Shape matcher 211/15/2022 It may be useful when we need to do additional work on that. Second method returns k best matches where k is specified by the user. Once it is created, two important methods are BFMatcher.match() and BFMatcher.knnMatch(). It provides consistent result, and is a good alternative to ratio test proposed by D.Lowe in SIFT paper. That is, the two features in both sets should match each other. If it is true, Matcher returns only those matches with value (i,j) such that i-th descriptor in set A has j-th descriptor in set B as the best match and vice-versa. Second param is boolean variable, crossCheck which is false by default. If ORB is using WTA_K = 3 or 4, cv.NORM_HAMMING2 should be used. For binary string based descriptors like ORB, BRIEF, BRISK etc, cv.NORM_HAMMING should be used, which used Hamming distance as measurement. It is good for SIFT, SURF etc ( cv.NORM_L1 is also there). It specifies the distance measurement to be used. And the closest one is returned.įor BF matcher, first we have to create the BFMatcher object using cv.BFMatcher(). It takes the descriptor of one feature in first set and is matched with all other features in second set using some distance calculation.
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