opencv Ratio Test

The ratio test in OpenCV is a method for filtering out false matches in feature matching algorithms.

In feature matching, keypoints are detected in two images and their descriptors are calculated. These descriptors are then matched to find corresponding keypoints in both images. However, some matches may be incorrect due to noise, occlusion, or other factors.

The ratio test helps filter out these incorrect matches. It works by comparing the distance between the best and second-best matches for each keypoint. If the distance ratio is below a certain threshold, the match is considered correct. If the ratio is above the threshold, the match is discarded.

The threshold value is typically set to 0.7, meaning that the distance to the second-best match should be at least 1.4 times greater than the distance to the best match.

Here's an example code snippet for implementing the ratio test in OpenCV:

# detect keypoints and compute descriptors for both images
kp1, des1 = detector.compute(img1, None)
kp2, des2 = detector.compute(img2, None)

# match descriptors using a matcher object
matches = matcher.knnMatch(des1, des2, k=2)

# apply ratio test to filter out false matches
good_matches = []
for m, n in matches:
    if m.distance < 0.7 * n.distance:
        good_matches.append(m)

In this code, `detector` is a keypoint detector object and `matcher` is a descriptor matcher object. The `knnMatch` function returns a list of the two best matches for each descriptor in the first image (`des1`) with descriptors in the second image (`des2`). The ratio test is then applied to filter out false matches, and the remaining matches are stored in `good_matches`.