import numpy as np from skimage._shared.testing import assert_equal from skimage import data from skimage import transform as tf from skimage.color import rgb2gray from skimage.feature import (BRIEF, match_descriptors, corner_peaks, corner_harris) from skimage._shared import testing def test_binary_descriptors_unequal_descriptor_sizes_error(): """Sizes of descriptors of keypoints to be matched should be equal.""" descs1 = np.array([[True, True, False, True], [False, True, False, True]]) descs2 = np.array([[True, False, False, True, False], [False, True, True, True, False]]) with testing.raises(ValueError): match_descriptors(descs1, descs2) def test_binary_descriptors(): descs1 = np.array([[True, True, False, True, True], [False, True, False, True, True]]) descs2 = np.array([[True, False, False, True, False], [False, False, True, True, True]]) matches = match_descriptors(descs1, descs2) assert_equal(matches, [[0, 0], [1, 1]]) def test_binary_descriptors_rotation_crosscheck_false(): """Verify matched keypoints and their corresponding masks results between image and its rotated version with the expected keypoint pairs with cross_check disabled.""" img = data.astronaut() img = rgb2gray(img) tform = tf.SimilarityTransform(scale=1, rotation=0.15, translation=(0, 0)) rotated_img = tf.warp(img, tform, clip=False) extractor = BRIEF(descriptor_size=512) keypoints1 = corner_peaks(corner_harris(img), min_distance=5, threshold_abs=0, threshold_rel=0.1) extractor.extract(img, keypoints1) descriptors1 = extractor.descriptors keypoints2 = corner_peaks(corner_harris(rotated_img), min_distance=5, threshold_abs=0, threshold_rel=0.1) extractor.extract(rotated_img, keypoints2) descriptors2 = extractor.descriptors matches = match_descriptors(descriptors1, descriptors2, cross_check=False) exp_matches1 = np.array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]) exp_matches2 = np.array([ 0, 31, 2, 3, 1, 4, 6, 4, 38, 5, 27, 7, 13, 10, 9, 27, 7, 11, 15, 8, 23, 14, 12, 16, 10, 25, 18, 19, 21, 20, 41, 24, 25, 26, 28, 27, 22, 23, 29, 30, 31, 32, 35, 33, 34, 30, 36]) assert_equal(matches[:, 0], exp_matches1) assert_equal(matches[:, 1], exp_matches2) def test_binary_descriptors_rotation_crosscheck_true(): """Verify matched keypoints and their corresponding masks results between image and its rotated version with the expected keypoint pairs with cross_check enabled.""" img = data.astronaut() img = rgb2gray(img) tform = tf.SimilarityTransform(scale=1, rotation=0.15, translation=(0, 0)) rotated_img = tf.warp(img, tform, clip=False) extractor = BRIEF(descriptor_size=512) keypoints1 = corner_peaks(corner_harris(img), min_distance=5, threshold_abs=0, threshold_rel=0.1) extractor.extract(img, keypoints1) descriptors1 = extractor.descriptors keypoints2 = corner_peaks(corner_harris(rotated_img), min_distance=5, threshold_abs=0, threshold_rel=0.1) extractor.extract(rotated_img, keypoints2) descriptors2 = extractor.descriptors matches = match_descriptors(descriptors1, descriptors2, cross_check=True) exp_matches1 = np.array([ 0, 2, 3, 4, 5, 6, 9, 11, 12, 13, 14, 17, 18, 19, 21, 22, 23, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46]) exp_matches2 = np.array([ 0, 2, 3, 1, 4, 6, 5, 7, 13, 10, 9, 11, 15, 8, 14, 12, 16, 18, 19, 21, 20, 24, 25, 26, 28, 27, 22, 23, 29, 30, 31, 32, 35, 33, 34, 36]) assert_equal(matches[:, 0], exp_matches1) assert_equal(matches[:, 1], exp_matches2) def test_max_distance(): descs1 = np.zeros((10, 128)) descs2 = np.zeros((15, 128)) descs1[0, :] = 1 matches = match_descriptors(descs1, descs2, metric='euclidean', max_distance=0.1, cross_check=False) assert len(matches) == 9 matches = match_descriptors(descs1, descs2, metric='euclidean', max_distance=np.sqrt(128.1), cross_check=False) assert len(matches) == 10 matches = match_descriptors(descs1, descs2, metric='euclidean', max_distance=0.1, cross_check=True) assert_equal(matches, [[1, 0]]) matches = match_descriptors(descs1, descs2, metric='euclidean', max_distance=np.sqrt(128.1), cross_check=True) assert_equal(matches, [[1, 0]]) def test_max_ratio(): descs1 = 10 * np.arange(10)[:, None].astype(np.float32) descs2 = 10 * np.arange(15)[:, None].astype(np.float32) descs2[0] = 5.0 matches = match_descriptors(descs1, descs2, metric='euclidean', max_ratio=1.0, cross_check=False) assert_equal(len(matches), 10) matches = match_descriptors(descs1, descs2, metric='euclidean', max_ratio=0.6, cross_check=False) assert_equal(len(matches), 10) matches = match_descriptors(descs1, descs2, metric='euclidean', max_ratio=0.5, cross_check=False) assert_equal(len(matches), 9) descs1[0] = 7.5 matches = match_descriptors(descs1, descs2, metric='euclidean', max_ratio=0.5, cross_check=False) assert_equal(len(matches), 9) descs2 = 10 * np.arange(1)[:, None].astype(np.float32) matches = match_descriptors(descs1, descs2, metric='euclidean', max_ratio=1.0, cross_check=False) assert_equal(len(matches), 10) matches = match_descriptors(descs1, descs2, metric='euclidean', max_ratio=0.5, cross_check=False) assert_equal(len(matches), 10) descs1 = 10 * np.arange(1)[:, None].astype(np.float32) matches = match_descriptors(descs1, descs2, metric='euclidean', max_ratio=1.0, cross_check=False) assert_equal(len(matches), 1) matches = match_descriptors(descs1, descs2, metric='euclidean', max_ratio=0.5, cross_check=False) assert_equal(len(matches), 1)