X-LINUX-AI - image classification using ONNX Python runtime

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Applicable for

STM32MP13x lines, STM32MP15x lines

This article explains how to experiment with ONNX Runtime ^[1] applications for image classification based on the MobileNet v1 model using ONNX Python™ runtime.

Information

Python applications are adequate for prototyping but they are less efficient than C/C++ applications.

1. Description[edit source]↑

The image classification neural network model allows the identification of the subject represented by an image. It classifies an image into various classes.

Python ONNX Runtime image classification application

The application enables three main features:

A camera streaming preview implemented using Gstreamer
An NN inference based on the camera inputs (or test data pictures) run by the ONNX Runtime ^[1] interpreter
A user interface implemented using Python GTK

The performance depends on the number of CPUs available. The camera preview is limited to one CPU core while the ONNX runtime^[1] interpreter is configured to use the maximum of the available resources.

The model used with this application is the MobileNet v1 downloaded from the Tensorflow Lite Hub^[2] and converted to ONNX opset 16 format using tf2onnx.

Information

To convert the Tensorflow Lite model to ONNX you can check this article : How to convert a Tensorflow™ Lite model to ONNX

2. Installation[edit source]↑

2.1. Install from the OpenSTLinux AI package repository[edit source]↑

Warning

The software package is provided AS IS, and by downloading it, you agree to be bound to the terms of the software license agreement (SLA). The detailed content licenses can be found here.

After having configured the AI OpenSTLinux package, the user can install the X-LINUX-AI components for this application:

 apt-get install onnx-cv-apps-image-classification-python

Then, the user can restart the demo launcher:

- For OpenSTLinux distribution with a version lower than 4.0 use:

 systemctl restart weston@root

- For other OpenSTLinux distribution use:

 systemctl restart weston-launch

2.2. Source code location[edit source]↑

The label_onnx.py Python script is available:

in the Openembedded OpenSTLinux Distribution with the X-LINUX-AI Expansion Package:

<Distribution Package installation directory>/layers/meta-st/meta-st-stm32mpu-ai/recipes-samples/onnxrt-cv-apps/files/image-classification/python/label_onnx.py

on the target:

/usr/local/demo-ai/computer-vision/onnx-image-classification/python/label_onnx.py

on GitHub:

https://github.com/STMicroelectronics/meta-st-stm32mpu-ai/tree/v3.0.0/recipes-samples/onnxrt-cv-apps/files/image-classification/python/label_onnx.py

3. How to use the application[edit source]↑

3.1. Launching via the demo launcher[edit source]↑

3.2. Executing with the command line[edit source]↑

The Python script label_onnx.py application is located in the userfs partition:

/usr/local/demo-ai/computer-vision/onnx-image-classification/python/label_onnx.py

It accepts the following input parameters:

usage: label_onnx.py [-h] [-i IMAGE] [-v VIDEO_DEVICE] [--frame_width FRAME_WIDTH] [--frame_height FRAME_HEIGHT] [--framerate FRAMERATE]
                     [-m MODEL_FILE] [-l LABEL_FILE] [--input_mean INPUT_MEAN] [--input_std INPUT_STD] [--validation]
                     [--num_threads NUM_THREADS]

options:
  -h, --help            show this help message and exit
  -i IMAGE, --image IMAGE
                        image directory with image to be classified
  -v VIDEO_DEVICE, --video_device VIDEO_DEVICE
                        video device (default /dev/video0)
  --frame_width FRAME_WIDTH
                        width of the camera frame (default is 640)
  --frame_height FRAME_HEIGHT
                        height of the camera frame (default is 480)
  --framerate FRAMERATE
                        framerate of the camera (default is 15fps)
  -m MODEL_FILE, --model_file MODEL_FILE
                        .onnx model to be executed
  -l LABEL_FILE, --label_file LABEL_FILE
                        name of file containing labels
  --input_mean INPUT_MEAN
                        input mean
  --input_std INPUT_STD
                        input standard deviation
  --validation          enable the validation mode
  --num_threads NUM_THREADS
                        Select the number of threads used by ONNX interpreter to run inference

3.3. Testing with MobileNet V1[edit source]↑

The model used for test is the mobilenet_v1_0.5_128_quant.onnx downloaded from Tensorflow Lite Hub^[2] and converted to ONNX format.

Information

The different objects that the neural network is able to classify are listed in the labels_onnx.txt file located in the target:

/usr/local/demo-ai/computer-vision/models/mobilenet/labels_onnx.txt

To launch the Python script more easily, two shell scripts are available:

launch image classification based on camera frame inputs

 /usr/local/demo-ai/computer-vision/onnx-image-classification/python/launch_python_label_onnx_mobilenet.sh

launch image classification based on the pictures located in /usr/local/demo-ai/computer-vision/models/mobilenet/testdata directory:

 /usr/local/demo-ai/computer-vision/onnx-image-classification/python/launch_python_label_onnx_mobilenet_testdata.sh

Information

The users must populate the testdata directory with their own data sets.

The pictures are then randomly read from the testdata directory

4. References[edit source]↑

↑ ^{Jump up to: 1.0} ^1.1 ^1.2 ONNX Runtime
↑ ^{Jump up to: 2.0} ^2.1 TFLite Hub

[onnxruntime_url-1] {Jump up to: 1.0} ^1.1 ^1.2 ONNX Runtime

[tflite_hub_url-2] {Jump up to: 2.0} ^2.1 TFLite Hub

[1]

[2]