mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-05 11:26:00 +08:00
Ankit Agrawal
50 lines
2.0 KiB
Python
50 lines
2.0 KiB
Python
import numpy as np
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from scipy.spatial.distance import hamming
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def _remove_border_keypoints(image, keypoints, dist):
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"""Removes keypoints that are within dist pixels from the image border."""
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width = image.shape[0]
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height = image.shape[1]
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keypoints = keypoints[(dist - 1 < keypoints[:, 0])
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& (keypoints[:, 0] < width - dist + 1)
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& (dist - 1 < keypoints[:, 1])
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& (keypoints[:, 1] < height - dist + 1)]
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return keypoints
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def hamming_distance(descriptors1, descriptors2):
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"""A dissimilarity measure used for matching keypoints in different images
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using binary feature descriptors like BRIEF etc.
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Parameters
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----------
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descriptors1 : (P1, D) array of dtype bool
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Binary feature descriptors for keypoints in the first image.
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2D ndarray with a binary descriptors of size D about P1 keypoints
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with value at an index (i, j) either being True or False representing
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the outcome of Intensity comparison about ith keypoint on jth decision
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pixel-pair.
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descriptors2 : (P2, D) array of dtype bool
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Binary feature descriptors for keypoints in the second image.
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2D ndarray with a binary descriptors of size D about P2 keypoints
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with value at an index (i, j) either being True or False representing
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the outcome of Intensity comparison about ith keypoint on jth decision
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pixel-pair.
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Returns
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-------
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distance : (P1, P2) array of dtype float
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2D ndarray with value at an index (i, j) in the range [0, 1]
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representing the extent of dissimilarity between ith keypoint of in
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first image and jth keypoint in second image.
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"""
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distance = np.zeros((descriptors1.shape[0], descriptors2.shape[0]), dtype=float)
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for i in range(descriptors1.shape[0]):
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for j in range(descriptors2.shape[0]):
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distance[i, j] = hamming(descriptors1[i, :], descriptors2[j, :])
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return distance
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