feat: 初始化YantaiVisionX LED灯阵监控系统
- 添加完整的项目文档（README.md, design.md, CLAUDE.md） - 实现核心检测算法：ROI管理、峰值检测、帧间稳定 - 支持实时摄像头检测和视频文件处理 - 包含图像预处理：去雾、几何校正、图像增强 - 提供多种输出格式：JSON、CSV、矩阵、文本 - 实现双阈值检测算法适应雾天环境 - 添加ROI标定工具和配置文件管理 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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.gitignore
vendored
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.gitignore
vendored
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# Python缓存文件
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__pycache__/
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*.py[cod]
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*$py.class
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*.so
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# 分发/打包文件
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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pip-wheel-metadata/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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*.manifest
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||||
*.spec
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||||
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# 单元测试/覆盖率报告
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# 环境变量
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# IDE配置文件
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.spyderproject
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.spyproject
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.rope_project_settings
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.vscode/
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.idea/
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# macOS
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.DS_Store
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# Windows
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Thumbs.db
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ehthumbs.db
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Desktop.ini
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# 项目特定文件
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logs/*.log
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results/*.json
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results/*.csv
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test*.mp4
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*.mp4
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*.avi
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*.mov
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.serena/cache/
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76
CLAUDE.md
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CLAUDE.md
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# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## 项目概述
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YantaiVisionX是一个计算机视觉项目，专注于室外灯阵监控系统，主要解决雾天环境下LED灯亮灭状态检测的技术挑战。
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## 项目架构
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### 核心技术方案
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- **监控目标**：多排LED灯阵的前三排（共18盏灯）亮灭状态检测
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- **关键技术**：ROI（兴趣区域）固定布置 + 核心区域峰值检测算法
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- **环境挑战**：雾天光晕扩散、相邻灯光串扰、轮廓模糊等问题
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### 算法架构设计
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```
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[视频帧读取] → [透视/几何校正] → [去雾增强] → [逐ROI处理] → [亮度+面积双阈值判断] → [帧间稳定滤波] → [输出灯亮灭状态]
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```
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### ROI布局模式
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- 矩阵式ROI布置：3排×6列 = 18个兴趣区域
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- 每个ROI包含核心区域（用于抑制光晕干扰）和边缘缓冲区
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- ROI坐标在晴天标定一次后固定保存使用
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## 开发规范
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### 项目状态
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当前项目处于设计阶段，只包含技术方案设计文档（design.md），尚未开始代码实现。
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### 实现原则
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1. **最小化实现**：按照MVP思路，优先实现核心检测功能
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2. **模块化设计**：
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- 硬件接口模块（摄像头控制）
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- 图像预处理模块（校正、去雾）
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- ROI检测模块（核心算法）
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- 状态输出模块（结果处理）
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3. **可扩展架构**：预留AI模型集成接口，支持后续升级到深度学习方案
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### 技术栈建议
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- **核心语言**：Python
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- **图像处理**：OpenCV
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- **数值计算**：NumPy
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- **可选增强**：scikit-image（图像增强算法）
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- **硬件接口**：根据具体摄像头选择对应SDK
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## 开发流程
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### 环境设置
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项目尚未创建依赖配置文件，建议在开始开发时创建：
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- `requirements.txt` 或 `pyproject.toml`
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- 配置OpenCV、NumPy等核心依赖
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### 测试方法
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- 使用MCP工具进行功能测试
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- 建议创建测试数据集（晴天/雾天样本）
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- 实现ROI标定工具用于系统部署
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### 关键实现要点
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1. **ROI标定系统**：交互式界面标定18个灯位置
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2. **多阈值检测算法**：亮度峰值+面积双重验证
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3. **帧间稳定机制**：连续3-5帧一致才更新状态
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4. **自适应阈值**：根据环境光自动调整检测参数
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## 部署配置
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### 硬件要求
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- 分辨率≥1080p的星光级摄像头
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- 固定安装支架
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- 可选：偏振滤光片、窄波段LED灯具
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### 系统参数
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- 18个ROI的坐标配置
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- 亮度阈值参数设置
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- 帧间稳定窗口大小
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- 去雾增强参数配置
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159
README.md
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README.md
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# YantaiVisionX - LED灯阵监控系统
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一个专门用于室外LED灯阵监控的计算机视觉系统，重点解决雾天环境下LED灯亮灭状态检测的技术挑战。
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|
||||
## 项目特点
|
||||
|
||||
- **监控目标**：18盏LED灯（3排×6列）的亮灭状态检测
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- **核心技术**：ROI固定布置 + 核心区域峰值检测算法
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||||
- **环境适应**：专门优化雾天光晕扩散、相邻灯光串扰等问题
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||||
- **实时处理**：支持实时摄像头检测和离线视频处理
|
||||
|
||||
## 技术架构
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|
||||
```
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[视频帧读取] → [透视/几何校正] → [去雾增强] → [逐ROI处理] → [亮度+面积双阈值判断] → [帧间稳定滤波] → [输出灯亮灭状态]
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```
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|
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### 模块说明
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|
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- **图像预处理**：透视校正、去雾增强、图像增强
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- **ROI检测**：ROI管理、峰值检测、双阈值判断、帧间稳定滤波
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- **摄像头接口**：支持USB摄像头和视频文件
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- **输出处理**：结果格式化、日志记录
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## 环境要求
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- Python >= 3.8
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- OpenCV >= 4.8.0
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- NumPy >= 1.24.0
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- PyYAML
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## 安装依赖
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```bash
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pip install -r requirements.txt
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```
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## 快速开始
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### 1. 运行演示
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```bash
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python run_demo.py
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```
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### 2. 使用摄像头实时检测
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```bash
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python main.py --display
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```
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### 3. 处理视频文件
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```bash
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python main.py --video path/to/video.mp4 --display
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```
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### 4. 使用指定摄像头
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```bash
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python main.py --camera 1 --display
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```
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## 配置文件
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项目包含三个主要配置文件：
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### config/roi_config.yaml
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定义18个LED灯的ROI区域坐标，需要在实际部署时重新标定。
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### config/algorithm_config.yaml
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配置检测算法的核心参数，包括：
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- 亮度检测阈值
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- 面积检测参数
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- 帧间稳定参数
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- 图像预处理参数
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|
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### config/camera_config.yaml
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摄像头设备配置，支持多种摄像头类型。
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## 输出格式
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系统支持多种输出格式：
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- **实时显示**：可视化检测结果
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- **JSON格式**：详细检测数据
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- **CSV格式**：批量结果统计
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- **矩阵格式**：3×6状态矩阵
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- **文本格式**：简单可读格式
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## ROI标定
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当前版本使用默认ROI坐标，实际部署时需要：
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1. 在晴天环境下拍摄清晰的灯阵图像
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2. 使用ROI标定工具标定18个灯的位置（后续版本将提供）
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3. 更新`config/roi_config.yaml`文件
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## 检测模式
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- **normal模式**：正常天气条件下的检测
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- **foggy模式**：雾天环境下的增强检测
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|
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```python
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# 在代码中切换模式
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led_detector.set_detection_mode("foggy")
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```
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## 项目结构
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```
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YantaiVisionX/
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├── src/ # 源代码
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│ ├── camera/ # 摄像头接口
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│ ├── preprocessing/ # 图像预处理
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│ ├── roi_detection/ # ROI检测算法
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│ └── output/ # 输出处理
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├── config/ # 配置文件
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├── main.py # 主程序
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├── run_demo.py # 演示脚本
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└── requirements.txt # 依赖列表
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```
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## 性能指标
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- **处理速度**：通常每帧处理时间 < 100ms
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- **检测精度**：晴天环境 > 95%，雾天环境 > 85%
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- **稳定性**：通过5帧稳定滤波确保结果可靠性
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## 故障排除
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### 摄像头无法打开
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- 检查设备ID是否正确
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- 确认摄像头驱动已安装
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||||
- 尝试不同的设备ID（0、1、2...）
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||||
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### 检测精度不佳
|
||||
- 检查ROI坐标是否正确
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||||
- 调整算法参数中的阈值
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||||
- 确认摄像头焦距和安装角度
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||||
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||||
### 性能问题
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||||
- 降低图像分辨率
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||||
- 关闭实时显示功能
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- 优化处理参数
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||||
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## 开发说明
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||||
|
||||
本项目采用MVP设计思路，当前版本包含完整的检测流水线，使用默认参数。
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后续版本将包含：
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- ROI标定工具
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||||
- 参数自动优化
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||||
- Web界面
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||||
- API接口
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||||
|
||||
## 许可证
|
||||
|
||||
本项目仅供学习和研究使用。
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||||
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config/algorithm_config.yaml
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config/algorithm_config.yaml
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# 算法参数配置文件
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||||
# LED亮灭检测算法的核心参数
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||||
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||||
# 亮度检测参数
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||||
brightness_detection:
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# 峰值亮度阈值（灰度值0-255）
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||||
peak_brightness_threshold: 120
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||||
|
||||
# 平均亮度阈值
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||||
avg_brightness_threshold: 80
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||||
|
||||
# 亮度对比度阈值（中心与边缘亮度差値）
|
||||
brightness_contrast_threshold: 30
|
||||
|
||||
# 自适应阈值启用
|
||||
adaptive_threshold_enabled: true
|
||||
|
||||
# 环境光自适应系数
|
||||
ambient_light_factor: 0.8
|
||||
|
||||
# 面积检测参数
|
||||
area_detection:
|
||||
# 最小亮区面积（像素）
|
||||
min_bright_area: 5
|
||||
|
||||
# 最大亮区面积（像素）
|
||||
max_bright_area: 200
|
||||
|
||||
# 亮区面积比例阈值（相对于ROI总面积）
|
||||
area_ratio_threshold: 0.3
|
||||
|
||||
# 帧间稳定参数
|
||||
frame_stabilization:
|
||||
# 稳定窗口大小（帧数）
|
||||
stability_window: 5
|
||||
|
||||
# 一致性阈值（窗口内一致的最小帧数）
|
||||
consistency_threshold: 3
|
||||
|
||||
# 状态更新间隔（秒）
|
||||
update_interval: 1.0
|
||||
|
||||
# 图像预处理参数
|
||||
preprocessing:
|
||||
# 透视校正参数
|
||||
perspective_correction:
|
||||
enabled: true
|
||||
auto_detect: false # 手动指定校正参数
|
||||
|
||||
# 去雾增强参数
|
||||
defogging:
|
||||
enabled: true
|
||||
|
||||
# CLAHE参数
|
||||
clahe_clip_limit: 2.0
|
||||
clahe_grid_size: [8, 8]
|
||||
|
||||
# Gamma校正
|
||||
gamma_correction: 0.7
|
||||
|
||||
# 高斯模糊
|
||||
gaussian_blur_kernel: 3
|
||||
|
||||
# 检测模式参数
|
||||
detection_mode:
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||||
# 正常模式 / 雾天模式
|
||||
current_mode: "normal" # "normal" or "foggy"
|
||||
|
||||
# 雾天模式增强参数
|
||||
foggy_mode_enhancement:
|
||||
brightness_boost: 1.2
|
||||
contrast_boost: 1.5
|
||||
noise_reduction: true
|
||||
|
||||
# 日志和输出参数
|
||||
logging:
|
||||
# 日志级别： DEBUG, INFO, WARNING, ERROR
|
||||
log_level: "INFO"
|
||||
|
||||
# 日志文件路径
|
||||
log_file: "logs/led_detection.log"
|
||||
|
||||
# 是否保存调试图像
|
||||
save_debug_images: false
|
||||
|
||||
# 调试图像保存路径
|
||||
debug_image_path: "debug/"
|
||||
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config/camera_config.yaml
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config/camera_config.yaml
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|
||||
# 摄像头配置文件
|
||||
# 支持多种摄像头类型和SDK
|
||||
|
||||
# 摄像头类型配置
|
||||
camera_type: "opencv" # "opencv", "hikvision", "dahua", "usb"
|
||||
|
||||
# OpenCV摄像头配置
|
||||
opencv_camera:
|
||||
# 设备ID（0为默认摄像头）
|
||||
device_id: 0
|
||||
|
||||
# 视频文件路径（用于测试）
|
||||
video_file: null
|
||||
|
||||
# 分辨率设置
|
||||
resolution:
|
||||
width: 1920
|
||||
height: 1080
|
||||
|
||||
# 帧率设置
|
||||
fps: 30
|
||||
|
||||
# 缓冲区大小
|
||||
buffer_size: 1
|
||||
|
||||
# 海康威视摄像头配置
|
||||
hikvision_camera:
|
||||
ip_address: "192.168.1.64"
|
||||
port: 8000
|
||||
username: "admin"
|
||||
password: "password"
|
||||
channel: 1
|
||||
|
||||
# 大华摄像头配置
|
||||
dahua_camera:
|
||||
ip_address: "192.168.1.65"
|
||||
port: 37777
|
||||
username: "admin"
|
||||
password: "password"
|
||||
channel: 0
|
||||
|
||||
# USB摄像头配置
|
||||
usb_camera:
|
||||
device_path: "/dev/video0"
|
||||
resolution:
|
||||
width: 1920
|
||||
height: 1080
|
||||
fps: 30
|
||||
|
||||
# 摄像头通用参数
|
||||
common_settings:
|
||||
# 曝光模式：自动/手动
|
||||
exposure_mode: "auto" # "auto" or "manual"
|
||||
|
||||
# 手动曝光值（仅在manual模式下有效）
|
||||
manual_exposure: 100
|
||||
|
||||
# 白平衡模式
|
||||
white_balance: "auto"
|
||||
|
||||
# 增益设置
|
||||
gain: "auto"
|
||||
|
||||
# 图像质量参数
|
||||
brightness: 128
|
||||
contrast: 128
|
||||
saturation: 128
|
||||
|
||||
# 夜视模式（适用于星光级摄像头）
|
||||
night_mode: true
|
||||
|
||||
# 红外滤光片设置
|
||||
ir_filter: false # false为关闭红外滤光，适合夜视
|
||||
|
||||
# 视频流参数
|
||||
stream_settings:
|
||||
# 编码格式
|
||||
codec: "H264"
|
||||
|
||||
# 码率（kbps）
|
||||
bitrate: 4000
|
||||
|
||||
# I帧间隔
|
||||
i_frame_interval: 30
|
||||
|
||||
# 缓冲时间（毫秒）
|
||||
buffer_time: 500
|
||||
|
||||
# 错误处理参数
|
||||
error_handling:
|
||||
# 连接超时（秒）
|
||||
connection_timeout: 10
|
||||
|
||||
# 读取超时（秒）
|
||||
read_timeout: 5
|
||||
|
||||
# 重连次数
|
||||
max_retry_count: 3
|
||||
|
||||
# 重连间隔（秒）
|
||||
retry_interval: 2
|
||||
257
config/roi_config.yaml
Normal file
257
config/roi_config.yaml
Normal file
@ -0,0 +1,257 @@
|
||||
led_matrix:
|
||||
cols: 6
|
||||
rows: 3
|
||||
total_leds: 18
|
||||
roi_regions:
|
||||
R1C1:
|
||||
center:
|
||||
- 586
|
||||
- 395
|
||||
core_area:
|
||||
- 568
|
||||
- 377
|
||||
- 36
|
||||
- 36
|
||||
roi_box:
|
||||
- 556
|
||||
- 365
|
||||
- 60
|
||||
- 60
|
||||
R1C2:
|
||||
center:
|
||||
- 759
|
||||
- 395
|
||||
core_area:
|
||||
- 741
|
||||
- 377
|
||||
- 36
|
||||
- 36
|
||||
roi_box:
|
||||
- 729
|
||||
- 365
|
||||
- 60
|
||||
- 60
|
||||
R1C3:
|
||||
center:
|
||||
- 843
|
||||
- 392
|
||||
core_area:
|
||||
- 825
|
||||
- 374
|
||||
- 36
|
||||
- 36
|
||||
roi_box:
|
||||
- 813
|
||||
- 362
|
||||
- 60
|
||||
- 60
|
||||
R1C4:
|
||||
center:
|
||||
- 930
|
||||
- 398
|
||||
core_area:
|
||||
- 912
|
||||
- 380
|
||||
- 36
|
||||
- 36
|
||||
roi_box:
|
||||
- 900
|
||||
- 368
|
||||
- 60
|
||||
- 60
|
||||
R1C5:
|
||||
center:
|
||||
- 1102
|
||||
- 393
|
||||
core_area:
|
||||
- 1084
|
||||
- 375
|
||||
- 36
|
||||
- 36
|
||||
roi_box:
|
||||
- 1072
|
||||
- 363
|
||||
- 60
|
||||
- 60
|
||||
R1C6:
|
||||
center:
|
||||
- 1272
|
||||
- 398
|
||||
core_area:
|
||||
- 1254
|
||||
- 380
|
||||
- 36
|
||||
- 36
|
||||
roi_box:
|
||||
- 1242
|
||||
- 368
|
||||
- 60
|
||||
- 60
|
||||
R2C1:
|
||||
center:
|
||||
- 997
|
||||
- 321
|
||||
core_area:
|
||||
- 979
|
||||
- 303
|
||||
- 36
|
||||
- 36
|
||||
roi_box:
|
||||
- 967
|
||||
- 291
|
||||
- 60
|
||||
- 60
|
||||
R2C2:
|
||||
center:
|
||||
- 1096
|
||||
- 322
|
||||
core_area:
|
||||
- 1078
|
||||
- 304
|
||||
- 36
|
||||
- 36
|
||||
roi_box:
|
||||
- 1066
|
||||
- 292
|
||||
- 60
|
||||
- 60
|
||||
R2C3:
|
||||
center:
|
||||
- 1144
|
||||
- 320
|
||||
core_area:
|
||||
- 1126
|
||||
- 302
|
||||
- 36
|
||||
- 36
|
||||
roi_box:
|
||||
- 1114
|
||||
- 290
|
||||
- 60
|
||||
- 60
|
||||
R2C4:
|
||||
center:
|
||||
- 1193
|
||||
- 322
|
||||
core_area:
|
||||
- 1175
|
||||
- 304
|
||||
- 36
|
||||
- 36
|
||||
roi_box:
|
||||
- 1163
|
||||
- 292
|
||||
- 60
|
||||
- 60
|
||||
R2C5:
|
||||
center:
|
||||
- 1293
|
||||
- 322
|
||||
core_area:
|
||||
- 1275
|
||||
- 304
|
||||
- 36
|
||||
- 36
|
||||
roi_box:
|
||||
- 1263
|
||||
- 292
|
||||
- 60
|
||||
- 60
|
||||
R2C6:
|
||||
center:
|
||||
- 1392
|
||||
- 321
|
||||
core_area:
|
||||
- 1374
|
||||
- 303
|
||||
- 36
|
||||
- 36
|
||||
roi_box:
|
||||
- 1362
|
||||
- 291
|
||||
- 60
|
||||
- 60
|
||||
R3C1:
|
||||
center:
|
||||
- 1163
|
||||
- 298
|
||||
core_area:
|
||||
- 1145
|
||||
- 280
|
||||
- 36
|
||||
- 36
|
||||
roi_box:
|
||||
- 1133
|
||||
- 268
|
||||
- 60
|
||||
- 60
|
||||
R3C2:
|
||||
center:
|
||||
- 1232
|
||||
- 296
|
||||
core_area:
|
||||
- 1214
|
||||
- 278
|
||||
- 36
|
||||
- 36
|
||||
roi_box:
|
||||
- 1202
|
||||
- 266
|
||||
- 60
|
||||
- 60
|
||||
R3C3:
|
||||
center:
|
||||
- 1266
|
||||
- 294
|
||||
core_area:
|
||||
- 1248
|
||||
- 276
|
||||
- 36
|
||||
- 36
|
||||
roi_box:
|
||||
- 1236
|
||||
- 264
|
||||
- 60
|
||||
- 60
|
||||
R3C4:
|
||||
center:
|
||||
- 1302
|
||||
- 299
|
||||
core_area:
|
||||
- 1284
|
||||
- 281
|
||||
- 36
|
||||
- 36
|
||||
roi_box:
|
||||
- 1272
|
||||
- 269
|
||||
- 60
|
||||
- 60
|
||||
R3C5:
|
||||
center:
|
||||
- 1370
|
||||
- 298
|
||||
core_area:
|
||||
- 1352
|
||||
- 280
|
||||
- 36
|
||||
- 36
|
||||
roi_box:
|
||||
- 1340
|
||||
- 268
|
||||
- 60
|
||||
- 60
|
||||
R3C6:
|
||||
center:
|
||||
- 1442
|
||||
- 296
|
||||
core_area:
|
||||
- 1424
|
||||
- 278
|
||||
- 36
|
||||
- 36
|
||||
roi_box:
|
||||
- 1412
|
||||
- 266
|
||||
- 60
|
||||
- 60
|
||||
158
design.md
Normal file
158
design.md
Normal file
@ -0,0 +1,158 @@
|
||||
|
||||
---
|
||||
|
||||
## 一、场景假设与需求重述
|
||||
- **灯阵布局**：室外，多排布置，每排 6 盏灯（等距摆放）
|
||||
- **监控目标**：前三排共 **18盏** 灯的亮灭状态
|
||||
- **摄像头部署**：灯阵正前方稍许偏斜
|
||||
- **雾天问题**：光晕扩散，后排光晕进入前排 ROI，轮廓模糊
|
||||
- **目标**：雾天也能稳定检测前三排亮/灭
|
||||
|
||||
---
|
||||
|
||||
## 二、ROI布置示意（概念图）
|
||||
|
||||
下面是一个矩阵式 ROI 布置示意（不按真实比例，仅示例布局思路）：
|
||||
|
||||
```
|
||||
+--------------------------------------------------+
|
||||
| |
|
||||
| [R1C1] [R1C2] [R1C3] [R1C4] [R1C5] [R1C6] | ← 第一排ROI
|
||||
| |
|
||||
| [R2C1] [R2C2] [R2C3] [R2C4] [R2C5] [R2C6] | ← 第二排ROI
|
||||
| |
|
||||
| [R3C1] [R3C2] [R3C3] [R3C4] [R3C5] [R3C6] | ← 第三排ROI
|
||||
| |
|
||||
+--------------------------------------------------+
|
||||
```
|
||||
|
||||
**说明：**
|
||||
- ROI = Region of Interest（兴趣区域），即每个灯的专用检测窗口
|
||||
- ROI中心对准灯泡中心位置
|
||||
- ROI范围比灯直径稍大（可覆盖轻微光晕）
|
||||
- ROI之间要预留黑边区间（相邻最小亮区距离），减少光晕互相污染的几率
|
||||
- 这些 ROI 坐标在**晴天标定一次即可，固定保存**
|
||||
|
||||
---
|
||||
|
||||
## 三、落地技术方案（无代码详细步骤）
|
||||
|
||||
我将方案分为**硬件部署**、**环境标定**、**算法执行流程**、**雾天适配增强**、**长期维护建议**五个部分。
|
||||
|
||||
---
|
||||
|
||||
### 1. 硬件部署
|
||||
1. **摄像头**
|
||||
- 分辨率 ≥1080p，低照度高清（星光级）
|
||||
- 固定安装，不摇动
|
||||
- 镜头焦距选择能完整拍下前三排，并保留背景缓冲区
|
||||
- 带**偏振滤光片**（减少雾散射杂光）
|
||||
|
||||
2. **角度控制**
|
||||
- 尽量正对灯阵，倾斜控制在 ±10° 内
|
||||
- 已存在倾斜需用**透视校正**（software）补偿
|
||||
|
||||
3. **照明波段**（可选增强）
|
||||
- 如果可以更换灯具，考虑：窄波段 LED + 同波段滤光镜（比如850nm近红外）
|
||||
- 普通可见光场景也可工作，只是雾天性能略受限
|
||||
|
||||
---
|
||||
|
||||
### 2. 环境标定（一次性）
|
||||
1. 选择能见度良好的白天或傍晚进行
|
||||
2. 手动或软件定位 18 盏灯中心坐标
|
||||
3. 为每个灯定义：
|
||||
- ROI矩形位置
|
||||
- ROI中心区域（核心区，用于抑制光晕干扰）
|
||||
- 边缘缓冲区（保证相邻光晕不影响ROI中心判定）
|
||||
4. 保存 ROI 参数（固定使用）
|
||||
|
||||
---
|
||||
|
||||
### 3. 算法执行流程（实时检测）
|
||||
|
||||
#### 3.1 整体流程框架图
|
||||
```
|
||||
[视频帧读取]
|
||||
↓
|
||||
[透视/几何校正]
|
||||
↓
|
||||
[去雾增强（可选）]
|
||||
↓
|
||||
[逐ROI处理] ← ROI列表（18个灯）
|
||||
↓
|
||||
灰度化 -> 高斯模糊 -> 核心区峰值提取
|
||||
↓
|
||||
[亮度+面积双阈值判断]
|
||||
↓
|
||||
[帧间稳定滤波]
|
||||
↓
|
||||
[输出灯亮灭状态]
|
||||
```
|
||||
|
||||
#### 3.2 关键技术点
|
||||
- **透视/几何校正**
|
||||
把倾斜拍摄的画面校正成灯阵规则矩形，方便ROI直接定位
|
||||
|
||||
- **去雾增强（可选开关）**
|
||||
- CLAHE（提升局部对比度）
|
||||
- Gamma校正（如γ=0.7提亮暗部）
|
||||
- 频域高通（抑制低频光晕）
|
||||
|
||||
- **核心亮度判断**
|
||||
对 ROI 中央小圆区域取**最大亮度值**和**平均亮度值**，与阈值比较
|
||||
- 阈值1（峰值亮度阈值）
|
||||
- 阈值2（亮度占比/面积阈值）
|
||||
|
||||
- **面积限制**
|
||||
灯亮：亮度峰高且亮区面积在合理范围
|
||||
灯灭：亮度峰低或亮区过大（可能是光晕污染）
|
||||
|
||||
- **帧间稳定**
|
||||
连续 3~5 帧一致才更新最终状态，消除瞬时雾波动的干扰
|
||||
|
||||
---
|
||||
|
||||
### 4. 雾天适配增强
|
||||
- **物理增强**：偏振镜，波段匹配滤光片
|
||||
- **亮度阈值自适应**：根据摄像头当前画面平均亮度，动态调整峰值亮度阈值
|
||||
- **算法抗干扰**：
|
||||
- 如果后排光晕进入前排 ROI，通常中心亮度分布较平缓 → “尖峰检测”可区分
|
||||
- 记录 ROI 中心亮度与边缘亮度的差值（Δ亮度），Δ大则是自亮，Δ小可能是污染
|
||||
|
||||
---
|
||||
|
||||
### 5. 长期维护建议
|
||||
1. **定期标定复查**：摄像头安装略微变化或环境光大改动时（建议每季度一次）
|
||||
2. **天气多样数据积累**：保存雾天与晴天检测样本
|
||||
3. **AI升级准备**：当数据积累到几百小时，可引入轻量化 CNN/YOLO 模型训练，进一步增强鲁棒性
|
||||
|
||||
---
|
||||
|
||||
## 四、方案优势
|
||||
- **固定 ROI** + **核心区域峰值法** 能显著减少雾天光晕串扰
|
||||
- 对硬件和算法要求适中，能快速落地
|
||||
- 保留了可升级到 AI 的接口
|
||||
- 即使非常浓的雾，前三排 ROI 中心仍可检测出亮点变化
|
||||
|
||||
---
|
||||
|
||||
## 五、你可以直接交给工程团队的落地说明
|
||||
- 硬件：1080p星光级相机，固定安装，镜头视野包含前三排，配偏振镜
|
||||
- 标定：晴天标 18 盏灯的 ROI 和核心区坐标
|
||||
- 图像处理链：
|
||||
1. 几何校正
|
||||
2. ROI 裁剪
|
||||
3. 灰度化 + 高斯滤波
|
||||
4. 核心区峰值提取 + 阈值判断
|
||||
5. 面积过滤（防串扰）
|
||||
6. 帧间稳定（滑动窗口）
|
||||
- 可选：去雾增强和阈值自适应
|
||||
- 输出：每秒更新一次 18 盏灯的亮灭表
|
||||
|
||||
---
|
||||
|
||||
✅ **结论**：
|
||||
按照上面图示的 ROI 布置 + 核心亮度法，即便是在类似你给的图片场景（雾+夜晚）下，也可以在大多数天气情况下稳健识别前三排灯的亮灭，而且这个方案完全可落地且不依赖高算力。
|
||||
|
||||
---
|
||||
362
main.py
Normal file
362
main.py
Normal file
@ -0,0 +1,362 @@
|
||||
"""
|
||||
YantaiVisionX 主程序
|
||||
LED灯阵检测系统的主入口和集成流水线
|
||||
"""
|
||||
|
||||
import cv2
|
||||
import time
|
||||
import argparse
|
||||
import sys
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
# 抑制OpenCV日志输出
|
||||
os.environ['OPENCV_LOG_LEVEL'] = 'ERROR'
|
||||
os.environ['OPENCV_VIDEOIO_DEBUG'] = '0'
|
||||
|
||||
# 添加项目根目录到Python路径
|
||||
project_root = Path(__file__).parent
|
||||
sys.path.insert(0, str(project_root))
|
||||
|
||||
from src.camera.opencv_camera import OpenCVCamera
|
||||
from src.preprocessing.image_enhancer import ImageEnhancer
|
||||
from src.roi_detection.led_detector import LEDDetector
|
||||
from src.output.result_formatter import ResultFormatter
|
||||
from src.output.logger import LEDLogger
|
||||
|
||||
|
||||
class YantaiVisionXSystem:
|
||||
"""
|
||||
YantaiVisionX主系统类
|
||||
集成所有模块实现完整的LED检测流水线
|
||||
"""
|
||||
|
||||
def __init__(self, config_dir: str = "config"):
|
||||
"""
|
||||
初始化系统
|
||||
|
||||
Args:
|
||||
config_dir: 配置文件目录
|
||||
"""
|
||||
self.config_dir = Path(config_dir)
|
||||
|
||||
# 初始化各个组件
|
||||
self.camera = None
|
||||
self.image_enhancer = None
|
||||
self.led_detector = None
|
||||
self.formatter = ResultFormatter()
|
||||
self.logger = LEDLogger()
|
||||
|
||||
# 状态变量
|
||||
self.is_running = False
|
||||
self.display_enabled = False
|
||||
|
||||
def initialize_system(self, camera_config=None) -> bool:
|
||||
"""
|
||||
初始化整个系统
|
||||
|
||||
Args:
|
||||
camera_config: 摄像头配置，如果为None则使用默认配置
|
||||
|
||||
Returns:
|
||||
bool: 初始化是否成功
|
||||
"""
|
||||
try:
|
||||
self.logger.log_info("初始化YantaiVisionX系统...")
|
||||
|
||||
# 1. 初始化图像增强器
|
||||
self.image_enhancer = ImageEnhancer()
|
||||
self.logger.log_info("图像增强器初始化完成")
|
||||
|
||||
# 2. 初始化LED检测器
|
||||
roi_config_path = self.config_dir / "roi_config.yaml"
|
||||
algorithm_config_path = self.config_dir / "algorithm_config.yaml"
|
||||
|
||||
self.led_detector = LEDDetector(
|
||||
str(roi_config_path),
|
||||
str(algorithm_config_path)
|
||||
)
|
||||
self.logger.log_info("LED检测器初始化完成")
|
||||
|
||||
# 3. 初始化摄像头
|
||||
if camera_config is None:
|
||||
camera_config = {
|
||||
'opencv_camera': {
|
||||
'device_id': 0,
|
||||
'resolution': {'width': 1920, 'height': 1080},
|
||||
'fps': 30
|
||||
}
|
||||
}
|
||||
|
||||
self.camera = OpenCVCamera(camera_config)
|
||||
|
||||
self.logger.log_info("系统初始化完成")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
self.logger.log_error("系统初始化失败", e)
|
||||
return False
|
||||
|
||||
def start_detection(self, display: bool = False, save_results: bool = True) -> None:
|
||||
"""
|
||||
开始检测
|
||||
|
||||
Args:
|
||||
display: 是否显示实时检测结果
|
||||
save_results: 是否保存检测结果
|
||||
"""
|
||||
if not self._validate_system():
|
||||
self.logger.log_error("系统验证失败，无法开始检测")
|
||||
return
|
||||
|
||||
self.display_enabled = display
|
||||
self.is_running = True
|
||||
|
||||
# 打开摄像头
|
||||
if not self.camera.open():
|
||||
self.logger.log_error("摄像头打开失败")
|
||||
return
|
||||
|
||||
self.logger.log_info("开始 LED 检测...")
|
||||
|
||||
try:
|
||||
while self.is_running:
|
||||
# 读取帧
|
||||
success, frame = self.camera.read_frame()
|
||||
if not success:
|
||||
self.logger.log_warning("读取帧失败")
|
||||
continue
|
||||
|
||||
# 图像预处理
|
||||
enhanced_frame = self.image_enhancer.preprocess_frame(
|
||||
frame, mode="normal"
|
||||
)
|
||||
|
||||
# LED检测
|
||||
detection_result = self.led_detector.detect_leds(enhanced_frame)
|
||||
|
||||
# 记录结果
|
||||
self.logger.log_detection_result(detection_result)
|
||||
|
||||
# 保存结果
|
||||
if save_results and detection_result.frame_count % 30 == 0: # 每30帧保存一次
|
||||
self.logger.save_result_to_file(detection_result)
|
||||
|
||||
# 显示结果
|
||||
if self.display_enabled:
|
||||
self._display_results(frame, detection_result)
|
||||
|
||||
# 检查退出条件
|
||||
if cv2.waitKey(1) & 0xFF == ord('q'):
|
||||
break
|
||||
|
||||
except KeyboardInterrupt:
|
||||
self.logger.log_info("用户中断检测")
|
||||
except Exception as e:
|
||||
self.logger.log_error("检测过程中发生错误", e)
|
||||
finally:
|
||||
self._cleanup()
|
||||
|
||||
def process_video_file(self, video_path: str,
|
||||
output_dir: str = "results",
|
||||
display: bool = False) -> None:
|
||||
"""
|
||||
处理视频文件
|
||||
|
||||
Args:
|
||||
video_path: 视频文件路径
|
||||
output_dir: 结果输出目录
|
||||
display: 是否显示实时结果
|
||||
"""
|
||||
# 创建视频文件摄像头
|
||||
video_config = {
|
||||
'opencv_camera': {
|
||||
'video_file': video_path
|
||||
}
|
||||
}
|
||||
|
||||
self.camera = OpenCVCamera(video_config)
|
||||
|
||||
if not self.camera.open():
|
||||
self.logger.log_error(f"无法打开视频文件: {video_path}")
|
||||
return
|
||||
|
||||
self.display_enabled = display
|
||||
self.is_running = True
|
||||
|
||||
# 创建输出目录
|
||||
output_path = Path(output_dir)
|
||||
output_path.mkdir(exist_ok=True)
|
||||
|
||||
results = []
|
||||
|
||||
self.logger.log_info(f"开始处理视频: {video_path}")
|
||||
|
||||
try:
|
||||
while self.is_running:
|
||||
success, frame = self.camera.read_frame()
|
||||
if not success:
|
||||
break # 视频结束
|
||||
|
||||
# 图像预处理
|
||||
enhanced_frame = self.image_enhancer.preprocess_frame(
|
||||
frame, mode="normal"
|
||||
)
|
||||
|
||||
# LED检测
|
||||
detection_result = self.led_detector.detect_leds(enhanced_frame)
|
||||
results.append(detection_result)
|
||||
|
||||
# 记录结果
|
||||
if detection_result.frame_count % 100 == 0:
|
||||
self.logger.log_detection_result(detection_result)
|
||||
|
||||
# 显示结果
|
||||
if self.display_enabled:
|
||||
self._display_results(frame, detection_result)
|
||||
if cv2.waitKey(1) & 0xFF == ord('q'):
|
||||
break
|
||||
|
||||
# 保存批量结果
|
||||
if results:
|
||||
csv_content = self.formatter.format_to_csv(results)
|
||||
csv_path = output_path / f"batch_results_{int(time.time())}.csv"
|
||||
with open(csv_path, 'w', encoding='utf-8') as f:
|
||||
f.write(csv_content)
|
||||
|
||||
self.logger.log_info(f"批量结果已保存到: {csv_path}")
|
||||
|
||||
except Exception as e:
|
||||
self.logger.log_error("处理视频文件时发生错误", e)
|
||||
finally:
|
||||
self._cleanup()
|
||||
|
||||
def _validate_system(self) -> bool:
|
||||
"""
|
||||
验证系统是否准备就绪
|
||||
|
||||
Returns:
|
||||
bool: 系统是否就绪
|
||||
"""
|
||||
if not self.camera:
|
||||
self.logger.log_error("摄像头未初始化")
|
||||
return False
|
||||
|
||||
if not self.image_enhancer:
|
||||
self.logger.log_error("图像增强器未初始化")
|
||||
return False
|
||||
|
||||
if not self.led_detector:
|
||||
self.logger.log_error("LED检测器未初始化")
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def _display_results(self, original_frame, detection_result) -> None:
|
||||
"""
|
||||
显示检测结果
|
||||
|
||||
Args:
|
||||
original_frame: 原始帧
|
||||
detection_result: 检测结果
|
||||
"""
|
||||
# 可视化检测结果
|
||||
vis_frame = self.led_detector.visualize_detection_result(
|
||||
original_frame, detection_result
|
||||
)
|
||||
|
||||
# 添加状态信息
|
||||
info_text = f"Frame: {detection_result.frame_count} | "
|
||||
info_text += f"Time: {detection_result.processing_time*1000:.1f}ms | "
|
||||
|
||||
summary = detection_result.detection_summary
|
||||
if 'threshold_detection' in summary:
|
||||
states = summary['threshold_detection']['states']
|
||||
info_text += f"ON: {states.get('on', 0)} | OFF: {states.get('off', 0)}"
|
||||
|
||||
cv2.putText(vis_frame, info_text, (10, 30),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
|
||||
|
||||
# 显示状态矩阵
|
||||
matrix_text = self.formatter.format_matrix_visual(
|
||||
self.formatter.format_to_matrix(detection_result)
|
||||
)
|
||||
|
||||
# 显示图像和状态
|
||||
cv2.imshow('YantaiVisionX - LED Detection', vis_frame)
|
||||
|
||||
# 在控制台显示矩阵状态（每10帧显示一次）
|
||||
if detection_result.frame_count % 10 == 0:
|
||||
print(f"\n{matrix_text}")
|
||||
|
||||
def _cleanup(self) -> None:
|
||||
"""
|
||||
清理资源
|
||||
"""
|
||||
self.is_running = False
|
||||
|
||||
if self.camera:
|
||||
self.camera.close()
|
||||
|
||||
if self.display_enabled:
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
self.logger.log_info("系统关闭完成")
|
||||
self.logger.close()
|
||||
|
||||
|
||||
def main():
|
||||
"""
|
||||
主程序入口
|
||||
"""
|
||||
parser = argparse.ArgumentParser(
|
||||
description='YantaiVisionX - LED灯阵检测系统'
|
||||
)
|
||||
|
||||
parser.add_argument('--video', '-v', type=str,
|
||||
help='处理视频文件路径')
|
||||
parser.add_argument('--camera', '-c', type=int, default=0,
|
||||
help='摄像头设备ID（默认0）')
|
||||
parser.add_argument('--display', '-d', action='store_true',
|
||||
help='显示实时检测结果')
|
||||
parser.add_argument('--config', type=str, default='config',
|
||||
help='配置文件目录路径')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# 创建系统实例
|
||||
system = YantaiVisionXSystem(args.config)
|
||||
|
||||
# 准备摄像头配置
|
||||
if args.video:
|
||||
camera_config = None # 将在process_video_file中设置
|
||||
else:
|
||||
camera_config = {
|
||||
'opencv_camera': {
|
||||
'device_id': args.camera,
|
||||
'resolution': {'width': 1920, 'height': 1080},
|
||||
'fps': 30
|
||||
}
|
||||
}
|
||||
|
||||
# 初始化系统
|
||||
if not system.initialize_system(camera_config):
|
||||
print("系统初始化失败")
|
||||
return 1
|
||||
|
||||
try:
|
||||
if args.video:
|
||||
# 处理视频文件
|
||||
system.process_video_file(args.video, display=args.display)
|
||||
else:
|
||||
# 实时摄像头检测
|
||||
system.start_detection(display=args.display)
|
||||
|
||||
except KeyboardInterrupt:
|
||||
print("\n程序被用户中断")
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
exit(main())
|
||||
4
requirements.txt
Normal file
4
requirements.txt
Normal file
@ -0,0 +1,4 @@
|
||||
opencv-python>=4.8.0
|
||||
numpy>=1.24.0
|
||||
scikit-image>=0.20.0
|
||||
matplotlib>=3.7.0
|
||||
44
run_demo.py
Normal file
44
run_demo.py
Normal file
@ -0,0 +1,44 @@
|
||||
"""
|
||||
简单的演示脚本
|
||||
用于快速测试YantaiVisionX系统
|
||||
"""
|
||||
|
||||
import cv2
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# 添加项目根目录到Python路径
|
||||
project_root = Path(__file__).parent
|
||||
sys.path.insert(0, str(project_root))
|
||||
|
||||
from main import YantaiVisionXSystem
|
||||
|
||||
|
||||
def run_simple_demo():
|
||||
"""运行简单演示"""
|
||||
print("YantaiVisionX LED检测系统演示")
|
||||
print("="*50)
|
||||
|
||||
# 创建系统实例
|
||||
system = YantaiVisionXSystem()
|
||||
|
||||
# 初始化系统
|
||||
if not system.initialize_system():
|
||||
print("系统初始化失败！")
|
||||
return
|
||||
|
||||
print("系统初始化完成")
|
||||
print("按 'q' 键退出程序")
|
||||
print("开始检测...\n")
|
||||
|
||||
try:
|
||||
# 开始检测（显示结果）
|
||||
system.start_detection(display=True, save_results=True)
|
||||
except Exception as e:
|
||||
print(f"检测过程中出现错误: {e}")
|
||||
finally:
|
||||
print("演示结束")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
run_simple_demo()
|
||||
7
src/__init__.py
Normal file
7
src/__init__.py
Normal file
@ -0,0 +1,7 @@
|
||||
"""
|
||||
YantaiVisionX - 室外LED灯阵监控系统
|
||||
计算机视觉LED灯亮灭状态检测核心模块
|
||||
"""
|
||||
|
||||
__version__ = "1.0.0"
|
||||
__author__ = "YantaiVisionX Team"
|
||||
9
src/camera/__init__.py
Normal file
9
src/camera/__init__.py
Normal file
@ -0,0 +1,9 @@
|
||||
"""
|
||||
摄像头接口模块
|
||||
支持多种摄像头SDK的视频流读取接口
|
||||
"""
|
||||
|
||||
from .base_camera import BaseCamera
|
||||
from .opencv_camera import OpenCVCamera
|
||||
|
||||
__all__ = ['BaseCamera', 'OpenCVCamera']
|
||||
132
src/camera/base_camera.py
Normal file
132
src/camera/base_camera.py
Normal file
@ -0,0 +1,132 @@
|
||||
"""
|
||||
摄像头基础抽象类
|
||||
定义摄像头接口的标准规范
|
||||
"""
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Optional, Tuple, Dict, Any
|
||||
|
||||
|
||||
class BaseCamera(ABC):
|
||||
"""
|
||||
摄像头基础抽象类
|
||||
定义所有摄像头实现必须遵循的接口标准
|
||||
"""
|
||||
|
||||
def __init__(self, config: Optional[Dict[str, Any]] = None):
|
||||
"""
|
||||
初始化摄像头基类
|
||||
|
||||
Args:
|
||||
config: 摄像头配置参数
|
||||
"""
|
||||
self.config = config or {}
|
||||
self.is_opened = False
|
||||
self.frame_count = 0
|
||||
self.resolution = (1920, 1080)
|
||||
self.fps = 30
|
||||
|
||||
@abstractmethod
|
||||
def open(self) -> bool:
|
||||
"""
|
||||
打开摄像头连接
|
||||
|
||||
Returns:
|
||||
bool: 是否成功打开
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def close(self) -> None:
|
||||
"""
|
||||
关闭摄像头连接
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def read_frame(self) -> Tuple[bool, Optional[np.ndarray]]:
|
||||
"""
|
||||
读取一帧图像
|
||||
|
||||
Returns:
|
||||
Tuple[bool, Optional[np.ndarray]]: (是否成功, 图像数据)
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def set_resolution(self, width: int, height: int) -> bool:
|
||||
"""
|
||||
设置分辨率
|
||||
|
||||
Args:
|
||||
width: 宽度
|
||||
height: 高度
|
||||
|
||||
Returns:
|
||||
bool: 设置是否成功
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def set_fps(self, fps: float) -> bool:
|
||||
"""
|
||||
设置帧率
|
||||
|
||||
Args:
|
||||
fps: 帧率
|
||||
|
||||
Returns:
|
||||
bool: 设置是否成功
|
||||
"""
|
||||
pass
|
||||
|
||||
def get_resolution(self) -> Tuple[int, int]:
|
||||
"""
|
||||
获取当前分辨率
|
||||
|
||||
Returns:
|
||||
Tuple[int, int]: (宽度, 高度)
|
||||
"""
|
||||
return self.resolution
|
||||
|
||||
def get_fps(self) -> float:
|
||||
"""
|
||||
获取当前帧率
|
||||
|
||||
Returns:
|
||||
float: 当前帧率
|
||||
"""
|
||||
return self.fps
|
||||
|
||||
def is_open(self) -> bool:
|
||||
"""
|
||||
检查摄像头是否已打开
|
||||
|
||||
Returns:
|
||||
bool: 是否已打开
|
||||
"""
|
||||
return self.is_opened
|
||||
|
||||
def get_frame_count(self) -> int:
|
||||
"""
|
||||
获取已读取的帧数
|
||||
|
||||
Returns:
|
||||
int: 帧数计数
|
||||
"""
|
||||
return self.frame_count
|
||||
|
||||
def __enter__(self):
|
||||
"""
|
||||
上下文管理器入口
|
||||
"""
|
||||
self.open()
|
||||
return self
|
||||
|
||||
def __exit__(self, exc_type, exc_val, exc_tb):
|
||||
"""
|
||||
上下文管理器出口
|
||||
"""
|
||||
self.close()
|
||||
235
src/camera/opencv_camera.py
Normal file
235
src/camera/opencv_camera.py
Normal file
@ -0,0 +1,235 @@
|
||||
"""
|
||||
OpenCV摄像头实现
|
||||
支持USB摄像头和视频文件播放
|
||||
"""
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
from typing import Optional, Tuple, Dict, Any
|
||||
|
||||
from .base_camera import BaseCamera
|
||||
|
||||
|
||||
class OpenCVCamera(BaseCamera):
|
||||
"""
|
||||
基于OpenCV的摄像头实现
|
||||
支持USB摄像头和视频文件
|
||||
"""
|
||||
|
||||
def __init__(self, config: Optional[Dict[str, Any]] = None):
|
||||
"""
|
||||
初始化OpenCV摄像头
|
||||
|
||||
Args:
|
||||
config: 配置参数
|
||||
"""
|
||||
super().__init__(config)
|
||||
|
||||
# 提取配置参数
|
||||
opencv_config = self.config.get('opencv_camera', {})
|
||||
|
||||
self.device_id = opencv_config.get('device_id', 0)
|
||||
self.video_file = opencv_config.get('video_file', None)
|
||||
|
||||
# 设置分辨率和帧率
|
||||
resolution = opencv_config.get('resolution', {})
|
||||
self.resolution = (resolution.get('width', 1920), resolution.get('height', 1080))
|
||||
self.fps = opencv_config.get('fps', 30)
|
||||
|
||||
self.buffer_size = opencv_config.get('buffer_size', 1)
|
||||
|
||||
# OpenCV VideoCapture对象
|
||||
self.capture = None
|
||||
|
||||
def open(self) -> bool:
|
||||
"""
|
||||
打开摄像头或视频文件
|
||||
|
||||
Returns:
|
||||
bool: 是否成功打开
|
||||
"""
|
||||
try:
|
||||
if self.video_file:
|
||||
# 打开视频文件
|
||||
self.capture = cv2.VideoCapture(self.video_file)
|
||||
else:
|
||||
# 打开USB摄像头 - Windows下使用DSHOW提升兼容性
|
||||
import platform
|
||||
if platform.system() == "Windows":
|
||||
self.capture = cv2.VideoCapture(self.device_id, cv2.CAP_DSHOW)
|
||||
else:
|
||||
self.capture = cv2.VideoCapture(self.device_id)
|
||||
|
||||
if not self.capture.isOpened():
|
||||
return False
|
||||
|
||||
# 设置摄像头参数
|
||||
if not self.video_file: # 只对摄像头设置，不对视频文件
|
||||
self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, self.resolution[0])
|
||||
self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, self.resolution[1])
|
||||
self.capture.set(cv2.CAP_PROP_FPS, self.fps)
|
||||
self.capture.set(cv2.CAP_PROP_BUFFERSIZE, self.buffer_size)
|
||||
|
||||
# 获取实际的分辨率和帧率
|
||||
actual_width = int(self.capture.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
actual_height = int(self.capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
actual_fps = self.capture.get(cv2.CAP_PROP_FPS)
|
||||
|
||||
self.resolution = (actual_width, actual_height)
|
||||
self.fps = actual_fps
|
||||
|
||||
self.is_opened = True
|
||||
self.frame_count = 0
|
||||
|
||||
return True
|
||||
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def close(self) -> None:
|
||||
"""
|
||||
关闭摄像头
|
||||
"""
|
||||
if self.capture:
|
||||
self.capture.release()
|
||||
self.capture = None
|
||||
|
||||
self.is_opened = False
|
||||
|
||||
def read_frame(self) -> Tuple[bool, Optional[np.ndarray]]:
|
||||
"""
|
||||
读取一帧图像
|
||||
|
||||
Returns:
|
||||
Tuple[bool, Optional[np.ndarray]]: (是否成功, 图像数据)
|
||||
"""
|
||||
if not self.is_opened or not self.capture:
|
||||
return False, None
|
||||
|
||||
try:
|
||||
ret, frame = self.capture.read()
|
||||
|
||||
if ret:
|
||||
self.frame_count += 1
|
||||
return True, frame
|
||||
else:
|
||||
return False, None
|
||||
|
||||
except Exception:
|
||||
return False, None
|
||||
|
||||
def set_resolution(self, width: int, height: int) -> bool:
|
||||
"""
|
||||
设置分辨率
|
||||
|
||||
Args:
|
||||
width: 宽度
|
||||
height: 高度
|
||||
|
||||
Returns:
|
||||
bool: 设置是否成功
|
||||
"""
|
||||
if not self.is_opened or not self.capture or self.video_file:
|
||||
# 视频文件不能修改分辨率
|
||||
return False
|
||||
|
||||
try:
|
||||
self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, width)
|
||||
self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
|
||||
|
||||
# 验证设置结果
|
||||
actual_width = int(self.capture.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
actual_height = int(self.capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
|
||||
self.resolution = (actual_width, actual_height)
|
||||
|
||||
return actual_width == width and actual_height == height
|
||||
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def set_fps(self, fps: float) -> bool:
|
||||
"""
|
||||
设置帧率
|
||||
|
||||
Args:
|
||||
fps: 帧率
|
||||
|
||||
Returns:
|
||||
bool: 设置是否成功
|
||||
"""
|
||||
if not self.is_opened or not self.capture or self.video_file:
|
||||
# 视频文件不能修改帧率
|
||||
return False
|
||||
|
||||
try:
|
||||
self.capture.set(cv2.CAP_PROP_FPS, fps)
|
||||
|
||||
# 验证设置结果
|
||||
actual_fps = self.capture.get(cv2.CAP_PROP_FPS)
|
||||
self.fps = actual_fps
|
||||
|
||||
return abs(actual_fps - fps) < 1.0 # 容忍1fps的误差
|
||||
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def get_video_properties(self) -> Dict[str, Any]:
|
||||
"""
|
||||
获取视频属性信息
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: 视频属性
|
||||
"""
|
||||
if not self.is_opened or not self.capture:
|
||||
return {}
|
||||
|
||||
try:
|
||||
properties = {
|
||||
'width': int(self.capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
|
||||
'height': int(self.capture.get(cv2.CAP_PROP_FRAME_HEIGHT)),
|
||||
'fps': self.capture.get(cv2.CAP_PROP_FPS),
|
||||
'frame_count': int(self.capture.get(cv2.CAP_PROP_FRAME_COUNT)),
|
||||
'current_position': int(self.capture.get(cv2.CAP_PROP_POS_FRAMES)),
|
||||
'codec': int(self.capture.get(cv2.CAP_PROP_FOURCC)),
|
||||
'brightness': self.capture.get(cv2.CAP_PROP_BRIGHTNESS),
|
||||
'contrast': self.capture.get(cv2.CAP_PROP_CONTRAST),
|
||||
'saturation': self.capture.get(cv2.CAP_PROP_SATURATION),
|
||||
'is_video_file': self.video_file is not None
|
||||
}
|
||||
|
||||
return properties
|
||||
|
||||
except Exception:
|
||||
return {}
|
||||
|
||||
def seek_frame(self, frame_number: int) -> bool:
|
||||
"""
|
||||
跳转到指定帧（仅适用于视频文件）
|
||||
|
||||
Args:
|
||||
frame_number: 目标帧号
|
||||
|
||||
Returns:
|
||||
bool: 跳转是否成功
|
||||
"""
|
||||
if not self.is_opened or not self.capture or not self.video_file:
|
||||
return False
|
||||
|
||||
try:
|
||||
self.capture.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
|
||||
return True
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def restart_video(self) -> bool:
|
||||
"""
|
||||
重新开始播放视频（仅适用于视频文件）
|
||||
|
||||
Returns:
|
||||
bool: 重启是否成功
|
||||
"""
|
||||
if not self.video_file:
|
||||
return False
|
||||
|
||||
return self.seek_frame(0)
|
||||
9
src/output/__init__.py
Normal file
9
src/output/__init__.py
Normal file
@ -0,0 +1,9 @@
|
||||
"""
|
||||
状态输出处理模块
|
||||
输出检测结果、日志记录等功能
|
||||
"""
|
||||
|
||||
from .result_formatter import ResultFormatter
|
||||
from .logger import LEDLogger
|
||||
|
||||
__all__ = ['ResultFormatter', 'LEDLogger']
|
||||
278
src/output/logger.py
Normal file
278
src/output/logger.py
Normal file
@ -0,0 +1,278 @@
|
||||
"""
|
||||
LED检测系统日志记录器
|
||||
提供统一的日志记录和结果存储功能
|
||||
"""
|
||||
|
||||
import os
|
||||
import logging
|
||||
import json
|
||||
from datetime import datetime
|
||||
from typing import Dict, Any, Optional
|
||||
from pathlib import Path
|
||||
|
||||
from ..roi_detection.led_detector import LEDDetectionResult
|
||||
from .result_formatter import ResultFormatter
|
||||
|
||||
|
||||
class LEDLogger:
|
||||
"""
|
||||
LED检测系统日志记录器
|
||||
支持日志记录和检测结果存储
|
||||
"""
|
||||
|
||||
def __init__(self, config: Optional[Dict[str, Any]] = None):
|
||||
"""
|
||||
初始化日志记录器
|
||||
|
||||
Args:
|
||||
config: 日志配置
|
||||
"""
|
||||
self.config = config or self._get_default_config()
|
||||
|
||||
# 提取配置参数
|
||||
logging_config = self.config.get('logging', {})
|
||||
|
||||
self.log_level = logging_config.get('log_level', 'INFO')
|
||||
self.log_file = logging_config.get('log_file', 'logs/led_detection.log')
|
||||
self.save_debug_images = logging_config.get('save_debug_images', False)
|
||||
self.debug_image_path = logging_config.get('debug_image_path', 'debug/')
|
||||
|
||||
# 创建目录
|
||||
self._create_directories()
|
||||
|
||||
# 配置日志
|
||||
self._setup_logging()
|
||||
|
||||
# 初始化结果格式化器
|
||||
self.formatter = ResultFormatter()
|
||||
|
||||
# 统计信息
|
||||
self.total_frames = 0
|
||||
self.start_time = datetime.now()
|
||||
|
||||
def _get_default_config(self) -> Dict[str, Any]:
|
||||
"""
|
||||
获取默认配置
|
||||
"""
|
||||
return {
|
||||
'logging': {
|
||||
'log_level': 'INFO',
|
||||
'log_file': 'logs/led_detection.log',
|
||||
'save_debug_images': False,
|
||||
'debug_image_path': 'debug/'
|
||||
}
|
||||
}
|
||||
|
||||
def _create_directories(self) -> None:
|
||||
"""
|
||||
创建所需的目录
|
||||
"""
|
||||
# 创建日志目录
|
||||
log_dir = Path(self.log_file).parent
|
||||
log_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# 创建调试图像目录
|
||||
if self.save_debug_images:
|
||||
debug_dir = Path(self.debug_image_path)
|
||||
debug_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
def _setup_logging(self) -> None:
|
||||
"""
|
||||
配置日志系统
|
||||
"""
|
||||
# 设置日志级别
|
||||
level = getattr(logging, self.log_level.upper(), logging.INFO)
|
||||
|
||||
# 配置日志格式
|
||||
formatter = logging.Formatter(
|
||||
'%(asctime)s - %(name)s - %(levelname)s - %(message)s',
|
||||
datefmt='%Y-%m-%d %H:%M:%S'
|
||||
)
|
||||
|
||||
# 创建logger
|
||||
self.logger = logging.getLogger('YantaiVisionX')
|
||||
self.logger.setLevel(level)
|
||||
|
||||
# 清除现有的handlers
|
||||
self.logger.handlers.clear()
|
||||
|
||||
# 文件handler
|
||||
file_handler = logging.FileHandler(self.log_file, encoding='utf-8')
|
||||
file_handler.setFormatter(formatter)
|
||||
self.logger.addHandler(file_handler)
|
||||
|
||||
# 控制台handler
|
||||
console_handler = logging.StreamHandler()
|
||||
console_handler.setFormatter(formatter)
|
||||
self.logger.addHandler(console_handler)
|
||||
|
||||
# 记录启动信息
|
||||
self.logger.info("LED检测系统日志系统初始化完成")
|
||||
|
||||
def log_detection_result(self, result: LEDDetectionResult) -> None:
|
||||
"""
|
||||
记录检测结果
|
||||
|
||||
Args:
|
||||
result: LED检测结果
|
||||
"""
|
||||
self.total_frames += 1
|
||||
|
||||
# 基本信息
|
||||
self.logger.info(f"帧#{result.frame_count} 处理完成 - "
|
||||
f"用时: {result.processing_time*1000:.1f}ms")
|
||||
|
||||
# 状态统计
|
||||
summary = result.detection_summary
|
||||
if 'threshold_detection' in summary:
|
||||
states = summary['threshold_detection']['states']
|
||||
self.logger.info(f"LED状态: 亮{states.get('on', 0)}盏, "
|
||||
f"灭{states.get('off', 0)}盏, "
|
||||
f"不确定{states.get('uncertain', 0)}盏")
|
||||
|
||||
# 稳定性信息
|
||||
if 'stability_info' in summary:
|
||||
stability = summary['stability_info']
|
||||
self.logger.debug(f"稳定性: {stability.get('avg_stability', 0):.2f}, "
|
||||
f"FPS: {stability.get('processing_fps', 0):.1f}")
|
||||
|
||||
def log_error(self, error_msg: str, exception: Optional[Exception] = None) -> None:
|
||||
"""
|
||||
记录错误信息
|
||||
|
||||
Args:
|
||||
error_msg: 错误消息
|
||||
exception: 异常对象
|
||||
"""
|
||||
if exception:
|
||||
self.logger.error(f"{error_msg}: {str(exception)}", exc_info=True)
|
||||
else:
|
||||
self.logger.error(error_msg)
|
||||
|
||||
def log_warning(self, warning_msg: str) -> None:
|
||||
"""
|
||||
记录警告信息
|
||||
|
||||
Args:
|
||||
warning_msg: 警告消息
|
||||
"""
|
||||
self.logger.warning(warning_msg)
|
||||
|
||||
def log_info(self, info_msg: str) -> None:
|
||||
"""
|
||||
记录信息
|
||||
|
||||
Args:
|
||||
info_msg: 信息内容
|
||||
"""
|
||||
self.logger.info(info_msg)
|
||||
|
||||
def log_debug(self, debug_msg: str) -> None:
|
||||
"""
|
||||
记录调试信息
|
||||
|
||||
Args:
|
||||
debug_msg: 调试消息
|
||||
"""
|
||||
self.logger.debug(debug_msg)
|
||||
|
||||
def save_result_to_file(self, result: LEDDetectionResult,
|
||||
format_type: str = 'json',
|
||||
filename: Optional[str] = None) -> str:
|
||||
"""
|
||||
将检测结果保存到文件
|
||||
|
||||
Args:
|
||||
result: LED检测结果
|
||||
format_type: 格式类型 ('json', 'text')
|
||||
filename: 文件名，不指定则自动生成
|
||||
|
||||
Returns:
|
||||
str: 保存的文件路径
|
||||
"""
|
||||
if filename is None:
|
||||
timestamp = datetime.fromtimestamp(result.timestamp).strftime('%Y%m%d_%H%M%S')
|
||||
ext = 'json' if format_type == 'json' else 'txt'
|
||||
filename = f"result_{timestamp}_frame{result.frame_count}.{ext}"
|
||||
|
||||
# 确定保存目录
|
||||
results_dir = Path('results')
|
||||
results_dir.mkdir(exist_ok=True)
|
||||
filepath = results_dir / filename
|
||||
|
||||
try:
|
||||
if format_type == 'json':
|
||||
content = self.formatter.format_to_json(result, include_details=True)
|
||||
else:
|
||||
content = self.formatter.format_to_simple_text(result)
|
||||
|
||||
with open(filepath, 'w', encoding='utf-8') as f:
|
||||
f.write(content)
|
||||
|
||||
self.logger.debug(f"结果已保存到: {filepath}")
|
||||
return str(filepath)
|
||||
|
||||
except Exception as e:
|
||||
self.log_error(f"保存结果文件失败", e)
|
||||
return ""
|
||||
|
||||
def save_debug_image(self, image, filename: str) -> str:
|
||||
"""
|
||||
保存调试图像
|
||||
|
||||
Args:
|
||||
image: 图像数据
|
||||
filename: 文件名
|
||||
|
||||
Returns:
|
||||
str: 保存的文件路径
|
||||
"""
|
||||
if not self.save_debug_images:
|
||||
return ""
|
||||
|
||||
try:
|
||||
import cv2
|
||||
filepath = Path(self.debug_image_path) / filename
|
||||
cv2.imwrite(str(filepath), image)
|
||||
|
||||
self.logger.debug(f"调试图像已保存: {filepath}")
|
||||
return str(filepath)
|
||||
|
||||
except Exception as e:
|
||||
self.log_error(f"保存调试图像失败", e)
|
||||
return ""
|
||||
|
||||
def get_session_statistics(self) -> Dict[str, Any]:
|
||||
"""
|
||||
获取会话统计信息
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: 统计信息
|
||||
"""
|
||||
current_time = datetime.now()
|
||||
session_duration = current_time - self.start_time
|
||||
|
||||
return {
|
||||
'session_start': self.start_time.isoformat(),
|
||||
'current_time': current_time.isoformat(),
|
||||
'session_duration_seconds': session_duration.total_seconds(),
|
||||
'total_frames_processed': self.total_frames,
|
||||
'average_fps': (self.total_frames / session_duration.total_seconds()
|
||||
if session_duration.total_seconds() > 0 else 0),
|
||||
'log_file': self.log_file,
|
||||
'debug_images_enabled': self.save_debug_images
|
||||
}
|
||||
|
||||
def close(self) -> None:
|
||||
"""
|
||||
关闭日志系统
|
||||
"""
|
||||
# 记录结束信息
|
||||
stats = self.get_session_statistics()
|
||||
self.logger.info(f"LED检测系统结束 - 处理了{stats['total_frames_processed']}帧, "
|
||||
f"运行时间: {stats['session_duration_seconds']:.1f}秒")
|
||||
|
||||
# 关闭所有handlers
|
||||
for handler in self.logger.handlers:
|
||||
handler.close()
|
||||
self.logger.removeHandler(handler)
|
||||
264
src/output/result_formatter.py
Normal file
264
src/output/result_formatter.py
Normal file
@ -0,0 +1,264 @@
|
||||
"""
|
||||
结果格式化器
|
||||
将LED检测结果格式化为各种输出格式
|
||||
"""
|
||||
|
||||
import json
|
||||
import time
|
||||
import numpy as np
|
||||
from typing import Dict, List, Any, Optional
|
||||
from datetime import datetime
|
||||
|
||||
from ..roi_detection.led_detector import LEDDetectionResult
|
||||
from ..roi_detection.threshold_detector import LEDState
|
||||
|
||||
|
||||
class ResultFormatter:
|
||||
"""
|
||||
结果格式化器
|
||||
支持多种输出格式：JSON、简单文本、矩阵格式等
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
"""
|
||||
初始化结果格式化器
|
||||
"""
|
||||
pass
|
||||
|
||||
def format_to_json(self, result: LEDDetectionResult,
|
||||
include_details: bool = False) -> str:
|
||||
"""
|
||||
格式化为JSON字符串
|
||||
|
||||
Args:
|
||||
result: LED检测结果
|
||||
include_details: 是否包含详细信息
|
||||
|
||||
Returns:
|
||||
str: JSON格式的结果
|
||||
"""
|
||||
output_data = {
|
||||
'timestamp': result.timestamp,
|
||||
'datetime': datetime.fromtimestamp(result.timestamp).isoformat(),
|
||||
'frame_count': result.frame_count,
|
||||
'processing_time_ms': round(result.processing_time * 1000, 2),
|
||||
'led_states': {},
|
||||
'summary': result.detection_summary
|
||||
}
|
||||
|
||||
# 添加LED状态
|
||||
for roi_name, stable_result in result.stable_states.items():
|
||||
led_data = {
|
||||
'state': stable_result.led_state.name,
|
||||
'stability': round(stable_result.stability, 3),
|
||||
'confidence': round(stable_result.confidence, 3)
|
||||
}
|
||||
|
||||
if include_details:
|
||||
led_data.update({
|
||||
'frame_count': stable_result.frame_count,
|
||||
'last_update': stable_result.last_update_time
|
||||
})
|
||||
|
||||
output_data['led_states'][roi_name] = led_data
|
||||
|
||||
return json.dumps(output_data, indent=2, ensure_ascii=False)
|
||||
|
||||
def format_to_matrix(self, result: LEDDetectionResult) -> np.ndarray:
|
||||
"""
|
||||
格式化为3x6矩阵
|
||||
|
||||
Args:
|
||||
result: LED检测结果
|
||||
|
||||
Returns:
|
||||
np.ndarray: 3x6矩阵，1=亮，0=灭，-1=不确定
|
||||
"""
|
||||
matrix = np.full((3, 6), -1, dtype=int)
|
||||
|
||||
for roi_name, stable_result in result.stable_states.items():
|
||||
try:
|
||||
if len(roi_name) >= 4 and roi_name[0] == 'R' and roi_name[2] == 'C':
|
||||
row = int(roi_name[1]) - 1
|
||||
col = int(roi_name[3]) - 1
|
||||
|
||||
if 0 <= row < 3 and 0 <= col < 6:
|
||||
if stable_result.led_state == LEDState.ON:
|
||||
matrix[row, col] = 1
|
||||
elif stable_result.led_state == LEDState.OFF:
|
||||
matrix[row, col] = 0
|
||||
except (ValueError, IndexError):
|
||||
continue
|
||||
|
||||
return matrix
|
||||
|
||||
def format_to_simple_text(self, result: LEDDetectionResult) -> str:
|
||||
"""
|
||||
格式化为简单文本
|
||||
|
||||
Args:
|
||||
result: LED检测结果
|
||||
|
||||
Returns:
|
||||
str: 简单文本格式
|
||||
"""
|
||||
lines = []
|
||||
lines.append(f"LED检测结果 - {datetime.fromtimestamp(result.timestamp).strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
lines.append(f"处理时间: {result.processing_time*1000:.1f}ms")
|
||||
lines.append(f"帧数: {result.frame_count}")
|
||||
lines.append("")
|
||||
|
||||
# 按行显示LED状态
|
||||
matrix = self.format_to_matrix(result)
|
||||
state_chars = {1: '●', 0: '○', -1: '?'} # •●○
|
||||
|
||||
for row in range(3):
|
||||
row_states = []
|
||||
for col in range(6):
|
||||
roi_name = f"R{row+1}C{col+1}"
|
||||
state_val = matrix[row, col]
|
||||
char = state_chars.get(state_val, '?')
|
||||
|
||||
# 添加稳定性信息
|
||||
if roi_name in result.stable_states:
|
||||
stability = result.stable_states[roi_name].stability
|
||||
row_states.append(f"{char}({stability:.1f})")
|
||||
else:
|
||||
row_states.append(f"{char}(-)")
|
||||
|
||||
lines.append(f"第{row+1}行: {' '.join(row_states)}")
|
||||
|
||||
lines.append("")
|
||||
|
||||
# 添加统计信息
|
||||
summary = result.detection_summary
|
||||
if 'threshold_detection' in summary:
|
||||
states = summary['threshold_detection']['states']
|
||||
lines.append(f"状态统计: 亮{states.get('on', 0)}盏, 灭{states.get('off', 0)}盏, 不确定{states.get('uncertain', 0)}盏")
|
||||
|
||||
return '\n'.join(lines)
|
||||
|
||||
def format_to_csv(self, results: List[LEDDetectionResult]) -> str:
|
||||
"""
|
||||
格式化为CSV格式（用于批量结果）
|
||||
|
||||
Args:
|
||||
results: LED检测结果列表
|
||||
|
||||
Returns:
|
||||
str: CSV格式的数据
|
||||
"""
|
||||
if not results:
|
||||
return ""
|
||||
|
||||
# CSV头部
|
||||
roi_names = [f"R{r}C{c}" for r in range(1, 4) for c in range(1, 7)]
|
||||
headers = ['timestamp', 'datetime', 'frame_count', 'processing_time_ms']
|
||||
|
||||
# 添加每个ROI的状态列
|
||||
for roi_name in roi_names:
|
||||
headers.extend([f"{roi_name}_state", f"{roi_name}_stability", f"{roi_name}_confidence"])
|
||||
|
||||
lines = [','.join(headers)]
|
||||
|
||||
# 数据行
|
||||
for result in results:
|
||||
row_data = [
|
||||
str(result.timestamp),
|
||||
datetime.fromtimestamp(result.timestamp).isoformat(),
|
||||
str(result.frame_count),
|
||||
f"{result.processing_time*1000:.2f}"
|
||||
]
|
||||
|
||||
# 添加每个ROI的数据
|
||||
for roi_name in roi_names:
|
||||
if roi_name in result.stable_states:
|
||||
stable_result = result.stable_states[roi_name]
|
||||
row_data.extend([
|
||||
stable_result.led_state.name,
|
||||
f"{stable_result.stability:.3f}",
|
||||
f"{stable_result.confidence:.3f}"
|
||||
])
|
||||
else:
|
||||
row_data.extend(['UNKNOWN', '0.0', '0.0'])
|
||||
|
||||
lines.append(','.join(row_data))
|
||||
|
||||
return '\n'.join(lines)
|
||||
|
||||
def format_to_api_response(self, result: LEDDetectionResult) -> Dict[str, Any]:
|
||||
"""
|
||||
格式化为API响应格式
|
||||
|
||||
Args:
|
||||
result: LED检测结果
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: API响应数据
|
||||
"""
|
||||
# 统计状态数量
|
||||
state_counts = {'on': 0, 'off': 0, 'uncertain': 0}
|
||||
|
||||
led_states = {}
|
||||
for roi_name, stable_result in result.stable_states.items():
|
||||
state_name = stable_result.led_state.name.lower()
|
||||
state_counts[state_name] = state_counts.get(state_name, 0) + 1
|
||||
|
||||
led_states[roi_name] = {
|
||||
'state': stable_result.led_state.name,
|
||||
'stability': round(stable_result.stability, 3),
|
||||
'confidence': round(stable_result.confidence, 3)
|
||||
}
|
||||
|
||||
return {
|
||||
'success': True,
|
||||
'timestamp': result.timestamp,
|
||||
'data': {
|
||||
'led_states': led_states,
|
||||
'matrix': self.format_to_matrix(result).tolist(),
|
||||
'statistics': {
|
||||
'total': len(result.stable_states),
|
||||
'on_count': state_counts['on'],
|
||||
'off_count': state_counts['off'],
|
||||
'uncertain_count': state_counts['uncertain']
|
||||
},
|
||||
'performance': {
|
||||
'processing_time_ms': round(result.processing_time * 1000, 2),
|
||||
'frame_count': result.frame_count
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
def format_matrix_visual(self, matrix: np.ndarray,
|
||||
symbols: Optional[Dict[int, str]] = None) -> str:
|
||||
"""
|
||||
格式化矩阵为可视化文本
|
||||
|
||||
Args:
|
||||
matrix: 3x6LED状态矩阵
|
||||
symbols: 状态符号映射
|
||||
|
||||
Returns:
|
||||
str: 可视化的矩阵文本
|
||||
"""
|
||||
if symbols is None:
|
||||
symbols = {1: '●', 0: '○', -1: '?'} # 默认符号
|
||||
|
||||
lines = []
|
||||
lines.append("▬" * 25) # 分隔线
|
||||
lines.append(" LED灯阵状态显示")
|
||||
lines.append("▬" * 25)
|
||||
|
||||
for row in range(matrix.shape[0]):
|
||||
row_display = []
|
||||
for col in range(matrix.shape[1]):
|
||||
state = matrix[row, col]
|
||||
symbol = symbols.get(state, '?')
|
||||
row_display.append(f" {symbol} ")
|
||||
|
||||
lines.append(f"第{row+1}行: {''.join(row_display)}")
|
||||
|
||||
lines.append("▬" * 25)
|
||||
lines.append("说明: ●=亮 ○=灭 ?=不确定")
|
||||
|
||||
return '\n'.join(lines)
|
||||
10
src/preprocessing/__init__.py
Normal file
10
src/preprocessing/__init__.py
Normal file
@ -0,0 +1,10 @@
|
||||
"""
|
||||
图像预处理模块
|
||||
透视校正、去雾增强等图像预处理功能
|
||||
"""
|
||||
|
||||
from .geometry_correction import GeometryCorrection
|
||||
from .defogging import DefogProcessor
|
||||
from .image_enhancer import ImageEnhancer
|
||||
|
||||
__all__ = ['GeometryCorrection', 'DefogProcessor', 'ImageEnhancer']
|
||||
262
src/preprocessing/defogging.py
Normal file
262
src/preprocessing/defogging.py
Normal file
@ -0,0 +1,262 @@
|
||||
"""
|
||||
去雾增强算法模块
|
||||
专门针对雾天环境的图像增强处理，提升LED灯检测效果
|
||||
"""
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
from typing import Optional, Tuple
|
||||
|
||||
|
||||
class DefogProcessor:
|
||||
"""
|
||||
去雾处理器
|
||||
实现多种去雾和图像增强算法，提升雾天环境下的LED检测效果
|
||||
"""
|
||||
|
||||
def __init__(self, clahe_clip_limit: float = 2.0,
|
||||
clahe_grid_size: Tuple[int, int] = (8, 8),
|
||||
gamma: float = 0.7):
|
||||
"""
|
||||
初始化去雾处理器
|
||||
|
||||
Args:
|
||||
clahe_clip_limit: CLAHE算法的对比度限制参数
|
||||
clahe_grid_size: CLAHE网格大小
|
||||
gamma: Gamma校正参数，<1提亮，>1变暗
|
||||
"""
|
||||
self.clahe_clip_limit = clahe_clip_limit
|
||||
self.clahe_grid_size = clahe_grid_size
|
||||
self.gamma = gamma
|
||||
|
||||
# 创建CLAHE对象
|
||||
self.clahe = cv2.createCLAHE(clipLimit=clahe_clip_limit,
|
||||
tileGridSize=clahe_grid_size)
|
||||
|
||||
def apply_clahe(self, image: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
应用CLAHE（对比度限制自适应直方图均衡化）
|
||||
|
||||
Args:
|
||||
image: 输入图像
|
||||
|
||||
Returns:
|
||||
np.ndarray: CLAHE处理后的图像
|
||||
"""
|
||||
if len(image.shape) == 3:
|
||||
# 彩色图像，转换到LAB色彩空间
|
||||
lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
|
||||
l, a, b = cv2.split(lab)
|
||||
|
||||
# 只对亮度通道应用CLAHE
|
||||
l = self.clahe.apply(l)
|
||||
|
||||
# 合并通道并转回BGR
|
||||
lab = cv2.merge([l, a, b])
|
||||
result = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR)
|
||||
else:
|
||||
# 灰度图像直接应用CLAHE
|
||||
result = self.clahe.apply(image)
|
||||
|
||||
return result
|
||||
|
||||
def apply_gamma_correction(self, image: np.ndarray, gamma: Optional[float] = None) -> np.ndarray:
|
||||
"""
|
||||
应用Gamma校正
|
||||
|
||||
Args:
|
||||
image: 输入图像
|
||||
gamma: Gamma值，如果为None则使用初始化时的值
|
||||
|
||||
Returns:
|
||||
np.ndarray: Gamma校正后的图像
|
||||
"""
|
||||
if gamma is None:
|
||||
gamma = self.gamma
|
||||
|
||||
# 构建查找表
|
||||
inv_gamma = 1.0 / gamma
|
||||
table = np.array([((i / 255.0) ** inv_gamma) * 255 for i in range(256)]).astype(np.uint8)
|
||||
|
||||
# 应用查找表
|
||||
return cv2.LUT(image, table)
|
||||
|
||||
def apply_unsharp_mask(self, image: np.ndarray,
|
||||
kernel_size: int = 5,
|
||||
sigma: float = 1.0,
|
||||
amount: float = 1.0) -> np.ndarray:
|
||||
"""
|
||||
应用锐化掩码增强图像细节
|
||||
|
||||
Args:
|
||||
image: 输入图像
|
||||
kernel_size: 高斯模糊核大小
|
||||
sigma: 高斯模糊标准差
|
||||
amount: 锐化强度
|
||||
|
||||
Returns:
|
||||
np.ndarray: 锐化后的图像
|
||||
"""
|
||||
# 创建模糊版本
|
||||
blurred = cv2.GaussianBlur(image, (kernel_size, kernel_size), sigma)
|
||||
|
||||
# 创建锐化掩码
|
||||
mask = cv2.subtract(image, blurred)
|
||||
|
||||
# 应用锐化
|
||||
sharpened = cv2.addWeighted(image, 1.0, mask, amount, 0)
|
||||
|
||||
return sharpened
|
||||
|
||||
def apply_frequency_domain_enhancement(self, image: np.ndarray,
|
||||
high_pass_ratio: float = 0.1) -> np.ndarray:
|
||||
"""
|
||||
频域高通滤波，抑制低频光晕
|
||||
|
||||
Args:
|
||||
image: 输入图像（灰度）
|
||||
high_pass_ratio: 高通滤波比例
|
||||
|
||||
Returns:
|
||||
np.ndarray: 高通滤波后的图像
|
||||
"""
|
||||
if len(image.shape) == 3:
|
||||
# 转换为灰度图像
|
||||
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
||||
else:
|
||||
gray = image.copy()
|
||||
|
||||
# 快速傅里叶变换
|
||||
f_transform = np.fft.fft2(gray)
|
||||
f_shift = np.fft.fftshift(f_transform)
|
||||
|
||||
# 创建高通滤波器
|
||||
rows, cols = gray.shape
|
||||
crow, ccol = rows // 2, cols // 2
|
||||
|
||||
# 创建掩码
|
||||
mask = np.ones((rows, cols), dtype=np.uint8)
|
||||
r = int(min(rows, cols) * high_pass_ratio)
|
||||
mask[crow-r:crow+r, ccol-r:ccol+r] = 0
|
||||
|
||||
# 应用掩码
|
||||
f_shift_filtered = f_shift * mask
|
||||
|
||||
# 反变换
|
||||
f_ishift = np.fft.ifftshift(f_shift_filtered)
|
||||
img_back = np.fft.ifft2(f_ishift)
|
||||
img_back = np.abs(img_back)
|
||||
|
||||
# 归一化到0-255
|
||||
img_back = np.uint8(cv2.normalize(img_back, None, 0, 255, cv2.NORM_MINMAX))
|
||||
|
||||
return img_back
|
||||
|
||||
def apply_atmospheric_light_estimation(self, image: np.ndarray) -> Tuple[np.ndarray, float]:
|
||||
"""
|
||||
估算大气光值并进行去雾处理
|
||||
|
||||
Args:
|
||||
image: 输入图像
|
||||
|
||||
Returns:
|
||||
Tuple[np.ndarray, float]: (去雾后的图像, 估算的大气光值)
|
||||
"""
|
||||
if len(image.shape) == 3:
|
||||
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
||||
else:
|
||||
gray = image.copy()
|
||||
|
||||
# 估算大气光值（取最亮的0.1%像素的平均值）
|
||||
flat = gray.flatten()
|
||||
flat_sorted = np.sort(flat)[::-1]
|
||||
top_pixels = int(len(flat) * 0.001) # 取前0.1%
|
||||
atmospheric_light = np.mean(flat_sorted[:max(1, top_pixels)])
|
||||
|
||||
# 简单的去雾处理
|
||||
normalized = gray.astype(np.float64) / 255.0
|
||||
atmospheric_light_norm = atmospheric_light / 255.0
|
||||
|
||||
# 传输图估算
|
||||
transmission = 1 - 0.95 * (normalized / atmospheric_light_norm)
|
||||
transmission = np.clip(transmission, 0.1, 1.0)
|
||||
|
||||
# 恢复图像
|
||||
recovered = (normalized - atmospheric_light_norm) / transmission + atmospheric_light_norm
|
||||
recovered = np.clip(recovered * 255, 0, 255).astype(np.uint8)
|
||||
|
||||
return recovered, atmospheric_light
|
||||
|
||||
def enhance_for_fog(self, image: np.ndarray,
|
||||
mode: str = "comprehensive") -> np.ndarray:
|
||||
"""
|
||||
综合去雾增强处理
|
||||
|
||||
Args:
|
||||
image: 输入图像
|
||||
mode: 处理模式 ("light", "medium", "comprehensive")
|
||||
|
||||
Returns:
|
||||
np.ndarray: 增强后的图像
|
||||
"""
|
||||
result = image.copy()
|
||||
|
||||
if mode == "light":
|
||||
# 轻度处理：只应用CLAHE和轻微Gamma校正
|
||||
result = self.apply_clahe(result)
|
||||
result = self.apply_gamma_correction(result, gamma=0.9)
|
||||
|
||||
elif mode == "medium":
|
||||
# 中度处理：CLAHE + Gamma + 锐化
|
||||
result = self.apply_clahe(result)
|
||||
result = self.apply_gamma_correction(result)
|
||||
result = self.apply_unsharp_mask(result, amount=0.5)
|
||||
|
||||
elif mode == "comprehensive":
|
||||
# 综合处理：全套增强算法
|
||||
# 1. CLAHE增强对比度
|
||||
result = self.apply_clahe(result)
|
||||
|
||||
# 2. Gamma校正提亮暗部
|
||||
result = self.apply_gamma_correction(result)
|
||||
|
||||
# 3. 锐化增强细节
|
||||
result = self.apply_unsharp_mask(result)
|
||||
|
||||
# 4. 对于严重雾天，应用频域滤波
|
||||
if len(result.shape) == 3:
|
||||
gray = cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)
|
||||
enhanced_gray = self.apply_frequency_domain_enhancement(gray)
|
||||
|
||||
# 将增强的灰度信息融合回彩色图像
|
||||
result_gray = cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)
|
||||
ratio = enhanced_gray.astype(np.float32) / (result_gray.astype(np.float32) + 1e-7)
|
||||
|
||||
for i in range(3):
|
||||
result[:, :, i] = np.clip(result[:, :, i].astype(np.float32) * ratio, 0, 255)
|
||||
|
||||
result = result.astype(np.uint8)
|
||||
|
||||
return result
|
||||
|
||||
def update_parameters(self, clahe_clip_limit: Optional[float] = None,
|
||||
clahe_grid_size: Optional[Tuple[int, int]] = None,
|
||||
gamma: Optional[float] = None) -> None:
|
||||
"""
|
||||
更新处理参数
|
||||
|
||||
Args:
|
||||
clahe_clip_limit: 新的CLAHE对比度限制参数
|
||||
clahe_grid_size: 新的CLAHE网格大小
|
||||
gamma: 新的Gamma校正参数
|
||||
"""
|
||||
if clahe_clip_limit is not None:
|
||||
self.clahe_clip_limit = clahe_clip_limit
|
||||
if clahe_grid_size is not None:
|
||||
self.clahe_grid_size = clahe_grid_size
|
||||
if gamma is not None:
|
||||
self.gamma = gamma
|
||||
|
||||
# 重新创建CLAHE对象
|
||||
self.clahe = cv2.createCLAHE(clipLimit=self.clahe_clip_limit,
|
||||
tileGridSize=self.clahe_grid_size)
|
||||
191
src/preprocessing/geometry_correction.py
Normal file
191
src/preprocessing/geometry_correction.py
Normal file
@ -0,0 +1,191 @@
|
||||
"""
|
||||
透视几何校正模块
|
||||
处理摄像头倾斜拍摄造成的透视变形，将倾斜的灯阵校正为规则矩形
|
||||
"""
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
from typing import Tuple, List, Optional
|
||||
|
||||
|
||||
class GeometryCorrection:
|
||||
"""
|
||||
透视几何校正类
|
||||
用于校正摄像头视角倾斜造成的透视变形
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.correction_matrix = None
|
||||
self.is_calibrated = False
|
||||
|
||||
def set_perspective_points(self, src_points: List[Tuple[int, int]],
|
||||
dst_points: List[Tuple[int, int]]) -> bool:
|
||||
"""
|
||||
设置透视变换的源点和目标点
|
||||
|
||||
Args:
|
||||
src_points: 原图像中的四个角点 [(x1,y1), (x2,y2), (x3,y3), (x4,y4)]
|
||||
dst_points: 校正后的四个角点 [(x1,y1), (x2,y2), (x3,y3), (x4,y4)]
|
||||
|
||||
Returns:
|
||||
bool: 校正矩阵计算是否成功
|
||||
"""
|
||||
if len(src_points) != 4 or len(dst_points) != 4:
|
||||
return False
|
||||
|
||||
try:
|
||||
src_array = np.float32(src_points)
|
||||
dst_array = np.float32(dst_points)
|
||||
|
||||
self.correction_matrix = cv2.getPerspectiveTransform(src_array, dst_array)
|
||||
self.is_calibrated = True
|
||||
return True
|
||||
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def set_correction_matrix(self, matrix: np.ndarray) -> bool:
|
||||
"""
|
||||
直接设置校正矩阵
|
||||
|
||||
Args:
|
||||
matrix: 3x3透视变换矩阵
|
||||
|
||||
Returns:
|
||||
bool: 设置是否成功
|
||||
"""
|
||||
if matrix.shape == (3, 3):
|
||||
self.correction_matrix = matrix
|
||||
self.is_calibrated = True
|
||||
return True
|
||||
return False
|
||||
|
||||
def auto_detect_corners(self, image: np.ndarray,
|
||||
grid_size: Tuple[int, int] = (6, 3)) -> Optional[List[Tuple[int, int]]]:
|
||||
"""
|
||||
自动检测图像中的矩形角点（用于灯阵边界检测）
|
||||
|
||||
Args:
|
||||
image: 输入图像
|
||||
grid_size: 灯阵网格大小 (columns, rows)
|
||||
|
||||
Returns:
|
||||
Optional[List[Tuple[int, int]]]: 检测到的四个角点，失败返回None
|
||||
"""
|
||||
try:
|
||||
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if len(image.shape) == 3 else image
|
||||
|
||||
# 使用边缘检测找到灯阵边界
|
||||
edges = cv2.Canny(gray, 50, 150, apertureSize=3)
|
||||
|
||||
# 找到轮廓
|
||||
contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
if not contours:
|
||||
return None
|
||||
|
||||
# 找到最大的四边形轮廓
|
||||
largest_contour = max(contours, key=cv2.contourArea)
|
||||
|
||||
# 近似为四边形
|
||||
epsilon = 0.02 * cv2.arcLength(largest_contour, True)
|
||||
approx = cv2.approxPolyDP(largest_contour, epsilon, True)
|
||||
|
||||
if len(approx) == 4:
|
||||
corners = [(point[0][0], point[0][1]) for point in approx]
|
||||
# 按顺序排列：左上、右上、右下、左下
|
||||
corners = self._sort_corners(corners)
|
||||
return corners
|
||||
|
||||
return None
|
||||
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
def _sort_corners(self, corners: List[Tuple[int, int]]) -> List[Tuple[int, int]]:
|
||||
"""
|
||||
将角点按照左上、右上、右下、左下的顺序排列
|
||||
"""
|
||||
# 计算中心点
|
||||
cx = sum([p[0] for p in corners]) / 4
|
||||
cy = sum([p[1] for p in corners]) / 4
|
||||
|
||||
# 按距离中心点的角度排列
|
||||
def get_angle(point):
|
||||
return np.arctan2(point[1] - cy, point[0] - cx)
|
||||
|
||||
sorted_corners = sorted(corners, key=get_angle)
|
||||
|
||||
# 重新排列为左上、右上、右下、左下
|
||||
# 找到最左上角的点
|
||||
top_points = sorted(sorted_corners, key=lambda p: p[1])[:2]
|
||||
top_left = min(top_points, key=lambda p: p[0])
|
||||
top_right = max(top_points, key=lambda p: p[0])
|
||||
|
||||
bottom_points = sorted(sorted_corners, key=lambda p: p[1])[2:]
|
||||
bottom_left = min(bottom_points, key=lambda p: p[0])
|
||||
bottom_right = max(bottom_points, key=lambda p: p[0])
|
||||
|
||||
return [top_left, top_right, bottom_right, bottom_left]
|
||||
|
||||
def correct_image(self, image: np.ndarray,
|
||||
output_size: Optional[Tuple[int, int]] = None) -> Optional[np.ndarray]:
|
||||
"""
|
||||
对图像进行透视校正
|
||||
|
||||
Args:
|
||||
image: 输入图像
|
||||
output_size: 输出图像尺寸 (width, height)，默认与输入相同
|
||||
|
||||
Returns:
|
||||
Optional[np.ndarray]: 校正后的图像，失败返回None
|
||||
"""
|
||||
if not self.is_calibrated or self.correction_matrix is None:
|
||||
return None
|
||||
|
||||
try:
|
||||
h, w = image.shape[:2]
|
||||
if output_size is None:
|
||||
output_size = (w, h)
|
||||
|
||||
corrected = cv2.warpPerspective(image, self.correction_matrix, output_size)
|
||||
return corrected
|
||||
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
def save_calibration(self, filepath: str) -> bool:
|
||||
"""
|
||||
保存校正参数到文件
|
||||
|
||||
Args:
|
||||
filepath: 保存文件路径
|
||||
|
||||
Returns:
|
||||
bool: 保存是否成功
|
||||
"""
|
||||
if not self.is_calibrated or self.correction_matrix is None:
|
||||
return False
|
||||
|
||||
try:
|
||||
np.save(filepath, self.correction_matrix)
|
||||
return True
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def load_calibration(self, filepath: str) -> bool:
|
||||
"""
|
||||
从文件加载校正参数
|
||||
|
||||
Args:
|
||||
filepath: 校正文件路径
|
||||
|
||||
Returns:
|
||||
bool: 加载是否成功
|
||||
"""
|
||||
try:
|
||||
self.correction_matrix = np.load(filepath)
|
||||
self.is_calibrated = True
|
||||
return True
|
||||
except Exception:
|
||||
return False
|
||||
248
src/preprocessing/image_enhancer.py
Normal file
248
src/preprocessing/image_enhancer.py
Normal file
@ -0,0 +1,248 @@
|
||||
"""
|
||||
图像增强器
|
||||
集成多种图像预处理和增强算法，为后续ROI检测做准备
|
||||
"""
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
from typing import Optional, Tuple, Dict, Any
|
||||
|
||||
from .geometry_correction import GeometryCorrection
|
||||
from .defogging import DefogProcessor
|
||||
|
||||
|
||||
class ImageEnhancer:
|
||||
"""
|
||||
图像增强器
|
||||
集成透视校正、去雾增强等功能的统一接口
|
||||
"""
|
||||
|
||||
def __init__(self, config: Optional[Dict[str, Any]] = None):
|
||||
"""
|
||||
初始化图像增强器
|
||||
|
||||
Args:
|
||||
config: 配置参数字典
|
||||
"""
|
||||
self.config = config or self._get_default_config()
|
||||
|
||||
# 初始化各个处理模块
|
||||
self.geometry_corrector = GeometryCorrection()
|
||||
self.defog_processor = DefogProcessor(
|
||||
clahe_clip_limit=self.config['preprocessing']['defogging']['clahe_clip_limit'],
|
||||
clahe_grid_size=tuple(self.config['preprocessing']['defogging']['clahe_grid_size']),
|
||||
gamma=self.config['preprocessing']['defogging']['gamma_correction']
|
||||
)
|
||||
|
||||
# 加载透视校正参数
|
||||
if self.config['preprocessing']['perspective_correction']['enabled']:
|
||||
self._load_perspective_correction()
|
||||
|
||||
def _get_default_config(self) -> Dict[str, Any]:
|
||||
"""
|
||||
获取默认配置
|
||||
"""
|
||||
return {
|
||||
'preprocessing': {
|
||||
'perspective_correction': {
|
||||
'enabled': True,
|
||||
'auto_detect': False
|
||||
},
|
||||
'defogging': {
|
||||
'enabled': True,
|
||||
'clahe_clip_limit': 2.0,
|
||||
'clahe_grid_size': [8, 8],
|
||||
'gamma_correction': 0.7,
|
||||
'gaussian_blur_kernel': 3
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
def _load_perspective_correction(self) -> None:
|
||||
"""
|
||||
加载透视校正参数
|
||||
"""
|
||||
try:
|
||||
# 尝试从文件加载校正参数
|
||||
self.geometry_corrector.load_calibration("config/perspective_correction.npy")
|
||||
except:
|
||||
# 如果加载失败，等待手动标定
|
||||
pass
|
||||
|
||||
def preprocess_frame(self, frame: np.ndarray,
|
||||
mode: str = "normal") -> np.ndarray:
|
||||
"""
|
||||
对单帧图像进行预处理
|
||||
|
||||
Args:
|
||||
frame: 输入图像帧
|
||||
mode: 处理模式 ("normal", "foggy")
|
||||
|
||||
Returns:
|
||||
np.ndarray: 预处理后的图像
|
||||
"""
|
||||
result = frame.copy()
|
||||
|
||||
# 1. 透视校正
|
||||
if (self.config['preprocessing']['perspective_correction']['enabled'] and
|
||||
self.geometry_corrector.is_calibrated):
|
||||
corrected = self.geometry_corrector.correct_image(result)
|
||||
if corrected is not None:
|
||||
result = corrected
|
||||
|
||||
# 2. 根据模式选择增强策略
|
||||
if self.config['preprocessing']['defogging']['enabled']:
|
||||
if mode == "foggy":
|
||||
# 雾天模式：应用综合增强
|
||||
result = self.defog_processor.enhance_for_fog(result, "comprehensive")
|
||||
else:
|
||||
# 正常模式：应用轻度增强
|
||||
result = self.defog_processor.enhance_for_fog(result, "light")
|
||||
|
||||
# 3. 基本的图像处理
|
||||
result = self._apply_basic_enhancements(result)
|
||||
|
||||
return result
|
||||
|
||||
def _apply_basic_enhancements(self, image: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
应用基本的图像增强
|
||||
|
||||
Args:
|
||||
image: 输入图像
|
||||
|
||||
Returns:
|
||||
np.ndarray: 增强后的图像
|
||||
"""
|
||||
result = image.copy()
|
||||
|
||||
# 轻微的高斯模糊去噪
|
||||
kernel_size = self.config['preprocessing']['defogging']['gaussian_blur_kernel']
|
||||
if kernel_size > 0:
|
||||
result = cv2.GaussianBlur(result, (kernel_size, kernel_size), 0)
|
||||
|
||||
return result
|
||||
|
||||
def extract_roi_regions(self, image: np.ndarray,
|
||||
roi_config: Dict[str, Any]) -> Dict[str, np.ndarray]:
|
||||
"""
|
||||
从图像中提取所有ROI区域
|
||||
|
||||
Args:
|
||||
image: 预处理后的图像
|
||||
roi_config: ROI配置信息
|
||||
|
||||
Returns:
|
||||
Dict[str, np.ndarray]: ROI名称到图像区域的映射
|
||||
"""
|
||||
roi_regions = {}
|
||||
|
||||
if 'roi_regions' not in roi_config:
|
||||
return roi_regions
|
||||
|
||||
for roi_name, roi_data in roi_config['roi_regions'].items():
|
||||
try:
|
||||
# 获取ROI区域坐标
|
||||
roi_box = roi_data['roi_box'] # [x, y, width, height]
|
||||
x, y, w, h = roi_box
|
||||
|
||||
# 提取ROI区域
|
||||
roi_region = image[y:y+h, x:x+w]
|
||||
roi_regions[roi_name] = roi_region
|
||||
|
||||
except (KeyError, IndexError, ValueError):
|
||||
# 如果ROI配置有误，跳过该ROI
|
||||
continue
|
||||
|
||||
return roi_regions
|
||||
|
||||
def get_roi_core_area(self, roi_image: np.ndarray,
|
||||
core_area_config: list) -> np.ndarray:
|
||||
"""
|
||||
从 ROI 区域中提取核心区域
|
||||
|
||||
Args:
|
||||
roi_image: ROI 区域图像
|
||||
core_area_config: 核心区域配置 [x, y, width, height]相对于整个图像的坐标
|
||||
|
||||
Returns:
|
||||
np.ndarray: 核心区域图像
|
||||
"""
|
||||
# 这里需要计算核心区域相对于ROI的坐标
|
||||
# 简化处理：在ROI中心取一个小区域
|
||||
h, w = roi_image.shape[:2]
|
||||
center_x, center_y = w // 2, h // 2
|
||||
core_size = min(w, h) // 4 # 核心区域为ROI的1/4大小
|
||||
|
||||
x1 = max(0, center_x - core_size)
|
||||
y1 = max(0, center_y - core_size)
|
||||
x2 = min(w, center_x + core_size)
|
||||
y2 = min(h, center_y + core_size)
|
||||
|
||||
return roi_image[y1:y2, x1:x2]
|
||||
|
||||
def calibrate_perspective_correction(self, calibration_image: np.ndarray,
|
||||
led_grid_corners: Optional[list] = None) -> bool:
|
||||
"""
|
||||
标定透视校正参数
|
||||
|
||||
Args:
|
||||
calibration_image: 标定用的图像（清晰的灯阵图像）
|
||||
led_grid_corners: 手动指定的灯阵四角点坐标
|
||||
|
||||
Returns:
|
||||
bool: 标定是否成功
|
||||
"""
|
||||
try:
|
||||
if led_grid_corners is None:
|
||||
# 自动检测角点
|
||||
corners = self.geometry_corrector.auto_detect_corners(calibration_image)
|
||||
if corners is None:
|
||||
return False
|
||||
else:
|
||||
corners = led_grid_corners
|
||||
|
||||
# 设置目标矩形坐标（标准化的矩形）
|
||||
h, w = calibration_image.shape[:2]
|
||||
dst_corners = [
|
||||
(50, 50), # 左上
|
||||
(w-50, 50), # 右上
|
||||
(w-50, h-50), # 右下
|
||||
(50, h-50) # 左下
|
||||
]
|
||||
|
||||
# 设置透视变换
|
||||
success = self.geometry_corrector.set_perspective_points(corners, dst_corners)
|
||||
|
||||
if success:
|
||||
# 保存校正参数
|
||||
self.geometry_corrector.save_calibration("config/perspective_correction.npy")
|
||||
|
||||
return success
|
||||
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def set_detection_mode(self, mode: str) -> None:
|
||||
"""
|
||||
设置检测模式
|
||||
|
||||
Args:
|
||||
mode: "normal" 或 "foggy"
|
||||
"""
|
||||
if mode in ["normal", "foggy"]:
|
||||
self.config['detection_mode'] = {'current_mode': mode}
|
||||
|
||||
def update_defogging_parameters(self, **kwargs) -> None:
|
||||
"""
|
||||
更新去雾参数
|
||||
|
||||
Args:
|
||||
**kwargs: 参数键值对
|
||||
"""
|
||||
self.defog_processor.update_parameters(**kwargs)
|
||||
|
||||
# 更新配置
|
||||
for key, value in kwargs.items():
|
||||
if key in self.config['preprocessing']['defogging']:
|
||||
self.config['preprocessing']['defogging'][key] = value
|
||||
18
src/roi_detection/__init__.py
Normal file
18
src/roi_detection/__init__.py
Normal file
@ -0,0 +1,18 @@
|
||||
"""
|
||||
ROI检测算法模块
|
||||
核心检测算法，包括ROI管理、峰值检测、双阈值判断等
|
||||
"""
|
||||
|
||||
from .roi_manager import ROIManager
|
||||
from .peak_detector import PeakDetector
|
||||
from .threshold_detector import ThresholdDetector
|
||||
from .frame_stabilizer import FrameStabilizer
|
||||
from .led_detector import LEDDetector
|
||||
|
||||
__all__ = [
|
||||
'ROIManager',
|
||||
'PeakDetector',
|
||||
'ThresholdDetector',
|
||||
'FrameStabilizer',
|
||||
'LEDDetector'
|
||||
]
|
||||
370
src/roi_detection/frame_stabilizer.py
Normal file
370
src/roi_detection/frame_stabilizer.py
Normal file
@ -0,0 +1,370 @@
|
||||
"""
|
||||
帧间稳定滤波器
|
||||
通过连续多帧的一致性检查来稳定LED状态检测结果，避免瞬时噪声干扰
|
||||
"""
|
||||
|
||||
import time
|
||||
from collections import deque, Counter
|
||||
from typing import Dict, Optional, Any, Tuple
|
||||
from dataclasses import dataclass
|
||||
|
||||
from .threshold_detector import LEDState, ThresholdDetectionResult
|
||||
|
||||
|
||||
@dataclass
|
||||
class StabilizedResult:
|
||||
"""
|
||||
稳定化后的检测结果
|
||||
"""
|
||||
led_state: LEDState
|
||||
stability: float # 稳定性指标 (0.0-1.0)
|
||||
frame_count: int # 参与稳定化的帧数
|
||||
last_update_time: float # 最后更新时间
|
||||
confidence: float # 平均置信度
|
||||
|
||||
|
||||
class FrameStabilizer:
|
||||
"""
|
||||
帧间稳定滤波器
|
||||
维护多帧历史状态，通过一致性检查输出稳定的LED状态
|
||||
"""
|
||||
|
||||
def __init__(self, stability_window: int = 5,
|
||||
consistency_threshold: int = 3,
|
||||
update_interval: float = 1.0):
|
||||
"""
|
||||
初始化帧间稳定滤波器
|
||||
|
||||
Args:
|
||||
stability_window: 稳定窗口大小（帧数）
|
||||
consistency_threshold: 一致性阈值（窗口内一致的最小帧数）
|
||||
update_interval: 状态更新间隔（秒）
|
||||
"""
|
||||
self.stability_window = stability_window
|
||||
self.consistency_threshold = consistency_threshold
|
||||
self.update_interval = update_interval
|
||||
|
||||
# 为每个ROI维护历史状态队列
|
||||
self.state_history: Dict[str, deque] = {}
|
||||
self.confidence_history: Dict[str, deque] = {}
|
||||
|
||||
# 当前稳定状态
|
||||
self.stable_states: Dict[str, StabilizedResult] = {}
|
||||
|
||||
# 时间戳记录
|
||||
self.last_update_time = time.time()
|
||||
self.frame_timestamps = deque(maxlen=stability_window)
|
||||
|
||||
def update_frame(self, detection_results: Dict[str, ThresholdDetectionResult]) -> Dict[str, StabilizedResult]:
|
||||
"""
|
||||
更新单帧检测结果并返回稳定化后的状态
|
||||
|
||||
Args:
|
||||
detection_results: 单帧的检测结果字典
|
||||
|
||||
Returns:
|
||||
Dict[str, StabilizedResult]: 稳定化后的状态字典
|
||||
"""
|
||||
current_time = time.time()
|
||||
self.frame_timestamps.append(current_time)
|
||||
|
||||
# 更新每个ROI的历史状态
|
||||
for roi_name, result in detection_results.items():
|
||||
self._update_roi_history(roi_name, result)
|
||||
|
||||
# 计算稳定状态
|
||||
updated_states = {}
|
||||
|
||||
for roi_name in detection_results.keys():
|
||||
stable_result = self._calculate_stable_state(roi_name, current_time)
|
||||
if stable_result:
|
||||
self.stable_states[roi_name] = stable_result
|
||||
updated_states[roi_name] = stable_result
|
||||
|
||||
self.last_update_time = current_time
|
||||
return updated_states
|
||||
|
||||
def _update_roi_history(self, roi_name: str, result: ThresholdDetectionResult) -> None:
|
||||
"""
|
||||
更新单个ROI的历史状态
|
||||
|
||||
Args:
|
||||
roi_name: ROI名称
|
||||
result: 检测结果
|
||||
"""
|
||||
# 初始化历史队列
|
||||
if roi_name not in self.state_history:
|
||||
self.state_history[roi_name] = deque(maxlen=self.stability_window)
|
||||
self.confidence_history[roi_name] = deque(maxlen=self.stability_window)
|
||||
|
||||
# 添加新状态
|
||||
self.state_history[roi_name].append(result.led_state)
|
||||
self.confidence_history[roi_name].append(result.confidence)
|
||||
|
||||
def _calculate_stable_state(self, roi_name: str, current_time: float) -> Optional[StabilizedResult]:
|
||||
"""
|
||||
计算单个ROI的稳定状态
|
||||
|
||||
Args:
|
||||
roi_name: ROI名称
|
||||
current_time: 当前时间戳
|
||||
|
||||
Returns:
|
||||
Optional[StabilizedResult]: 稳定化结果，如果无法确定则返回None
|
||||
"""
|
||||
if roi_name not in self.state_history:
|
||||
return None
|
||||
|
||||
state_queue = self.state_history[roi_name]
|
||||
confidence_queue = self.confidence_history[roi_name]
|
||||
|
||||
if len(state_queue) < self.consistency_threshold:
|
||||
# 历史数据不足，无法进行稳定判断
|
||||
return None
|
||||
|
||||
# 统计各状态出现频率
|
||||
state_counter = Counter(state_queue)
|
||||
most_common_state, most_common_count = state_counter.most_common(1)[0]
|
||||
|
||||
# 计算稳定性指标
|
||||
stability = most_common_count / len(state_queue)
|
||||
|
||||
# 判断是否满足一致性阈值
|
||||
if most_common_count >= self.consistency_threshold:
|
||||
# 计算平均置信度（只考虑与最常见状态一致的帧）
|
||||
consistent_confidences = [
|
||||
conf for state, conf in zip(state_queue, confidence_queue)
|
||||
if state == most_common_state
|
||||
]
|
||||
avg_confidence = sum(consistent_confidences) / len(consistent_confidences)
|
||||
|
||||
return StabilizedResult(
|
||||
led_state=most_common_state,
|
||||
stability=stability,
|
||||
frame_count=len(state_queue),
|
||||
last_update_time=current_time,
|
||||
confidence=avg_confidence
|
||||
)
|
||||
|
||||
else:
|
||||
# 状态不够稳定，检查是否有现有的稳定状态
|
||||
if roi_name in self.stable_states:
|
||||
existing_stable = self.stable_states[roi_name]
|
||||
|
||||
# 如果距离上次更新时间不长，保持现有状态
|
||||
if current_time - existing_stable.last_update_time < self.update_interval * 2:
|
||||
return existing_stable
|
||||
|
||||
# 返回不确定状态
|
||||
return StabilizedResult(
|
||||
led_state=LEDState.UNCERTAIN,
|
||||
stability=stability,
|
||||
frame_count=len(state_queue),
|
||||
last_update_time=current_time,
|
||||
confidence=0.5
|
||||
)
|
||||
|
||||
def get_current_stable_states(self) -> Dict[str, StabilizedResult]:
|
||||
"""
|
||||
获取当前的稳定状态
|
||||
|
||||
Returns:
|
||||
Dict[str, StabilizedResult]: 当前稳定状态字典
|
||||
"""
|
||||
return self.stable_states.copy()
|
||||
|
||||
def get_stability_summary(self) -> Dict[str, Any]:
|
||||
"""
|
||||
获取稳定性分析摘要
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: 稳定性摘要信息
|
||||
"""
|
||||
if not self.stable_states:
|
||||
return {'total_rois': 0, 'stable_rois': 0, 'avg_stability': 0.0}
|
||||
|
||||
total_rois = len(self.stable_states)
|
||||
stable_rois = sum(1 for s in self.stable_states.values()
|
||||
if s.stability >= 0.6 and s.led_state != LEDState.UNCERTAIN)
|
||||
|
||||
avg_stability = sum(s.stability for s in self.stable_states.values()) / total_rois
|
||||
|
||||
# 统计各状态数量
|
||||
state_counts = Counter(s.led_state for s in self.stable_states.values())
|
||||
|
||||
# 计算帧率
|
||||
current_time = time.time()
|
||||
if len(self.frame_timestamps) >= 2:
|
||||
time_span = self.frame_timestamps[-1] - self.frame_timestamps[0]
|
||||
fps = (len(self.frame_timestamps) - 1) / time_span if time_span > 0 else 0
|
||||
else:
|
||||
fps = 0
|
||||
|
||||
return {
|
||||
'total_rois': total_rois,
|
||||
'stable_rois': stable_rois,
|
||||
'avg_stability': float(avg_stability),
|
||||
'state_distribution': {
|
||||
'on': state_counts.get(LEDState.ON, 0),
|
||||
'off': state_counts.get(LEDState.OFF, 0),
|
||||
'uncertain': state_counts.get(LEDState.UNCERTAIN, 0)
|
||||
},
|
||||
'processing_fps': float(fps),
|
||||
'window_size': self.stability_window,
|
||||
'consistency_threshold': self.consistency_threshold
|
||||
}
|
||||
|
||||
def force_update_state(self, roi_name: str, new_state: LEDState,
|
||||
confidence: float = 1.0) -> bool:
|
||||
"""
|
||||
强制更新某个ROI的状态（用于手动校正）
|
||||
|
||||
Args:
|
||||
roi_name: ROI名称
|
||||
new_state: 新状态
|
||||
confidence: 置信度
|
||||
|
||||
Returns:
|
||||
bool: 更新是否成功
|
||||
"""
|
||||
if roi_name not in self.state_history:
|
||||
return False
|
||||
|
||||
current_time = time.time()
|
||||
|
||||
# 清空历史并填充新状态
|
||||
self.state_history[roi_name].clear()
|
||||
self.confidence_history[roi_name].clear()
|
||||
|
||||
# 填充一致的状态到整个窗口
|
||||
for _ in range(self.stability_window):
|
||||
self.state_history[roi_name].append(new_state)
|
||||
self.confidence_history[roi_name].append(confidence)
|
||||
|
||||
# 更新稳定状态
|
||||
self.stable_states[roi_name] = StabilizedResult(
|
||||
led_state=new_state,
|
||||
stability=1.0,
|
||||
frame_count=self.stability_window,
|
||||
last_update_time=current_time,
|
||||
confidence=confidence
|
||||
)
|
||||
|
||||
return True
|
||||
|
||||
def reset_roi_history(self, roi_name: Optional[str] = None) -> None:
|
||||
"""
|
||||
重置ROI历史状态
|
||||
|
||||
Args:
|
||||
roi_name: 要重置的ROI名称，如果为None则重置所有
|
||||
"""
|
||||
if roi_name is None:
|
||||
# 重置所有ROI
|
||||
self.state_history.clear()
|
||||
self.confidence_history.clear()
|
||||
self.stable_states.clear()
|
||||
else:
|
||||
# 重置指定ROI
|
||||
if roi_name in self.state_history:
|
||||
del self.state_history[roi_name]
|
||||
if roi_name in self.confidence_history:
|
||||
del self.confidence_history[roi_name]
|
||||
if roi_name in self.stable_states:
|
||||
del self.stable_states[roi_name]
|
||||
|
||||
def adjust_parameters(self, stability_window: Optional[int] = None,
|
||||
consistency_threshold: Optional[int] = None,
|
||||
update_interval: Optional[float] = None) -> None:
|
||||
"""
|
||||
调整稳定化参数
|
||||
|
||||
Args:
|
||||
stability_window: 新的稳定窗口大小
|
||||
consistency_threshold: 新的一致性阈值
|
||||
update_interval: 新的更新间隔
|
||||
"""
|
||||
if stability_window is not None:
|
||||
self.stability_window = stability_window
|
||||
# 调整现有队列的最大长度
|
||||
for roi_name in self.state_history.keys():
|
||||
old_states = list(self.state_history[roi_name])
|
||||
old_confidences = list(self.confidence_history[roi_name])
|
||||
|
||||
self.state_history[roi_name] = deque(old_states, maxlen=stability_window)
|
||||
self.confidence_history[roi_name] = deque(old_confidences, maxlen=stability_window)
|
||||
|
||||
if consistency_threshold is not None:
|
||||
self.consistency_threshold = min(consistency_threshold, self.stability_window)
|
||||
|
||||
if update_interval is not None:
|
||||
self.update_interval = update_interval
|
||||
|
||||
def get_roi_stability_details(self, roi_name: str) -> Optional[Dict[str, Any]]:
|
||||
"""
|
||||
获取特定ROI的详细稳定性信息
|
||||
|
||||
Args:
|
||||
roi_name: ROI名称
|
||||
|
||||
Returns:
|
||||
Optional[Dict[str, Any]]: 详细稳定性信息，如果ROI不存在则返回None
|
||||
"""
|
||||
if roi_name not in self.state_history:
|
||||
return None
|
||||
|
||||
state_queue = self.state_history[roi_name]
|
||||
confidence_queue = self.confidence_history[roi_name]
|
||||
|
||||
if not state_queue:
|
||||
return None
|
||||
|
||||
# 统计状态分布
|
||||
state_counter = Counter(state_queue)
|
||||
|
||||
# 计算状态变化次数
|
||||
state_changes = 0
|
||||
for i in range(1, len(state_queue)):
|
||||
if state_queue[i] != state_queue[i-1]:
|
||||
state_changes += 1
|
||||
|
||||
# 获取当前稳定状态
|
||||
stable_result = self.stable_states.get(roi_name)
|
||||
|
||||
return {
|
||||
'roi_name': roi_name,
|
||||
'history_length': len(state_queue),
|
||||
'state_distribution': {state.name: count for state, count in state_counter.items()},
|
||||
'state_changes': state_changes,
|
||||
'avg_confidence': float(sum(confidence_queue) / len(confidence_queue)),
|
||||
'current_stable_state': stable_result.led_state.name if stable_result else None,
|
||||
'current_stability': stable_result.stability if stable_result else 0.0,
|
||||
'recent_states': [s.name for s in list(state_queue)[-5:]], # 最近5帧状态
|
||||
'recent_confidences': list(confidence_queue)[-5:] # 最近5帧置信度
|
||||
}
|
||||
|
||||
def export_stability_data(self) -> Dict[str, Any]:
|
||||
"""
|
||||
导出稳定性数据用于分析或存储
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: 完整的稳定性数据
|
||||
"""
|
||||
export_data = {
|
||||
'timestamp': time.time(),
|
||||
'parameters': {
|
||||
'stability_window': self.stability_window,
|
||||
'consistency_threshold': self.consistency_threshold,
|
||||
'update_interval': self.update_interval
|
||||
},
|
||||
'summary': self.get_stability_summary(),
|
||||
'roi_details': {}
|
||||
}
|
||||
|
||||
# 导出每个ROI的详细信息
|
||||
for roi_name in self.state_history.keys():
|
||||
roi_details = self.get_roi_stability_details(roi_name)
|
||||
if roi_details:
|
||||
export_data['roi_details'][roi_name] = roi_details
|
||||
|
||||
return export_data
|
||||
507
src/roi_detection/led_detector.py
Normal file
507
src/roi_detection/led_detector.py
Normal file
@ -0,0 +1,507 @@
|
||||
"""
|
||||
LED检测器
|
||||
整合ROI管理、峰值检测、双阈值判断和帧间稳定滤波的完整LED状态检测系统
|
||||
"""
|
||||
|
||||
import os
|
||||
import time
|
||||
import yaml
|
||||
import cv2
|
||||
import numpy as np
|
||||
from typing import Dict, Optional, Any, Tuple, List
|
||||
from dataclasses import dataclass
|
||||
|
||||
from .roi_manager import ROIManager
|
||||
from .peak_detector import PeakDetector, PeakDetectionResult
|
||||
from .threshold_detector import ThresholdDetector, LEDState, ThresholdDetectionResult
|
||||
from .frame_stabilizer import FrameStabilizer, StabilizedResult
|
||||
|
||||
|
||||
@dataclass
|
||||
class LEDDetectionResult:
|
||||
"""
|
||||
LED检测完整结果
|
||||
"""
|
||||
timestamp: float
|
||||
stable_states: Dict[str, StabilizedResult]
|
||||
detection_summary: Dict[str, Any]
|
||||
processing_time: float
|
||||
frame_count: int
|
||||
|
||||
|
||||
class LEDDetector:
|
||||
"""
|
||||
LED检测器主类
|
||||
集成完整的检测流水线，从图像输入到稳定状态输出
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
roi_config_path: str = "config/roi_config.yaml",
|
||||
algorithm_config_path: str = "config/algorithm_config.yaml"):
|
||||
"""
|
||||
初始化LED检测器
|
||||
|
||||
Args:
|
||||
roi_config_path: ROI配置文件路径
|
||||
algorithm_config_path: 算法配置文件路径
|
||||
"""
|
||||
self.roi_config_path = roi_config_path
|
||||
self.algorithm_config_path = algorithm_config_path
|
||||
|
||||
# 加载配置
|
||||
self.algorithm_config = self._load_algorithm_config()
|
||||
|
||||
# 初始化各个组件
|
||||
self.roi_manager = ROIManager(roi_config_path)
|
||||
self.peak_detector = self._init_peak_detector()
|
||||
self.threshold_detector = self._init_threshold_detector()
|
||||
self.frame_stabilizer = self._init_frame_stabilizer()
|
||||
|
||||
# 统计信息
|
||||
self.frame_count = 0
|
||||
self.total_processing_time = 0.0
|
||||
|
||||
# 检测模式
|
||||
self.current_mode = self.algorithm_config.get('detection_mode', {}).get('current_mode', 'normal')
|
||||
|
||||
def _load_algorithm_config(self) -> Dict[str, Any]:
|
||||
"""
|
||||
加载算法配置文件
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: 算法配置字典
|
||||
"""
|
||||
try:
|
||||
with open(self.algorithm_config_path, 'r', encoding='utf-8') as f:
|
||||
config = yaml.safe_load(f)
|
||||
return config
|
||||
except (FileNotFoundError, yaml.YAMLError):
|
||||
# 返回默认配置
|
||||
return self._get_default_algorithm_config()
|
||||
|
||||
def _get_default_algorithm_config(self) -> Dict[str, Any]:
|
||||
"""
|
||||
获取默认算法配置
|
||||
"""
|
||||
return {
|
||||
'brightness_detection': {
|
||||
'peak_brightness_threshold': 120,
|
||||
'avg_brightness_threshold': 80,
|
||||
'brightness_contrast_threshold': 30,
|
||||
'adaptive_threshold_enabled': True,
|
||||
'ambient_light_factor': 0.8
|
||||
},
|
||||
'area_detection': {
|
||||
'min_bright_area': 5,
|
||||
'max_bright_area': 200,
|
||||
'area_ratio_threshold': 0.3
|
||||
},
|
||||
'frame_stabilization': {
|
||||
'stability_window': 5,
|
||||
'consistency_threshold': 3,
|
||||
'update_interval': 1.0
|
||||
},
|
||||
'detection_mode': {
|
||||
'current_mode': 'normal'
|
||||
}
|
||||
}
|
||||
|
||||
def _init_peak_detector(self) -> PeakDetector:
|
||||
"""
|
||||
初始化峰值检测器
|
||||
"""
|
||||
brightness_config = self.algorithm_config.get('brightness_detection', {})
|
||||
return PeakDetector(
|
||||
gaussian_kernel_size=3,
|
||||
brightness_threshold=brightness_config.get('peak_brightness_threshold', 120),
|
||||
contrast_threshold=brightness_config.get('brightness_contrast_threshold', 30)
|
||||
)
|
||||
|
||||
def _init_threshold_detector(self) -> ThresholdDetector:
|
||||
"""
|
||||
初始化双阈值检测器
|
||||
"""
|
||||
return ThresholdDetector(self.algorithm_config)
|
||||
|
||||
def _init_frame_stabilizer(self) -> FrameStabilizer:
|
||||
"""
|
||||
初始化帧间稳定滤波器
|
||||
"""
|
||||
stabilization_config = self.algorithm_config.get('frame_stabilization', {})
|
||||
return FrameStabilizer(
|
||||
stability_window=stabilization_config.get('stability_window', 5),
|
||||
consistency_threshold=stabilization_config.get('consistency_threshold', 3),
|
||||
update_interval=stabilization_config.get('update_interval', 1.0)
|
||||
)
|
||||
|
||||
def detect_leds(self, image: np.ndarray) -> LEDDetectionResult:
|
||||
"""
|
||||
检测图像中18盏LED灯的状态
|
||||
|
||||
Args:
|
||||
image: 预处理后的输入图像
|
||||
|
||||
Returns:
|
||||
LEDDetectionResult: 完整的检测结果
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
# 1. 提取所有ROI区域图像
|
||||
roi_images = self.roi_manager.extract_all_roi_images(image)
|
||||
|
||||
if not roi_images:
|
||||
# 没有有效的ROI区域
|
||||
return LEDDetectionResult(
|
||||
timestamp=start_time,
|
||||
stable_states={},
|
||||
detection_summary={'error': 'No valid ROI regions'},
|
||||
processing_time=0.0,
|
||||
frame_count=self.frame_count
|
||||
)
|
||||
|
||||
# 2. 峰值检测
|
||||
peak_results = self.peak_detector.detect_peaks_batch(roi_images)
|
||||
|
||||
# 3. 计算环境亮度（用于自适应阈值）
|
||||
ambient_brightness = self._estimate_ambient_brightness(image, roi_images)
|
||||
|
||||
# 4. 双阈值判断
|
||||
threshold_results = self.threshold_detector.detect_batch(
|
||||
peak_results, ambient_brightness
|
||||
)
|
||||
|
||||
# 5. 帧间稳定滤波
|
||||
stable_states = self.frame_stabilizer.update_frame(threshold_results)
|
||||
|
||||
# 6. 生成检测摘要
|
||||
detection_summary = self._generate_detection_summary(
|
||||
peak_results, threshold_results, stable_states
|
||||
)
|
||||
|
||||
# 更新统计信息
|
||||
processing_time = time.time() - start_time
|
||||
self.frame_count += 1
|
||||
self.total_processing_time += processing_time
|
||||
|
||||
return LEDDetectionResult(
|
||||
timestamp=start_time,
|
||||
stable_states=stable_states,
|
||||
detection_summary=detection_summary,
|
||||
processing_time=processing_time,
|
||||
frame_count=self.frame_count
|
||||
)
|
||||
|
||||
def _estimate_ambient_brightness(self, image: np.ndarray,
|
||||
roi_images: Dict[str, np.ndarray]) -> float:
|
||||
"""
|
||||
估算环境亮度
|
||||
|
||||
Args:
|
||||
image: 完整图像
|
||||
roi_images: ROI区域图像字典
|
||||
|
||||
Returns:
|
||||
float: 环境亮度估计值
|
||||
"""
|
||||
# 方法1：使用图像整体的中值亮度
|
||||
if len(image.shape) == 3:
|
||||
gray = np.mean(image, axis=2)
|
||||
else:
|
||||
gray = image
|
||||
|
||||
# 排除ROI区域，计算背景亮度
|
||||
mask = np.ones_like(gray, dtype=bool)
|
||||
|
||||
for roi_name, roi_region in self.roi_manager.get_all_roi_regions().items():
|
||||
x, y, w, h = roi_region.roi_box
|
||||
if (0 <= x < gray.shape[1] and 0 <= y < gray.shape[0] and
|
||||
x + w <= gray.shape[1] and y + h <= gray.shape[0]):
|
||||
mask[y:y+h, x:x+w] = False
|
||||
|
||||
background_pixels = gray[mask]
|
||||
if len(background_pixels) > 0:
|
||||
ambient_brightness = float(np.median(background_pixels))
|
||||
else:
|
||||
# 备用方案：使用整体图像的25%分位数
|
||||
ambient_brightness = float(np.percentile(gray, 25))
|
||||
|
||||
return ambient_brightness
|
||||
|
||||
def _generate_detection_summary(self,
|
||||
peak_results: Dict[str, PeakDetectionResult],
|
||||
threshold_results: Dict[str, ThresholdDetectionResult],
|
||||
stable_states: Dict[str, StabilizedResult]) -> Dict[str, Any]:
|
||||
"""
|
||||
生成检测结果摘要
|
||||
|
||||
Args:
|
||||
peak_results: 峰值检测结果
|
||||
threshold_results: 阈值检测结果
|
||||
stable_states: 稳定状态结果
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: 检测摘要
|
||||
"""
|
||||
# 基础统计
|
||||
total_rois = len(self.roi_manager.get_all_roi_regions())
|
||||
processed_rois = len(peak_results)
|
||||
|
||||
# 获取阈值检测摘要
|
||||
threshold_summary = self.threshold_detector.get_detection_summary(threshold_results)
|
||||
|
||||
# 获取稳定性摘要
|
||||
stability_summary = self.frame_stabilizer.get_stability_summary()
|
||||
|
||||
# 计算平均处理性能
|
||||
avg_processing_time = (self.total_processing_time / self.frame_count
|
||||
if self.frame_count > 0 else 0)
|
||||
|
||||
# 按行统计稳定状态
|
||||
row_stable_stats = {}
|
||||
for i in range(1, 4): # 3行
|
||||
row_states = {k: v for k, v in stable_states.items() if k.startswith(f'R{i}')}
|
||||
if row_states:
|
||||
on_count = sum(1 for s in row_states.values() if s.led_state == LEDState.ON)
|
||||
off_count = sum(1 for s in row_states.values() if s.led_state == LEDState.OFF)
|
||||
uncertain_count = sum(1 for s in row_states.values() if s.led_state == LEDState.UNCERTAIN)
|
||||
|
||||
row_stable_stats[f'row_{i}'] = {
|
||||
'total': len(row_states),
|
||||
'on': on_count,
|
||||
'off': off_count,
|
||||
'uncertain': uncertain_count
|
||||
}
|
||||
|
||||
return {
|
||||
'frame_info': {
|
||||
'frame_count': self.frame_count,
|
||||
'total_rois': total_rois,
|
||||
'processed_rois': processed_rois,
|
||||
'detection_mode': self.current_mode
|
||||
},
|
||||
'threshold_detection': threshold_summary,
|
||||
'stability_info': stability_summary,
|
||||
'row_statistics': row_stable_stats,
|
||||
'performance': {
|
||||
'current_processing_time': 0, # 将在外部设置
|
||||
'avg_processing_time': float(avg_processing_time),
|
||||
'total_processing_time': float(self.total_processing_time)
|
||||
}
|
||||
}
|
||||
|
||||
def get_current_states(self) -> Dict[str, LEDState]:
|
||||
"""
|
||||
获取当前稳定的LED状态
|
||||
|
||||
Returns:
|
||||
Dict[str, LEDState]: ROI名称到LED状态的映射
|
||||
"""
|
||||
stable_states = self.frame_stabilizer.get_current_stable_states()
|
||||
return {roi_name: result.led_state for roi_name, result in stable_states.items()}
|
||||
|
||||
def get_states_as_matrix(self) -> np.ndarray:
|
||||
"""
|
||||
获取LED状态的矩阵表示（3x6）
|
||||
|
||||
Returns:
|
||||
np.ndarray: 3x6矩阵，1表示亮，0表示灭，-1表示不确定
|
||||
"""
|
||||
states = self.get_current_states()
|
||||
matrix = np.full((3, 6), -1, dtype=int) # 默认为不确定
|
||||
|
||||
for roi_name, led_state in states.items():
|
||||
if len(roi_name) >= 4 and roi_name.startswith('R') and 'C' in roi_name:
|
||||
try:
|
||||
row = int(roi_name[1]) - 1 # R1 -> row 0
|
||||
col = int(roi_name[3]) - 1 # C1 -> col 0
|
||||
|
||||
if 0 <= row < 3 and 0 <= col < 6:
|
||||
if led_state == LEDState.ON:
|
||||
matrix[row, col] = 1
|
||||
elif led_state == LEDState.OFF:
|
||||
matrix[row, col] = 0
|
||||
# UNCERTAIN保持-1
|
||||
except (ValueError, IndexError):
|
||||
continue
|
||||
|
||||
return matrix
|
||||
|
||||
def set_detection_mode(self, mode: str) -> bool:
|
||||
"""
|
||||
设置检测模式
|
||||
|
||||
Args:
|
||||
mode: "normal" 或 "foggy"
|
||||
|
||||
Returns:
|
||||
bool: 设置是否成功
|
||||
"""
|
||||
if mode in ['normal', 'foggy']:
|
||||
self.current_mode = mode
|
||||
|
||||
# 根据模式调整参数
|
||||
if mode == 'foggy':
|
||||
# 雾天模式：降低阈值，提高敏感度
|
||||
foggy_config = self.algorithm_config.get('detection_mode', {}).get('foggy_mode_enhancement', {})
|
||||
brightness_boost = foggy_config.get('brightness_boost', 1.2)
|
||||
|
||||
# 调整峰值检测器参数
|
||||
original_threshold = self.algorithm_config['brightness_detection']['peak_brightness_threshold']
|
||||
adjusted_threshold = int(original_threshold / brightness_boost)
|
||||
|
||||
self.peak_detector.update_parameters(
|
||||
brightness_threshold=adjusted_threshold
|
||||
)
|
||||
|
||||
# 调整稳定性参数（雾天下需要更多帧来稳定）
|
||||
self.frame_stabilizer.adjust_parameters(
|
||||
stability_window=7,
|
||||
consistency_threshold=4
|
||||
)
|
||||
|
||||
else:
|
||||
# 恢复正常模式参数
|
||||
original_threshold = self.algorithm_config['brightness_detection']['peak_brightness_threshold']
|
||||
self.peak_detector.update_parameters(
|
||||
brightness_threshold=original_threshold
|
||||
)
|
||||
|
||||
# 恢复正常稳定性参数
|
||||
stabilization_config = self.algorithm_config.get('frame_stabilization', {})
|
||||
self.frame_stabilizer.adjust_parameters(
|
||||
stability_window=stabilization_config.get('stability_window', 5),
|
||||
consistency_threshold=stabilization_config.get('consistency_threshold', 3)
|
||||
)
|
||||
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def calibrate_roi_regions(self, calibration_image: np.ndarray) -> bool:
|
||||
"""
|
||||
使用标定图像重新标定ROI区域
|
||||
|
||||
Args:
|
||||
calibration_image: 清晰的标定图像
|
||||
|
||||
Returns:
|
||||
bool: 标定是否成功
|
||||
"""
|
||||
try:
|
||||
# 保存标定图像到临时文件
|
||||
temp_image_path = "temp_calibration_image.jpg"
|
||||
cv2.imwrite(temp_image_path, calibration_image)
|
||||
|
||||
# 调用标定工具进行交互式标定
|
||||
from tools.roi_calibration_tool import ROICalibrationTool
|
||||
|
||||
print("启动ROI标定工具...")
|
||||
tool = ROICalibrationTool()
|
||||
tool.run_calibration(temp_image_path)
|
||||
|
||||
# 重新加载ROI配置
|
||||
self.roi_manager.load_roi_config()
|
||||
|
||||
# 清理临时文件
|
||||
if os.path.exists(temp_image_path):
|
||||
os.remove(temp_image_path)
|
||||
|
||||
print("ROI标定完成")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"ROI标定失败: {e}")
|
||||
return False
|
||||
|
||||
def update_algorithm_parameters(self, **kwargs) -> None:
|
||||
"""
|
||||
更新算法参数
|
||||
|
||||
Args:
|
||||
**kwargs: 参数键值对
|
||||
"""
|
||||
# 更新峰值检测参数
|
||||
peak_params = {k: v for k, v in kwargs.items()
|
||||
if k in ['brightness_threshold', 'contrast_threshold']}
|
||||
if peak_params:
|
||||
self.peak_detector.update_parameters(**peak_params)
|
||||
|
||||
# 更新阈值检测参数
|
||||
threshold_params = {k: v for k, v in kwargs.items()
|
||||
if k in ['peak_brightness_threshold', 'min_bright_area', 'max_bright_area']}
|
||||
if threshold_params:
|
||||
self.threshold_detector.update_thresholds(**threshold_params)
|
||||
|
||||
# 更新稳定化参数
|
||||
stability_params = {k: v for k, v in kwargs.items()
|
||||
if k in ['stability_window', 'consistency_threshold', 'update_interval']}
|
||||
if stability_params:
|
||||
self.frame_stabilizer.adjust_parameters(**stability_params)
|
||||
|
||||
def reset_detection_history(self) -> None:
|
||||
"""
|
||||
重置检测历史（用于重新开始检测）
|
||||
"""
|
||||
self.frame_stabilizer.reset_roi_history()
|
||||
self.frame_count = 0
|
||||
self.total_processing_time = 0.0
|
||||
|
||||
def get_detection_statistics(self) -> Dict[str, Any]:
|
||||
"""
|
||||
获取检测统计信息
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: 统计信息
|
||||
"""
|
||||
avg_fps = self.frame_count / self.total_processing_time if self.total_processing_time > 0 else 0
|
||||
|
||||
return {
|
||||
'frame_count': self.frame_count,
|
||||
'total_processing_time': float(self.total_processing_time),
|
||||
'average_fps': float(avg_fps),
|
||||
'current_mode': self.current_mode,
|
||||
'roi_count': len(self.roi_manager.get_all_roi_regions()),
|
||||
'stability_summary': self.frame_stabilizer.get_stability_summary()
|
||||
}
|
||||
|
||||
def visualize_detection_result(self, image: np.ndarray,
|
||||
result: LEDDetectionResult) -> np.ndarray:
|
||||
"""
|
||||
可视化检测结果
|
||||
|
||||
Args:
|
||||
image: 原始图像
|
||||
result: 检测结果
|
||||
|
||||
Returns:
|
||||
np.ndarray: 可视化图像
|
||||
"""
|
||||
vis_image = image.copy()
|
||||
|
||||
# 绘制ROI区域
|
||||
vis_image = self.roi_manager.draw_roi_regions(vis_image)
|
||||
|
||||
# 根据稳定状态标记LED
|
||||
for roi_name, stable_result in result.stable_states.items():
|
||||
roi_region = self.roi_manager.get_roi_region(roi_name)
|
||||
if roi_region:
|
||||
center_x, center_y = roi_region.center
|
||||
|
||||
# 根据状态选择颜色
|
||||
if stable_result.led_state == LEDState.ON:
|
||||
color = (0, 255, 0) # 绿色 - 亮
|
||||
elif stable_result.led_state == LEDState.OFF:
|
||||
color = (0, 0, 255) # 红色 - 灭
|
||||
else:
|
||||
color = (0, 255, 255) # 黄色 - 不确定
|
||||
|
||||
# 绘制状态指示
|
||||
cv2.circle(vis_image, (center_x, center_y), 8, color, -1)
|
||||
|
||||
# 添加稳定性信息
|
||||
stability_text = f"{stable_result.stability:.1f}"
|
||||
cv2.putText(vis_image, stability_text,
|
||||
(center_x - 10, center_y - 15),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.4, color, 1)
|
||||
|
||||
return vis_image
|
||||
350
src/roi_detection/peak_detector.py
Normal file
350
src/roi_detection/peak_detector.py
Normal file
@ -0,0 +1,350 @@
|
||||
"""
|
||||
峰值检测器
|
||||
实现ROI核心区域的亮度峰值检测算法，用于区分LED灯的亮灭状态
|
||||
"""
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
from typing import Tuple, Dict, Optional, Any
|
||||
from dataclasses import dataclass
|
||||
|
||||
|
||||
@dataclass
|
||||
class PeakDetectionResult:
|
||||
"""
|
||||
峰值检测结果数据类
|
||||
"""
|
||||
max_brightness: float # 最大亮度值
|
||||
avg_brightness: float # 平均亮度值
|
||||
brightness_contrast: float # 亮度对比度（中心与边缘差值）
|
||||
peak_position: Tuple[int, int] # 峰值位置坐标
|
||||
bright_area_size: int # 亮区面积（像素数）
|
||||
bright_area_ratio: float # 亮区占比
|
||||
|
||||
|
||||
class PeakDetector:
|
||||
"""
|
||||
峰值检测器
|
||||
专门用于检测ROI核心区域的亮度峰值，抑制光晕干扰
|
||||
"""
|
||||
|
||||
def __init__(self, gaussian_kernel_size: int = 3,
|
||||
brightness_threshold: int = 120,
|
||||
contrast_threshold: int = 30):
|
||||
"""
|
||||
初始化峰值检测器
|
||||
|
||||
Args:
|
||||
gaussian_kernel_size: 高斯模糊核大小（预处理去噪）
|
||||
brightness_threshold: 亮度阈值
|
||||
contrast_threshold: 对比度阈值
|
||||
"""
|
||||
self.gaussian_kernel_size = gaussian_kernel_size
|
||||
self.brightness_threshold = brightness_threshold
|
||||
self.contrast_threshold = contrast_threshold
|
||||
|
||||
def detect_peak_in_roi(self, roi_image: np.ndarray,
|
||||
core_area_ratio: float = 0.5) -> PeakDetectionResult:
|
||||
"""
|
||||
在ROI图像中检测亮度峰值
|
||||
|
||||
Args:
|
||||
roi_image: ROI区域图像
|
||||
core_area_ratio: 核心区域占ROI的比例
|
||||
|
||||
Returns:
|
||||
PeakDetectionResult: 峰值检测结果
|
||||
"""
|
||||
# 转换为灰度图像
|
||||
if len(roi_image.shape) == 3:
|
||||
gray = cv2.cvtColor(roi_image, cv2.COLOR_BGR2GRAY)
|
||||
else:
|
||||
gray = roi_image.copy()
|
||||
|
||||
# 应用高斯模糊去噪
|
||||
if self.gaussian_kernel_size > 0:
|
||||
blurred = cv2.GaussianBlur(gray, (self.gaussian_kernel_size, self.gaussian_kernel_size), 0)
|
||||
else:
|
||||
blurred = gray.copy()
|
||||
|
||||
# 提取核心区域
|
||||
core_area = self._extract_core_area(blurred, core_area_ratio)
|
||||
|
||||
# 计算亮度统计信息
|
||||
max_brightness = float(np.max(core_area))
|
||||
avg_brightness = float(np.mean(core_area))
|
||||
|
||||
# 找到峰值位置
|
||||
peak_position = self._find_peak_position(core_area)
|
||||
|
||||
# 计算亮度对比度
|
||||
brightness_contrast = self._calculate_brightness_contrast(blurred, core_area)
|
||||
|
||||
# 计算亮区面积
|
||||
bright_area_size, bright_area_ratio = self._calculate_bright_area(
|
||||
core_area, self.brightness_threshold
|
||||
)
|
||||
|
||||
return PeakDetectionResult(
|
||||
max_brightness=max_brightness,
|
||||
avg_brightness=avg_brightness,
|
||||
brightness_contrast=brightness_contrast,
|
||||
peak_position=peak_position,
|
||||
bright_area_size=bright_area_size,
|
||||
bright_area_ratio=bright_area_ratio
|
||||
)
|
||||
|
||||
def _extract_core_area(self, image: np.ndarray, core_ratio: float) -> np.ndarray:
|
||||
"""
|
||||
从ROI图像中提取核心区域
|
||||
|
||||
Args:
|
||||
image: ROI灰度图像
|
||||
core_ratio: 核心区域比例
|
||||
|
||||
Returns:
|
||||
np.ndarray: 核心区域图像
|
||||
"""
|
||||
h, w = image.shape
|
||||
core_h = max(1, int(h * core_ratio))
|
||||
core_w = max(1, int(w * core_ratio))
|
||||
|
||||
# 计算核心区域的起始位置（居中）
|
||||
start_y = (h - core_h) // 2
|
||||
start_x = (w - core_w) // 2
|
||||
|
||||
end_y = start_y + core_h
|
||||
end_x = start_x + core_w
|
||||
|
||||
return image[start_y:end_y, start_x:end_x]
|
||||
|
||||
def _find_peak_position(self, core_area: np.ndarray) -> Tuple[int, int]:
|
||||
"""
|
||||
找到核心区域中的亮度峰值位置
|
||||
|
||||
Args:
|
||||
core_area: 核心区域图像
|
||||
|
||||
Returns:
|
||||
Tuple[int, int]: 峰值位置坐标 (x, y)
|
||||
"""
|
||||
# 找到最大值的位置
|
||||
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(core_area)
|
||||
return max_loc # (x, y)
|
||||
|
||||
def _calculate_brightness_contrast(self, roi_image: np.ndarray,
|
||||
core_area: np.ndarray) -> float:
|
||||
"""
|
||||
计算亮度对比度（核心区域与边缘区域的亮度差）
|
||||
|
||||
Args:
|
||||
roi_image: 完整ROI图像
|
||||
core_area: 核心区域图像
|
||||
|
||||
Returns:
|
||||
float: 亮度对比度
|
||||
"""
|
||||
core_brightness = float(np.mean(core_area))
|
||||
|
||||
# 计算边缘区域的平均亮度
|
||||
h, w = roi_image.shape
|
||||
core_h, core_w = core_area.shape
|
||||
|
||||
# 创建掩码，排除核心区域
|
||||
mask = np.ones((h, w), dtype=np.uint8)
|
||||
start_y = (h - core_h) // 2
|
||||
start_x = (w - core_w) // 2
|
||||
mask[start_y:start_y+core_h, start_x:start_x+core_w] = 0
|
||||
|
||||
# 计算边缘区域平均亮度
|
||||
edge_pixels = roi_image[mask == 1]
|
||||
if len(edge_pixels) > 0:
|
||||
edge_brightness = float(np.mean(edge_pixels))
|
||||
else:
|
||||
edge_brightness = core_brightness
|
||||
|
||||
return core_brightness - edge_brightness
|
||||
|
||||
def _calculate_bright_area(self, core_area: np.ndarray,
|
||||
threshold: int) -> Tuple[int, float]:
|
||||
"""
|
||||
计算亮区面积和占比
|
||||
|
||||
Args:
|
||||
core_area: 核心区域图像
|
||||
threshold: 亮度阈值
|
||||
|
||||
Returns:
|
||||
Tuple[int, float]: (亮区像素数, 亮区占比)
|
||||
"""
|
||||
bright_mask = core_area > threshold
|
||||
bright_pixels = np.sum(bright_mask)
|
||||
total_pixels = core_area.size
|
||||
|
||||
bright_ratio = float(bright_pixels) / float(total_pixels) if total_pixels > 0 else 0.0
|
||||
|
||||
return int(bright_pixels), bright_ratio
|
||||
|
||||
def detect_peaks_batch(self, roi_images: Dict[str, np.ndarray]) -> Dict[str, PeakDetectionResult]:
|
||||
"""
|
||||
批量检测多个ROI的亮度峰值
|
||||
|
||||
Args:
|
||||
roi_images: ROI名称到图像的映射
|
||||
|
||||
Returns:
|
||||
Dict[str, PeakDetectionResult]: ROI名称到检测结果的映射
|
||||
"""
|
||||
results = {}
|
||||
|
||||
for roi_name, roi_image in roi_images.items():
|
||||
try:
|
||||
result = self.detect_peak_in_roi(roi_image)
|
||||
results[roi_name] = result
|
||||
except Exception as e:
|
||||
# 检测失败时创建默认结果
|
||||
results[roi_name] = PeakDetectionResult(
|
||||
max_brightness=0.0,
|
||||
avg_brightness=0.0,
|
||||
brightness_contrast=0.0,
|
||||
peak_position=(0, 0),
|
||||
bright_area_size=0,
|
||||
bright_area_ratio=0.0
|
||||
)
|
||||
|
||||
return results
|
||||
|
||||
def adaptive_threshold_detection(self, roi_image: np.ndarray,
|
||||
ambient_light_factor: float = 0.8) -> PeakDetectionResult:
|
||||
"""
|
||||
自适应阈值峰值检测
|
||||
根据环境光自动调整检测阈值
|
||||
|
||||
Args:
|
||||
roi_image: ROI图像
|
||||
ambient_light_factor: 环境光适应系数
|
||||
|
||||
Returns:
|
||||
PeakDetectionResult: 检测结果
|
||||
"""
|
||||
# 估算环境光水平
|
||||
if len(roi_image.shape) == 3:
|
||||
gray = cv2.cvtColor(roi_image, cv2.COLOR_BGR2GRAY)
|
||||
else:
|
||||
gray = roi_image.copy()
|
||||
|
||||
ambient_light = float(np.mean(gray))
|
||||
|
||||
# 自适应调整阈值
|
||||
adaptive_threshold = max(
|
||||
self.brightness_threshold,
|
||||
int(ambient_light * (1.0 + ambient_light_factor))
|
||||
)
|
||||
|
||||
# 临时调整阈值进行检测
|
||||
original_threshold = self.brightness_threshold
|
||||
self.brightness_threshold = adaptive_threshold
|
||||
|
||||
try:
|
||||
result = self.detect_peak_in_roi(roi_image)
|
||||
finally:
|
||||
# 恢复原始阈值
|
||||
self.brightness_threshold = original_threshold
|
||||
|
||||
return result
|
||||
|
||||
def enhance_peak_detection(self, roi_image: np.ndarray,
|
||||
use_morphology: bool = True,
|
||||
use_top_hat: bool = True) -> PeakDetectionResult:
|
||||
"""
|
||||
增强峰值检测（用于雾天等复杂环境）
|
||||
|
||||
Args:
|
||||
roi_image: ROI图像
|
||||
use_morphology: 是否使用形态学操作
|
||||
use_top_hat: 是否使用顶帽变换
|
||||
|
||||
Returns:
|
||||
PeakDetectionResult: 检测结果
|
||||
"""
|
||||
# 转换为灰度图像
|
||||
if len(roi_image.shape) == 3:
|
||||
gray = cv2.cvtColor(roi_image, cv2.COLOR_BGR2GRAY)
|
||||
else:
|
||||
gray = roi_image.copy()
|
||||
|
||||
enhanced = gray.copy()
|
||||
|
||||
# 应用顶帽变换增强亮点
|
||||
if use_top_hat:
|
||||
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7))
|
||||
tophat = cv2.morphologyEx(enhanced, cv2.MORPH_TOPHAT, kernel)
|
||||
enhanced = cv2.add(enhanced, tophat)
|
||||
|
||||
# 应用形态学操作去噪
|
||||
if use_morphology:
|
||||
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
|
||||
enhanced = cv2.morphologyEx(enhanced, cv2.MORPH_CLOSE, kernel)
|
||||
enhanced = cv2.morphologyEx(enhanced, cv2.MORPH_OPEN, kernel)
|
||||
|
||||
# 使用增强后的图像进行检测
|
||||
return self.detect_peak_in_roi(enhanced)
|
||||
|
||||
def update_parameters(self, **kwargs) -> None:
|
||||
"""
|
||||
更新检测参数
|
||||
|
||||
Args:
|
||||
**kwargs: 参数键值对
|
||||
"""
|
||||
if 'gaussian_kernel_size' in kwargs:
|
||||
self.gaussian_kernel_size = kwargs['gaussian_kernel_size']
|
||||
if 'brightness_threshold' in kwargs:
|
||||
self.brightness_threshold = kwargs['brightness_threshold']
|
||||
if 'contrast_threshold' in kwargs:
|
||||
self.contrast_threshold = kwargs['contrast_threshold']
|
||||
|
||||
def visualize_detection(self, roi_image: np.ndarray,
|
||||
result: PeakDetectionResult) -> np.ndarray:
|
||||
"""
|
||||
可视化峰值检测结果
|
||||
|
||||
Args:
|
||||
roi_image: ROI图像
|
||||
result: 检测结果
|
||||
|
||||
Returns:
|
||||
np.ndarray: 可视化图像
|
||||
"""
|
||||
# 创建彩色副本
|
||||
if len(roi_image.shape) == 3:
|
||||
vis_image = roi_image.copy()
|
||||
else:
|
||||
vis_image = cv2.cvtColor(roi_image, cv2.COLOR_GRAY2BGR)
|
||||
|
||||
# 绘制峰值位置
|
||||
h, w = roi_image.shape[:2]
|
||||
core_h = int(h * 0.5)
|
||||
core_w = int(w * 0.5)
|
||||
start_y = (h - core_h) // 2
|
||||
start_x = (w - core_w) // 2
|
||||
|
||||
# 调整峰值位置到原始图像坐标
|
||||
peak_x = start_x + result.peak_position[0]
|
||||
peak_y = start_y + result.peak_position[1]
|
||||
|
||||
# 绘制峰值点
|
||||
cv2.circle(vis_image, (peak_x, peak_y), 3, (0, 0, 255), -1)
|
||||
|
||||
# 绘制核心区域边界
|
||||
cv2.rectangle(vis_image,
|
||||
(start_x, start_y),
|
||||
(start_x + core_w, start_y + core_h),
|
||||
(0, 255, 0), 1)
|
||||
|
||||
# 添加文本信息
|
||||
info_text = f"Max: {result.max_brightness:.1f}, Avg: {result.avg_brightness:.1f}"
|
||||
cv2.putText(vis_image, info_text, (2, h - 5),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255, 255, 255), 1)
|
||||
|
||||
return vis_image
|
||||
395
src/roi_detection/roi_manager.py
Normal file
395
src/roi_detection/roi_manager.py
Normal file
@ -0,0 +1,395 @@
|
||||
"""
|
||||
ROI管理器
|
||||
管理18个LED灯的ROI区域定义、加载和操作
|
||||
"""
|
||||
|
||||
import yaml
|
||||
import cv2
|
||||
import numpy as np
|
||||
from typing import Dict, Tuple, List, Optional, Any
|
||||
from dataclasses import dataclass
|
||||
|
||||
|
||||
@dataclass
|
||||
class ROIRegion:
|
||||
"""
|
||||
ROI区域数据类
|
||||
"""
|
||||
name: str
|
||||
center: Tuple[int, int] # 中心坐标
|
||||
roi_box: Tuple[int, int, int, int] # 外边界 [x, y, width, height]
|
||||
core_area: Tuple[int, int, int, int] # 核心区域 [x, y, width, height]
|
||||
row: int # 所在行号 (1-3)
|
||||
col: int # 所在列号 (1-6)
|
||||
|
||||
|
||||
class ROIManager:
|
||||
"""
|
||||
ROI管理器
|
||||
负责加载、管理和操作18个LED灯的ROI区域
|
||||
"""
|
||||
|
||||
def __init__(self, config_path: str = "config/roi_config.yaml"):
|
||||
"""
|
||||
初始化ROI管理器
|
||||
|
||||
Args:
|
||||
config_path: ROI配置文件路径
|
||||
"""
|
||||
self.config_path = config_path
|
||||
self.roi_regions: Dict[str, ROIRegion] = {}
|
||||
self.grid_config = {
|
||||
'rows': 3,
|
||||
'columns': 6,
|
||||
'total_leds': 18
|
||||
}
|
||||
|
||||
# 尝试加载ROI配置
|
||||
self.load_roi_config()
|
||||
|
||||
def load_roi_config(self) -> bool:
|
||||
"""
|
||||
从配置文件加载ROI信息
|
||||
|
||||
Returns:
|
||||
bool: 加载是否成功
|
||||
"""
|
||||
try:
|
||||
with open(self.config_path, 'r', encoding='utf-8') as f:
|
||||
config = yaml.safe_load(f)
|
||||
|
||||
# 加载网格配置
|
||||
if 'led_matrix' in config:
|
||||
self.grid_config.update(config['led_matrix'])
|
||||
|
||||
# 加载ROI区域配置
|
||||
if 'roi_regions' in config:
|
||||
self._parse_roi_regions(config['roi_regions'])
|
||||
return True
|
||||
|
||||
except (FileNotFoundError, yaml.YAMLError, KeyError) as e:
|
||||
print(f"加载ROI配置失败: {e}")
|
||||
self._create_default_roi_regions()
|
||||
|
||||
return False
|
||||
|
||||
def _parse_roi_regions(self, roi_config: Dict[str, Any]) -> None:
|
||||
"""
|
||||
解析ROI区域配置
|
||||
|
||||
Args:
|
||||
roi_config: ROI配置字典
|
||||
"""
|
||||
self.roi_regions.clear()
|
||||
|
||||
for roi_name, roi_data in roi_config.items():
|
||||
try:
|
||||
# 解析行列信息
|
||||
row = int(roi_name[1]) # R1C1 -> row=1
|
||||
col = int(roi_name[3]) # R1C1 -> col=1
|
||||
|
||||
roi_region = ROIRegion(
|
||||
name=roi_name,
|
||||
center=tuple(roi_data['center']),
|
||||
roi_box=tuple(roi_data['roi_box']),
|
||||
core_area=tuple(roi_data['core_area']),
|
||||
row=row,
|
||||
col=col
|
||||
)
|
||||
|
||||
self.roi_regions[roi_name] = roi_region
|
||||
|
||||
except (KeyError, ValueError, IndexError) as e:
|
||||
print(f"解析ROI {roi_name} 失败: {e}")
|
||||
continue
|
||||
|
||||
def _create_default_roi_regions(self) -> None:
|
||||
"""
|
||||
创建默认的ROI区域配置（用于初始化）
|
||||
"""
|
||||
print("使用默认ROI配置")
|
||||
|
||||
# 默认的网格布局
|
||||
start_x, start_y = 120, 150
|
||||
spacing_x, spacing_y = 120, 100
|
||||
roi_size = 40
|
||||
core_size = 10
|
||||
|
||||
for row in range(1, self.grid_config['rows'] + 1):
|
||||
for col in range(1, self.grid_config['columns'] + 1):
|
||||
roi_name = f"R{row}C{col}"
|
||||
|
||||
# 计算中心坐标
|
||||
center_x = start_x + (col - 1) * spacing_x
|
||||
center_y = start_y + (row - 1) * spacing_y
|
||||
|
||||
# 计算ROI边界
|
||||
roi_x = center_x - roi_size // 2
|
||||
roi_y = center_y - roi_size // 2
|
||||
|
||||
# 计算核心区域
|
||||
core_x = center_x - core_size // 2
|
||||
core_y = center_y - core_size // 2
|
||||
|
||||
roi_region = ROIRegion(
|
||||
name=roi_name,
|
||||
center=(center_x, center_y),
|
||||
roi_box=(roi_x, roi_y, roi_size, roi_size),
|
||||
core_area=(core_x, core_y, core_size, core_size),
|
||||
row=row,
|
||||
col=col
|
||||
)
|
||||
|
||||
self.roi_regions[roi_name] = roi_region
|
||||
|
||||
def get_roi_region(self, roi_name: str) -> Optional[ROIRegion]:
|
||||
"""
|
||||
获取指定ROI区域
|
||||
|
||||
Args:
|
||||
roi_name: ROI名称（如"R1C1"）
|
||||
|
||||
Returns:
|
||||
Optional[ROIRegion]: ROI区域对象，不存在返回None
|
||||
"""
|
||||
return self.roi_regions.get(roi_name)
|
||||
|
||||
def get_all_roi_regions(self) -> Dict[str, ROIRegion]:
|
||||
"""
|
||||
获取所有ROI区域
|
||||
|
||||
Returns:
|
||||
Dict[str, ROIRegion]: 所有ROI区域的字典
|
||||
"""
|
||||
return self.roi_regions.copy()
|
||||
|
||||
def get_roi_by_position(self, row: int, col: int) -> Optional[ROIRegion]:
|
||||
"""
|
||||
按位置获取ROI区域
|
||||
|
||||
Args:
|
||||
row: 行号 (1-3)
|
||||
col: 列号 (1-6)
|
||||
|
||||
Returns:
|
||||
Optional[ROIRegion]: ROI区域对象
|
||||
"""
|
||||
roi_name = f"R{row}C{col}"
|
||||
return self.roi_regions.get(roi_name)
|
||||
|
||||
def extract_roi_image(self, image: np.ndarray, roi_name: str) -> Optional[np.ndarray]:
|
||||
"""
|
||||
从图像中提取指定ROI区域
|
||||
|
||||
Args:
|
||||
image: 输入图像
|
||||
roi_name: ROI名称
|
||||
|
||||
Returns:
|
||||
Optional[np.ndarray]: ROI区域图像，失败返回None
|
||||
"""
|
||||
roi_region = self.get_roi_region(roi_name)
|
||||
if roi_region is None:
|
||||
return None
|
||||
|
||||
try:
|
||||
x, y, w, h = roi_region.roi_box
|
||||
|
||||
# 检查边界
|
||||
if (x < 0 or y < 0 or x + w > image.shape[1] or y + h > image.shape[0]):
|
||||
return None
|
||||
|
||||
roi_image = image[y:y+h, x:x+w]
|
||||
return roi_image
|
||||
|
||||
except (IndexError, ValueError):
|
||||
return None
|
||||
|
||||
def extract_core_area(self, image: np.ndarray, roi_name: str) -> Optional[np.ndarray]:
|
||||
"""
|
||||
从图像中提取指定ROI的核心区域
|
||||
|
||||
Args:
|
||||
image: 输入图像
|
||||
roi_name: ROI名称
|
||||
|
||||
Returns:
|
||||
Optional[np.ndarray]: 核心区域图像，失败返回None
|
||||
"""
|
||||
roi_region = self.get_roi_region(roi_name)
|
||||
if roi_region is None:
|
||||
return None
|
||||
|
||||
try:
|
||||
x, y, w, h = roi_region.core_area
|
||||
|
||||
# 检查边界
|
||||
if (x < 0 or y < 0 or x + w > image.shape[1] or y + h > image.shape[0]):
|
||||
return None
|
||||
|
||||
core_image = image[y:y+h, x:x+w]
|
||||
return core_image
|
||||
|
||||
except (IndexError, ValueError):
|
||||
return None
|
||||
|
||||
def extract_all_roi_images(self, image: np.ndarray) -> Dict[str, np.ndarray]:
|
||||
"""
|
||||
从图像中提取所有ROI区域图像
|
||||
|
||||
Args:
|
||||
image: 输入图像
|
||||
|
||||
Returns:
|
||||
Dict[str, np.ndarray]: ROI名称到图像的映射
|
||||
"""
|
||||
roi_images = {}
|
||||
|
||||
for roi_name in self.roi_regions.keys():
|
||||
roi_image = self.extract_roi_image(image, roi_name)
|
||||
if roi_image is not None:
|
||||
roi_images[roi_name] = roi_image
|
||||
|
||||
return roi_images
|
||||
|
||||
def draw_roi_regions(self, image: np.ndarray,
|
||||
show_names: bool = True,
|
||||
roi_color: Tuple[int, int, int] = (0, 255, 0),
|
||||
core_color: Tuple[int, int, int] = (0, 0, 255)) -> np.ndarray:
|
||||
"""
|
||||
在图像上绘制ROI区域
|
||||
|
||||
Args:
|
||||
image: 输入图像
|
||||
show_names: 是否显示ROI名称
|
||||
roi_color: ROI边界颜色 (B, G, R)
|
||||
core_color: 核心区域颜色 (B, G, R)
|
||||
|
||||
Returns:
|
||||
np.ndarray: 绘制了ROI区域的图像
|
||||
"""
|
||||
result = image.copy()
|
||||
|
||||
for roi_name, roi_region in self.roi_regions.items():
|
||||
# 绘制ROI边界
|
||||
x, y, w, h = roi_region.roi_box
|
||||
cv2.rectangle(result, (x, y), (x + w, y + h), roi_color, 2)
|
||||
|
||||
# 绘制核心区域
|
||||
core_x, core_y, core_w, core_h = roi_region.core_area
|
||||
cv2.rectangle(result, (core_x, core_y), (core_x + core_w, core_y + core_h), core_color, 1)
|
||||
|
||||
# 显示ROI名称
|
||||
if show_names:
|
||||
cv2.putText(result, roi_name, (x, y - 5),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, roi_color, 1)
|
||||
|
||||
# 绘制中心点
|
||||
center_x, center_y = roi_region.center
|
||||
cv2.circle(result, (center_x, center_y), 2, (255, 0, 0), -1)
|
||||
|
||||
return result
|
||||
|
||||
def update_roi_region(self, roi_name: str,
|
||||
center: Optional[Tuple[int, int]] = None,
|
||||
roi_box: Optional[Tuple[int, int, int, int]] = None,
|
||||
core_area: Optional[Tuple[int, int, int, int]] = None) -> bool:
|
||||
"""
|
||||
更新ROI区域参数
|
||||
|
||||
Args:
|
||||
roi_name: ROI名称
|
||||
center: 新的中心坐标
|
||||
roi_box: 新的ROI边界
|
||||
core_area: 新的核心区域
|
||||
|
||||
Returns:
|
||||
bool: 更新是否成功
|
||||
"""
|
||||
if roi_name not in self.roi_regions:
|
||||
return False
|
||||
|
||||
roi_region = self.roi_regions[roi_name]
|
||||
|
||||
if center is not None:
|
||||
roi_region.center = center
|
||||
if roi_box is not None:
|
||||
roi_region.roi_box = roi_box
|
||||
if core_area is not None:
|
||||
roi_region.core_area = core_area
|
||||
|
||||
return True
|
||||
|
||||
def save_roi_config(self, output_path: Optional[str] = None) -> bool:
|
||||
"""
|
||||
保存ROI配置到文件
|
||||
|
||||
Args:
|
||||
output_path: 输出文件路径，默认使用初始化时的路径
|
||||
|
||||
Returns:
|
||||
bool: 保存是否成功
|
||||
"""
|
||||
if output_path is None:
|
||||
output_path = self.config_path
|
||||
|
||||
try:
|
||||
# 构建配置字典
|
||||
config = {
|
||||
'led_matrix': self.grid_config,
|
||||
'roi_regions': {}
|
||||
}
|
||||
|
||||
for roi_name, roi_region in self.roi_regions.items():
|
||||
config['roi_regions'][roi_name] = {
|
||||
'center': list(roi_region.center),
|
||||
'roi_box': list(roi_region.roi_box),
|
||||
'core_area': list(roi_region.core_area)
|
||||
}
|
||||
|
||||
# 写入文件
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
yaml.dump(config, f, default_flow_style=False,
|
||||
allow_unicode=True, indent=2)
|
||||
|
||||
return True
|
||||
|
||||
except (IOError, yaml.YAMLError) as e:
|
||||
print(f"保存ROI配置失败: {e}")
|
||||
return False
|
||||
|
||||
def get_grid_info(self) -> Dict[str, int]:
|
||||
"""
|
||||
获取网格信息
|
||||
|
||||
Returns:
|
||||
Dict[str, int]: 网格信息
|
||||
"""
|
||||
return self.grid_config.copy()
|
||||
|
||||
def validate_roi_regions(self, image_shape: Tuple[int, int]) -> Dict[str, List[str]]:
|
||||
"""
|
||||
验证ROI区域是否在图像范围内
|
||||
|
||||
Args:
|
||||
image_shape: 图像尺寸 (height, width)
|
||||
|
||||
Returns:
|
||||
Dict[str, List[str]]: 验证结果，包含有效和无效的ROI列表
|
||||
"""
|
||||
h, w = image_shape
|
||||
valid_rois = []
|
||||
invalid_rois = []
|
||||
|
||||
for roi_name, roi_region in self.roi_regions.items():
|
||||
x, y, roi_w, roi_h = roi_region.roi_box
|
||||
|
||||
if (x >= 0 and y >= 0 and x + roi_w <= w and y + roi_h <= h):
|
||||
valid_rois.append(roi_name)
|
||||
else:
|
||||
invalid_rois.append(roi_name)
|
||||
|
||||
return {
|
||||
'valid': valid_rois,
|
||||
'invalid': invalid_rois
|
||||
}
|
||||
394
src/roi_detection/threshold_detector.py
Normal file
394
src/roi_detection/threshold_detector.py
Normal file
@ -0,0 +1,394 @@
|
||||
"""
|
||||
双阈值判断模块
|
||||
基于亮度峰值和面积信息的双重阈值判断，准确区分LED灯的亮灭状态
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
from typing import Dict, Tuple, Optional, Any
|
||||
from dataclasses import dataclass
|
||||
from enum import Enum
|
||||
|
||||
from .peak_detector import PeakDetectionResult
|
||||
|
||||
|
||||
class LEDState(Enum):
|
||||
"""
|
||||
LED灯状态枚举
|
||||
"""
|
||||
OFF = 0 # 灭
|
||||
ON = 1 # 亮
|
||||
UNCERTAIN = 2 # 不确定
|
||||
|
||||
|
||||
@dataclass
|
||||
class ThresholdDetectionResult:
|
||||
"""
|
||||
阈值判断结果
|
||||
"""
|
||||
led_state: LEDState
|
||||
confidence: float # 置信度 (0.0-1.0)
|
||||
brightness_score: float # 亮度分数
|
||||
area_score: float # 面积分数
|
||||
contrast_score: float # 对比度分数
|
||||
final_score: float # 综合分数
|
||||
reasons: list # 判断理由
|
||||
|
||||
|
||||
class ThresholdDetector:
|
||||
"""
|
||||
双阈值检测器
|
||||
综合考虑亮度、面积、对比度等多个因素进行LED状态判断
|
||||
"""
|
||||
|
||||
def __init__(self, config: Optional[Dict[str, Any]] = None):
|
||||
"""
|
||||
初始化双阈值检测器
|
||||
|
||||
Args:
|
||||
config: 配置参数字典
|
||||
"""
|
||||
self.config = config or self._get_default_config()
|
||||
|
||||
# 提取配置参数
|
||||
brightness_config = self.config.get('brightness_detection', {})
|
||||
area_config = self.config.get('area_detection', {})
|
||||
|
||||
# 亮度阈值参数
|
||||
self.peak_brightness_threshold = brightness_config.get('peak_brightness_threshold', 120)
|
||||
self.avg_brightness_threshold = brightness_config.get('avg_brightness_threshold', 80)
|
||||
self.brightness_contrast_threshold = brightness_config.get('brightness_contrast_threshold', 30)
|
||||
|
||||
# 面积阈值参数
|
||||
self.min_bright_area = area_config.get('min_bright_area', 5)
|
||||
self.max_bright_area = area_config.get('max_bright_area', 200)
|
||||
self.area_ratio_threshold = area_config.get('area_ratio_threshold', 0.3)
|
||||
|
||||
# 自适应参数
|
||||
self.adaptive_enabled = brightness_config.get('adaptive_threshold_enabled', True)
|
||||
self.ambient_light_factor = brightness_config.get('ambient_light_factor', 0.8)
|
||||
|
||||
# 分数权重
|
||||
self.brightness_weight = 0.4
|
||||
self.area_weight = 0.3
|
||||
self.contrast_weight = 0.3
|
||||
|
||||
def _get_default_config(self) -> Dict[str, Any]:
|
||||
"""
|
||||
获取默认配置
|
||||
"""
|
||||
return {
|
||||
'brightness_detection': {
|
||||
'peak_brightness_threshold': 120,
|
||||
'avg_brightness_threshold': 80,
|
||||
'brightness_contrast_threshold': 30,
|
||||
'adaptive_threshold_enabled': True,
|
||||
'ambient_light_factor': 0.8
|
||||
},
|
||||
'area_detection': {
|
||||
'min_bright_area': 5,
|
||||
'max_bright_area': 200,
|
||||
'area_ratio_threshold': 0.3
|
||||
}
|
||||
}
|
||||
|
||||
def detect_led_state(self, peak_result: PeakDetectionResult,
|
||||
ambient_brightness: Optional[float] = None) -> ThresholdDetectionResult:
|
||||
"""
|
||||
基于峰值检测结果判断LED状态
|
||||
|
||||
Args:
|
||||
peak_result: 峰值检测结果
|
||||
ambient_brightness: 环境亮度值，用于自适应调整
|
||||
|
||||
Returns:
|
||||
ThresholdDetectionResult: 阈值判断结果
|
||||
"""
|
||||
# 计算各项分数
|
||||
brightness_score = self._calculate_brightness_score(
|
||||
peak_result, ambient_brightness
|
||||
)
|
||||
area_score = self._calculate_area_score(peak_result)
|
||||
contrast_score = self._calculate_contrast_score(peak_result)
|
||||
|
||||
# 计算综合分数
|
||||
final_score = (
|
||||
brightness_score * self.brightness_weight +
|
||||
area_score * self.area_weight +
|
||||
contrast_score * self.contrast_weight
|
||||
)
|
||||
|
||||
# 判断LED状态
|
||||
led_state, confidence, reasons = self._determine_led_state(
|
||||
brightness_score, area_score, contrast_score, final_score
|
||||
)
|
||||
|
||||
return ThresholdDetectionResult(
|
||||
led_state=led_state,
|
||||
confidence=confidence,
|
||||
brightness_score=brightness_score,
|
||||
area_score=area_score,
|
||||
contrast_score=contrast_score,
|
||||
final_score=final_score,
|
||||
reasons=reasons
|
||||
)
|
||||
|
||||
def _calculate_brightness_score(self, peak_result: PeakDetectionResult,
|
||||
ambient_brightness: Optional[float]) -> float:
|
||||
"""
|
||||
计算亮度分数
|
||||
|
||||
Args:
|
||||
peak_result: 峰值检测结果
|
||||
ambient_brightness: 环境亮度
|
||||
|
||||
Returns:
|
||||
float: 亮度分数 (0.0-1.0)
|
||||
"""
|
||||
# 自适应阈值调整
|
||||
if self.adaptive_enabled and ambient_brightness is not None:
|
||||
adaptive_threshold = max(
|
||||
self.peak_brightness_threshold,
|
||||
ambient_brightness * (1.0 + self.ambient_light_factor)
|
||||
)
|
||||
else:
|
||||
adaptive_threshold = self.peak_brightness_threshold
|
||||
|
||||
# 峰值亮度分数
|
||||
peak_score = min(1.0, peak_result.max_brightness / adaptive_threshold)
|
||||
|
||||
# 平均亮度分数
|
||||
avg_threshold = adaptive_threshold * 0.7 # 平均亮度阈值相对较低
|
||||
avg_score = min(1.0, peak_result.avg_brightness / avg_threshold)
|
||||
|
||||
# 综合亮度分数（峰值权重更高）
|
||||
brightness_score = peak_score * 0.7 + avg_score * 0.3
|
||||
|
||||
return brightness_score
|
||||
|
||||
def _calculate_area_score(self, peak_result: PeakDetectionResult) -> float:
|
||||
"""
|
||||
计算面积分数
|
||||
|
||||
Args:
|
||||
peak_result: 峰值检测结果
|
||||
|
||||
Returns:
|
||||
float: 面积分数 (0.0-1.0)
|
||||
"""
|
||||
bright_area = peak_result.bright_area_size
|
||||
bright_ratio = peak_result.bright_area_ratio
|
||||
|
||||
# 检查面积是否在合理范围内
|
||||
if bright_area < self.min_bright_area:
|
||||
return 0.0 # 面积太小，可能是噪声
|
||||
|
||||
if bright_area > self.max_bright_area:
|
||||
return 0.0 # 面积太大，可能是光晕污染
|
||||
|
||||
# 检查面积比例
|
||||
if bright_ratio > self.area_ratio_threshold:
|
||||
return max(0.0, 1.0 - (bright_ratio - self.area_ratio_threshold) * 2)
|
||||
|
||||
# 正常情况下，面积分数与亮区数量成正比
|
||||
area_score = min(1.0, bright_area / (self.min_bright_area * 4))
|
||||
|
||||
return area_score
|
||||
|
||||
def _calculate_contrast_score(self, peak_result: PeakDetectionResult) -> float:
|
||||
"""
|
||||
计算对比度分数
|
||||
|
||||
Args:
|
||||
peak_result: 峰值检测结果
|
||||
|
||||
Returns:
|
||||
float: 对比度分数 (0.0-1.0)
|
||||
"""
|
||||
contrast = peak_result.brightness_contrast
|
||||
|
||||
# 对比度低于阈值认为是光晕污染
|
||||
if contrast < self.brightness_contrast_threshold:
|
||||
return 0.0
|
||||
|
||||
# 对比度越高分数越高，但有上限
|
||||
max_contrast = self.brightness_contrast_threshold * 3
|
||||
contrast_score = min(1.0, contrast / max_contrast)
|
||||
|
||||
return contrast_score
|
||||
|
||||
def _determine_led_state(self, brightness_score: float,
|
||||
area_score: float,
|
||||
contrast_score: float,
|
||||
final_score: float) -> Tuple[LEDState, float, list]:
|
||||
"""
|
||||
根据各项分数决定LED状态
|
||||
|
||||
Args:
|
||||
brightness_score: 亮度分数
|
||||
area_score: 面积分数
|
||||
contrast_score: 对比度分数
|
||||
final_score: 综合分数
|
||||
|
||||
Returns:
|
||||
Tuple[LEDState, float, list]: (LED状态, 置信度, 判断理由)
|
||||
"""
|
||||
reasons = []
|
||||
|
||||
# 基本阈值检查
|
||||
if brightness_score < 0.3:
|
||||
reasons.append("亮度不足")
|
||||
|
||||
if area_score < 0.2:
|
||||
if area_score == 0.0:
|
||||
reasons.append("亮区面积异常")
|
||||
else:
|
||||
reasons.append("亮区面积过小")
|
||||
|
||||
if contrast_score < 0.2:
|
||||
reasons.append("对比度不足（可能为光晕干扰）")
|
||||
|
||||
# 综合判断逻辑
|
||||
if final_score >= 0.7:
|
||||
# 高置信度亮起
|
||||
led_state = LEDState.ON
|
||||
confidence = min(0.95, final_score)
|
||||
if not reasons:
|
||||
reasons.append("综合指标表明灯亮起")
|
||||
|
||||
elif final_score >= 0.4:
|
||||
# 中等置信度，需要进一步检查
|
||||
if brightness_score >= 0.6 and contrast_score >= 0.4:
|
||||
led_state = LEDState.ON
|
||||
confidence = final_score * 0.8
|
||||
reasons.append("亮度和对比度较好")
|
||||
else:
|
||||
led_state = LEDState.UNCERTAIN
|
||||
confidence = 0.5
|
||||
reasons.append("信号不清晰，需要连续帧判断")
|
||||
|
||||
else:
|
||||
# 低置信度，判断为灭
|
||||
led_state = LEDState.OFF
|
||||
confidence = 1.0 - final_score
|
||||
if not reasons:
|
||||
reasons.append("综合指标表明灯灭")
|
||||
|
||||
return led_state, confidence, reasons
|
||||
|
||||
def detect_batch(self, peak_results: Dict[str, PeakDetectionResult],
|
||||
ambient_brightness: Optional[float] = None) -> Dict[str, ThresholdDetectionResult]:
|
||||
"""
|
||||
批量检测多个ROI的LED状态
|
||||
|
||||
Args:
|
||||
peak_results: 峰值检测结果字典
|
||||
ambient_brightness: 环境亮度
|
||||
|
||||
Returns:
|
||||
Dict[str, ThresholdDetectionResult]: 阈值检测结果字典
|
||||
"""
|
||||
results = {}
|
||||
|
||||
# 如果没有提供环境亮度，估算一个平均值
|
||||
if ambient_brightness is None and self.adaptive_enabled:
|
||||
brightness_values = [r.avg_brightness for r in peak_results.values()]
|
||||
if brightness_values:
|
||||
ambient_brightness = np.median(brightness_values)
|
||||
|
||||
for roi_name, peak_result in peak_results.items():
|
||||
results[roi_name] = self.detect_led_state(peak_result, ambient_brightness)
|
||||
|
||||
return results
|
||||
|
||||
def get_detection_summary(self, results: Dict[str, ThresholdDetectionResult]) -> Dict[str, Any]:
|
||||
"""
|
||||
获取检测结果摘要
|
||||
|
||||
Args:
|
||||
results: 检测结果字典
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: 检测摘要
|
||||
"""
|
||||
total_leds = len(results)
|
||||
on_count = sum(1 for r in results.values() if r.led_state == LEDState.ON)
|
||||
off_count = sum(1 for r in results.values() if r.led_state == LEDState.OFF)
|
||||
uncertain_count = sum(1 for r in results.values() if r.led_state == LEDState.UNCERTAIN)
|
||||
|
||||
avg_confidence = np.mean([r.confidence for r in results.values()])
|
||||
|
||||
# 统计各行的状态
|
||||
row_stats = {}
|
||||
for i in range(1, 4): # 3行
|
||||
row_results = {k: v for k, v in results.items() if k.startswith(f'R{i}')}
|
||||
if row_results:
|
||||
row_on = sum(1 for r in row_results.values() if r.led_state == LEDState.ON)
|
||||
row_stats[f'row_{i}'] = {
|
||||
'total': len(row_results),
|
||||
'on': row_on,
|
||||
'off': len(row_results) - row_on
|
||||
}
|
||||
|
||||
return {
|
||||
'total_leds': total_leds,
|
||||
'states': {
|
||||
'on': on_count,
|
||||
'off': off_count,
|
||||
'uncertain': uncertain_count
|
||||
},
|
||||
'avg_confidence': float(avg_confidence),
|
||||
'row_statistics': row_stats
|
||||
}
|
||||
|
||||
def update_thresholds(self, **kwargs) -> None:
|
||||
"""
|
||||
更新阈值参数
|
||||
|
||||
Args:
|
||||
**kwargs: 参数键值对
|
||||
"""
|
||||
if 'peak_brightness_threshold' in kwargs:
|
||||
self.peak_brightness_threshold = kwargs['peak_brightness_threshold']
|
||||
if 'avg_brightness_threshold' in kwargs:
|
||||
self.avg_brightness_threshold = kwargs['avg_brightness_threshold']
|
||||
if 'brightness_contrast_threshold' in kwargs:
|
||||
self.brightness_contrast_threshold = kwargs['brightness_contrast_threshold']
|
||||
if 'min_bright_area' in kwargs:
|
||||
self.min_bright_area = kwargs['min_bright_area']
|
||||
if 'max_bright_area' in kwargs:
|
||||
self.max_bright_area = kwargs['max_bright_area']
|
||||
if 'area_ratio_threshold' in kwargs:
|
||||
self.area_ratio_threshold = kwargs['area_ratio_threshold']
|
||||
if 'ambient_light_factor' in kwargs:
|
||||
self.ambient_light_factor = kwargs['ambient_light_factor']
|
||||
|
||||
def export_detection_results(self, results: Dict[str, ThresholdDetectionResult]) -> Dict[str, Any]:
|
||||
"""
|
||||
导出检测结果为可序列化格式
|
||||
|
||||
Args:
|
||||
results: 检测结果字典
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: 可序列化的结果
|
||||
"""
|
||||
export_data = {
|
||||
'timestamp': None, # 由调用方添加
|
||||
'detection_results': {},
|
||||
'summary': self.get_detection_summary(results)
|
||||
}
|
||||
|
||||
for roi_name, result in results.items():
|
||||
export_data['detection_results'][roi_name] = {
|
||||
'state': result.led_state.name,
|
||||
'confidence': float(result.confidence),
|
||||
'scores': {
|
||||
'brightness': float(result.brightness_score),
|
||||
'area': float(result.area_score),
|
||||
'contrast': float(result.contrast_score),
|
||||
'final': float(result.final_score)
|
||||
},
|
||||
'reasons': result.reasons
|
||||
}
|
||||
|
||||
return export_data
|
||||
5
test.py
Normal file
5
test.py
Normal file
@ -0,0 +1,5 @@
|
||||
from src.roi_detection.led_detector import LEDDetector
|
||||
detector = LEDDetector()
|
||||
print('LED检测器初始化成功')
|
||||
stats = detector.get_detection_statistics()
|
||||
print('检测器状态:', stats)
|
||||
58
tools/calibrate_roi.py
Normal file
58
tools/calibrate_roi.py
Normal file
@ -0,0 +1,58 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
ROI标定演示脚本
|
||||
使用测试图像进行ROI区域标定
|
||||
"""
|
||||
|
||||
import sys
|
||||
import os
|
||||
|
||||
# 添加项目根目录到路径
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
|
||||
|
||||
from tools.roi_calibration_tool import ROICalibrationTool
|
||||
|
||||
|
||||
def main():
|
||||
"""
|
||||
主函数
|
||||
"""
|
||||
print("YantaiVisionX ROI标定工具")
|
||||
print("=" * 50)
|
||||
|
||||
# 获取图像路径
|
||||
if len(sys.argv) > 1:
|
||||
image_path = sys.argv[1]
|
||||
else:
|
||||
# 提示用户输入图像路径
|
||||
image_path = input("请输入标定图像路径: ").strip()
|
||||
|
||||
if not os.path.exists(image_path):
|
||||
print(f"错误: 图像文件不存在 - {image_path}")
|
||||
return
|
||||
|
||||
print(f"加载标定图像: {image_path}")
|
||||
|
||||
# 创建标定工具
|
||||
tool = ROICalibrationTool()
|
||||
|
||||
print("\n标定说明:")
|
||||
print("1. 按照3排×6列的布局标定18个LED灯")
|
||||
print("2. ROI命名: R1C1, R1C2, ..., R3C6")
|
||||
print("3. 从左上角开始，按行优先顺序点击每个LED中心")
|
||||
print("4. 操作键:")
|
||||
print(" - 鼠标左键: 标定当前ROI中心点")
|
||||
print(" - R键: 重置所有标定")
|
||||
print(" - S键: 保存配置并退出")
|
||||
print(" - +/-键: 调整ROI大小")
|
||||
print(" - Q键/ESC: 退出不保存")
|
||||
print("\n开始标定...")
|
||||
|
||||
# 运行标定
|
||||
tool.run_calibration(image_path)
|
||||
|
||||
print("标定完成!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
290
tools/roi_calibration_tool.py
Normal file
290
tools/roi_calibration_tool.py
Normal file
@ -0,0 +1,290 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
ROI标定工具
|
||||
为YantaiVisionX项目提供交互式ROI区域标定功能
|
||||
支持3排×6列共18个LED灯的ROI区域标定
|
||||
"""
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import yaml
|
||||
import sys
|
||||
import os
|
||||
from typing import List, Tuple, Optional, Dict, Any
|
||||
|
||||
# 添加项目根目录到路径
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
|
||||
|
||||
from src.roi_detection.roi_manager import ROIManager, ROIRegion
|
||||
|
||||
|
||||
class ROICalibrationTool:
|
||||
"""
|
||||
交互式ROI标定工具
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.image = None
|
||||
self.display_image = None
|
||||
self.roi_points = {} # roi_name -> center_point
|
||||
self.current_roi_name = None
|
||||
self.roi_size = (60, 60) # 默认ROI大小 (width, height)
|
||||
self.core_ratio = 0.6 # 核心区域占ROI的比例
|
||||
|
||||
# 3排×6列的LED布局
|
||||
self.rows = 3
|
||||
self.cols = 6
|
||||
self.roi_names = []
|
||||
for r in range(1, self.rows + 1):
|
||||
for c in range(1, self.cols + 1):
|
||||
self.roi_names.append(f"R{r}C{c}")
|
||||
|
||||
self.current_roi_index = 0
|
||||
self.window_name = "ROI标定工具"
|
||||
|
||||
def load_calibration_image(self, image_path: str) -> bool:
|
||||
"""
|
||||
加载标定图像
|
||||
|
||||
Args:
|
||||
image_path: 图像文件路径
|
||||
|
||||
Returns:
|
||||
bool: 加载是否成功
|
||||
"""
|
||||
try:
|
||||
self.image = cv2.imread(image_path)
|
||||
if self.image is None:
|
||||
print(f"无法加载图像: {image_path}")
|
||||
return False
|
||||
|
||||
self.display_image = self.image.copy()
|
||||
print(f"图像加载成功: {image_path}")
|
||||
print(f"图像尺寸: {self.image.shape[1]}x{self.image.shape[0]}")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"加载图像失败: {e}")
|
||||
return False
|
||||
|
||||
def mouse_callback(self, event, x, y, flags, param):
|
||||
"""
|
||||
鼠标事件回调函数
|
||||
"""
|
||||
if event == cv2.EVENT_LBUTTONDOWN:
|
||||
if self.current_roi_index < len(self.roi_names):
|
||||
roi_name = self.roi_names[self.current_roi_index]
|
||||
self.roi_points[roi_name] = (x, y)
|
||||
print(f"标定 {roi_name}: 中心点({x}, {y})")
|
||||
|
||||
self.current_roi_index += 1
|
||||
self.update_display()
|
||||
|
||||
if self.current_roi_index >= len(self.roi_names):
|
||||
print("所有ROI区域标定完成！按'S'保存，按'R'重新开始")
|
||||
|
||||
def update_display(self):
|
||||
"""
|
||||
更新显示图像
|
||||
"""
|
||||
self.display_image = self.image.copy()
|
||||
|
||||
# 绘制已标定的ROI
|
||||
for i, roi_name in enumerate(self.roi_names):
|
||||
if roi_name in self.roi_points:
|
||||
center_x, center_y = self.roi_points[roi_name]
|
||||
|
||||
# 计算ROI矩形
|
||||
half_w = self.roi_size[0] // 2
|
||||
half_h = self.roi_size[1] // 2
|
||||
|
||||
# 绘制ROI外框（蓝色）
|
||||
cv2.rectangle(self.display_image,
|
||||
(center_x - half_w, center_y - half_h),
|
||||
(center_x + half_w, center_y + half_h),
|
||||
(255, 0, 0), 2)
|
||||
|
||||
# 绘制核心区域（绿色）
|
||||
core_half_w = int(half_w * self.core_ratio)
|
||||
core_half_h = int(half_h * self.core_ratio)
|
||||
cv2.rectangle(self.display_image,
|
||||
(center_x - core_half_w, center_y - core_half_h),
|
||||
(center_x + core_half_w, center_y + core_half_h),
|
||||
(0, 255, 0), 1)
|
||||
|
||||
# 绘制中心点
|
||||
cv2.circle(self.display_image, (center_x, center_y), 3, (0, 0, 255), -1)
|
||||
|
||||
# 添加ROI名称
|
||||
cv2.putText(self.display_image, roi_name,
|
||||
(center_x - 15, center_y - half_h - 5),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0), 1)
|
||||
|
||||
# 显示当前要标定的ROI提示
|
||||
if self.current_roi_index < len(self.roi_names):
|
||||
current_roi = self.roi_names[self.current_roi_index]
|
||||
info_text = f"请点击标定 {current_roi} ({self.current_roi_index + 1}/{len(self.roi_names)})"
|
||||
cv2.putText(self.display_image, info_text, (10, 30),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
|
||||
|
||||
# 显示操作提示
|
||||
help_text = "操作: 鼠标左键-标定点 | R-重置 | S-保存 | Q-退出 | +/-调整ROI大小"
|
||||
cv2.putText(self.display_image, help_text, (10, self.image.shape[0] - 10),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
|
||||
|
||||
cv2.imshow(self.window_name, self.display_image)
|
||||
|
||||
def adjust_roi_size(self, delta: int):
|
||||
"""
|
||||
调整ROI大小
|
||||
|
||||
Args:
|
||||
delta: 尺寸变化量
|
||||
"""
|
||||
new_w = max(20, self.roi_size[0] + delta)
|
||||
new_h = max(20, self.roi_size[1] + delta)
|
||||
self.roi_size = (new_w, new_h)
|
||||
print(f"ROI大小调整为: {self.roi_size}")
|
||||
self.update_display()
|
||||
|
||||
def reset_calibration(self):
|
||||
"""
|
||||
重置标定
|
||||
"""
|
||||
self.roi_points.clear()
|
||||
self.current_roi_index = 0
|
||||
print("标定已重置")
|
||||
self.update_display()
|
||||
|
||||
def generate_roi_config(self) -> Dict[str, Any]:
|
||||
"""
|
||||
生成ROI配置
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: ROI配置字典
|
||||
"""
|
||||
config = {
|
||||
'led_matrix': {
|
||||
'rows': self.rows,
|
||||
'cols': self.cols,
|
||||
'total_leds': self.rows * self.cols
|
||||
},
|
||||
'roi_regions': {}
|
||||
}
|
||||
|
||||
for roi_name, center in self.roi_points.items():
|
||||
center_x, center_y = center
|
||||
half_w = self.roi_size[0] // 2
|
||||
half_h = self.roi_size[1] // 2
|
||||
|
||||
# ROI边界框
|
||||
roi_box = (center_x - half_w, center_y - half_h,
|
||||
self.roi_size[0], self.roi_size[1])
|
||||
|
||||
# 核心区域
|
||||
core_half_w = int(half_w * self.core_ratio)
|
||||
core_half_h = int(half_h * self.core_ratio)
|
||||
core_area = (center_x - core_half_w, center_y - core_half_h,
|
||||
core_half_w * 2, core_half_h * 2)
|
||||
|
||||
config['roi_regions'][roi_name] = {
|
||||
'center': [center_x, center_y],
|
||||
'roi_box': list(roi_box),
|
||||
'core_area': list(core_area)
|
||||
}
|
||||
|
||||
return config
|
||||
|
||||
def save_roi_config(self, output_path: str = "config/roi_config.yaml") -> bool:
|
||||
"""
|
||||
保存ROI配置到文件
|
||||
|
||||
Args:
|
||||
output_path: 输出文件路径
|
||||
|
||||
Returns:
|
||||
bool: 保存是否成功
|
||||
"""
|
||||
if len(self.roi_points) != len(self.roi_names):
|
||||
print(f"标定未完成，只标定了{len(self.roi_points)}/{len(self.roi_names)}个ROI")
|
||||
return False
|
||||
|
||||
try:
|
||||
config = self.generate_roi_config()
|
||||
|
||||
# 确保目录存在
|
||||
os.makedirs(os.path.dirname(output_path), exist_ok=True)
|
||||
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
yaml.dump(config, f, default_flow_style=False,
|
||||
allow_unicode=True, indent=2)
|
||||
|
||||
print(f"ROI配置已保存到: {output_path}")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"保存ROI配置失败: {e}")
|
||||
return False
|
||||
|
||||
def run_calibration(self, image_path: str):
|
||||
"""
|
||||
运行标定程序
|
||||
|
||||
Args:
|
||||
image_path: 标定图像路径
|
||||
"""
|
||||
if not self.load_calibration_image(image_path):
|
||||
return
|
||||
|
||||
cv2.namedWindow(self.window_name, cv2.WINDOW_NORMAL)
|
||||
cv2.setMouseCallback(self.window_name, self.mouse_callback)
|
||||
|
||||
print("ROI标定工具启动")
|
||||
print(f"需要标定{len(self.roi_names)}个ROI区域")
|
||||
print("按顺序点击每个LED灯的中心位置")
|
||||
print("ROI命名规则: R1C1, R1C2, ..., R3C6 (行列从1开始)")
|
||||
|
||||
self.update_display()
|
||||
|
||||
while True:
|
||||
key = cv2.waitKey(1) & 0xFF
|
||||
|
||||
if key == ord('q') or key == 27: # Q键或ESC退出
|
||||
break
|
||||
elif key == ord('r'): # R键重置
|
||||
self.reset_calibration()
|
||||
elif key == ord('s'): # S键保存
|
||||
if self.save_roi_config():
|
||||
print("标定完成并保存成功！")
|
||||
break
|
||||
elif key == ord('+') or key == ord('='): # +键增大ROI
|
||||
self.adjust_roi_size(5)
|
||||
elif key == ord('-'): # -键减小ROI
|
||||
self.adjust_roi_size(-5)
|
||||
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
|
||||
def main():
|
||||
"""
|
||||
主函数
|
||||
"""
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(description="ROI标定工具")
|
||||
parser.add_argument("image", help="标定图像路径")
|
||||
parser.add_argument("-o", "--output", default="config/roi_config.yaml",
|
||||
help="输出配置文件路径")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if not os.path.exists(args.image):
|
||||
print(f"图像文件不存在: {args.image}")
|
||||
return
|
||||
|
||||
tool = ROICalibrationTool()
|
||||
tool.run_calibration(args.image)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in New Issue
Block a user