opendatacam

Development notes

Run in simulation mode

Simulation mode is useful to work on the UI and node.js feature deployment without having to run the neural network / the webcam.

Dependency: Mongodb installed (optional, only to record data) : see tutorial

# Clone repo
git clone https://github.com/opendatacam/opendatacam.git

or

git@github.com:opendatacam/opendatacam.git
# Install dependencies
npm i
# Run in dev mode
npm run dev
# Open browser on http://localhost:8080/

If you have an error while doing npm install it is probably a problem with node-gyp, you need to install additional dependencies depending on your platform: https://github.com/nodejs/node-gyp#on-unix

Simulation Mode

The new simulation mode allows to feed YOLO JSON detections into OpenDataCam. As for the video either pre-extracted frames or a video file where the frames will be extracted using ffmpeg.

The simulation can be customized in the OpenDataCam config by adding it as a new video source.

"VIDEO_INPUTS_PARAMS": {
  "simulation": "--yolo_json public/static/placeholder/alexeydetections30FPS.json --video_file_or_folder public/static/placeholder/frames --isLive true --jsonFps 20 --mjpgFps 0.2"
}

Whereas

• yolo_json: A relative or absolute path to the JSON file to use. For relative paths, the repository root will be used as the base.
• video_file_or_folder: A file or folder to find JPGs. If it’s a file the images will be extracted using ffmpeg. If it’s a file it will expect 001.jpg, 002.jpg, …, 101.jpg, … to be present there.
• isLive: Should the simulation behave like a live source (e.g. WebCam), or like a file. If true, the simulation will silently loop from the beginning without killing the stream. If false, the simulation will kill the streams at the end of JSON file just like Darknet.
• jsonFps: Approximate frames per second for the JSON stream.
• mjpgFps: Only when using ffmpeg. Approximate frames per second for the MJPG stream. Having this set lower than jsonFps, will make the video skip a few frames.
• darknetStdout: If the simulation should mimic the output of Darknet on stdout.
• json_port: The TCP port for JSON streaming
• mjpg_port: The TCP port for MJGP streeaming

The simulation JSON and MJPG streams can also be started without Opendatacam by invoking node scripts/YoloSimulation.js from the repository root folder.

Release checklist

• For next release only: Set $VERSION instead of master for the Kubernete install script, see: https://github.com/opendatacam/opendatacam/pull/247
• Make sure that config.json has the TO_REPLACE_VIDEO_INPUT, TO_REPLACE_VIDEO_INPUT values that will be replaced by sed on installation
• Search and replace OLD_VERSION with NEW_VERSION in all documentation
• Make sure correct version in config.json > OPENDATACAM_VERSION
• Make sure correct version in package.json
• Make sure correct version in README “Install and start OpenDataCam” wget install script
• Make sure correct version in JETSON_NANO.md “Install OpenDataCam” wget install script
• Make sure correct VERSION in /docker/install-opendatacam.sh
• Generate up to date api documentation npm run generateapidoc
• Push and Tag version on github
• Compile docker image on 4 platforms ( nano, tx2, xavier, nvidia-docker ) , upload them to dockerhub and tag them properly
• Add Release on github

Tags commands

# Tag latest commit
git tag v3.0.1

# Push tag
git push origin v3.0.1

# List tags
git tag --list

# Remove tag on remote
git push origin :v3.0.1

# Delete local tag
git tag --delete v3.0.1

# Push all tag
git push --tags

Markdown table of content generator

https://ecotrust-canada.github.io/markdown-toc/

List all cams

v4l2-ctl --list-devices

Technical architecture

Code Style

Opendatacam uses the https://github.com/airbnb/javascript style. You can run npm run lint to check the whole code base. Or npx eslint yourfile.js to check only a single file.

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