This library lets you run machine learning models and collect sensor data on Linux machines using Python. The SDK is open source and hosted on GitHub: edgeimpulse/linux-sdk-python. See our Linux EIM executable guide to learn more about the .eim file format.

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Installation guide

  1. Install a recent version of Python 3 (>=3.7).
  2. Install the SDK Raspberry Pi 
    sudo apt-get install libatlas-base-dev libportaudio0 libportaudio2 libportaudiocpp0 portaudio19-dev
pip3 install edge_impulse_linux -i https://pypi.python.org/simple
    Jetson Nano 
    sudo apt-get install libatlas-base-dev libportaudio2 libportaudiocpp0 portaudio19-dev python3-pip
    It is possible you will need to install Cython for building numpy package: 
    pip3 install Cython
    After that proceed with installing Linux Python SDK: 
    pip3 install pyaudio edge_impulse_linux
  3. Clone this repository to get the examples: 
    git clone https://github.com/edgeimpulse/linux-sdk-python
    Windows Subsystem for Linux (WSL) If you are using WSL, you will need to install the following npm packages, and may need to install audio / video dependencies for your machine: e.g. audio dependencies for Ubuntu: 
    sudo apt-get install libportaudio2 libportaudiocpp0 portaudio19-dev
    npm route for WSL use administrator user permissions: 
    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
sudo npm install -g edge-impulse-linux --unsafe-perm
    Then proceed with installing the Linux Python SDK: 
    sudo npm install -g edge-impulse-linux --unsafe-perm

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Collecting data

Before you can classify data you’ll first need to collect it. If you want to collect data from the camera or microphone on your system you can use the Edge Impulse CLI, and if you want to collect data from different sensors (like accelerometers or proprietary control systems) you can do so in a few lines of code.

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Collecting data from the camera or microphone

To collect data from the camera or microphone, follow the getting started guide) for your development board.

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Collecting data from other sensors

To collect data from other sensors you’ll need to write some code to collect the data from an external sensor, wrap it in the Edge Impulse Data Acquisition format, and upload the data to the Ingestion service. Here’s an end-to-end example.

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Classifying data

To classify data (whether this is from the camera, the microphone, or a custom sensor) you’ll need a model file. This model file contains all signal processing code, classical ML algorithms and neural networks - and typically contains hardware optimizations to run as fast as possible. To grab a model file:
  1. Train your model in Edge Impulse.
  2. Install the Edge Impulse for Linux CLI.
  3. Download the model file via: 
    edge-impulse-linux-runner --download modelfile.eim
    This downloads the file into modelfile.eim. (Want to switch projects? Add --clean)
Then you can start classifying realtime sensor data. We have examples for:
  • Audio - grabs data from the microphone and classifies it in realtime.
  • Camera - grabs data from a webcam and classifies it in realtime.
  • Custom data - classifies custom sensor data.

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Troubleshooting

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[Errno -9986] Internal PortAudio error (macOS)

If you see this error you can re-install portaudio via: 
brew uninstall --ignore-dependencies portaudio
brew install portaudio --HEAD​

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Abort trap (6) (macOS)

This error shows when you want to gain access to the camera or the microphone on macOS from a virtual shell (like the terminal in Visual Studio Code). Try to run the command from the normal Terminal.app.

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Exception: No data or corrupted data received

This error is due to the length of the results output. To fix this, you can overwrite this line in the class ImpulseRunner from the runner.py. 
data = self._client.recv(1 * 1024 * 1024)
with: 
data = b""
while True:
    chunk = self._client.recv(1024)
    # end chunk has \x00 in the end
    if chunk[-1] == 0:
        data = data + chunk[:-1]
        break
    data = data + chunk