OpenCV 3 Computer Vision with Python Cookbook

How to do it...

To complete this recipe, perform the following steps:

1. Import the module:

import cv2

2. Create the main window and loop over the different trackers:

cv2.namedWindow('frame')for name, tracker in (('KCF', cv2.TrackerKCF_create),                       ('MIL', cv2.TrackerMIL_create),                       ('TLD', cv2.TrackerTLD_create)):    tracker = tracker()    initialized = False

3. Open the test video file and select an object:

video = cv2.VideoCapture('../data/traffic.mp4')bbox = (878, 266, 1153-878, 475-266)

4. Track until the video ends or ESC is pressed, and visualize the current tracked object:

    while True:        t0 = time.time()        ok, frame = video.read()        if not ok:             break        if initialized:            tracked, bbox = tracker.update(frame)        else:            cv2.imwrite('/tmp/frame.png', frame) tracked ...