def compute_lk_optical_flow()

in video_processing/modules/optical_flow.py [0:0]

• 26 lines of code
• 8 McCabe index (conditional complexity)

def compute_lk_optical_flow(frames):
    # params for ShiTomasi corner detection
    maxCorners = 50
    feature_params = dict(maxCorners=maxCorners, qualityLevel=0.3, minDistance=7, blockSize=7)
    # Parameters for lucas kanade optical flow
    lk_params = dict(
        winSize=(15, 15),
        maxLevel=2,
        criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03),
    )
    # Create some random colors
    color = np.random.randint(0, 255, (maxCorners, 3))
    # Take first frame and find corners in it
    old_frame = frames[0]
    old_gray = cv2.cvtColor(np.array(old_frame), cv2.COLOR_BGR2GRAY)
    p0 = cv2.goodFeaturesToTrack(old_gray, mask=None, **feature_params)
    # Create a mask image for drawing purposes
    mask = np.zeros_like(old_frame)

    for frame in frames[1:]:
        frame_gray = cv2.cvtColor(np.array(frame), cv2.COLOR_BGR2GRAY)
        # calculate optical flow
        p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
        # Select good points
        if p1 is not None:
            good_new = p1[st == 1]
            good_old = p0[st == 1]
        # draw the tracks
        for i, (new, old) in enumerate(zip(good_new, good_old)):
            a, b = new.ravel()
            c, d = old.ravel()
            mask = cv2.line(mask, (int(a), int(b)), (int(c), int(d)), color[i].tolist(), 2)
        old_gray = frame_gray.copy()
        p0 = good_new.reshape(-1, 1, 2)
    return mask