- Carry over uncommitted changes from original repo - Update old references: rk3588-agent → safesight-agent
99 lines
3.2 KiB
Markdown
99 lines
3.2 KiB
Markdown
# 命令指南
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- 运行后台服务
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./build/media-server -c configs/test_face_det_zoned_only.json
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[在windows上安装ffmpeg]
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- 打开 PowerShell 或 CMD,执行:
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winget install Gyan.FFmpeg
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- 安装完成后,关闭并重新打开终端,验证:
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ffmpeg -version
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-查看本地摄像头信息
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ffmpeg -list_devices true -f dshow -i dummy
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查看分辨率
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ffmpeg -f dshow -list_options true -i video="你的摄像头名称"
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ffmpeg -f dshow -list_options true -i video="HD Webcam eMeet C960"
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- 本地运行RTSP服务器
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C:\Software\mediamtx\mediamtx.exe
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- 推流到RTSP服务器(设置摄像头的分辨率为720P)
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ffmpeg -f dshow -rtbufsize 100M -video_size 1280x720 -framerate 30 -vcodec mjpeg -i video="4K AutoFocus Webcam" -c:v libx264 -preset ultrafast -pix_fmt yuv420p -f rtsp rtsp://localhost:8554/cam
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ffmpeg -f dshow -rtbufsize 100M -video_size 1920x1080 -framerate 30 -vcodec mjpeg -i video="HD Webcam eMeet C960" -c:v libx264 -preset ultrafast -pix_fmt yuv420p -f rtsp rtsp://localhost:8554/cam
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ffmpeg -re -stream_loop -1 -i 行人.flv -c copy -rtsp_transport tcp -f rtsp rtsp://10.0.0.49:8554/cam
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ffmpeg -re -stream_loop -1 -i 监控.mp4 -c copy -rtsp_transport tcp -f rtsp rtsp://10.0.0.49:8554/cam
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ffmpeg -stream_loop -1 -re -i "boots.mp4" -c:v libx264 -preset fast -tune zerolatency -r 30 -f rtsp -rtsp_transport tcp rtsp://localhost:8554/cam
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ffmpeg -re -stream_loop -1 -i reg_001_单人_侧面_黑色鞋_1.mp4 -c copy -rtsp_transport tcp -f rtsp rtsp://10.0.0.49:8554/cam
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C:\Users\Tellme\Pictures\人脸库> ffmpeg -re -stream_loop -1 -i reg_008_unk_011_多人_正面_黑色鞋_白色鞋_1.mp4 -c copy -rtsp_transport tcp -f rtsp rtsp://10.0.0.49:8554/cam
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- 本地验证RTSP拉流正确
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ffplay rtsp://localhost:8554/cam
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在3588上测试RTSP输出
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ffplay rtsp://localhost:8555/live/cam1
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查看HLC输出
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http://10.0.0.50:9000/hls_player.html
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- 在 Windows 上用 VLC 播放处理后的流:
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rtsp://10.0.0.50:8555/live/cam1
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- 编译agent
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go build -o safesight-agent_linux_arm64 ./cmd/safesight-agent
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- 运行模拟告警服务
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C:\Users\Tellme\apps\safesight-edge\python .\mock_alarm_server.py
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或
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uv run --with flask scripts/mock_alarm_server.py
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- 运行minio
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C:\Users\Tellme\minio\minio.exe server C:\Users\Tellme\minio\myminio --address ":9000" --console-address ":9001"
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用户名密码是:admin/password
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然后在 RK3588 上测试 token 接口:
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curl -X POST http://10.0.0.49:8080/api/getToken
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- 运行后台管理服务
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go run .\cmd\managerd\main.go .\managerd.json
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# 综合监控(运行脚本)
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~/apps/safesight-edge/scripts/ops.sh hw
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- 标准单路全流程测试:
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./build/media-server -c configs/sample_cam_ppe12.json
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- 5路全流程压力测试
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./build/media-server --config configs/stress_5ch_stretch.json
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- PT模型转RKNN
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在Linux系统上,先克隆RKNN修改后的项目到本地:
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https://gitcode.com/GitHub_Trending/ul/ultralytics
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安装依赖:
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pip install -e . rknn-toolkit2
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pip install "onnx==1.16.1"
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激活python环境
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source ./venv_rknn/bin/activate
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进入模型目录,执行:
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yolo export model=yolov8s_ppe.pt format=rknn name=rk3588
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- 插件可以通过以下方式构建(以ai_face_det_zoned为例):
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cmake --build build --target ai_face_det_zoned |