mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-16 11:21:25 +08:00
Made recarray changes in docstrings and tests
This commit is contained in:
Ankit Agrawal
committed by
Johannes Schönberger
Johannes Schönberger
parent
5611da485f
commit
38bdd3e523
+43
-53
@@ -57,12 +57,8 @@ def keypoints_orb(image, n_keypoints=500, fast_n=9, fast_threshold=0.08,
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Returns
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-------
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keypoints : (N, 2) ndarray
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The oFAST keypoints.
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orientations : (N,) ndarray
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The orientations of the N extracted keypoints.
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scales : (N,) ndarray
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The scales of the N extracted keypoints.
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keypoints : record array
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Record array with fields row, col, octave, orientation, response.
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References
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----------
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@@ -75,25 +71,20 @@ def keypoints_orb(image, n_keypoints=500, fast_n=9, fast_threshold=0.08,
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>>> from skimage.feature import keypoints_orb, descriptor_orb
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>>> square = np.zeros((50, 50))
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>>> square[20:30, 20:30] = 1
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>>> keypoints, orientations, scales = keypoints_orb(square, n_keypoints=8, n_scales=2)
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>>> keypoints = keypoints_orb(square, n_keypoints=8, n_scales=2)
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>>> keypoints.shape
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(8, 2)
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>>> keypoints
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array([[29, 29],
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[29, 20],
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[20, 29],
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[20, 20],
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[15, 15],
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[15, 20],
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[20, 15],
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[20, 20]])
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>>> orientations
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array([-2.35619449, -0.78539816, 2.35619449, 0.78539816, 0.78539816,
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2.35619449, -0.78539816, -2.35619449])
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>>> np.rad2deg(orientations)
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array([-135., -45., 135., 45., 45., 135., -45., -135.])
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>>> scales
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array([0, 0, 0, 0, 1, 1, 1, 1])
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(8,)
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>>> keypoints.row
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array([ 29. , 29. , 20. , 20. , 20.4, 20.4, 28.8, 28.8])
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>>> keypoints.col
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array([ 29. , 20. , 29. , 20. , 28.8, 20.4, 28.8, 20.4])
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>>> keypoints.octave
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array([ 1. , 1. , 1. , 1. , 1.2, 1.2, 1.2, 1.2])
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>>> np.rad2deg(keypoints.orientation)
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array([-135., -45., 135., 45., 135., 45., -135., -45.])
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>>> keypoints.response
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array([ 21.4776577 , 21.4776577 , 21.4776577 , 21.4776577 ,
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14.03845308, 14.03845308, 14.03845308, 14.03845308])
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"""
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@@ -121,30 +112,31 @@ def keypoints_orb(image, n_keypoints=500, fast_n=9, fast_threshold=0.08,
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harris_response_list.append(harris_response[corners[:, 0],
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corners[:, 1]])
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keypoints = np.vstack(keypoints_list)
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keypoints_array = np.vstack(keypoints_list)
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orientations = np.hstack(orientations_list)
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octaves = downscale ** np.hstack(scales_list)
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harris_measure = np.hstack(harris_response_list)
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kpts_recarray = _create_keypoint_recarray(keypoints[:, 0], keypoints[:, 1],
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octaves, orientations,
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harris_measure)
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keypoints = _create_keypoint_recarray(keypoints_array[:, 0],
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keypoints_array[:, 1],
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octaves, orientations,
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harris_measure)
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if kpts_recarray.shape[0] < n_keypoints:
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return kpts_recarray
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if keypoints.shape[0] < n_keypoints:
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return keypoints
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else:
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best_indices = harris_measure.argsort()[::-1][:n_keypoints]
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return kpts_recarray[best_indices]
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return keypoints[best_indices]
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def descriptor_orb(image, kpts_recarray, downscale=1.2, n_scales=8):
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def descriptor_orb(image, keypoints, downscale=1.2, n_scales=8):
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"""Compute rBRIEF descriptors of input keypoints.
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Parameters
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----------
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image : 2D ndarray
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Input grayscale image.
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kpts_recarray : (N, 2) ndarray
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Array of N input keypoint locations in the format (row, col).
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keypoints : record array
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Record array with fields row, col, octave, orientation, response.
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downscale : float
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Downscale factor for the image pyramid. Should be the same as that
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used in ``keypoints_orb``.
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@@ -159,8 +151,9 @@ def descriptor_orb(image, kpts_recarray, downscale=1.2, n_scales=8):
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filtering out those near the image border. Size of each descriptor
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is 32 bytes or 256 bits.
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filtered_keypoints : (P, 2) ndarray
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Location i.e. (row, col) of P keypoints after removing out those that
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are near border.
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Record array with fields row, col, octave, orientation, response for
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P keypoints obtained after removing out those that are near the
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border.
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References
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----------
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@@ -174,15 +167,12 @@ def descriptor_orb(image, kpts_recarray, downscale=1.2, n_scales=8):
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>>> from skimage.feature import keypoints_orb, descriptor_orb
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>>> square = np.zeros((50, 50))
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>>> square[20:30, 20:30] = 1
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>>> keypoints, orientations, scales = keypoints_orb(square, n_keypoints=8,
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... n_scales=2)
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>>> keypoints = keypoints_orb(square, n_keypoints=8, n_scales=2)
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>>> keypoints.shape
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(8, 2)
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>>> descriptors, filtered_keypoints = descriptor_orb(square, keypoints,
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... orientations, scales,
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... n_scales=2)
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(8,)
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>>> descriptors, filtered_keypoints = descriptor_orb(square, keypoints, n_scales=2)
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>>> filtered_keypoints.shape
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(8, 2)
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(8,)
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>>> descriptors.shape
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(8, 256)
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@@ -192,31 +182,31 @@ def descriptor_orb(image, kpts_recarray, downscale=1.2, n_scales=8):
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pyramid = list(pyramid_gaussian(image, n_scales - 1, downscale))
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descriptors_list = []
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kpts_recarray_list = []
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keypoints_list = []
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for scale in range(n_scales):
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curr_image = np.ascontiguousarray(pyramid[scale])
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curr_scale_mask = (np.log(kpts_recarray.octave) /
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curr_scale_mask = (np.log(keypoints.octave) /
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np.log(downscale)).astype(np.intp) == scale
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if np.sum(curr_scale_mask) > 0:
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curr_kpts_recarray = kpts_recarray[curr_scale_mask]
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curr_scale_kpts = np.squeeze(np.dstack((curr_kpts_recarray.row / curr_kpts_recarray.octave,
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curr_kpts_recarray.col / curr_kpts_recarray.octave)))
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curr_keypoints = keypoints[curr_scale_mask]
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curr_scale_kpts = np.squeeze(np.dstack((curr_keypoints.row / curr_keypoints.octave,
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curr_keypoints.col / curr_keypoints.octave)))
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border_mask = _mask_border_keypoints(curr_image,
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curr_scale_kpts,
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dist=16)
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curr_kpts_recarray = curr_kpts_recarray[border_mask]
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curr_keypoints = curr_keypoints[border_mask]
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curr_scale_kpts = np.ascontiguousarray(curr_scale_kpts[border_mask].astype(np.intp))
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curr_scale_orientation = np.ascontiguousarray(curr_kpts_recarray.orientation)
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curr_scale_orientation = np.ascontiguousarray(curr_keypoints.orientation)
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curr_scale_descriptors = _orb_loop(curr_image, curr_scale_kpts,
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curr_scale_orientation)
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descriptors_list.append(curr_scale_descriptors)
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kpts_recarray_list.append(curr_kpts_recarray)
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keypoints_list.append(curr_keypoints)
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descriptors = np.vstack(descriptors_list).view(np.bool)
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filtered_kpts_recarray = np.hstack(kpts_recarray_list)
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return descriptors, filtered_kpts_recarray
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filtered_keypoints = np.hstack(keypoints_list)
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return descriptors, filtered_keypoints.view(np.recarray)
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@@ -7,73 +7,66 @@ from skimage.color import rgb2gray
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def test_keypoints_orb_desired_no_of_keypoints():
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img = rgb2gray(lena())
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keypoints, orientations, scales = keypoints_orb(img, n_keypoints=10,
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fast_n=12,
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fast_threshold=0.20)
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exp_keypoints = np.array([[435, 180],
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[436, 180],
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[376, 156],
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[455, 176],
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[435, 180],
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[269, 111],
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[376, 156],
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[311, 173],
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[413, 70],
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[311, 173]])
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exp_scales = np.array([0, 1, 0, 0, 2, 0, 1, 1, 0, 3])
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keypoints = keypoints_orb(img, n_keypoints=10, fast_n=12,
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fast_threshold=0.20)
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exp_row = np.array([ 435. , 435.6 , 376. , 455. , 434.88, 269. ,
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375.6 , 310.8 , 413. , 311.04])
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exp_col = np.array([ 180. , 180. , 156. , 176. , 180. , 111. ,
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156. , 172.8, 70. , 172.8])
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exp_octaves = np.array([ 1. , 1.2 , 1. , 1. , 1.44 , 1. ,
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1.2 , 1.2 , 1. , 1.728])
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exp_orientations = np.array([-175.64733392, -167.94842949, -148.98350192,
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-142.03599837, -176.08535837, -53.08162354,
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-150.89208271, 97.7693776 , -173.4479964 ,
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38.66312042])
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assert_array_equal(exp_keypoints, keypoints)
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assert_array_equal(exp_scales, scales)
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assert_almost_equal(exp_orientations, np.rad2deg(orientations))
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exp_response = np.array([ 0.96770745, 0.81027306, 0.72376257,
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0.5626413 , 0.5097993 , 0.44351774,
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0.39154173, 0.39084861, 0.39063076,
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0.37602487])
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assert_almost_equal(exp_row, keypoints.row)
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assert_almost_equal(exp_col, keypoints.col)
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assert_almost_equal(exp_octaves, keypoints.octave)
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assert_almost_equal(exp_response, keypoints.response)
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assert_almost_equal(exp_orientations, np.rad2deg(keypoints.orientation))
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def test_keypoints_orb_less_than_desired_no_of_keypoints():
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img = rgb2gray(lena())
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keypoints, orientations, scales = keypoints_orb(img, n_keypoints=15,
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keypoints = keypoints_orb(img, n_keypoints=15,
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fast_n=12,
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fast_threshold=0.33,
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downscale=2, n_scales=2)
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exp_keypoints = np.array([[ 67, 157],
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[247, 146],
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[269, 111],
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[413, 70],
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[435, 180],
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[230, 136],
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[264, 336],
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[330, 148],
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[372, 156]])
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exp_scales = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1])
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exp_row = np.array([ 67., 247., 269., 413., 435., 230., 264.,
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330., 372.])
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exp_col = np.array([ 157., 146., 111., 70., 180., 136., 336.,
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148., 156.])
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exp_octaves = np.array([ 1., 1., 1., 1., 1., 2., 2., 2., 2.])
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exp_orientations = np.array([-105.76503839, -96.28973044, -53.08162354,
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-173.4479964 , -175.64733392, -106.07927215,
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-163.40016243, 75.80865813, -154.73195911])
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assert_array_equal(exp_keypoints, keypoints)
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assert_array_equal(exp_scales, scales)
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assert_almost_equal(exp_orientations, np.rad2deg(orientations))
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exp_response = np.array([ 0.13197835, 0.24931321, 0.44351774,
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0.39063076, 0.96770745, 0.04935129,
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0.21431068, 0.15826555, 0.42403573])
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assert_almost_equal(exp_row, keypoints.row)
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assert_almost_equal(exp_col, keypoints.col)
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assert_almost_equal(exp_octaves, keypoints.octave)
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assert_almost_equal(exp_response, keypoints.response)
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assert_almost_equal(exp_orientations, np.rad2deg(keypoints.orientation))
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def test_descriptor_orb():
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img = rgb2gray(lena())
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keypoints, orientations, scales = keypoints_orb(img, n_keypoints=10,
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fast_n=12,
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fast_threshold=0.20)
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descriptors, filtered_keypoints = descriptor_orb(img, keypoints, orientations, scales)
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exp_filtered_keypoints = np.array([[435, 180],
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[376, 156],
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[455, 176],
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[269, 111],
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[413, 70],
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[436, 180],
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[376, 156],
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[311, 173],
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[435, 180],
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[311, 173]])
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keypoints = keypoints_orb(img, n_keypoints=10, fast_n=12,
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fast_threshold=0.20)
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descriptors, filtered_keypoints = descriptor_orb(img, keypoints)
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descriptors_120_129 = np.array([[ True, False, False, True, False, False, False, False, False, False],
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[ True, True, False, False, True, False, False, True, False, True],
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[ True, True, False, False, True, False, False, True, False, True],
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[False, True, True, False, True, False, True, True, True, True],
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[False, False, False, True, True, False, True, False, True, False],
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[False, True, True, True, True, False, True, True, True, False],
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@@ -81,10 +74,10 @@ def test_descriptor_orb():
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[ True, False, True, False, True, False, True, True, False, True],
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[ True, True, True, True, True, True, False, True, True, True],
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[ True, True, True, False, True, False, True, True, True, False],
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[ True, True, False, True, True, True, False, True, False, True]],
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[ True, False, False, False, False, False, True, True, True, False]],
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dtype=bool)
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assert_array_equal(exp_filtered_keypoints, filtered_keypoints)
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assert_array_equal(descriptors_120_129, descriptors[:, 120:130])
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