- Last edited 91 days ago
X-LINUX-AI - object detection using TensorFlow Lite Python runtime
This article explains how to experiment with TensorFlow Lite applications for object detection based on the COCO SSD MobileNet v1 model using TensorFlow Lite Python runtime.
|Python applications are good for prototyping but are less efficient than C/C++ applications|
The object detection neural network model allows identification and localization of a known object within an image.
The application enables OpenCV camera streaming (or test data picture) and the TensorFlow Lite interpreter runing the NN inference based on the camera (or test data pictures) inputs.
It is a multi-process Python application that allows the camera preview (on the CPU core 0) and the neural network inference (using the CPU core 1) to run in parallel .
The user interface is implemented using Python GTK.
The model used with this application is the COCO SSD MobileNet v1 downloaded from the TensorFlow Lite object detection overview page.
2.1 Install from the OpenSTLinux AI package repository
|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 you can install the X-LINUX-AI components for this application:
Board $> apt-get install tflite-cv-apps-object-detection-python
Then restart the demo launcher:
Board $> systemctl restart weston@root
2.2 Source code location
The objdetect_tfl_multiprocessing.py Python script is available:
- in the Openembedded OpenSTLinux Distribution with X-LINUX-AI Expansion Package:
- <Distribution Package installation directory>/layers/meta-st/meta-st-stm32mpu-ai/recipes-samples/tflite-cv-apps/files/object-detection/python/objdetect_tfl_multiprocessing.py
- on the target:
- on GitHub:
3 How to use the application
3.1 Launching via the demo launcher
3.2 Executing with the command line
The Python script objdetect_tfl_multiprocessing.py application is located in the userfs partition:
It accepts the following input parameters:
usage: objdetect_tfl_multiprocessing.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] optional arguments: -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 320) --frame_height FRAME_HEIGHT height of the camera frame (default is 240) --framerate FRAMERATE framerate of the camera (default is 15fps) -m MODEL_FILE, --model_file MODEL_FILE .tflite 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
3.3 Testing with COCO ssd MobileNet v1
The model used for test is the detect.tflite downloaded from object detection overview
|The different objects the neural network is able to detect are listed in the labels.txt file located in the target:
To ease launching of the Python script, two shell scripts are available:
- launch object detection based on camera frame inputs
Board $> /usr/local/demo-ai/computer-vision/tflite-object-detection/python/launch_python_objdetect_tfl_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
Board $> /usr/local/demo-ai/computer-vision/tflite-object-detection/python/launch_python_objdetect_tfl_coco_ssd_mobilenet_testdata.sh
|Note that you need to populate the testdata directory with your own data sets.|
The pictures are then randomly read from the testdata directory