• Due to recent changes in MicroPython some modifications to the micropython-modules are needed:
  • The last parameter in MP_REGISTER_MODULE (enable flag) must be removed in modcamera.c and tensorflow-microlite.c
  • mp_obj_type_t must be modified according to https://github.com/micropython/micropython/wiki/Build-Troubleshooting#define_const_obj_type in tensorflow-microlite.c
• 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
  

• • 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.

• 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
  

• • git apply ../boards/esp32/MICROLITE_SPIRAM_CAM/micropython.patch
    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.

• • git submodule update --init lib/berkeley-db-1.xx

• 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:

• • git apply ../boards/esp32/MICROLITE_SPIRAM_CAM/micropython.patch
    !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.
• cd boards/esp32/MICROLITE_SPIRAM_16M (when using the TTGO T7 Mini32 V1.5 ESP32-WROVER-B CPU board)

• 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:

• Regenerate the microlite/tfm directory
  • cd tensorflow
  • ../micropython-modules/microlite/prepare-tflm-esp.sh

• • cd ../micropython

https://github.com/mocleiri/tensorflow-micropython-examples

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
  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
• Setup the sub-modules for the ESP32 port of MicroPython
  • cd micropython
  • git submodule update --init lib/axtls
  • git 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
    !MicroPython allocates the full SPIRAM installed on the esp32-cam module for its heap. However, the esp32-camera driver needs some of the 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.
  • idf.py clean build (this builds the MicroPython firmware)
• flash the custom MicroPython interpreter
  • idf.py flash

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

Preparing a custom version of MicroPython with TensorFlow

Introduction

For work with ML algorithms I use the esp32-cam module because it is small, cheap and has a camera installed on it.

-- Uli Raich - 2022-01-31

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