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Adding tests for orb; fixing some bugs

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This commit is contained in:
Ankit Agrawal
2013-10-02 20:15:22 +05:30
committed by Johannes Schönberger
parent 58515ad14e
commit 142ad4e774
3 changed files with 111 additions and 15 deletions
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skimage/feature/orb.py
+16 -14
View File
@@ -18,7 +18,7 @@ for i in range(-15, 16):
def keypoints_orb(image, n_keypoints=500, fast_n=9, fast_threshold=0.08,
harris_k=0.04, downscale=1.2, n_scales=8):
harris_k=0.04, downscale=1.2, n_scales=8):
"""Detect Oriented Fast keypoints.
@@ -201,22 +201,24 @@ def descriptor_orb(image, keypoints, orientations, scales,
curr_image = np.ascontiguousarray(pyramid[scale])
curr_scale_mask = scales == scale
curr_scale_kpts = keypoints[curr_scale_mask] / (downscale ** scale)
curr_scale_kpts = np.round(curr_scale_kpts).astype(np.intp)
curr_scale_orientation = orientations[curr_scale_mask]
if np.sum(curr_scale_mask) > 0:
curr_scale_kpts = keypoints[curr_scale_mask] / (downscale ** scale)
curr_scale_kpts = np.round(curr_scale_kpts).astype(np.intp)
curr_scale_orientation = orientations[curr_scale_mask]
border_mask = _mask_border_keypoints(curr_image, curr_scale_kpts,
dist=16)
curr_scale_kpts = curr_scale_kpts[border_mask]
curr_scale_orientation = curr_scale_orientation[border_mask]
border_mask = _mask_border_keypoints(curr_image, curr_scale_kpts,
dist=16)
curr_scale_kpts = np.ascontiguousarray(curr_scale_kpts)
curr_scale_orientation = np.ascontiguousarray(curr_scale_orientation)
curr_scale_descriptors = _orb_loop(curr_image, curr_scale_kpts,
curr_scale_orientation)
curr_scale_kpts = curr_scale_kpts[border_mask]
curr_scale_orientation = curr_scale_orientation[border_mask]
descriptors_list.append(curr_scale_descriptors)
filtered_keypoints_list.append(curr_scale_kpts * downscale ** scale)
curr_scale_kpts = np.ascontiguousarray(curr_scale_kpts)
curr_scale_orientation = np.ascontiguousarray(curr_scale_orientation)
curr_scale_descriptors = _orb_loop(curr_image, curr_scale_kpts,
curr_scale_orientation)
descriptors_list.append(curr_scale_descriptors)
filtered_keypoints_list.append(curr_scale_kpts * downscale ** scale)
descriptors = np.vstack(descriptors_list).view(np.bool)
filtered_keypoints = np.vstack(filtered_keypoints_list)
skimage/feature/orb_cy.pyx
+2 -1
View File
@@ -9,7 +9,8 @@ import numpy as np
from libc.math cimport sin, cos, round
pos = np.loadtxt("orb_descriptor_positions.txt", dtype=np.int8)
pos = np.loadtxt(os.path.join(os.path.dirname(__file__),
"orb_descriptor_positions.txt"), dtype=np.int8)
pos0 = np.ascontiguousarray(pos[:, :2])
pos1 = np.ascontiguousarray(pos[:, 2:])
skimage/feature/tests/test_orb.py
+93
View File
@@ -0,0 +1,93 @@
import numpy as np
from numpy.testing import assert_array_equal, assert_almost_equal
from skimage.feature import keypoints_orb, descriptor_orb
from skimage.data import lena
from skimage.color import rgb2gray
def test_keypoints_orb_desired_no_of_keypoints():
img = rgb2gray(lena())
keypoints, orientations, scales = keypoints_orb(img, n_keypoints=10,
fast_n=12,
fast_threshold=0.20)
exp_keypoints = np.array([[435, 180],
[436, 180],
[376, 156],
[455, 176],
[435, 180],
[269, 111],
[376, 156],
[311, 173],
[413, 70],
[311, 173]])
exp_scales = np.array([0, 1, 0, 0, 2, 0, 1, 1, 0, 3])
exp_orientations = np.array([-175.64733392, -167.94842949, -148.98350192,
-142.03599837, -176.08535837, -53.08162354,
-150.89208271, 97.7693776 , -173.4479964 ,
38.66312042])
assert_array_equal(exp_keypoints, keypoints)
assert_array_equal(exp_scales, scales)
assert_almost_equal(exp_orientations, np.rad2deg(orientations))
def test_keypoints_orb_less_than_desired_no_of_keypoints():
img = rgb2gray(lena())
keypoints, orientations, scales = keypoints_orb(img, n_keypoints=15,
fast_n=12,
fast_threshold=0.33,
downscale=2, n_scales=2)
exp_keypoints = np.array([[ 67, 157],
[247, 146],
[269, 111],
[413, 70],
[435, 180],
[230, 136],
[264, 336],
[330, 148],
[372, 156]])
exp_scales = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1])
exp_orientations = np.array([-105.76503839, -96.28973044, -53.08162354,
-173.4479964 , -175.64733392, -106.07927215,
-163.40016243, 75.80865813, -154.73195911])
assert_array_equal(exp_keypoints, keypoints)
assert_array_equal(exp_scales, scales)
assert_almost_equal(exp_orientations, np.rad2deg(orientations))
def test_descriptor_orb():
img = rgb2gray(lena())
keypoints, orientations, scales = keypoints_orb(img, n_keypoints=10,
fast_n=12,
fast_threshold=0.20)
descriptors, filtered_keypoints = descriptor_orb(img, keypoints, orientations, scales)
exp_filtered_keypoints = np.array([[435, 180],
[376, 156],
[455, 176],
[269, 111],
[413, 70],
[436, 180],
[376, 156],
[311, 173],
[435, 180],
[311, 173]])
descriptors_120_129 = np.array([[ True, False, False, True, False, False, False, False, False, False],
[ True, True, False, False, True, False, False, True, False, True],
[False, True, True, False, True, False, True, True, True, True],
[False, False, False, True, True, False, True, False, True, False],
[False, True, True, True, True, False, True, True, True, False],
[ True, False, True, True, True, False, False, False, True, False],
[ True, False, True, False, True, False, True, True, False, True],
[ True, True, True, True, True, True, False, True, True, True],
[ True, True, True, False, True, False, True, True, True, False],
[ True, True, False, True, True, True, False, True, False, True]],
dtype=bool)
assert_array_equal(exp_filtered_keypoints, filtered_keypoints)
assert_array_equal(descriptors_120_129, descriptors[:, 120:130])
if __name__ == '__main__':
from numpy import testing
testing.run_module_suite()