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Made recarray changes in the brief docstring

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This commit is contained in:
Ankit Agrawal
2013-11-14 22:48:22 +05:30
committed by Johannes Schönberger
parent 0d79b3963e
commit 172c5ae1ec
1 changed files with 27 additions and 34 deletions
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skimage/feature/_brief.py
+27 -34
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@@ -46,9 +46,9 @@ def descriptor_brief(image, keypoints, descriptor_size=256, mode='normal',
keypoints after filtering out border keypoints with value at an index
(i, j) either being True or False representing the outcome
of Intensity comparison about ith keypoint on jth decision pixel-pair.
keypoints : (Q, 2) ndarray
Location i.e. (row, col) of keypoints after removing out those that
are near border.
keypoints : record array with Q rows
Record array with fields row, col, octave, orientation, response.
Octave, orientation and response can be None.
References
----------
@@ -58,9 +58,10 @@ def descriptor_brief(image, keypoints, descriptor_size=256, mode='normal',
Examples
--------
>> import numpy as np
>> from skimage.feature.corner import corner_peaks, corner_harris
>> from skimage.feature import pairwise_hamming_distance, descriptor_brief, match_binary_descriptors
>> from skimage.feature import (pairwise_hamming_distance, descriptor_brief,
... match_binary_descriptors,
... create_keypoint_recarray)
>> square1 = np.zeros([8, 8], dtype=np.int32)
>> square1[2:6, 2:6] = 1
>> square1
@@ -73,17 +74,13 @@ def descriptor_brief(image, keypoints, descriptor_size=256, mode='normal',
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)
>> keypoints1 = corner_peaks(corner_harris(square1), min_distance=1)
>> keypoints1
array([[2, 2],
[2, 5],
[5, 2],
[5, 5]])
>> keypoints1 = create_keypoint_recarray(keypoints1[:, 0], keypoints1[:, 1])
>> descriptors1, keypoints1 = descriptor_brief(square1, keypoints1, patch_size=5)
>> keypoints1
array([[2, 2],
[2, 5],
[5, 2],
[5, 5]])
rec.array([(2.0, 2.0, nan, nan, nan), (2.0, 5.0, nan, nan, nan),
(5.0, 2.0, nan, nan, nan), (5.0, 5.0, nan, nan, nan)],
dtype=[('row', '<f8'), ('col', '<f8'), ('octave', '<f8'),
('orientation', '<f8'), ('response', '<f8')])
>> square2 = np.zeros([9, 9], dtype=np.int32)
>> square2[2:7, 2:7] = 1
>> square2
@@ -97,38 +94,34 @@ def descriptor_brief(image, keypoints, descriptor_size=256, mode='normal',
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)
>> keypoints2 = corner_peaks(corner_harris(square2), min_distance=1)
>> keypoints2 = create_keypoint_recarray(keypoints2[:, 0], keypoints2[:, 1])
>> keypoints2
array([[2, 2],
[2, 6],
[6, 2],
[6, 6]])
rec.array([(2.0, 2.0, nan, nan, nan), (2.0, 6.0, nan, nan, nan),
(6.0, 2.0, nan, nan, nan), (6.0, 6.0, nan, nan, nan)],
dtype=[('row', '<f8'), ('col', '<f8'), ('octave', '<f8'),
('orientation', '<f8'), ('response', '<f8')])
>> descriptors2, keypoints2 = descriptor_brief(square2, keypoints2, patch_size=5)
>> keypoints2
array([[2, 2],
[2, 6],
[6, 2],
[6, 6]])
>> pairwise_hamming_distance(descriptors1, descriptors2)
array([[ 0.03125 , 0.3203125, 0.3671875, 0.6171875],
[ 0.3203125, 0.03125 , 0.640625 , 0.375 ],
[ 0.375 , 0.6328125, 0.0390625, 0.328125 ],
[ 0.625 , 0.3671875, 0.34375 , 0.0234375]])
>> matched_kpts, mask1, mask2 = match_binary_descriptors(keypoints1,
descriptors1,
keypoints2,
descriptors2)
... descriptors1,
... keypoints2,
... descriptors2)
>> matched_kpts
array([[[ 2, 2],
[ 2, 2]],
array([[[2, 2],
[2, 2]],
[[ 2, 5],
[ 2, 6]],
[[2, 5],
[2, 6]],
[[ 5, 2],
[ 6, 2]],
[[5, 2],
[6, 2]],
[[ 5, 5],
[ 6, 6]]])
[[5, 5],
[6, 6]]])
"""