Implementation under progress
- OpenCV 3.4
- imutils
Download weights here and place them in model_data/
$python3 src/main.py -h
usage: main.py [-h] [--input INPUT] [--output OUTPUT] --model MODEL
[--config CONFIG] [--classes CLASSES] [--thr THR]
Object Detection and Tracking on Video Streams
optional arguments:
-h, --help show this help message and exit
--input INPUT Path to input image or video file. Skip this argument to
capture frames from a camera.
--output OUTPUT Path to save output as video file. Skip this argument if
you don't want the output to be saved.
--model MODEL Path to a binary file of model that contains trained weights.
It could be a file with extensions .caffemodel (Caffe) or
.weights (Darknet)
--config CONFIG Path to a text file of model that contains network
configuration. It could be a file with extensions
.prototxt (Caffe) or .cfg (Darknet)
--classes CLASSES Optional path to a text file with names of classes to
label detected objects.
--thr THR Confidence threshold for detection. Default: 0.35
Execute code from root directory. Example:
python3 src/main.py --model model_data/yolov2.weights --config model_data/yolov2.cfg --classes model_data/coco_classes.txt --input media/sample_video.mp4 --output out/sample_output.avi
or
python3 src/main.py --model model_data/MobileNetSSD_deploy.caffemodel --config model_data/MobileNetSSD_deploy.prototxt --classes model_data/MobileNet_classes.txt --input media/sample_video.mp4 --output out/sample_output.avi
Note: --input can be ommitted, which will activate stream from webcam. New objects are detected when current objects being tracked are lost, or when 'q' is pressed
