137 lines
3.3 KiB
Markdown
137 lines
3.3 KiB
Markdown
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# 安保异常行为识别系统
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## 项目概述
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基于 YOLO + TensorRT 的安保异常行为识别系统，支持离岗检测和周界入侵检测。
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## 快速开始
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### 1. 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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### 2. 生成 TensorRT 引擎
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```bash
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python scripts/build_engine.py --model models/yolo11n.pt --fp16
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```
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### 3. 配置数据库
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编辑 `config.yaml` 配置数据库连接。
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### 4. 启动服务
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```bash
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python main.py
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```
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### 5. 访问前端
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打开浏览器访问 `http://localhost:3000`
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## 目录结构
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```
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project_root/
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├── main.py # FastAPI 入口
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├── config.yaml # 配置文件
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├── requirements.txt # Python 依赖
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├── inference/
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│ ├── engine.py # TensorRT 引擎封装
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│ ├── stream.py # RTSP 流处理
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│ ├── pipeline.py # 推理主流程
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│ ├── roi/
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│ │ └── roi_filter.py # ROI 过滤
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│ └── rules/
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│ └── algorithms.py # 规则算法
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├── db/
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│ ├── models.py # SQLAlchemy ORM 模型
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│ └── crud.py # 数据库操作
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├── api/
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│ ├── camera.py # 摄像头管理接口
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│ ├── roi.py # ROI 管理接口
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│ └── alarm.py # 告警管理接口
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├── utils/
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│ ├── logger.py # 日志工具
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│ ├── helpers.py # 辅助函数
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│ └── metrics.py # Prometheus 监控
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├── frontend/ # React 前端
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├── scripts/
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│ └── build_engine.py # TensorRT 引擎构建脚本
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├── tests/ # 单元测试
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└── Dockerfile # Docker 配置
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```
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## API 接口
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### 摄像头管理
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- `GET /api/cameras` - 获取摄像头列表
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- `POST /api/cameras` - 添加摄像头
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- `PUT /api/cameras/{id}` - 更新摄像头
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- `DELETE /api/cameras/{id}` - 删除摄像头
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### ROI 管理
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- `GET /api/camera/{id}/rois` - 获取摄像头 ROI 列表
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- `POST /api/camera/{id}/roi` - 添加 ROI
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- `PUT /api/camera/{id}/roi/{roi_id}` - 更新 ROI
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- `DELETE /api/camera/{id}/roi/{roi_id}` - 删除 ROI
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### 告警管理
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- `GET /api/alarms` - 获取告警列表
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- `GET /api/alarms/stats` - 获取告警统计
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- `PUT /api/alarms/{id}` - 更新告警状态
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- `POST /api/alarms/{id}/llm-check` - 触发大模型检查
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### 其他接口
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- `GET /api/camera/{id}/snapshot` - 获取实时截图
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- `GET /api/camera/{id}/detect` - 获取带检测框的截图
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- `GET /api/pipeline/status` - 获取 Pipeline 状态
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- `GET /health` - 健康检查
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## 配置说明
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### config.yaml
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```yaml
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database:
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dialect: "sqlite" # sqlite 或 mysql
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name: "security_monitor"
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model:
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engine_path: "models/yolo11n_fp16_480.engine"
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imgsz: [480, 480]
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batch_size: 8
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half: true
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stream:
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buffer_size: 2
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reconnect_delay: 3.0
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alert:
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snapshot_path: "data/alerts"
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cooldown_sec: 300
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```
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## Docker 部署
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```bash
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docker-compose up -d
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```
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## 监控指标
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系统暴露 Prometheus 格式的监控指标：
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- `camera_count` - 活跃摄像头数量
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- `camera_fps{camera_id="*"}` - 各摄像头 FPS
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- `inference_latency_seconds{camera_id="*"}` - 推理延迟
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- `alert_total{camera_id="*", event_type="*"}` - 告警总数
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- `event_queue_size` - 事件队列大小
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