179 lines
7.1 KiB
Python
179 lines
7.1 KiB
Python
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)
|