The Behavior of the cv2.ORB algorithm changes from version 4.1.2.30 to 4.2.0.32

Expected behaviour

I use the ORB image registration algorithm and it worked until 4.1.2.30. Same solution on same data!

Actual behaviour

From Version 4.1.2.32 to newest (4.5.1.48) it has a different (wrong) solution on the same data AND it needs about 3 times more time.

Steps to reproduce

def alignImages(im, imRef):
    MAX_FEATURES = 1000
    GOOD_MATCH_PERCENT = 0.5

    # Detect ORB features and compute descriptors.
    orb = cv2.ORB_create(MAX_FEATURES, scaleFactor=2, WTA_K=4, scoreType=cv2.ORB_HARRIS_SCORE, patchSize=61)

    keypoints1, descriptors1 = orb.detectAndCompute(im, None)
    keypoints2, descriptors2 = orb.detectAndCompute(imRef, None)
    #print('after kp2: ', time.time() - t)

    print('len keypoints:', len(keypoints1), ' ',len(keypoints2))

    # Match features.
    matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)
    matches = matcher.match(descriptors1, descriptors2, None)

    #print('after matcher: ', time.time() - t)

    # Sort matches by score
    matches.sort(key=lambda x: x.distance, reverse=False)

    # Remove not so good matches
    numGoodMatches = int(len(matches) * GOOD_MATCH_PERCENT)
    matches = matches[:numGoodMatches]

    # Extract location of good matches
    points1 = np.zeros((len(matches), 2), dtype=np.float32)
    points2 = np.zeros((len(matches), 2), dtype=np.float32)

    for i, match in enumerate(matches):
        points1[i, :] = keypoints1[match.queryIdx].pt
        points2[i, :] = keypoints2[match.trainIdx].pt

    # Find homography
    h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)
    return h

• operating system: WIN 10 64-bit

Issue submission checklist

sorry, i don't know if it is a opencv-python issue or a opencv issue.

Start with the inline alignImages reproducer and run it against the listed OpenCV-Python versions on Windows 10 using identical input images. Compare ORB keypoints, matches, homography, and timing to determine whether the regression is in the Python package or OpenCV core. Done means the regression is explained and fixed, or the issue is routed to the appropriate project with a minimal reproducible case.