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Do not use lena in test_orb

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This commit is contained in:
Himanshu Mishra
2016-01-24 16:58:56 +05:30
parent b9c951335a
commit d677015806
1 changed files with 41 additions and 48 deletions
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skimage/feature/tests/test_orb.py
+41 -48
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@@ -2,11 +2,10 @@ import numpy as np
from numpy.testing import assert_equal, assert_almost_equal, run_module_suite
from skimage.feature import ORB
from skimage import data
from skimage.color import rgb2gray
from skimage._shared.testing import test_parallel
img = rgb2gray(data.lena())
img = data.coins()
@test_parallel()
@@ -14,22 +13,21 @@ def test_keypoints_orb_desired_no_of_keypoints():
detector_extractor = ORB(n_keypoints=10, fast_n=12, fast_threshold=0.20)
detector_extractor.detect(img)
exp_rows = np.array([ 435. , 435.6 , 376. , 455. , 434.88, 269. ,
375.6 , 310.8 , 413. , 311.04])
exp_cols = np.array([ 180. , 180. , 156. , 176. , 180. , 111. ,
156. , 172.8, 70. , 172.8])
exp_rows = np.array([ 141. , 108. , 214.56 , 131. , 214.272,
67. , 206. , 177. , 108. , 141. ])
exp_cols = np.array([ 323. , 328. , 282.24 , 292. , 281.664,
85. , 260. , 284. , 328.8 , 267. ])
exp_scales = np.array([ 1. , 1.2 , 1. , 1. , 1.44 , 1. ,
1.2 , 1.2 , 1. , 1.728])
exp_scales = np.array([ 323. , 328. , 282.24 , 292. , 281.664,
85. , 260. , 284. , 328.8 , 267. ])
exp_orientations = np.array([-175.64733392, -167.94842949, -148.98350192,
-142.03599837, -176.08535837, -53.08162354,
-150.89208271, 97.7693776 , -173.4479964 ,
38.66312042])
exp_response = np.array([ 0.96770745, 0.81027306, 0.72376257,
0.5626413 , 0.5097993 , 0.44351774,
0.39154173, 0.39084861, 0.39063076,
0.37602487])
exp_orientations = np.array([ -53.97446153, 59.5055285 , -96.01885186,
-149.70789506, -94.70171899, -45.76429535,
-51.49752849, 113.57081195, 63.30428063,
-79.56091118])
exp_response = np.array([ 1.01168357, 0.82934145, 0.67784179, 0.57176438,
0.56637459, 0.52248355, 0.43696175, 0.42992376,
0.37700486, 0.36126832])
assert_almost_equal(exp_rows, detector_extractor.keypoints[:, 0])
assert_almost_equal(exp_cols, detector_extractor.keypoints[:, 1])
@@ -48,20 +46,16 @@ def test_keypoints_orb_less_than_desired_no_of_keypoints():
fast_threshold=0.33, downscale=2, n_scales=2)
detector_extractor.detect(img)
exp_rows = np.array([ 67., 247., 269., 413., 435., 230., 264.,
330., 372.])
exp_cols = np.array([ 157., 146., 111., 70., 180., 136., 336.,
148., 156.])
exp_rows = np.array([ 58., 65., 108., 140., 203.])
exp_cols = np.array([ 291., 130., 293., 202., 267.])
exp_scales = np.array([ 1., 1., 1., 1., 1., 2., 2., 2., 2.])
exp_scales = np.array([1., 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])
exp_orientations = np.array([-158.26941428, -59.42996346, 151.93905955,
-79.46341354, -56.90052451])
exp_response = np.array([ 0.13197835, 0.24931321, 0.44351774,
0.39063076, 0.96770745, 0.04935129,
0.21431068, 0.15826555, 0.42403573])
exp_response = np.array([ 0.2667641 , 0.04009017, -0.17641695, -0.03243431,
0.26521259])
assert_almost_equal(exp_rows, detector_extractor.keypoints[:, 0])
assert_almost_equal(exp_cols, detector_extractor.keypoints[:, 1])
@@ -78,27 +72,26 @@ def test_keypoints_orb_less_than_desired_no_of_keypoints():
def test_descriptor_orb():
detector_extractor = ORB(fast_n=12, fast_threshold=0.20)
exp_descriptors = np.array([[ True, False, True, True, False, False, False, False, False, False],
[False, False, True, True, False, True, True, False, True, True],
[ True, False, False, False, True, False, True, True, True, False],
[ True, False, False, True, False, True, True, False, False, False],
[False, True, True, True, False, False, False, True, True, False],
[False, False, False, False, False, True, False, True, True, True],
[False, True, True, True, True, False, False, True, False, True],
[ True, True, True, False, True, True, True, True, False, False],
[ True, True, False, True, True, True, True, False, False, False],
[ True, False, False, False, False, True, False, False, True, True],
[ True, False, False, False, True, True, True, False, False, False],
[False, False, True, False, True, False, False, True, False, False],
[False, False, True, True, False, False, False, False, False, True],
[ True, True, False, False, False, True, True, True, True, True],
[ True, True, True, False, False, True, False, True, True, False],
[False, True, True, False, False, True, True, True, True, True],
[ True, True, True, False, False, False, False, True, True, True],
[False, False, False, False, True, False, False, True, True, False],
[False, True, False, False, True, False, False, False, True, True],
[ True, False, True, False, False, False, True, True, False, False]], dtype=bool)
exp_descriptors = np.array([[0, 1, 1, 1, 0, 1, 0, 1, 0, 1],
[1, 1, 1, 0, 0, 1, 0, 0, 1, 1],
[1, 0, 1, 1, 0, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 1, 0],
[1, 1, 0, 1, 1, 1, 0, 0, 1, 1],
[1, 1, 0, 1, 0, 0, 1, 0, 1, 1],
[0, 0, 1, 0, 1, 0, 0, 1, 1, 0],
[1, 0, 0, 0, 1, 0, 0, 0, 0, 1],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 0, 1, 0, 1, 0, 0, 1, 1],
[1, 1, 1, 0, 0, 0, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
[1, 1, 1, 0, 1, 1, 1, 1, 0, 0],
[1, 1, 0, 0, 1, 0, 0, 1, 0, 1],
[1, 1, 0, 0, 0, 0, 1, 0, 0, 1],
[0, 0, 0, 0, 1, 1, 1, 0, 1, 0],
[0, 0, 0, 0, 1, 1, 1, 0, 0, 1],
[0, 0, 0, 0, 0, 1, 1, 0, 1, 1],
[0, 0, 0, 0, 1, 0, 1, 0, 1, 1]], dtype=bool)
detector_extractor.detect(img)
detector_extractor.extract(img, detector_extractor.keypoints,
detector_extractor.scales,