OpenCV 3 Computer Vision with Python Cookbook

How to do it

You need to complete the following steps:

1. Import the necessary modules:

import cv2

2. Open a test video and initialize the auxiliary variables:

video = cv2.VideoCapture('../data/traffic.mp4')prev_pts = Noneprev_gray_frame = Nonetracks = None

3. Start reading the frames from the video, converting each image into grayscale:

while True:    retval, frame = video.read()    if not retval: break    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

4. Track the keypoints from a previous frame using the sparse Lucas-Kanade optical flow algorithm or, if you've just started or pressed C, detect the keypoints so that we have something to track in the next frame:

    if prev_pts is not None:        pts, status, errors = cv2.calcOpticalFlowPyrLK( prev_gray_frame, ...