X-LINUX-AI - object detection using ONNX Python runtime

Applicable for

STM32MP13x lines, STM32MP15x lines

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This article explains how to experiment with ONNX Runtime applications for object detection based on the COCO SSD 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

The object detection neural network model allows the identification and localization of a known object within an image.

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 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 interpreter is configured to use the maximum of the available resources.

The model used with this application is the COCO SSD MobileNet v1 downloaded from the TensorFlow™ For Mobile & Edge as a .tflite model and converted to the 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

2.1. Install from the OpenSTLinux AI package repository

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-object-detection-python

Then, the user can restart the demo launcher:

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

 systemctl restart weston@root

- For other OpenSTLinux distributions, use:

 systemctl restart weston-launch

2.2. Source code location

The objdetect_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/object-detection/python/objdetect_onnx.py

• on the target:

/usr/local/demo-ai/computer-vision/onnx-object-detection/python/objdetect_onnx.py

• on GitHub:

https://github.com/STMicroelectronics/meta-st-stm32mpu-ai/tree/v3.0.0/recipes-samples/onnxrt-cv-apps/files/object-detection/python/objdetect_onnx.py

3. How to use the application

3.1. Launching via the demo launcher

3.2. Executing with the command line

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

/usr/local/demo-ai/computer-vision/onnx-object-detection/python/objdetect_onnx.py

It accepts the following input parameters:

usage: objdetect_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]
                        [--maximum_detection MAXIMUM_DETECTION] [--threshold THRESHOLD]

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 the ONNX interpreter to run inference
  --maximum_detection MAXIMUM_DETECTION
                        Adjust the maximum number of objects detected in a frame according to your NN model (default is 10)
  --threshold THRESHOLD
                        threshold of accuracy above which the boxes are displayed (default 0.62)

3.3. Testing with COCO SSD MobileNet v1

The model used for test is the detect.onnx downloaded from TensorFlow™ For Mobile & Edge and converted to the ONNX format.

Information

The different objects that the neural network is able to detect are listed in the labels.txt file located in the target: /usr/local/demo-ai/computer-vision/models/coco_ssd_mobilenet/labels.txt

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

• launch object detection based on camera frame inputs:

 /usr/local/demo-ai/computer-vision/onnx-object-detection/python/launch_python_objdetect_onnx_coco_ssd_mobilenet.sh

• launch object detection based on the pictures located in /usr/local/demo-ai/computer-vision/models/coco_ssd_mobilenet/testdata directory:

 /usr/local/demo-ai/computer-vision/onnx-object-detection/python/launch_python_objdetect_onnx_coco_ssd_mobilenet_testdata.sh

Information

Note that you need to populate the testdata directory with your own data sets. The pictures are then randomly read from the testdata directory.

Neural network

Central processing unit

Artificial intelligence

Neural network

Central processing unit

Artificial intelligence