162 lines
4.1 KiB
Markdown
162 lines
4.1 KiB
Markdown
# Store Dwell Alert on 192.168.5.108
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Local planning project for a single-camera RTSP long-stay alert system running on `192.168.5.108`.
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Documents:
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- `docs/plans/2026-04-15-store-dwell-alert-design.md`
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- `docs/plans/2026-04-15-store-dwell-alert.md`
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Scope:
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- Single RTSP store camera
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- `YOLO11n` person detection
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- Full-frame tracking and lightweight re-association
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- Staff whitelist exclusion
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- Local JSONL event sink
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- Half-hour dwell reports
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- Offline bundle packaging for `.108`-class Ubuntu hosts
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- Chinese operations guide: `README_zh.md`
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Runtime:
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- Default config path: `config/108.local.yaml`
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- Event sink path: `logs/events.jsonl`
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- Continuous mode: `python -m app.main`
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- Single-frame smoke test: `python -m app.main --once`
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- Management API mode: `python -m app.manage_api --config config/local.yaml --port 18081`
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Staff gallery:
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- Directory: `data/staff_gallery/`
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- Supported formats:
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- `*.json` signature files
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- `*.jpg`, `*.jpeg`, `*.png`, `*.bmp`, `*.webp` sample crops
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- JSON example:
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```json
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{
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"staff_id": "staff_001",
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"signature": [0.182, 0.244, 0.317]
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}
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```
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Offline bundle:
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- Target host assumption: Ubuntu 24.04, Python 3.12, NVIDIA GPU compatible with the `.108` runtime stack
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- Bundle script: `bash scripts/package_bundle.sh`
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- Bundle output: `dist/store_dwell_alert_bundle_<date>.tar.gz`
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- Bundle prerequisites before packaging:
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- place Linux wheels under `wheelhouse/`
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- place `yolo11n.pt` under `weights/yolo11n.pt`
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Offline install on target host:
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1. Extract `store_dwell_alert_bundle_<date>.tar.gz`
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2. Edit `scripts/run.sh` and set `RTSP_URL`
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3. Run `sudo bash scripts/install.sh`
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4. The installer enables and starts `store-dwell-alert.service`
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Optional service install:
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1. Run `bash scripts/install_service.sh`
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2. Start with `sudo systemctl start store-dwell-alert.service`
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Docker image:
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- Required runtime weight in build context: `weights/yolo11n.pt`
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- Build: `docker build -t store-dwell-alert:test .`
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- Compose: `docker compose up --build store-dwell-alert`
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- Health: `http://127.0.0.1:18081/api/manage/health`
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- Default container entrypoint seeds `config/local.yaml` from `config/config.example.yaml` and then starts the management API
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- Persisted host paths:
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- `./config -> /app/config`
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- `./data -> /app/data`
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- `./logs -> /app/logs`
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## 中文离线部署说明
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适用环境：
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- `Ubuntu 24.04`
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- `Python 3.12`
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- 已安装 NVIDIA 驱动，`nvidia-smi` 可用
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- 已安装 `ffmpeg`
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离线包准备：
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- 成品包示例：`store_dwell_alert_bundle_2026-04-16.tar.gz`
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- 包内已经包含：
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- 项目源码
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- `wheelhouse/` 离线依赖
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- `weights/yolo11n.pt`
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- `scripts/install.sh`
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- `scripts/run.sh`
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- `scripts/install_service.sh`
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部署步骤：
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1. 把离线包传到目标机器
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2. 解压：
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```bash
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tar -xzf store_dwell_alert_bundle_2026-04-16.tar.gz
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cd store_dwell_alert_bundle
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```
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3. 执行离线安装：
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```bash
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bash scripts/install.sh
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```
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4. 编辑运行脚本，至少改这几个变量：
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- `RTSP_URL`
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- `CAMERA_ID`
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- `EVENT_SINK_PATH`
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```bash
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vim scripts/run.sh
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```
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5. 手工启动：
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```bash
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bash scripts/run.sh
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```
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常见输出位置：
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- 事件结果：`logs/events.jsonl`
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- 本地配置：`config/local.yaml`
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- 店员图库：`data/staff_gallery/`
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如果需要做店员排除：
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- 把店员样本图放到 `data/staff_gallery/`
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- 或者放入对应的 `*.json` 特征文件
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配置完成后的常驻运行：
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1. 安装服务：
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```bash
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bash scripts/install_service.sh
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```
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2. 启动服务：
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```bash
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sudo systemctl start store-dwell-alert.service
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```
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3. 查看状态：
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```bash
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sudo systemctl status store-dwell-alert.service
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```
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4. 停止服务：
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```bash
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sudo systemctl stop store-dwell-alert.service
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```
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5. 开机自启：
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```bash
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sudo systemctl enable store-dwell-alert.service
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```
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排查建议：
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- `scripts/install.sh` 失败时，先确认 `python3.12`、`ffmpeg`、`nvidia-smi` 都存在
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- `scripts/run.sh` 失败时，先确认 `RTSP_URL` 已改成真实地址
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- 如果程序启动了但没有结果，先看 `logs/events.jsonl` 是否生成
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- 如果识别不到店员，先检查 `data/staff_gallery/` 是否有有效样本
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