CustomNopMicroPython < IoT_Course

---+ 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. 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:<br />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<br />In my case the Espressif development environment <b>espidf </b>has already been downloaded and set up earlier. I use espidf version 4.3.1, the latest stable version at time of writing.<br /><br />Activate the virtual Python environment needed for espidf (if venvwrapper is installed: _workon espidf_)<br />Make sure that the modules _Pillow_ and <i>Wave </i>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 <br />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<br />cd boards/esp32/MICROLITE_SPIRAM_CAM when using the esp32-cam module<br />
      * 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:

<img alt="tflite_micropython.png" height="180" src="%ATTACHURL%/tflite_micropython.png" title="tflite_micropython.png" width="811" />

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

-- %USERSIG{UliRaich - 2022-01-31}%

---++ Comments

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png	tflite_micropython.png	r1	manage	22.3 K	2022-01-31 - 13:25	UliRaich
This topic: IoT_Course_English > WebHome > TinyML > CustomNopMicroPython
Topic revision: r7 - 2022-09-22 - UliRaich