mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-30 05:09:30 +08:00
Ankit Agrawal
80 lines
3.2 KiB
Python
80 lines
3.2 KiB
Python
import numpy as np
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from .util import pairwise_hamming_distance
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from .match_cy import _binary_cross_check_loop
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def match_binary_descriptors(keypoints1, descriptors1, keypoints2,
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descriptors2, threshold=0.40, cross_check=True):
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"""Match keypoints described using binary descriptors in one image to
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those in second image.
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Parameters
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----------
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keypoints1 : (M, 2) ndarray
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M Keypoints from the first image described using skimage.feature.brief
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descriptors1 : (M, P) ndarray
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Binary descriptors of size P about M keypoints in the first image.
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keypoints2 : (N, 2) ndarray
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N Keypoints from the second image described using skimage.feature.brief
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descriptors2 : (N, P) ndarray
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Binary descriptors of size P about N keypoints in the second image.
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threshold : float in range [0, 1]
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Maximum allowable hamming 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 iff keypoint2 is the best
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match for keypoint1 in second image and keypoint1 is the best match for
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keypoint2 in first image.
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Returns
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-------
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matches : (Q, 2, 2) ndarray
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Location of Q matched keypoint pairs from two images.
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mask1 : (Q,) ndarray
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Indices of keypoints in keypoints1 that have been matched.
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mask2 : (Q,) ndarray
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Indices of keypoints in keypoints2 that have been matched.
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"""
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if (keypoints1.shape[0] != descriptors1.shape[0]
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or keypoints2.shape[0] != descriptors2.shape[0]):
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raise ValueError("The number of keypoints and number of described "
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"keypoints do not match.")
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if descriptors1.shape[1] != descriptors2.shape[1]:
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raise ValueError("Descriptor sizes for matching keypoints in both "
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"the images should be equal.")
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# Get hamming distances between keypoints1 and keypoints2
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distance = pairwise_hamming_distance(descriptors1, descriptors2)
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if cross_check:
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matched_keypoints1_index = np.argmin(distance, axis=1)
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matched_keypoints2_index = np.argmin(distance, axis=0)
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matched_index = _binary_cross_check_loop(matched_keypoints1_index,
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matched_keypoints2_index,
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distance, threshold)
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matches = np.zeros((matched_index.shape[0], 2, 2),
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dtype=np.intp)
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mask1 = matched_index[:, 0]
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mask2 = matched_index[:, 1]
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matches[:, 0, :] = keypoints1[mask1]
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matches[:, 1, :] = keypoints2[mask2]
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else:
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temp = distance > threshold
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row_check = np.any(~temp, axis=1)
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matched_keypoints2 = keypoints2[np.argmin(distance, axis=1)]
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matches = np.zeros((np.sum(row_check), 2, 2),
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dtype=np.intp)
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matches[:, 0, :] = keypoints1[row_check]
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matches[:, 1, :] = matched_keypoints2[row_check]
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mask1 = np.where(row_check == True)
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mask2 = np.argmin(distance, axis=1)[row_check]
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return matches, mask1, mask2
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