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Preparing a custom version of MicroPython with TensorFlow


For work with ML algorithms I use the esp32-cam module because it is small, cheap and has a camera installed on it. To get it ready for ML models we must first create a custom version of MicroPython. Michael O'Cleirigh's github repository (https://github.com/mocleiri/tensorflow-micropython-examples) contains all the tools to do just this.

Here are the steps to build this custom microPython version:

  • Download the repository:
    git clone https://github.com/mocleiri/tensorflow-micropython-examples tflite-micro-micropython
  • The steps to build the firmware can be found in tflite-micro-micropython/.github/workflows/build_esp32.yml
    In my case the Espressif development environment espidf has already been downloaded and set up earlier. I use espidf version 4.3.1, the latest stable version at time of writing.

    Activate the virtual Python environment needed for espidf (if venvwrapper is installed: workon espidf)
    Make sure that the modules Pillow and Wave have been installed on this virtual environment. If not, install them with pip:
    • pip3 install Pillow
    • pip3 install Wave
  • Setup the sub-modules needed for tflm:
    • cd tflite-micro-micropython
    • git submodule init
    • git submodule update --recursive
  • Regenerate the microlite/tfm directory
    • cd tensorflow
    • ../micropython-modules/microlite/prepare-tflm-esp.sh
  • Setup the sub-modules for the ESP32 port of MicroPython
    • cd ../micropython
    • git submodule update --init lib/axtls
    • git submodule update --init lib/berkeley-db-1.xx
  • Get the esp32-camera driver from Espressif
    • cd ..
    • cd tflm-esp-kernels
    • git submodule update --init examples/person_detection/esp32-camera
  • Build the MicroPython cross compiler
    • cd ../micropython/mpy_cross
    • make
  • cd .. (the micropython folder of tflite-micro-micropython)
    • git apply ../boards/esp32/MICROLITE_SPIRAM_CAM/micropython.patch !!! This is not needed any longer and the patch does not work anymore !!!
      MicroPython allocates the full SPIRAM installed on the esp32-cam module for its heap. However, the esp32-camera driver needs some SPIRAM for its image buffer. The patch modifies main.c in micropython/ports/esp32 such that part of the SPIRAM is kept free for the camera driver.
  • Due to recent changes in MicroPython some modifications to the micropython-modules are needed:
  • cd boards/esp32/MICROLITE_SPIRAM_16M, when using the TTGO T7 Mini32 V1.5 ESP32-WROVER-B CPU board or
    cd boards/esp32/MICROLITE_SPIRAM_CAM, when using the esp32-cam module
    • idf.py clean build (this builds the MicroPython firmware)
  • flash the custom MicroPython interpreter
    • idf.py flash
Once you have flashed the custom interpreter you can check if all the Python modules are available:


Since all the necessary drivers and libraries are now ready in MicroPython we can go ahead and try the TensorFlow Lite Micro examples.

-- Uli Raich - 2022-01-31


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Topic revision: r9 - 2022-09-23 - UliRaich
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