From d67701580650af4c3863c99cba2ff6abb8a18fa8 Mon Sep 17 00:00:00 2001 From: Himanshu Mishra Date: Sun, 24 Jan 2016 16:58:56 +0530 Subject: [PATCH] Do not use lena in test_orb --- skimage/feature/tests/test_orb.py | 89 ++++++++++++++----------------- 1 file changed, 41 insertions(+), 48 deletions(-) diff --git a/skimage/feature/tests/test_orb.py b/skimage/feature/tests/test_orb.py index 8895d857..d95d2d4a 100644 --- a/skimage/feature/tests/test_orb.py +++ b/skimage/feature/tests/test_orb.py @@ -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,