mirror of
https://github.com/wassname/scikit-image.git
synced 2026-09-09 11:33:41 +08:00
Get rid of trailing underscores
This commit is contained in:
Johannes Schönberger
+46
-63
@@ -54,6 +54,23 @@ class ORB(FeatureDetector, DescriptorExtractor):
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Maximum number of scales from the bottom of the image pyramid to
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extract the features from.
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Attributes
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----------
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keypoints : (N, 2) array
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Keypoint coordinates as ``(row, col)``.
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scales : (N, ) array
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Corresponding scales.
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orientations : (N, ) array
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Corresponding orientations in radians.
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responses : (N, ) array
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Corresponding Harris corner responses.
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descriptors : (Q, `descriptor_size`) array of dtype bool
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2D array of binary descriptors of size `descriptor_size` for Q
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keypoints after filtering out border keypoints with value at an
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index ``(i, j)`` either being ``True`` or ``False`` representing
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the outcome of the intensity comparison for i-th keypoint on j-th
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decision pixel-pair. It is ``Q == np.sum(mask)``.
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References
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----------
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.. [1] Ethan Rublee, Vincent Rabaud, Kurt Konolige and Gary Bradski
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@@ -73,21 +90,21 @@ class ORB(FeatureDetector, DescriptorExtractor):
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>>> detector_extractor2 = ORB(n_keypoints=5)
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>>> detector_extractor1.detect_and_extract(img1)
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>>> detector_extractor2.detect_and_extract(img2)
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>>> matches = match_descriptors(detector_extractor1.descriptors_,
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... detector_extractor2.descriptors_)
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>>> matches = match_descriptors(detector_extractor1.descriptors,
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... detector_extractor2.descriptors)
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>>> matches
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array([[0, 0],
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[1, 1],
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[2, 2],
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[3, 3],
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[4, 4]])
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>>> detector_extractor1.keypoints_[matches[:, 0]]
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>>> detector_extractor1.keypoints[matches[:, 0]]
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array([[ 42., 40.],
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[ 47., 58.],
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[ 44., 40.],
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[ 59., 42.],
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[ 45., 44.]])
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>>> detector_extractor2.keypoints_[matches[:, 1]]
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>>> detector_extractor2.keypoints[matches[:, 1]]
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array([[ 55., 53.],
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[ 60., 71.],
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[ 57., 53.],
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@@ -106,6 +123,12 @@ class ORB(FeatureDetector, DescriptorExtractor):
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self.fast_threshold = fast_threshold
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self.harris_k = harris_k
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self.keypoints = None
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self.scales = None
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self.responses = None
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self.orientations = None
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self.descriptors = None
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def _build_pyramid(self, image):
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image = _prepare_grayscale_input_2D(image)
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return list(pyramid_gaussian(image, self.n_scales - 1, self.downscale))
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@@ -142,17 +165,6 @@ class ORB(FeatureDetector, DescriptorExtractor):
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image : 2D array
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Input image.
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Attributes
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----------
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keypoints_ : (N, 2) array
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Keypoint coordinates as ``(row, col)``.
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scales_ : (N, ) array
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Corresponding scales.
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orientations_ : (N, ) array
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Corresponding orientations in radians.
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responses_ : (N, ) array
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Corresponding Harris corner responses.
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"""
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pyramid = self._build_pyramid(image)
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@@ -181,17 +193,17 @@ class ORB(FeatureDetector, DescriptorExtractor):
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responses = np.hstack(responses_list)
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if keypoints.shape[0] < self.n_keypoints:
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self.keypoints_ = keypoints
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self.scales_ = scales
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self.orientations_ = orientations
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self.responses_ = responses
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self.keypoints = keypoints
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self.scales = scales
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self.orientations = orientations
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self.responses = responses
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else:
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# Choose best n_keypoints according to Harris corner response
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best_indices = responses.argsort()[::-1][:self.n_keypoints]
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self.keypoints_ = keypoints[best_indices]
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self.scales_ = scales[best_indices]
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self.orientations_ = orientations[best_indices]
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self.responses_ = responses[best_indices]
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self.keypoints = keypoints[best_indices]
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self.scales = scales[best_indices]
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self.orientations = orientations[best_indices]
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self.responses = responses[best_indices]
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def _extract_octave(self, octave_image, keypoints, orientations):
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mask = _mask_border_keypoints(octave_image.shape, keypoints,
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@@ -224,18 +236,6 @@ class ORB(FeatureDetector, DescriptorExtractor):
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orientations : (N, ) array
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Corresponding orientations in radians.
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Attributes
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----------
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descriptors_ : (Q, `descriptor_size`) array of dtype bool
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2D array of binary descriptors of size `descriptor_size` for Q
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keypoints after filtering out border keypoints with value at an
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index ``(i, j)`` either being ``True`` or ``False`` representing
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the outcome of the intensity comparison for i-th keypoint on j-th
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decision pixel-pair. It is ``Q == np.sum(mask)``.
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mask_ : (N, ) array of dtype bool
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Mask indicating whether a keypoint has been filtered out
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(``False``) or is described in the `descriptors` array (``True``).
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"""
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pyramid = self._build_pyramid(image)
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@@ -267,7 +267,7 @@ class ORB(FeatureDetector, DescriptorExtractor):
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descriptors_list.append(descriptors)
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mask_list.append(mask)
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self.descriptors_ = np.vstack(descriptors_list).view(np.bool)
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self.descriptors = np.vstack(descriptors_list).view(np.bool)
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self.mask_ = np.hstack(mask_list)
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def detect_and_extract(self, image):
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@@ -281,23 +281,6 @@ class ORB(FeatureDetector, DescriptorExtractor):
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image : 2D array
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Input image.
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Attributes
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----------
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keypoints_ : (N, 2) array
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Keypoint coordinates as ``(row, col)``.
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scales_ : (N, ) array
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Corresponding scales.
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orientations_ : (N, ) array
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Corresponding orientations in radians.
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responses_ : (N, ) array
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Corresponding Harris corner responses.
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descriptors_ : (Q, `descriptor_size`) array of dtype bool
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2D array of binary descriptors of size `descriptor_size` for Q
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keypoints after filtering out border keypoints with value at an
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index ``(i, j)`` either being ``True`` or ``False`` representing
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the outcome of the intensity comparison for i-th keypoint on j-th
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decision pixel-pair. It is ``Q == np.sum(mask)``.
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"""
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pyramid = self._build_pyramid(image)
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@@ -338,16 +321,16 @@ class ORB(FeatureDetector, DescriptorExtractor):
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descriptors = np.vstack(descriptors_list).view(np.bool)
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if keypoints.shape[0] < self.n_keypoints:
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self.keypoints_ = keypoints
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self.scales_ = scales
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self.orientations_ = orientations
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self.responses_ = responses
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self.descriptors_ = descriptors
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self.keypoints = keypoints
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self.scales = scales
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self.orientations = orientations
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self.responses = responses
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self.descriptors = descriptors
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else:
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# Choose best n_keypoints according to Harris corner response
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best_indices = responses.argsort()[::-1][:self.n_keypoints]
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self.keypoints_ = keypoints[best_indices]
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self.scales_ = scales[best_indices]
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self.orientations_ = orientations[best_indices]
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self.responses_ = responses[best_indices]
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self.descriptors_ = descriptors[best_indices]
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self.keypoints = keypoints[best_indices]
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self.scales = scales[best_indices]
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self.orientations = orientations[best_indices]
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self.responses = responses[best_indices]
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self.descriptors = descriptors[best_indices]
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