match-descriptors

match_descriptors

skimage.feature.match_descriptors(descriptors1, descriptors2, metric=None, p=2, max_distance=inf, cross_check=True) [source]

Brute-force matching of descriptors.

For each descriptor in the first set this matcher finds the closest descriptor in the second set (and vice-versa in the case of enabled cross-checking).

Parameters:

descriptors1 : (M, P) array Binary descriptors of size P about M keypoints in the first image. descriptors2 : (N, P) array Binary descriptors of size P about N keypoints in the second image. metric : {‘euclidean’, ‘cityblock’, ‘minkowski’, ‘hamming’, ...} The metric to compute the distance between two descriptors. See scipy.spatial.distance.cdist for all possible types. The hamming distance should be used for binary descriptors. By default the L2-norm is used for all descriptors of dtype float or double and the Hamming distance is used for binary descriptors automatically. p : int The p-norm to apply for metric='minkowski'. max_distance : float Maximum allowed distance between descriptors of two keypoints in separate images to be regarded as a match. cross_check : bool If True, the matched keypoints are returned after cross checking i.e. a matched pair (keypoint1, keypoint2) is returned if keypoint2 is the best match for keypoint1 in second image and keypoint1 is the best match for keypoint2 in first image.

Returns:

matches : (Q, 2) array Indices of corresponding matches in first and second set of descriptors, where matches[:, 0] denote the indices in the first and matches[:, 1] the indices in the second set of descriptors.