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98 lines
4.1 KiB
Python
98 lines
4.1 KiB
Python
import numpy as np
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from numpy.testing import assert_equal, assert_raises
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from skimage import data
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from skimage import transform as tf
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from skimage.color import rgb2gray
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from skimage.feature import (BRIEF, match_descriptors,
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corner_peaks, corner_harris)
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def test_binary_descriptors_unequal_descriptor_sizes_error():
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"""Sizes of descriptors of keypoints to be matched should be equal."""
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des1 = np.array([[True, True, False, True],
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[False, True, False, True]])
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des2 = np.array([[True, False, False, True, False],
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[False, True, True, True, False]])
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assert_raises(ValueError, match_descriptors, des1, des2)
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def test_binary_descriptors():
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des1 = np.array([[True, True, False, True, True],
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[False, True, False, True, True]])
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des2 = np.array([[True, False, False, True, False],
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[False, False, True, True, True]])
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indices1, indices2 = match_descriptors(des1, des2)
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assert_equal(indices1, [0, 1])
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assert_equal(indices2, [0, 1])
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def test_binary_descriptors_lena_rotation_crosscheck_false():
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"""Verify matched keypoints and their corresponding masks results between
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lena image and its rotated version with the expected keypoint pairs with
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cross_check disabled."""
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img = data.lena()
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img = rgb2gray(img)
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tform = tf.SimilarityTransform(scale=1, rotation=0.15, translation=(0, 0))
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rotated_img = tf.warp(img, tform)
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descriptor = BRIEF(descriptor_size=512)
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keypoints1 = corner_peaks(corner_harris(img), min_distance=5)
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descriptors1, mask1 = descriptor.extract(img, keypoints1)
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keypoints1 = keypoints1[mask1]
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keypoints2 = corner_peaks(corner_harris(rotated_img), min_distance=5)
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descriptors2, mask2 = descriptor.extract(rotated_img, keypoints2)
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keypoints2 = keypoints1[mask2]
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m1, m2 = match_descriptors(descriptors1, descriptors2, threshold=0.13,
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cross_check=False)
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expected_mask1 = np.array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
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12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
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24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
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36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46])
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expected_mask2 = np.array([33, 0, 35, 7, 1, 35, 3, 2, 3, 6, 4, 9,
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11, 10, 28, 7, 8, 5, 31, 14, 13, 15, 21, 16,
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16, 13, 17, 18, 19, 21, 22, 23, 0, 24, 1, 24,
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23, 0, 26, 27, 25, 34, 28, 14, 29, 30, 21])
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assert_equal(m1, expected_mask1)
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assert_equal(m2, expected_mask2)
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def test_binary_descriptors_lena_rotation_crosscheck_true():
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"""Verify matched keypoints and their corresponding masks results between
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lena image and its rotated version with the expected keypoint pairs with
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cross_check enabled."""
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img = data.lena()
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img = rgb2gray(img)
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tform = tf.SimilarityTransform(scale=1, rotation=0.15, translation=(0, 0))
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rotated_img = tf.warp(img, tform)
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descriptor = BRIEF(descriptor_size=512)
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keypoints1 = corner_peaks(corner_harris(img), min_distance=5)
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descriptors1, mask1 = descriptor.extract(img, keypoints1)
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keypoints1 = keypoints1[mask1]
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keypoints2 = corner_peaks(corner_harris(rotated_img), min_distance=5)
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descriptors2, mask2 = descriptor.extract(rotated_img, keypoints2)
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keypoints2 = keypoints1[mask2]
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m1, m2 = match_descriptors(descriptors1, descriptors2, threshold=0.13,
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cross_check=True)
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expected_mask1 = np.array([ 0, 1, 2, 4, 6, 7, 9, 10, 11, 12, 13, 15,
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16, 17, 19, 20, 21, 24, 26, 27, 28, 29, 30, 35,
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36, 38, 39, 40, 42, 44, 45])
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expected_mask2 = np.array([33, 0, 35, 1, 3, 2, 6, 4, 9, 11, 10, 7,
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8, 5, 14, 13, 15, 16, 17, 18, 19, 21, 22, 24,
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23, 26, 27, 25, 28, 29, 30])
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assert_equal(m1, expected_mask1)
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assert_equal(m2, expected_mask2)
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if __name__ == '__main__':
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from numpy import testing
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testing.run_module_suite()
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