A concept project that uses a low-code/no-code approach to implement deep learning inference on devices. It provides a componentized framework and a visual flow-based programming development environment.


This project implemented the “Edge Impulse for LinuxPython SDK on the Raspberry Pi 4 development board and used “Tutorial: Object Detection” as a demonstration. The operation steps on the Raspberry Pi 4 development board are as follows:

  • Install the Edge Impulse for Linux CLI:
    curl -sL https://deb.nodesource.com/setup_12.x | sudo bash -
    sudo apt install -y gcc g++ make build-essential nodejs sox gstreamer1.0-tools gstreamer1.0-plugins-good gstreamer1.0-plugins-base gstreamer1.0-plugins-base-apps
    npm config set user root && sudo npm install edge-impulse-linux -g --unsafe-perm

  • Connecting to Edge Impulse:
    edge-impulse-linux --disable-camera

For unknown reasons, the edge-impulse-linux program will not detect that my USB Camera (UVC) is on the Raspberry Pi 4. So add the –disable-camera option to avoid this issue.

  • Install the SDK:
    sudo apt-get install libatlas-base-dev libportaudio0 libportaudio2 libportaudiocpp0 portaudio19-dev
    pip3 install edge_impulse_linux -i https://pypi.python.org/simple

  • Update the NumPy:
    sudo pip3 install -U numpy

  • Download the model file via:
    edge-impulse-linux-runner --download modelfile.eim
    and put the model file in the project directory.

  • Install the PyQt5:
    sudo apt-get install python3-pyqt5

  • Run the EzEdgeAI project:
    python diagram-editor.py


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