def test_match_keypoints_brief_lena_rotation():
"""Verify matched keypoints result between lena image and its rotated
version with the expected keypoint pairs."""
img = data.lena()
img = rgb2gray(img)
img.shape
tform = tf.SimilarityTransform(scale=1, rotation=0.10, translation=(0, 0))
rotated_img = tf.warp(img, tform)
keypoints1 = corner_peaks(corner_harris(img), min_distance=5)
descriptors1, keypoints1 = brief(img, keypoints1, descriptor_size=512)
keypoints2 = corner_peaks(corner_harris(rotated_img), min_distance=5)
descriptors2, keypoints2 = brief(rotated_img,
keypoints2,
descriptor_size=512)
matched_keypoints = match_keypoints_brief(keypoints1,
descriptors1,
keypoints2,
descriptors2,
threshold=0.07)
expected = np.array([[[263, 272], [234, 298]], [[271, 120], [258, 146]],
[[323, 164], [305, 195]], [[414, 70], [405, 111]],
[[435, 181], [415, 223]], [[454, 176], [435, 221]]])
assert_array_equal(matched_keypoints, expected)
def test_match_keypoints_brief_lena_translation():
"""Test matched keypoints between lena image and its translated version."""
img = data.lena()
img = rgb2gray(img)
img.shape
tform = tf.SimilarityTransform(scale=1, rotation=0, translation=(15, 20))
translated_img = tf.warp(img, tform)
keypoints1 = corner_peaks(corner_harris(img), min_distance=5)
descriptors1, keypoints1 = brief(img, keypoints1, descriptor_size=512)
keypoints2 = corner_peaks(corner_harris(translated_img), min_distance=5)
descriptors2, keypoints2 = brief(translated_img, keypoints2,
descriptor_size=512)
matched_keypoints = match_keypoints_brief(keypoints1, descriptors1,
keypoints2, descriptors2,
threshold=0.10)
assert_array_equal(matched_keypoints[:, 0, :], matched_keypoints[:, 1, :] +
[20, 15])
def test_match_keypoints_brief_lena_rotation():
"""Verify matched keypoints result between lena image and its rotated
version with the expected keypoint pairs."""
img = data.lena()
img = rgb2gray(img)
img.shape
tform = tf.SimilarityTransform(scale=1, rotation=0.10, translation=(0, 0))
rotated_img = tf.warp(img, tform)
keypoints1 = corner_peaks(corner_harris(img), min_distance=5)
descriptors1, keypoints1 = brief(img, keypoints1, descriptor_size=512)
keypoints2 = corner_peaks(corner_harris(rotated_img), min_distance=5)
descriptors2, keypoints2 = brief(rotated_img, keypoints2,
descriptor_size=512)
matched_keypoints = match_keypoints_brief(keypoints1, descriptors1,
keypoints2, descriptors2,
threshold=0.07)
expected = np.array([[[263, 272],
[234, 298]],
[[271, 120],
[258, 146]],
[[323, 164],
[305, 195]],
[[414, 70],
[405, 111]],
[[435, 181],
[415, 223]],
[[454, 176],
[435, 221]]])
assert_array_equal(matched_keypoints, expected)
transformed_img_color = tf.warp(img_color, tform) img = rgb2gray(img_color) transformed_img = rgb2gray(transformed_img_color) # Extracting keypoints using Harris corner response and describing them # using BRIEF for both the images keypoints1 = corner_peaks(corner_harris(img), min_distance=5) descriptors1, keypoints1 = brief(img, keypoints1, descriptor_size=512) keypoints2 = corner_peaks(corner_harris(transformed_img), min_distance=5) descriptors2, keypoints2 = brief(transformed_img, keypoints2, descriptor_size=512) # Matching the BRIEF described keypoints in both the images using # Hamming distance dissimilarity measure pairwise_hamming_distance(descriptors1, descriptors2) matched_keypoints = match_keypoints_brief(keypoints1, descriptors1, keypoints2, descriptors2, threshold=0.15) print "Pairs of matched keypoints :\n" print matched_keypoints # Plotting the matched correspondences in both the images using matplotlib src = matched_keypoints[:, 0, :] dst = matched_keypoints[:, 1, :] img_combined = np.concatenate((img_as_float(img_color), transformed_img_color), axis=1) offset = img.shape fig, ax = plt.subplots(nrows=1, ncols=1) plt.gray() ax.imshow(img_combined, interpolation='nearest')