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Introduction

This project intends to re-wrap the openvino's Multi Camera Multi Target Demo into a more accessible way. All dependents( pre-built models) are included.

Requirements

  • a x86 Linux host
  • docker runtime
  • X Window System display server

Prepare

  • pull the image
docker pull ghcr.io/changtimwu/retail_ovmc_demo:main
  • On your Linux desktop, launch xterm and use xhost command to permit X clients
xhost +

Run the image

  • fixed arguments

    • -e DISPLAY=:0 --net=host -v /tmp/.X11-unix:/tmp/.X11-unix:rw: make X server transparent between host and containers

  • arguments

    • -v work_path: specify the path where the input video files and output results to be store
    • -e DET_MODEL: specify the detection model
    • -e REID_MODEL: specify the ReID model
    • -e video_file: the video files to be analyzed in work_path or video source in URL form

  • optional arguments

    • -e no_show_id=1: show only the bounding box without the identification number

Examples

  • analyze a mp4 file
docker run -itu root -e DISPLAY=:0 --net=host -v /tmp/.X11-unix:/tmp/.X11-unix:rw --name openvino_multicam_reid -v <your media/save path>:/opt/work -e video_file=abc.mp4 --rm retail_ovmc_demo
  • analyze multiple RTSP streams
docker run -itu root -e DISPLAY=:0 --net=host -v /tmp/.X11-unix:/tmp/.X11-unix:rw --name openvino_multicam_reid -v <your media/save path>:/opt/work -e video_file="rtsp://user:password@192.168.1.12/stream2,rtsp://user:password@192.168.1.13/stream2" --rm retail_ovmc_demo

Custom configs

  • Create a directory named configs in your work_path
  • take configs/person.py in this repo as a template and copy it into the work_path/configs/
  • now you can customize it. For example, you can enable the analyzer
analyzer = namespace(
    enable=True,
    show_distances=True,
    save_distances='',
    concatenate_imgs_with_distances=True,
    plot_timeline_freq=0,
    save_timeline='',
    crop_size=(32, 64)
)

the analyzer window would pop up Alt text

List of models

  • Detection model -- specified with -e DET_MODEL=
    • person-detection-retail-0013
  • ReID model -- specified with -e REID_MODEL=
    • person-reidentification-retail-0277
    • person-reidentification-retail-0286
    • person-reidentification-retail-0287
    • person-reidentification-retail-0288

Output

while input video is playing, you're supposed to see the people identifed with colored rectangle and number.

Alt text

detections.json and history_results.json would be saved into the working directory

Build and Deploy

  • build the image by the command
make
  • Proper Github Actions have been setup to automatically build the whole demo as a docker image. By default, it will publish the result image to github's Container registry.

Reference