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This commit is contained in:
Ankit Agrawal
2013-07-16 20:16:23 +05:30
parent cb49e1ce7d
commit 2466df39e1
5 changed files with 72 additions and 69 deletions
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skimage/feature/__init__.py
+3 -2
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@@ -2,8 +2,9 @@ from ._daisy import daisy
from ._hog import hog
from .texture import greycomatrix, greycoprops, local_binary_pattern
from .peak import peak_local_max
from .corner import (corner_kitchen_rosenfeld, corner_harris, corner_shi_tomasi,
corner_foerstner, corner_subpix, corner_peaks)
from .corner import (corner_kitchen_rosenfeld, corner_harris,
corner_shi_tomasi, corner_foerstner, corner_subpix,
corner_peaks)
from .corner_cy import corner_moravec
from .template import match_template
from ._brief import brief, match_keypoints_brief
skimage/feature/_brief.py
+13 -12
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@@ -31,7 +31,7 @@ def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
length patch_size, pixel pairs are sampled using the `mode` parameter
to build the descriptors using intensity comparison. The value of
`sample_seed` should be the same for the images to be matched while
building the descriptors. Default is 1.
building the descriptors. Default is 1.
variance : float
Variance of the Gaussian Low Pass filter applied on the image to
alleviate noise sensitivity. Default is 2.
@@ -75,7 +75,7 @@ def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
[2, 5],
[5, 2],
[5, 5]])
>>> descriptors1, keypoints1 = brief(square1, keypoints1, patch_size = 5)
>>> descriptors1, keypoints1 = brief(square1, keypoints1, patch_size=5)
>>> keypoints1
array([[2, 2],
[2, 5],
@@ -99,7 +99,7 @@ def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
[2, 6],
[6, 2],
[6, 6]])
>>> descriptors2, keypoints2 = brief(square2, keypoints2, patch_size = 5)
>>> descriptors2, keypoints2 = brief(square2, keypoints2, patch_size=5)
>>> keypoints2
array([[2, 2],
[2, 6],
@@ -163,7 +163,8 @@ def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
else:
samples = np.random.randint(-(patch_size - 2) // 2, (patch_size // 2) + 1,
samples = np.random.randint(-(patch_size - 2) // 2,
(patch_size // 2) + 1,
(descriptor_size * 2, 2))
pos1, pos2 = np.split(samples, 2)
@@ -200,21 +201,21 @@ def match_keypoints_brief(keypoints1, descriptors1, keypoints2,
Location of Q matched keypoint pairs from two images.
"""
if keypoints1.shape[0] != descriptors1.shape[0] or \
keypoints2.shape[0] != descriptors2.shape[0]:
raise ValueError("The number of keypoints and number of described \
keypoints do not match. Make the optional parameter \
return_keypoints True to get described keypoints.")
if (keypoints1.shape[0] != descriptors1.shape[0]
or keypoints2.shape[0] != descriptors2.shape[0]):
raise ValueError("The number of keypoints and number of described "
"keypoints do not match. Make the optional parameter "
"return_keypoints True to get described keypoints.")
if descriptors1.shape[1] != descriptors2.shape[1]:
raise ValueError("Descriptor sizes for matching keypoints in both \
the images should be equal.")
raise ValueError("Descriptor sizes for matching keypoints in both "
"the images should be equal.")
# Get hamming distances between keeypoints1 and keypoints2
distance = pairwise_hamming_distance(descriptors1, descriptors2)
temp = distance > threshold
row_check = np.any(~temp, axis = 1)
row_check = np.any(~temp, axis=1)
matched_keypoints2 = keypoints2[np.argmin(distance, axis=1)]
matched_keypoint_pairs = np.zeros((np.sum(row_check), 2, 2), dtype=np.intp)
matched_keypoint_pairs[:, 0, :] = keypoints1[row_check]
skimage/feature/tests/test_brief.py
+34 -34
View File
@@ -15,48 +15,49 @@ def test_brief_color_image_unsupported_error():
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)
"""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)
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)
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)
matched_keypoints = match_keypoints_brief(keypoints1, descriptors1,
keypoints2, descriptors2,
threshold=0.10)
assert_array_equal(matched_keypoints[:, 0,:], matched_keypoints[:, 1,:] +
[20, 15])
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)
"""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)
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)
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],
matched_keypoints = match_keypoints_brief(keypoints1, descriptors1,
keypoints2, descriptors2,
threshold=0.07)
expected = np.array([[[263, 272],
[234, 298]],
[[271, 120],
@@ -74,5 +75,4 @@ def test_match_keypoints_brief_lena_rotation():
[[454, 176],
[435, 221]]])
assert_array_equal(matched_keypoints, expected)
assert_array_equal(matched_keypoints, expected)
skimage/feature/tests/test_util.py
+20 -20
View File
@@ -2,26 +2,26 @@ import numpy as np
from numpy.testing import assert_array_equal
from skimage.feature.util import pairwise_hamming_distance
def test_pairwise_hamming_distance_range():
"""Values of all the pairwise hamming distances should be in the range
[0, 1].
"""
a = np.random.random_sample((10, 50)) > 0.5
b = np.random.random_sample((20, 50)) > 0.5
dist = pairwise_hamming_distance(a, b)
assert np.all((0 <= dist) & (dist <= 1))
"""Values of all the pairwise hamming distances should be in the range
[0, 1]."""
a = np.random.random_sample((10, 50)) > 0.5
b = np.random.random_sample((20, 50)) > 0.5
dist = pairwise_hamming_distance(a, b)
assert np.all((0 <= dist) & (dist <= 1))
def test_pairwise_hamming_distance_value():
"""The result of pairwise_hamming_distance of two fixed sets of boolean
vectors should be same as expected.
"""
np.random.seed(10)
a = np.random.random_sample((4, 100)) > 0.5
np.random.seed(20)
b = np.random.random_sample((3, 100)) > 0.5
result = pairwise_hamming_distance(a, b)
expected = np.array([[ 0.5 , 0.49, 0.44],
[ 0.44, 0.53, 0.52],
[ 0.4 , 0.55, 0.5 ],
[ 0.47, 0.48, 0.57]])
assert_array_equal(result, expected)
"""The result of pairwise_hamming_distance of two fixed sets of boolean
vectors should be same as expected."""
np.random.seed(10)
a = np.random.random_sample((4, 100)) > 0.5
np.random.seed(20)
b = np.random.random_sample((3, 100)) > 0.5
result = pairwise_hamming_distance(a, b)
expected = np.array([[0.5 , 0.49, 0.44],
[0.44, 0.53, 0.52],
[0.4 , 0.55, 0.5 ],
[0.47, 0.48, 0.57]])
assert_array_equal(result, expected)
skimage/feature/util.py
+2 -1
View File
@@ -1,4 +1,5 @@
def _remove_border_keypoints(image, keypoints, dist):
"""Removes keypoints that are within dist pixels from the image border."""
width = image.shape[0]
@@ -31,5 +32,5 @@ def pairwise_hamming_distance(array1, array2):
vector in array2.
"""
distance = (array1[:,None] != array2[None]).mean(axis=2)
distance = (array1[:, None] != array2[None]).mean(axis=2)
return distance