mirror of
https://github.com/wassname/scikit-image.git
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Johannes Schönberger
71 lines
2.6 KiB
Python
71 lines
2.6 KiB
Python
import numpy as np
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from scipy.spatial.distance import cdist
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def match_descriptors(descriptors1, descriptors2, metric=None, p=2,
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max_distance=np.inf, cross_check=True):
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"""Brute-force matching of descriptors.
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For each descriptor in the first set this matcher finds the closest
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descriptor in the second set (and vice-versa in the case of enabled
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cross-checking).
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Parameters
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----------
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descriptors1 : (M, P) array
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Binary descriptors of size P about M keypoints in the first image.
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descriptors2 : (N, P) array
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Binary descriptors of size P about N keypoints in the second image.
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metric : {'euclidean', 'cityblock', 'minkowski', 'hamming', ...}
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The metric to compute the distance between two descriptors. See
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`scipy.spatial.distance.cdist` for all possible types. The hamming
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distance should be used for binary descriptors. By default the L2-norm
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is used for all descriptors of dtype float or double and the Hamming
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distance is used for binary descriptors automatically.
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p : int
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The p-norm to apply for ``metric='minkowski'``.
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max_distance : float
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Maximum allowed distance between descriptors of two keypoints
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in separate images to be regarded as a match.
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cross_check : bool
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If True, the matched keypoints are returned after cross checking i.e. a
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matched pair (keypoint1, keypoint2) is returned if keypoint2 is the
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best match for keypoint1 in second image and keypoint1 is the best
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match for keypoint2 in first image.
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Returns
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-------
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matches : (Q, 2) array
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Indices of corresponding matches in first and second set of
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descriptors, where ``matches[:, 0]`` denote the indices in the first
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and ``matches[:, 1]`` the indices in the second set of descriptors.
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"""
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if descriptors1.shape[1] != descriptors2.shape[1]:
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raise ValueError("Descriptor length must equal.")
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if metric is None:
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if np.issubdtype(descriptors1.dtype, np.bool):
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metric = 'hamming'
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else:
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metric = 'euclidean'
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distances = cdist(descriptors1, descriptors2, metric=metric, p=p)
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indices1 = np.arange(descriptors1.shape[0])
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indices2 = np.argmin(distances, axis=1)
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if cross_check:
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matches1 = np.argmin(distances, axis=0)
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mask = indices1 == matches1[indices2]
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indices1 = indices1[mask]
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indices2 = indices2[mask]
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matches = np.column_stack((indices1, indices2))
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if max_distance < np.inf:
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matches = matches[distances[indices1, indices2] < max_distance]
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return matches
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