671 lines
16 KiB
Markdown
671 lines
16 KiB
Markdown
# 配置文件编写指南
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本文档说明当前项目主线配置的写法，重点覆盖两阶段鞋检测、劳保鞋颜色判断、告警节流，以及 RK3588 上与性能稳定性直接相关的参数。
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---
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## 1. 当前推荐架构
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对车间劳保鞋场景，当前推荐链路是：
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```text
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input_rtsp
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-> preprocess(rgb)
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-> ai_yolo(person only)
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-> tracker(person only)
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-> ai_shoe_det(dynamic roi)
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-> logic_gate(person_shoe_check)
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-> logic_gate(ppe_boots_check)
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-> osd
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-> preprocess(nv12)
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-> publish
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-> alarm
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```
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业务目标：
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- 先检测到人
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- 再只在脚部 ROI 检鞋
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- 只关心“检测到鞋后，颜色是否接近黑色劳保鞋”
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- 不再把“没穿鞋”作为主告警目标
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---
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## 2. 配置结构
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当前项目常用的是 graph 模式：
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```json
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{
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"queue": {
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"size": 8,
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"strategy": "drop_oldest"
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},
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"graphs": [
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{
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"name": "person_shoe_two_stage_workshoe_alarm",
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"executor": {
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"batch_size": 2,
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"run_budget": 8
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},
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"nodes": [ ... ],
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"edges": [ ... ]
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}
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]
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}
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```
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### 2.1 queue
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```json
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{
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"queue": {
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"size": 8,
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"strategy": "drop_oldest"
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}
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}
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```
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说明：
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- `size`：默认队列长度
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- `strategy`
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- `drop_oldest`：推荐，实时性最好
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- `drop_newest`
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- `block`
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### 2.2 executor
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```json
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{
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"executor": {
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"batch_size": 2,
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"run_budget": 8
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}
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}
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```
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说明：
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- `batch_size`：执行器单次批量处理帧数
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- `run_budget`：单次调度预算
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在 RK3588 上，如果出现“隔几秒卡一下”，通常先减小 `run_budget`，再看是否需要调 `batch_size`。
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---
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## 3. 关键节点
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### 3.1 input_rtsp
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```json
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{
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"id": "in",
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"type": "input_rtsp",
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"role": "source",
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"enable": true,
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"url": "rtsp://10.0.0.49:8554/cam",
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"fps": 30,
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"width": 1920,
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"height": 1080,
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"use_ffmpeg": true,
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"use_mpp": false,
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"force_tcp": true
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}
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```
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当前推荐：
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- 对会在固定画面卡顿的 RTSP 源，优先使用 `use_ffmpeg: true`、`use_mpp: false`
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- 原因是部分源流在 `ffmpeg demux + mpp decode` 路径上会出现固定位置卡顿，而 VLC 直拉源流正常
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### 3.2 preprocess
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```json
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{
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"id": "pre_rgb",
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"type": "preprocess",
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"role": "filter",
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"enable": true,
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"dst_w": 1920,
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"dst_h": 1080,
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"dst_format": "rgb",
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"dst_packed": true,
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"resize_mode": "stretch",
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"rga_gate": "person_shoe_two_stage_workshoe_alarm",
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"use_rga": true
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}
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```
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说明：
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- 前级 `pre_rgb` 准备共享的高分辨率 RGB 主帧
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- 后级 `post` 再转回 `nv12` 给编码器
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- `use_rga: true` 是 RK3588 上的推荐项
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### 3.3 ai_yolo
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```json
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{
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"id": "person_det",
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"type": "ai_yolo",
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"role": "filter",
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"enable": true,
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"infer_fps": 2,
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"infer_phase_ms": 0,
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"use_rga": true,
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"use_dma_input": true,
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"model_path": "./models/yolov8n-640.rknn",
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"model_version": "v8",
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"model_w": 640,
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"model_h": 640,
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"num_classes": 80,
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"conf": 0.35,
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"nms": 0.45,
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"class_filter": [0],
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"bbox_expand": {
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"enable": true,
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"class_id": 0,
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"left": 0.06,
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"right": 0.06,
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"top": 0.04,
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"bottom": 0.16
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}
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}
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```
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当前用途：
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- 只做人检测前级
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- 不再直接承担鞋检测
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关键参数：
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| 参数 | 作用 | 当前建议 |
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|------|------|----------|
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| `infer_fps` | 人体检测频率 | `2` |
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| `infer_phase_ms` | 与鞋检错峰 | `0` |
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| `class_filter` | 只保留 `person` | `[0]` |
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| `bbox_expand.bottom` | 补偿漏脚 | `0.16` 左右 |
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| `use_rga` / `use_dma_input` | 输入性能优化 | 建议开启 |
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### 3.4 tracker
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```json
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{
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"id": "person_trk",
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"type": "tracker",
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"role": "filter",
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"enable": true,
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"mode": "bytetrack_lite",
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"per_class": true,
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"track_classes": [0],
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"high_th": 0.55,
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"low_th": 0.10,
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"iou_th": 0.3,
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"max_age_ms": 900,
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"min_hits": 2
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}
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```
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当前用途：
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- 稳定人框
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- 为鞋子关联和按人节流提供 `track_id`
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### 3.5 ai_shoe_det
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```json
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{
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"id": "shoe_det",
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"type": "ai_shoe_det",
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"role": "filter",
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"enable": true,
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"infer_fps": 2,
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"infer_phase_ms": 150,
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"use_rga": true,
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"use_dma_input": false,
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"model_path": "./models/shoe_detector_openimages_ppe_v1.rknn",
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"model_w": 640,
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"model_h": 640,
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"conf": 0.15,
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"nms": 0.45,
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"v8_box_format": "cxcywh",
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"append_detections": true,
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"dynamic_roi": {
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"enable": true,
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"person_class_id": 0,
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"shoe_class_id": 1,
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"debug_roi_class_id": -1,
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"max_rois": 3,
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"min_person_height": 60,
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"x_offset": -0.24,
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"y_offset": 0.64,
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"width_scale": 1.48,
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"height_scale": 0.58
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}
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}
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```
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当前用途：
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- 从人框动态生成脚部 ROI
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- 只在 ROI 内跑鞋模型
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- 将鞋框追加回 `frame->det`
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关键参数：
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| 参数 | 作用 | 当前建议 |
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|------|------|----------|
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| `conf` | 鞋候选召回阈值 | `0.15` |
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| `append_detections` | 保留人框并追加鞋框 | 必须 `true` |
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| `dynamic_roi.max_rois` | 每帧最多处理多少人 | `3` |
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| `dynamic_roi.min_person_height` | 过滤太远的人 | `60` |
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| `x_offset/y_offset/width_scale/height_scale` | 脚部 ROI 形状 | 按高机位场景调优 |
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说明：
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- `shoe_det.conf` 看起来偏低，这是有意为之
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- 当前方案依赖“低阈值召回 + 人鞋关联 + 颜色判断 + 告警节流”整体收敛
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- 在高机位小鞋场景中，如果把这里直接调到 `0.4` 或 `0.5`，通常会明显漏检
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### 3.6 logic_gate
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#### 模式一：person_shoe_check
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```json
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{
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"id": "shoe_assoc",
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"type": "logic_gate",
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"role": "filter",
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"enable": true,
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"mode": "person_shoe_check",
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"person_shoe_check": {
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"person_class": 0,
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"shoe_class": 1,
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"violation_class": 2,
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"min_person_score": 0.30,
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"min_shoe_score": 0.15,
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"require_person_track_id": true,
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"attach_person_track_to_shoe": true,
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"emit_missing_violation": false,
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"foot_region": {
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"x_offset": -0.24,
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"y_offset": 0.64,
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"width_scale": 1.48,
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"height_scale": 0.58
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}
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}
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}
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```
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当前用途：
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- 把鞋关联到对应的人
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- 把人的 `track_id` 传给鞋
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- 不再输出“没鞋违规”
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#### 模式二：ppe_boots_check
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```json
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{
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"id": "shoe_color",
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"type": "logic_gate",
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"role": "filter",
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"enable": true,
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"mode": "ppe_boots_check",
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"anchor_class": 0,
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"boots_class": 1,
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"violation_class": 2,
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"color_check": {
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"enable": true,
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"method": "brightness",
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"dark_threshold": 90,
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"roi_expand": 1.0
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}
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}
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```
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当前用途：
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- 只对已检测到的鞋做颜色判断
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- 深色鞋：视为合规
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- 非深色鞋：追加 `cls=2` 的违规框，供 OSD 和 alarm 使用
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颜色参数建议：
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| 参数 | 含义 | 当前建议 |
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|------|------|----------|
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| `method` | 颜色判断方式 | `brightness` |
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| `dark_threshold` | 深色阈值 | `90` |
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| `roi_expand` | 颜色分析区域扩展 | `1.0` |
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### 3.7 osd
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```json
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{
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"id": "osd",
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"type": "osd",
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"role": "filter",
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"enable": true,
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"draw_bbox": true,
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"draw_text": false,
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"use_rga_bbox": false,
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"labels": ["person", "shoe", "non_black_shoe"]
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}
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```
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当前显示含义：
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- `person`
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- `shoe`
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- `non_black_shoe`
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### 3.8 publish
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```json
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{
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"id": "pub",
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"type": "publish",
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"role": "filter",
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"enable": true,
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"codec": "h264",
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"fps": 30,
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"bitrate_kbps": 2000,
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"mpp_output_timeout_ms": 50,
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"mpp_packet_wait_ms": 10,
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"use_mpp": true,
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"outputs": [
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{"proto": "rtsp_server", "port": 8555, "path": "/live/cam1"}
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]
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}
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```
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当前推荐：
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- 输入端优先 FFmpeg CPU decode
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- 输出端继续使用 MPP 编码
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### 3.9 alarm
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```json
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{
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"id": "alarm",
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"type": "alarm",
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"role": "sink",
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"enable": true,
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"eval_fps": 2,
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"labels": ["person", "shoe", "non_black_shoe"],
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"rules": [
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{
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"name": "non_compliant_workshoe",
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"class_ids": [2],
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"roi": {"x": 0.0, "y": 0.0, "w": 1.0, "h": 1.0},
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"min_score": 0.30,
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"require_track_id": false,
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"min_duration_ms": 800,
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"min_hits": 2,
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"hit_window_ms": 2000,
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"cooldown_ms": 15000,
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"per_track_cooldown_ms": 0
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}
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],
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"actions": {
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"log": {
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"enable": true,
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"level": "info",
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"include_detections": true,
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"min_interval_ms": 2000
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}
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}
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}
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```
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当前告警策略：
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- 只对 `cls=2 non_black_shoe` 告警
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- 蓝框稳定出现 2 次以上，且持续约 `800ms`，才触发
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- 触发后进入 `15s` 冷却，避免反复刷屏
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---
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## 4. 连接关系
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```json
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{
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"edges": [
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["in", "pre_rgb"],
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["pre_rgb", "person_det"],
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["person_det", "person_trk"],
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["person_trk", "shoe_det"],
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["shoe_det", "shoe_assoc"],
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["shoe_assoc", "shoe_color"],
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["shoe_color", "osd"],
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["osd", "post"],
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["post", "pub"],
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["pub", "alarm"]
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]
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}
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```
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说明：
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- `publish -> alarm` 是合法链路
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- `alarm` 会继续读取前面节点保留下来的 `frame->det`
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---
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## 5. 推荐配置
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| 配置文件 | 说明 |
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|----------|------|
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| `configs/person_shoe_two_stage_workshoe_alarm_v8s_shoe640.json` | 单路劳保鞋颜色告警主线配置 |
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| `configs/person_shoe_two_stage_workshoe_alarm_v8s_shoe640_strict.json` | 单路劳保鞋颜色告警严格版 |
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| `configs/full_pipeline_1080p.json` | 人脸检测/识别 + 劳保鞋颜色告警完整流程 |
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---
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## 6. 调参顺序
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建议按下面顺序调，不要同时乱改：
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1. `ai_yolo.conf` 和 `bbox_expand`
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目标：先保证人体框稳定且脚下留出空间
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2. `ai_shoe_det.conf` 和 `dynamic_roi`
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目标：先让鞋框能出来
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3. `logic_gate.color_check.dark_threshold`
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目标：区分黑色劳保鞋和拖鞋/浅色鞋
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4. `alarm.min_hits / min_duration_ms / cooldown_ms`
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目标：把“会报警”收敛成“稳一点再报警”
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### 6.1 实施人员重点参数
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实施时优先看下面这些参数，不建议一开始改其它项。
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| 节点 | 参数 | 期望效果 | 调大后的典型结果 | 调小后的典型结果 |
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|------|------|----------|------------------|------------------|
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| `input_rtsp` | `use_ffmpeg / use_mpp` | 解决固定画面卡顿 | `use_ffmpeg=true` 一般更稳 | `use_mpp=true` 某些源可能卡顿 |
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| `person_det` | `infer_fps` | 控制人体检测负载 | 召回更及时，但更吃 NPU | 更省资源，但人框更新更慢 |
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| `person_det` | `bbox_expand.bottom` | 给脚部 ROI 留空间 | 更不容易漏脚 | 人框更紧，鞋 ROI 更容易裁偏 |
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| `shoe_det` | `conf` | 控制鞋框召回和误报平衡 | 误报减少，但漏鞋增多 | 鞋框更多，但误报也更多 |
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| `shoe_det.dynamic_roi` | `max_rois` | 控制每帧最多处理多少人 | 覆盖更多人，但 FPS 下降 | 更省资源，但多人时会漏掉部分鞋 |
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| `shoe_det.dynamic_roi` | `min_person_height` | 过滤远景小人 | 远处小人不参与鞋检 | 更多远景人进入鞋检 |
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| `shoe_det.dynamic_roi` | `max_box_area_ratio` | 过滤接近整块脚区的大误框 | 大蓝框减少 | 更容易保留整块 ROI 级误报 |
|
||
| `shoe_assoc` | `min_shoe_score` | 控制低分鞋框是否参与人鞋关联 | 低分误框减少 | 弱鞋框更容易进入后续逻辑 |
|
||
| `shoe_color` | `dark_threshold` | 控制黑鞋/非黑鞋边界 | 更多深灰鞋被判合规 | 更多鞋会被判非黑鞋 |
|
||
| `alarm` | `min_duration_ms` | 控制要稳定多久才报警 | 更稳，但慢一点 | 更灵敏，但更容易闪报 |
|
||
| `alarm` | `cooldown_ms` | 控制两次告警间隔 | 减少重复告警 | 同一事件会更频繁重复报 |
|
||
|
||
### 6.2 推荐调参动作
|
||
|
||
现场遇到问题时，优先按下面方式处理：
|
||
|
||
- 鞋子漏报多：
|
||
- 先降低 `shoe_det.conf`
|
||
- 再降低 `shoe_assoc.min_shoe_score`
|
||
- 必要时降低 `dynamic_roi.min_person_height`
|
||
|
||
- 蓝框误报多：
|
||
- 先提高 `shoe_det.conf`
|
||
- 再提高 `shoe_assoc.min_shoe_score`
|
||
- 如出现整块脚区级误框，再降低 `max_box_area_ratio`
|
||
|
||
- 黑色劳保鞋被误报：
|
||
- 提高 `shoe_color.dark_threshold`
|
||
|
||
- 非黑鞋不报警：
|
||
- 降低 `shoe_color.dark_threshold`
|
||
- 检查 `alarm.min_score`
|
||
- 检查 `alarm.min_duration_ms / min_hits`
|
||
|
||
- 完整流程 FPS 不够：
|
||
- 先降低 `face_det.infer_fps`
|
||
- 再降低 `face_recog.infer_fps`
|
||
- 再考虑降低 `dynamic_roi.max_rois`
|
||
|
||
---
|
||
|
||
## 7. 常见问题
|
||
|
||
### Q1: 为什么鞋子检测阈值只有 0.15？
|
||
|
||
因为当前场景是高机位、小鞋目标，单纯靠高阈值会先漏掉大量鞋框。系统依赖多级过滤，而不是单级高阈值。
|
||
|
||
### Q2: 为什么有蓝框却不告警？
|
||
|
||
优先检查：
|
||
|
||
- 是否跑的是 `person_shoe_two_stage_workshoe_alarm_v8s_shoe640.json`
|
||
- `alarm.rules[].class_ids` 是否包含 `2`
|
||
- `min_score / min_hits / min_duration_ms` 是否过严
|
||
|
||
### Q3: 为什么 RTSP 会固定画面卡顿？
|
||
|
||
如果 VLC 直拉源流不卡，而项目里卡，优先切到：
|
||
|
||
```json
|
||
"use_ffmpeg": true,
|
||
"use_mpp": false
|
||
```
|
||
|
||
这通常说明问题在输入解码兼容性，而不是 AI 链路。
|
||
|
||
---
|
||
|
||
**版本**：v2.0
|
||
**更新日期**：2026-03-15
|
||
## Pose-Enabled Behavior Configuration
|
||
|
||
This repository now supports two behavior deployment modes.
|
||
|
||
### Mode 1: Baseline Behavior Graph
|
||
|
||
Use this when you only need tracked detections and rule-based region behavior:
|
||
|
||
```text
|
||
input_rtsp -> preprocess -> ai_yolo -> tracker -> region_event -> action_recog -> event_fusion
|
||
```
|
||
|
||
Reference config:
|
||
|
||
- `configs/sample_region_behavior_intrusion.json`
|
||
|
||
Characteristics:
|
||
|
||
- No pose model dependency
|
||
- Lowest compute cost
|
||
- `fall` and `fight` rely on bbox-only rules
|
||
|
||
### Mode 2: Pose-Enabled Behavior Graph
|
||
|
||
Use this when you need pose-aware `fall` and `fight`:
|
||
|
||
```text
|
||
input_rtsp -> preprocess -> ai_yolo -> tracker -> ai_pose -> pose_assoc -> region_event -> action_recog -> event_fusion
|
||
```
|
||
|
||
Reference config:
|
||
|
||
- `configs/sample_region_behavior_full.json`
|
||
|
||
Characteristics:
|
||
|
||
- `ai_pose` writes `Frame.pose`
|
||
- `pose_assoc` assigns `PoseItem.track_id`
|
||
- `action_recog` can fuse bbox and pose signals per event rule
|
||
|
||
### Optional Integration Rules
|
||
|
||
- `ai_pose` is optional. Do not add it to graphs that only need intrusion or climb.
|
||
- `pose_assoc` should be placed after `ai_pose` and after tracked detections are already available on the frame.
|
||
- `action_recog` must remain runnable when `Frame.pose` is missing or empty.
|
||
|
||
### Degradation Semantics
|
||
|
||
- Without `ai_pose`, the graph must still behave correctly with bbox-only behavior logic.
|
||
- With `ai_pose` but without `pose_assoc`, `pose.track_id` is not guaranteed and downstream logic should treat it as optional compatibility mode.
|
||
- If pose inference returns no items for a frame, the rest of the graph must keep using `Frame.det` and `track_id`.
|
||
|
||
### Structured `action_recog` Rules
|
||
|
||
`action_recog` supports a structured event format with `bbox`, `pose`, and `fusion` sections.
|
||
|
||
Example:
|
||
|
||
```json
|
||
{
|
||
"id": "action_evt",
|
||
"type": "action_recog",
|
||
"events": [
|
||
{
|
||
"type": "fall",
|
||
"window_ms": 1500,
|
||
"activate_duration_ms": 300,
|
||
"bbox": {
|
||
"enabled": true,
|
||
"min_drop_pixels": 120,
|
||
"min_aspect_ratio_delta": 0.35
|
||
},
|
||
"pose": {
|
||
"enabled": true,
|
||
"min_torso_drop_pixels": 120,
|
||
"max_upright_ratio": 0.60
|
||
},
|
||
"fusion": {
|
||
"match_mode": "any"
|
||
}
|
||
},
|
||
{
|
||
"type": "fight",
|
||
"window_ms": 1200,
|
||
"activate_duration_ms": 200,
|
||
"bbox": {
|
||
"enabled": true,
|
||
"proximity_pixels": 220,
|
||
"min_motion_pixels": 90
|
||
},
|
||
"pose": {
|
||
"enabled": true,
|
||
"min_wrist_motion_pixels": 120,
|
||
"max_wrist_distance_pixels": 120
|
||
},
|
||
"fusion": {
|
||
"match_mode": "any"
|
||
}
|
||
}
|
||
]
|
||
}
|
||
```
|
||
|
||
Rules:
|
||
|
||
- `bbox.enabled=false` disables bbox signal evaluation for that event.
|
||
- `pose.enabled=false` disables pose signal evaluation for that event.
|
||
- `fusion.match_mode="any"` means either bbox or pose may activate the event.
|
||
- `fusion.match_mode="all"` means bbox and pose must both activate the event.
|
||
|
||
Backward compatibility:
|
||
|
||
- Old flat keys such as `min_drop_pixels` and `pose_min_torso_drop_pixels` are still accepted.
|
||
- New configs should prefer the structured form above.
|