180 lines
7.3 KiB
Python
180 lines
7.3 KiB
Python
import numpy as np
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from skimage._shared.testing import assert_equal
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from skimage import data
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from skimage import transform
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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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from skimage._shared import testing
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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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descs1 = np.array([[True, True, False, True],
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[False, True, False, True]])
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descs2 = np.array([[True, False, False, True, False],
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[False, True, True, True, False]])
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with testing.raises(ValueError):
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match_descriptors(descs1, descs2)
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def test_binary_descriptors():
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descs1 = np.array([[True, True, False, True, True],
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[False, True, False, True, True]])
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descs2 = np.array([[True, False, False, True, False],
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[False, False, True, True, True]])
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matches = match_descriptors(descs1, descs2)
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assert_equal(matches, [[0, 0], [1, 1]])
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def test_binary_descriptors_rotation_crosscheck_false():
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"""Verify matched keypoints and their corresponding masks results between
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image and its rotated version with the expected keypoint pairs with
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cross_check disabled."""
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img = data.astronaut()
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img = rgb2gray(img)
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tform = transform.SimilarityTransform(scale=1, rotation=0.15, translation=(0, 0))
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rotated_img = transform.warp(img, tform, clip=False)
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extractor = BRIEF(descriptor_size=512)
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keypoints1 = corner_peaks(corner_harris(img), min_distance=5,
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threshold_abs=0, threshold_rel=0.1)
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extractor.extract(img, keypoints1)
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descriptors1 = extractor.descriptors
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keypoints2 = corner_peaks(corner_harris(rotated_img), min_distance=5,
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threshold_abs=0, threshold_rel=0.1)
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extractor.extract(rotated_img, keypoints2)
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descriptors2 = extractor.descriptors
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matches = match_descriptors(descriptors1, descriptors2, cross_check=False)
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exp_matches1 = np.arange(47)
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exp_matches2 = np.array([0, 2, 1, 3, 4, 5, 7, 8, 14, 9, 11, 13,
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23, 15, 16, 22, 17, 19, 34, 18, 24, 27,
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30, 25, 26, 32, 28, 35, 37, 42, 29, 38,
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33, 40, 36, 3, 10, 32, 43, 15, 29, 41,
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1, 18, 32, 24, 11])
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assert_equal(matches[:, 0], exp_matches1)
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assert_equal(matches[:, 1], exp_matches2)
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# minkowski takes a different code path, therefore we test it explicitly
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matches = match_descriptors(descriptors1, descriptors2,
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metric='minkowski', cross_check=False)
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assert_equal(matches[:, 0], exp_matches1)
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assert_equal(matches[:, 1], exp_matches2)
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# it also has an extra parameter
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matches = match_descriptors(descriptors1, descriptors2,
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metric='minkowski', p=4, cross_check=False)
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assert_equal(matches[:, 0], exp_matches1)
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assert_equal(matches[:, 1], exp_matches2)
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def test_binary_descriptors_rotation_crosscheck_true():
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"""Verify matched keypoints and their corresponding masks results between
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image and its rotated version with the expected keypoint pairs with
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cross_check enabled."""
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img = data.astronaut()
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img = rgb2gray(img)
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tform = transform.SimilarityTransform(scale=1, rotation=0.15, translation=(0, 0))
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rotated_img = transform.warp(img, tform, clip=False)
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extractor = BRIEF(descriptor_size=512)
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keypoints1 = corner_peaks(corner_harris(img), min_distance=5,
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threshold_abs=0, threshold_rel=0.1)
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extractor.extract(img, keypoints1)
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descriptors1 = extractor.descriptors
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keypoints2 = corner_peaks(corner_harris(rotated_img), min_distance=5,
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threshold_abs=0, threshold_rel=0.1)
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extractor.extract(rotated_img, keypoints2)
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descriptors2 = extractor.descriptors
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matches = match_descriptors(descriptors1, descriptors2, cross_check=True)
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exp_matches1 = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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13, 14, 15, 16, 17, 19, 20, 21, 22, 23,
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24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
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34, 38, 41])
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exp_matches2 = np.array([0, 2, 1, 3, 4, 5, 7, 8, 14, 9, 11, 13,
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23, 15, 16, 22, 17, 19, 18, 24, 27, 30,
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25, 26, 32, 28, 35, 37, 42, 29, 38, 33,
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40, 36, 43, 41])
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assert_equal(matches[:, 0], exp_matches1)
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assert_equal(matches[:, 1], exp_matches2)
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def test_max_distance():
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descs1 = np.zeros((10, 128))
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descs2 = np.zeros((15, 128))
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descs1[0, :] = 1
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matches = match_descriptors(descs1, descs2, metric='euclidean',
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max_distance=0.1, cross_check=False)
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assert len(matches) == 9
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matches = match_descriptors(descs1, descs2, metric='euclidean',
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max_distance=np.sqrt(128.1),
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cross_check=False)
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assert len(matches) == 10
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matches = match_descriptors(descs1, descs2, metric='euclidean',
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max_distance=0.1,
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cross_check=True)
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assert_equal(matches, [[1, 0]])
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matches = match_descriptors(descs1, descs2, metric='euclidean',
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max_distance=np.sqrt(128.1),
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cross_check=True)
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assert_equal(matches, [[1, 0]])
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def test_max_ratio():
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descs1 = 10 * np.arange(10)[:, None].astype(np.float32)
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descs2 = 10 * np.arange(15)[:, None].astype(np.float32)
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descs2[0] = 5.0
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matches = match_descriptors(descs1, descs2, metric='euclidean',
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max_ratio=1.0, cross_check=False)
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assert_equal(len(matches), 10)
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matches = match_descriptors(descs1, descs2, metric='euclidean',
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max_ratio=0.6, cross_check=False)
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assert_equal(len(matches), 10)
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matches = match_descriptors(descs1, descs2, metric='euclidean',
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max_ratio=0.5, cross_check=False)
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assert_equal(len(matches), 9)
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descs1[0] = 7.5
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matches = match_descriptors(descs1, descs2, metric='euclidean',
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max_ratio=0.5, cross_check=False)
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assert_equal(len(matches), 9)
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descs2 = 10 * np.arange(1)[:, None].astype(np.float32)
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matches = match_descriptors(descs1, descs2, metric='euclidean',
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max_ratio=1.0, cross_check=False)
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assert_equal(len(matches), 10)
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matches = match_descriptors(descs1, descs2, metric='euclidean',
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max_ratio=0.5, cross_check=False)
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assert_equal(len(matches), 10)
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descs1 = 10 * np.arange(1)[:, None].astype(np.float32)
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matches = match_descriptors(descs1, descs2, metric='euclidean',
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max_ratio=1.0, cross_check=False)
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assert_equal(len(matches), 1)
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matches = match_descriptors(descs1, descs2, metric='euclidean',
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max_ratio=0.5, cross_check=False)
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assert_equal(len(matches), 1)
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