wassname/scikit-image
1
0
Fork 0
mirror of https://github.com/wassname/scikit-image.git synced 2026-07-01 09:27:54 +08:00
Code Issues Packages Projects Releases Wiki Activity

Get rid of trailing underscores

Browse Source
This commit is contained in:
Johannes Schönberger
2014-01-22 19:04:57 -05:00
parent 279d892789
commit c3cf88edd3
8 changed files with 122 additions and 133 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/examples/plot_brief.py
+6 -6
View File
@@ -34,16 +34,16 @@ keypoints3 = corner_peaks(corner_harris(img3), min_distance=5)
extractor = BRIEF()
extractor.extract(img1, keypoints1)
keypoints1 = keypoints1[extractor.mask_]
descriptors1 = extractor.descriptors_
keypoints1 = keypoints1[extractor.mask]
descriptors1 = extractor.descriptors
extractor.extract(img2, keypoints2)
keypoints2 = keypoints2[extractor.mask_]
descriptors2 = extractor.descriptors_
keypoints2 = keypoints2[extractor.mask]
descriptors2 = extractor.descriptors
extractor.extract(img3, keypoints3)
keypoints3 = keypoints3[extractor.mask_]
descriptors3 = extractor.descriptors_
keypoints3 = keypoints3[extractor.mask]
descriptors3 = extractor.descriptors
matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True)
matches13 = match_descriptors(descriptors1, descriptors3, cross_check=True)
doc/examples/plot_censure.py
+4 -4
View File
@@ -30,14 +30,14 @@ detector.detect(img1)
ax[0].imshow(img1)
ax[0].axis('off')
ax[0].scatter(detector.keypoints_[:, 1], detector.keypoints_[:, 0],
2 ** detector.scales_, facecolors='none', edgecolors='r')
ax[0].scatter(detector.keypoints[:, 1], detector.keypoints[:, 0],
2 ** detector.scales, facecolors='none', edgecolors='r')
detector.detect(img2)
ax[1].imshow(img2)
ax[1].axis('off')
ax[1].scatter(detector.keypoints_[:, 1], detector.keypoints_[:, 0],
2 ** detector.scales_, facecolors='none', edgecolors='r')
ax[1].scatter(detector.keypoints[:, 1], detector.keypoints[:, 0],
2 ** detector.scales, facecolors='none', edgecolors='r')
plt.show()
skimage/feature/brief.py
+21 -18
View File
@@ -44,6 +44,18 @@ class BRIEF(DescriptorExtractor):
to alleviate noise sensitivity, which is strongly recommended to obtain
discriminative and good descriptors.
Attributes
----------
descriptors : (Q, `descriptor_size`) array of dtype bool
2D ndarray of binary descriptors of size `descriptor_size` for Q
keypoints after filtering out border keypoints with value at an
index ``(i, j)`` either being ``True`` or ``False`` representing
the outcome of the intensity comparison for i-th keypoint on j-th
decision pixel-pair. It is ``Q == np.sum(mask)``.
mask : (N, ) array of dtype bool
Mask indicating whether a keypoint has been filtered out
(``False``) or is described in the `descriptors` array (``True``).
Examples
--------
>>> from skimage.feature import (corner_harris, corner_peaks, BRIEF,
@@ -76,9 +88,9 @@ class BRIEF(DescriptorExtractor):
>>> keypoints2 = corner_peaks(corner_harris(square2), min_distance=1)
>>> extractor = BRIEF(patch_size=5)
>>> extractor.extract(square1, keypoints1)
>>> descriptors1 = extractor.descriptors_
>>> descriptors1 = extractor.descriptors
>>> extractor.extract(square2, keypoints2)
>>> descriptors2 = extractor.descriptors_
>>> descriptors2 = extractor.descriptors
>>> matches = match_descriptors(descriptors1, descriptors2)
>>> matches
array([[0, 0],
@@ -111,6 +123,9 @@ class BRIEF(DescriptorExtractor):
self.sigma = sigma
self.sample_seed = sample_seed
self.descriptors = None
self.mask = None
def extract(self, image, keypoints):
"""Extract BRIEF binary descriptors for given keypoints in image.
@@ -121,18 +136,6 @@ class BRIEF(DescriptorExtractor):
keypoints : (N, 2) array
Keypoint coordinates as ``(row, col)``.
Attributes
----------
descriptors_ : (Q, `descriptor_size`) array of dtype bool
2D ndarray of binary descriptors of size `descriptor_size` for Q
keypoints after filtering out border keypoints with value at an
index ``(i, j)`` either being ``True`` or ``False`` representing
the outcome of the intensity comparison for i-th keypoint on j-th
decision pixel-pair. It is ``Q == np.sum(mask)``.
mask_ : (N, ) array of dtype bool
Mask indicating whether a keypoint has been filtered out
(``False``) or is described in the `descriptors` array (``True``).
"""
np.random.seed(self.sample_seed)
@@ -165,14 +168,14 @@ class BRIEF(DescriptorExtractor):
# Removing keypoints that are within (patch_size / 2) distance from the
# image border
self.mask_ = _mask_border_keypoints(image.shape, keypoints,
self.mask = _mask_border_keypoints(image.shape, keypoints,
patch_size // 2)
keypoints = np.array(keypoints[self.mask_, :], dtype=np.intp,
keypoints = np.array(keypoints[self.mask, :], dtype=np.intp,
order='C', copy=False)
self.descriptors_ = np.zeros((keypoints.shape[0], desc_size),
self.descriptors = np.zeros((keypoints.shape[0], desc_size),
dtype=bool, order='C')
_brief_loop(image, self.descriptors_.view(np.uint8), keypoints,
_brief_loop(image, self.descriptors.view(np.uint8), keypoints,
pos1, pos2)
skimage/feature/censure.py
+16 -13
View File
@@ -139,6 +139,13 @@ class CenSurE(FeatureDetector):
Threshold for rejecting interest points which have ratio of principal
curvatures greater than this value.
Attributes
----------
keypoints : (N, 2) array
Keypoint coordinates as ``(row, col)``.
scales : (N, ) array
Corresponding scales.
References
----------
.. [1] Motilal Agrawal, Kurt Konolige and Morten Rufus Blas
@@ -159,7 +166,7 @@ class CenSurE(FeatureDetector):
>>> img = rgb2gray(lena()[100:300, 100:300])
>>> censure = CenSurE()
>>> censure.detect(img)
>>> censure.keypoints_
>>> censure.keypoints
array([[ 71, 148],
[ 77, 186],
[ 78, 189],
@@ -175,7 +182,7 @@ class CenSurE(FeatureDetector):
[171, 29],
[179, 20],
[194, 65]])
>>> censure.scales_
>>> censure.scales
array([2, 4, 2, 3, 4, 2, 2, 3, 4, 6, 3, 2, 3, 4, 2])
"""
@@ -197,6 +204,9 @@ class CenSurE(FeatureDetector):
self.non_max_threshold = non_max_threshold
self.line_threshold = line_threshold
self.keypoints = None
self.scales = None
def detect(self, image):
"""Detect CenSurE keypoints along with the corresponding scale.
@@ -205,13 +215,6 @@ class CenSurE(FeatureDetector):
image : 2D ndarray
Input image.
Attributes
----------
keypoints : (N, 2) array
Keypoint coordinates as ``(row, col)``.
scales : (N, ) array
Corresponding scales.
"""
# (1) First we generate the required scales on the input grayscale
@@ -258,8 +261,8 @@ class CenSurE(FeatureDetector):
scales = scales + self.min_scale + 1
if self.mode == 'dob':
self.keypoints_ = keypoints
self.scales_ = scales
self.keypoints = keypoints
self.scales = scales
return
cumulative_mask = np.zeros(keypoints.shape[0], dtype=np.bool)
@@ -279,5 +282,5 @@ class CenSurE(FeatureDetector):
_mask_border_keypoints(image.shape, keypoints, c)
& (scales == i))
self.keypoints_ = keypoints[cumulative_mask]
self.scales_ = scales[cumulative_mask]
self.keypoints = keypoints[cumulative_mask]
self.scales = scales[cumulative_mask]
skimage/feature/orb.py
+46 -63
View File
@@ -54,6 +54,23 @@ class ORB(FeatureDetector, DescriptorExtractor):
Maximum number of scales from the bottom of the image pyramid to
extract the features from.
Attributes
----------
keypoints : (N, 2) array
Keypoint coordinates as ``(row, col)``.
scales : (N, ) array
Corresponding scales.
orientations : (N, ) array
Corresponding orientations in radians.
responses : (N, ) array
Corresponding Harris corner responses.
descriptors : (Q, `descriptor_size`) array of dtype bool
2D array of binary descriptors of size `descriptor_size` for Q
keypoints after filtering out border keypoints with value at an
index ``(i, j)`` either being ``True`` or ``False`` representing
the outcome of the intensity comparison for i-th keypoint on j-th
decision pixel-pair. It is ``Q == np.sum(mask)``.
References
----------
.. [1] Ethan Rublee, Vincent Rabaud, Kurt Konolige and Gary Bradski
@@ -73,21 +90,21 @@ class ORB(FeatureDetector, DescriptorExtractor):
>>> detector_extractor2 = ORB(n_keypoints=5)
>>> detector_extractor1.detect_and_extract(img1)
>>> detector_extractor2.detect_and_extract(img2)
>>> matches = match_descriptors(detector_extractor1.descriptors_,
... detector_extractor2.descriptors_)
>>> matches = match_descriptors(detector_extractor1.descriptors,
... detector_extractor2.descriptors)
>>> matches
array([[0, 0],
[1, 1],
[2, 2],
[3, 3],
[4, 4]])
>>> detector_extractor1.keypoints_[matches[:, 0]]
>>> detector_extractor1.keypoints[matches[:, 0]]
array([[ 42., 40.],
[ 47., 58.],
[ 44., 40.],
[ 59., 42.],
[ 45., 44.]])
>>> detector_extractor2.keypoints_[matches[:, 1]]
>>> detector_extractor2.keypoints[matches[:, 1]]
array([[ 55., 53.],
[ 60., 71.],
[ 57., 53.],
@@ -106,6 +123,12 @@ class ORB(FeatureDetector, DescriptorExtractor):
self.fast_threshold = fast_threshold
self.harris_k = harris_k
self.keypoints = None
self.scales = None
self.responses = None
self.orientations = None
self.descriptors = None
def _build_pyramid(self, image):
image = _prepare_grayscale_input_2D(image)
return list(pyramid_gaussian(image, self.n_scales - 1, self.downscale))
@@ -142,17 +165,6 @@ class ORB(FeatureDetector, DescriptorExtractor):
image : 2D array
Input image.
Attributes
----------
keypoints_ : (N, 2) array
Keypoint coordinates as ``(row, col)``.
scales_ : (N, ) array
Corresponding scales.
orientations_ : (N, ) array
Corresponding orientations in radians.
responses_ : (N, ) array
Corresponding Harris corner responses.
"""
pyramid = self._build_pyramid(image)
@@ -181,17 +193,17 @@ class ORB(FeatureDetector, DescriptorExtractor):
responses = np.hstack(responses_list)
if keypoints.shape[0] < self.n_keypoints:
self.keypoints_ = keypoints
self.scales_ = scales
self.orientations_ = orientations
self.responses_ = responses
self.keypoints = keypoints
self.scales = scales
self.orientations = orientations
self.responses = responses
else:
# Choose best n_keypoints according to Harris corner response
best_indices = responses.argsort()[::-1][:self.n_keypoints]
self.keypoints_ = keypoints[best_indices]
self.scales_ = scales[best_indices]
self.orientations_ = orientations[best_indices]
self.responses_ = responses[best_indices]
self.keypoints = keypoints[best_indices]
self.scales = scales[best_indices]
self.orientations = orientations[best_indices]
self.responses = responses[best_indices]
def _extract_octave(self, octave_image, keypoints, orientations):
mask = _mask_border_keypoints(octave_image.shape, keypoints,
@@ -224,18 +236,6 @@ class ORB(FeatureDetector, DescriptorExtractor):
orientations : (N, ) array
Corresponding orientations in radians.
Attributes
----------
descriptors_ : (Q, `descriptor_size`) array of dtype bool
2D array of binary descriptors of size `descriptor_size` for Q
keypoints after filtering out border keypoints with value at an
index ``(i, j)`` either being ``True`` or ``False`` representing
the outcome of the intensity comparison for i-th keypoint on j-th
decision pixel-pair. It is ``Q == np.sum(mask)``.
mask_ : (N, ) array of dtype bool
Mask indicating whether a keypoint has been filtered out
(``False``) or is described in the `descriptors` array (``True``).
"""
pyramid = self._build_pyramid(image)
@@ -267,7 +267,7 @@ class ORB(FeatureDetector, DescriptorExtractor):
descriptors_list.append(descriptors)
mask_list.append(mask)
self.descriptors_ = np.vstack(descriptors_list).view(np.bool)
self.descriptors = np.vstack(descriptors_list).view(np.bool)
self.mask_ = np.hstack(mask_list)
def detect_and_extract(self, image):
@@ -281,23 +281,6 @@ class ORB(FeatureDetector, DescriptorExtractor):
image : 2D array
Input image.
Attributes
----------
keypoints_ : (N, 2) array
Keypoint coordinates as ``(row, col)``.
scales_ : (N, ) array
Corresponding scales.
orientations_ : (N, ) array
Corresponding orientations in radians.
responses_ : (N, ) array
Corresponding Harris corner responses.
descriptors_ : (Q, `descriptor_size`) array of dtype bool
2D array of binary descriptors of size `descriptor_size` for Q
keypoints after filtering out border keypoints with value at an
index ``(i, j)`` either being ``True`` or ``False`` representing
the outcome of the intensity comparison for i-th keypoint on j-th
decision pixel-pair. It is ``Q == np.sum(mask)``.
"""
pyramid = self._build_pyramid(image)
@@ -338,16 +321,16 @@ class ORB(FeatureDetector, DescriptorExtractor):
descriptors = np.vstack(descriptors_list).view(np.bool)
if keypoints.shape[0] < self.n_keypoints:
self.keypoints_ = keypoints
self.scales_ = scales
self.orientations_ = orientations
self.responses_ = responses
self.descriptors_ = descriptors
self.keypoints = keypoints
self.scales = scales
self.orientations = orientations
self.responses = responses
self.descriptors = descriptors
else:
# Choose best n_keypoints according to Harris corner response
best_indices = responses.argsort()[::-1][:self.n_keypoints]
self.keypoints_ = keypoints[best_indices]
self.scales_ = scales[best_indices]
self.orientations_ = orientations[best_indices]
self.responses_ = responses[best_indices]
self.descriptors_ = descriptors[best_indices]
self.keypoints = keypoints[best_indices]
self.scales = scales[best_indices]
self.orientations = orientations[best_indices]
self.responses = responses[best_indices]
self.descriptors = descriptors[best_indices]
skimage/feature/tests/test_brief.py
+4 -4
View File
@@ -32,7 +32,7 @@ def test_normal_mode():
[False, True, True, True, False, False, True, False],
[False, False, False, False, True, False, False, False]], dtype=bool)
assert_array_equal(extractor.descriptors_, expected)
assert_array_equal(extractor.descriptors, expected)
def test_uniform_mode():
@@ -54,7 +54,7 @@ def test_uniform_mode():
[False, False, True, True, False, False, True, True],
[ True, True, False, False, False, False, False, False]], dtype=bool)
assert_array_equal(extractor.descriptors_, expected)
assert_array_equal(extractor.descriptors, expected)
def test_unsupported_mode():
@@ -68,8 +68,8 @@ def test_border():
extractor = BRIEF(patch_size=41)
extractor.extract(img, keypoints)
assert extractor.descriptors_.shape[0] == 3
assert_array_equal(extractor.mask_, (False, True, True, True))
assert extractor.descriptors.shape[0] == 3
assert_array_equal(extractor.mask, (False, True, True, True))
if __name__ == '__main__':
skimage/feature/tests/test_censure.py
+6 -6
View File
@@ -40,8 +40,8 @@ def test_keypoints_censure_moon_image_dob():
[467, 260]])
expected_scales = np.array([3, 4, 4, 2, 2, 3, 2, 2, 2])
assert_array_equal(expected_keypoints, detector.keypoints_)
assert_array_equal(expected_scales, detector.scales_)
assert_array_equal(expected_keypoints, detector.keypoints)
assert_array_equal(expected_scales, detector.scales)
def test_keypoints_censure_moon_image_octagon():
@@ -58,8 +58,8 @@ def test_keypoints_censure_moon_image_octagon():
expected_scales = np.array([3, 4, 2, 2, 2])
assert_array_equal(expected_keypoints, detector.keypoints_)
assert_array_equal(expected_scales, detector.scales_)
assert_array_equal(expected_keypoints, detector.keypoints)
assert_array_equal(expected_scales, detector.scales)
def test_keypoints_censure_moon_image_star():
@@ -80,8 +80,8 @@ def test_keypoints_censure_moon_image_star():
expected_scales = np.array([3, 3, 6, 2, 3, 2, 3, 5, 2, 2])
assert_array_equal(expected_keypoints, detector.keypoints_)
assert_array_equal(expected_scales, detector.scales_)
assert_array_equal(expected_keypoints, detector.keypoints)
assert_array_equal(expected_scales, detector.scales)
if __name__ == '__main__':
skimage/feature/tests/test_orb.py
+19 -19
View File
@@ -29,16 +29,16 @@ def test_keypoints_orb_desired_no_of_keypoints():
0.39154173, 0.39084861, 0.39063076,
0.37602487])
assert_almost_equal(exp_rows, detector_extractor.keypoints_[:, 0])
assert_almost_equal(exp_cols, detector_extractor.keypoints_[:, 1])
assert_almost_equal(exp_scales, detector_extractor.scales_)
assert_almost_equal(exp_response, detector_extractor.responses_)
assert_almost_equal(exp_rows, detector_extractor.keypoints[:, 0])
assert_almost_equal(exp_cols, detector_extractor.keypoints[:, 1])
assert_almost_equal(exp_scales, detector_extractor.scales)
assert_almost_equal(exp_response, detector_extractor.responses)
assert_almost_equal(exp_orientations,
np.rad2deg(detector_extractor.orientations_), 5)
np.rad2deg(detector_extractor.orientations), 5)
detector_extractor.detect_and_extract(img)
assert_almost_equal(exp_rows, detector_extractor.keypoints_[:, 0])
assert_almost_equal(exp_cols, detector_extractor.keypoints_[:, 1])
assert_almost_equal(exp_rows, detector_extractor.keypoints[:, 0])
assert_almost_equal(exp_cols, detector_extractor.keypoints[:, 1])
def test_keypoints_orb_less_than_desired_no_of_keypoints():
@@ -62,16 +62,16 @@ def test_keypoints_orb_less_than_desired_no_of_keypoints():
0.39063076, 0.96770745, 0.04935129,
0.21431068, 0.15826555, 0.42403573])
assert_almost_equal(exp_rows, detector_extractor.keypoints_[:, 0])
assert_almost_equal(exp_cols, detector_extractor.keypoints_[:, 1])
assert_almost_equal(exp_scales, detector_extractor.scales_)
assert_almost_equal(exp_response, detector_extractor.responses_)
assert_almost_equal(exp_rows, detector_extractor.keypoints[:, 0])
assert_almost_equal(exp_cols, detector_extractor.keypoints[:, 1])
assert_almost_equal(exp_scales, detector_extractor.scales)
assert_almost_equal(exp_response, detector_extractor.responses)
assert_almost_equal(exp_orientations,
np.rad2deg(detector_extractor.orientations_), 5)
np.rad2deg(detector_extractor.orientations), 5)
detector_extractor.detect_and_extract(img)
assert_almost_equal(exp_rows, detector_extractor.keypoints_[:, 0])
assert_almost_equal(exp_cols, detector_extractor.keypoints_[:, 1])
assert_almost_equal(exp_rows, detector_extractor.keypoints[:, 0])
assert_almost_equal(exp_cols, detector_extractor.keypoints[:, 1])
def test_descriptor_orb():
@@ -99,15 +99,15 @@ def test_descriptor_orb():
[ True, False, True, False, False, False, True, True, False, False]], dtype=bool)
detector_extractor.detect(img)
detector_extractor.extract(img, detector_extractor.keypoints_,
detector_extractor.scales_,
detector_extractor.orientations_)
detector_extractor.extract(img, detector_extractor.keypoints,
detector_extractor.scales,
detector_extractor.orientations)
assert_array_equal(exp_descriptors,
detector_extractor.descriptors_[100:120, 10:20])
detector_extractor.descriptors[100:120, 10:20])
detector_extractor.detect_and_extract(img)
assert_array_equal(exp_descriptors,
detector_extractor.descriptors_[100:120, 10:20])
detector_extractor.descriptors[100:120, 10:20])
if __name__ == '__main__':