This article explains how to experiment TensorFlow Lite applications for object detection based on COCO SSD MobileNet v1 model using TensorFlow Lite Python runtime.
1. Description[edit source]
The object detection[1] 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 TensorFlowLite interpreter runing the NN inference based on the camera (or test data pictures) inputs.
It is a multi-process Python application that allow to run in parallel the camera preview (on the CPU core 0) and the neural network inference (using the CPU core 1).
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[1].
2. Installation[edit source]
2.1. Install from the OpenSTLinux AI package repository[edit source]
After having configured the AI OpenSTLinux package you can install the X-LINUX-AI components for this application:
apt-get install apt-get install tflite-cv-apps-object-detection-python
And restart the demo launcher:
systemctl restart weston@root
2.2. Source code location[edit source]
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:
- /usr/local/demo-ai/computer-vision/tflite-object-detection/python/objdetect_tfl_multiprocessing.py
- on GitHub:
3. How to use it[edit source]
3.1. Launch application via the demo launcher[edit source]
3.2. Or execute with command line[edit source]
The Python script objdetect_tfl_multiprocessing.py application is located in the userfs partition:
/usr/local/demo-ai/computer-vision/tflite-object-detection/python/objdetect_tfl_multiprocessing.py
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[edit source]
The model used for test is the detect.tflite downloaded from object detection overview[1]
To ease launching of the Python script, two shell scripts are available:
- launch object detection based on camera frame inputs
/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
/usr/local/demo-ai/computer-vision/tflite-object-detection/python/launch_python_objdetect_tfl_coco_ssd_mobilenet_testdata.sh