import numpy as np
from skimage._shared.testing import assert_equal
from skimage import data
from skimage import transform
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 = transform.SimilarityTransform(scale=1, rotation=0.15, translation=(0, 0))
rotated_img = transform.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.arange(47)
exp_matches2 = np.array([0, 2, 1, 3, 4, 5, 7, 8, 14, 9, 11, 13,
23, 15, 16, 22, 17, 19, 34, 18, 24, 27,
30, 25, 26, 32, 28, 35, 37, 42, 29, 38,
33, 40, 36, 3, 10, 32, 43, 15, 29, 41,
1, 18, 32, 24, 11])
assert_equal(matches[:, 0], exp_matches1)
assert_equal(matches[:, 1], exp_matches2)
# minkowski takes a different code path, therefore we test it explicitly
matches = match_descriptors(descriptors1, descriptors2,
metric='minkowski', cross_check=False)
assert_equal(matches[:, 0], exp_matches1)
assert_equal(matches[:, 1], exp_matches2)
# it also has an extra parameter
matches = match_descriptors(descriptors1, descriptors2,
metric='minkowski', p=4, cross_check=False)
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 = transform.SimilarityTransform(scale=1, rotation=0.15, translation=(0, 0))
rotated_img = transform.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, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34, 38, 41])
exp_matches2 = np.array([0, 2, 1, 3, 4, 5, 7, 8, 14, 9, 11, 13,
23, 15, 16, 22, 17, 19, 18, 24, 27, 30,
25, 26, 32, 28, 35, 37, 42, 29, 38, 33,
40, 36, 43, 41])
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)