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I am trying to use Lowe's criteria to remove bad matches but I realised that I get only one match per keypoint. Why is that? Those are my matcher, detector and descriptor

    self.brisk = cv2.DescriptorExtractor_create("BRISK")
    self.detector = cv2.GFTTDetector(maxCorners=100, minDistance=1)
    self.bf = cv2.BFMatcher(cv2.NORM_L2)

And the function I use to remove bad matches by comparing the distance of the first best and second best matches, but I only have the first best match (for each m in matches there is only one DMatch object and not an array).

def filterMatches( kp1, kp2, matches, ratio = 0.75 ):
    mkp1, mkp2 = [], []
    for m in matches:
        if len( m ) == 2 and m[0].distance < m[1].distance * ratio:
            m = m[0]
            mkp1.append( kp1[m.queryIdx] )
            mkp2.append( kp2[m.trainIdx] )

    pairs = zip( mkp1, mkp2 )

    return pairs

I am trying to use Lowe's criteria to remove bad matches but I realised that I get only one match per keypoint. Why is that? Those are my matcher, detector and descriptor

    self.brisk = cv2.DescriptorExtractor_create("BRISK")
    self.detector = cv2.GFTTDetector(maxCorners=100, minDistance=1)
    self.bf = cv2.BFMatcher(cv2.NORM_L2)

And the function I use to remove bad matches by comparing the distance of the first best and second best matches, but I only have the first best match (for each m in matches there is only one DMatch object and not an array).

def filterMatches( kp1, kp2, matches, ratio = 0.75 ):
    mkp1, mkp2 = [], []
    for m in matches:
        if len( m ) == 2 and m[0].distance < m[1].distance * ratio:
            m = m[0]
            mkp1.append( kp1[m.queryIdx] )
            mkp2.append( kp2[m.trainIdx] )

    pairs = zip( mkp1, mkp2 )

    return pairs