real_time_object_detection_and_tracking's Introduction

Real-time Object Tracking and Detection for Video-streams

Pre-req:

OpenCV 3.4
imutils

Download weights here and place them in model_data/

Arguments:

$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

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 all current objects being tracked are lost, or when 'q' is pressed