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Adding docs for match_keypoints_brief
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Ankit Agrawal
+85
-60
@@ -28,6 +28,9 @@ def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
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the keypoints. Default is 49.
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sample_seed : int
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Seed for sampling the decision pixel-pairs. Default is 1.
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variance : float
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Variance of the Gaussian Low Pass filter applied on the image to
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alleviate noise sensitivity. Default is 2.
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return_keypoints : bool
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If True, return the Q keypoints (after filtering out the border
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keypoints) about which the descriptors are extracted. Default is False.
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@@ -46,81 +49,76 @@ def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
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References
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----------
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.. [1] Michael Calonder, Vincent Lepetit, Christoph Strecha and Pascal Fua
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"BRIEF : Binary robust independent elementary features",
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http://cvlabwww.epfl.ch/~lepetit/papers/calonder_eccv10.pdf
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"BRIEF : Binary robust independent elementary features",
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http://cvlabwww.epfl.ch/~lepetit/papers/calonder_eccv10.pdf
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Examples
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--------
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>>> from skimage.feature.corner import *
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>>> from skimage.feature import brief, hamming_distance
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>>> from skimage.feature import hamming_distance
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>>> from skimage.feature._brief import *
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>>> square1 = np.zeros([10, 10])
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>>> square1[2:8, 2:8] = 1
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>>> square1 = np.zeros([8, 8], dtype=np.int32)
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>>> square1[2:6, 2:6] = 1
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>>> square1
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array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
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[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
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[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
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[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
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[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
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[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
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array([[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)
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>>> keypoints1 = corner_peaks(corner_harris(square1), min_distance=1)
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>>> keypoints1
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array([[2, 2],
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[2, 7],
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[7, 2],
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[7, 7]])
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[2, 5],
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[5, 2],
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[5, 5]])
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>>> descriptors1, keypoints1 = brief(square1, keypoints1, patch_size = 5, return_keypoints=True)
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>>> keypoints1
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array([[2, 2],
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[2, 7],
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[7, 2],
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[7, 7]])
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>>> square2 = np.zeros([12, 12])
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>>> square2[3:9, 3:9] = 1
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[2, 5],
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[5, 2],
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[5, 5]])
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>>> square2 = np.zeros([9, 9], dtype=np.int32)
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>>> square2[2:7, 2:7] = 1
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>>> square2
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array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
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array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 1, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)
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>>> keypoints2 = corner_peaks(corner_harris(square2), min_distance=1)
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>>> keypoints2
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array([[3, 3],
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[3, 8],
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[8, 3],
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[8, 8]])
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array([[2, 2],
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[2, 6],
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[6, 2],
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[6, 6]])
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>>> descriptors2, keypoints2 = brief(square2, keypoints2, patch_size = 5, return_keypoints=True)
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>>> keypoints2
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array([[3, 3],
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[3, 8],
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[8, 3],
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[8, 8]])
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array([[2, 2],
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[2, 6],
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[6, 2],
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[6, 6]])
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>>> hamming_distance(descriptors1, descriptors2)
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array([[ 0.00390625, 0.33984375, 0.35546875, 0.63671875],
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[ 0.3359375 , 0. , 0.65625 , 0.3515625 ],
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[ 0.359375 , 0.65625 , 0. , 0.3515625 ],
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[ 0.6328125 , 0.3515625 , 0.3515625 , 0. ]])
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array([[ 0.03125 , 0.3203125, 0.3671875, 0.6171875],
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[ 0.3203125, 0.03125 , 0.640625 , 0.375 ],
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[ 0.375 , 0.6328125, 0.0390625, 0.328125 ],
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[ 0.625 , 0.3671875, 0.34375 , 0.0234375]])
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>>> match_keypoints_brief(keypoints1, descriptors1, keypoints2, descriptors2)
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array([[[ 2., 2.],
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[ 2., 7.],
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[ 7., 2.],
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[ 7., 7.]],
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[ 2., 5.],
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[ 5., 2.],
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[ 5., 5.]],
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[[ 3., 3.],
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[ 3., 8.],
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[ 8., 3.],
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[ 8., 8.]]])
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[[ 2., 2.],
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[ 2., 6.],
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[ 6., 2.],
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[ 6., 6.]]])
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"""
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@@ -178,8 +176,30 @@ def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
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def match_keypoints_brief(keypoints1, descriptors1, keypoints2,
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descriptors2, threshold=0.15):
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"""Match keypoints described using BRIEF descriptors.
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if keypoints1.shape[0] != descriptors1.shape[0] or keypoints2.shape[0] != descriptors2.shape[0]:
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Parameters
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----------
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keypoints1 : (M, 2) ndarray
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M Keypoints from the first image described using feature._brief.brief
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descriptors1 : (M, P) ndarray
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BRIEF descriptors of size P about M keypoints in the first image.
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keypoints2 : (N, 2) ndarray
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N Keypoints from the second image described using feature._brief.brief
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descriptors2 : (N, P) ndarray
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BRIEF descriptors of size P about N keypoints in the second image.
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threshold : float in range [0, 1]
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Threshold for removing matched keypoint pairs with hamming distance
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greater than it. Default is 0.15
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Returns
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-------
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match_keypoints_brief : (2, Q, 2) ndarray
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Location of Q matched keypoint pairs from two images.
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"""
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if keypoints1.shape[0] != descriptors1.shape[0] or \
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keypoints2.shape[0] != descriptors2.shape[0]:
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raise ValueError("The number of keypoints and number of described \
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keypoints do not match. Make the optional parameter \
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return_keypoints True to get described keypoints.")
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@@ -187,12 +207,16 @@ def match_keypoints_brief(keypoints1, descriptors1, keypoints2,
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if descriptors1.shape[1] != descriptors2.shape[1]:
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raise ValueError("Descriptor sizes for matching keypoints in both \
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the images should be equal.")
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# Get hamming distances between keeypoints1 and keypoints2
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distance = hamming_distance(descriptors1, descriptors2)
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# For each keypoint in keypoints1, match it with the keypoint in keypoints2
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# that has minimum hamming distance
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dist_matched_kp = np.amin(distance, axis=1)
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index_matched_kp2 = distance.argmin(axis=1)
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# Remove the matched pairs which have hamming distance greater than the
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# threshold
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temp = np.zeros((keypoints1.shape[0], 3))
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temp[:, 0] = range(keypoints1.shape[0])
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temp[:, 1] = index_matched_kp2
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@@ -202,8 +226,9 @@ def match_keypoints_brief(keypoints1, descriptors1, keypoints2,
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matched_kp1 = keypoints1[np.int16(temp[:, 0])]
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matched_kp2 = keypoints2[np.int16(temp[:, 1])]
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matched_kp = np.zeros((2, matched_kp1.shape[0], 2))
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matched_kp[0, :, :] = matched_kp1
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matched_kp[1, :, :] = matched_kp2
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# Collecting matched keypoint pairs from their index pairs
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matched_keypoint_pairs = np.zeros((2, matched_kp1.shape[0], 2))
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matched_keypoint_pairs[0, :, :] = matched_kp1
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matched_keypoint_pairs[1, :, :] = matched_kp2
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return matched_kp
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return matched_keypoint_pairs
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