Cummulative object counting with Tensorflow 2 and Tensorflow Lite.
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Clone the repository
git clone https://github.com/TannerGilbert/Tensorflow-2-Object-Counting -
Install dependencies
cd Tensorflow-2-Object-Counting pip3 install -r requirements.txt
To run cumulative counting with a Tensorflow object detection model use the tensorflow_cumulative_object_counting.py script.
usage: tensorflow_cumulative_object_counting.py [-h] -m MODEL -l LABELMAP [-v VIDEO_PATH] [-t THRESHOLD] [-roi ROI_POSITION] [-la LABELS [LABELS ...]] [-a] [-s SKIP_FRAMES] [-sh] [-sp SAVE_PATH] Detect objects inside webcam videostream optional arguments: -h, --help show this help message and exit -m MODEL, --model MODEL Model Path -l LABELMAP, --labelmap LABELMAP Path to Labelmap -v VIDEO_PATH, --video_path VIDEO_PATH Path to video. If None camera will be used -t THRESHOLD, --threshold THRESHOLD Detection threshold -roi ROI_POSITION, --roi_position ROI_POSITION ROI Position (0-1) -la LABELS [LABELS ...], --labels LABELS [LABELS ...] Label names to detect (default="all-labels") -a, --axis Axis for cumulative counting (default=x axis) -s SKIP_FRAMES, --skip_frames SKIP_FRAMES Number of frames to skip between using object detection model -sh, --show Show output -sp SAVE_PATH, --save_path SAVE_PATH Path to save the output. If None output won't be saved Example: python tensorflow_cumulative_object_counting.py -m model_path/saved_model -l labelmap.pbtxt -v video.mp4 -a
To run cumulative counting with a Tensorflow Lite model use the tflite_cumulative_object_counting.py script.
usage: tflite_cumulative_object_counting.py [-h] -m MODEL -l LABELMAP [-v VIDEO_PATH] [-t THRESHOLD] [-roi ROI_POSITION] [-la LABELS [LABELS ...]] [-a] [-e] [-s SKIP_FRAMES] [-sh] [-sp SAVE_PATH] [--type {tensorflow,yolo,yolov3-tiny}] optional arguments: -h, --help show this help message and exit -m MODEL, --model MODEL File path of .tflite file. (default: None) -l LABELMAP, --labelmap LABELMAP File path of labels file. (default: None) -v VIDEO_PATH, --video_path VIDEO_PATH Path to video. If None camera will be used (default: ) -t THRESHOLD, --threshold THRESHOLD Detection threshold (default: 0.5) -roi ROI_POSITION, --roi_position ROI_POSITION ROI Position (0-1) (default: 0.6) -la LABELS [LABELS ...], --labels LABELS [LABELS ...] Label names to detect (default="all-labels") (default: None) -a, --axis Axis for cumulative counting (default=x axis) (default: True) -e, --use_edgetpu Use EdgeTPU (default: False) -s SKIP_FRAMES, --skip_frames SKIP_FRAMES Number of frames to skip between using object detection model (default: 20) -sh, --show Show output (default: True) -sp SAVE_PATH, --save_path SAVE_PATH Path to save the output. If None output won't be saved (default: ) --type {tensorflow,yolo,yolov3-tiny} Whether the original model was a Tensorflow or YOLO model (default: tensorflow) Example: python tflite_cumulative_object_counting.py -m model.tflite -l labelmap.txt -v video.mp4 -a
This project was inspired by OpenCV People Counter and the tensorflow_object_counting_api.
