def test_high_pass_2_3(self):
r = np.array([[[0.67840929, 0.42564602, 0.56367731],
[-0.06834775, -0.14577867, 0.09220944],
[-0.21991411, 0.57926609, 0.32041161],
[0.19807773, -0.14934361, 0.16678943]],
[[0.43283425, 0.13444749, 0.52570046],
[0.27778178, 0.53327464, 0.30796907],
[0.14707917, -0.3018394, 0.30696447],
[0.30284919, 0.73104188, 0.23689022]]])
# alternate result, which is based on more exact numeric integral
r_alternate = np.array([[[0.67879973, 0.42581689, 0.56405514],
[-0.06831375, -0.1455644, 0.09238308],
[-0.22003481, 0.57934385, 0.32063781],
[0.19818479, -0.14915996, 0.1669471]],
[[0.43323599, 0.13470246, 0.52612553],
[0.27797546, 0.53340689, 0.30820639],
[0.14721724, -0.30164113, 0.30726631],
[0.30309852, 0.73129418, 0.23706999]]])
self.assertTrue(
np.allclose(
hybrid.high_pass(self.img1, 2, 3), r, rtol=1e-4, atol=1e-08)
or np.allclose(hybrid.high_pass(self.img1, 2, 3),
r_alternate,
rtol=1e-4,
atol=1e-08))
def test_high_pass_9_7(self):
r = np.array([[[0.2862107, -0.04295958, 0.29505309],
[0.46900091, 0.43362076, 0.12043671],
[0.07635266, 0.34369499, 0.34341876],
[0.74036798, 0.45551239, -0.04270926],
[0.75775172, 0.5654669, 0.16100456]],
[[-0.07602991, 0.62244331, 0.01915625],
[0.37805486, 0.04694034, 0.50508338],
[0.12437849, 0.66055268, 0.09461144],
[0.74389022, 0.55967782, 0.35313881],
[-0.00238069, 0.11131494, -0.00425285]],
[[0.52875691, 0.22471744, 0.22277958],
[0.09027023, 0.41082914, 0.27969589],
[0.29011503, 0.15634128, 0.21811742],
[0.65650664, 0.36706042, -0.01747312],
[0.1936762, -0.1121314, 0.81590494]],
[[0.05740758, 0.22434804, 0.16188927],
[0.39432313, -0.07318352, -0.05993572],
[0.2590762, 0.75856393, 0.48518876],
[0.39822023, -0.00237932, 0.53796125],
[0.14244267, 0.45042029, 0.36582769]]])
# alternate result, which is based on more exact numeric integral
r_alternate = np.array([[[0.2862109, -0.04295852, 0.29505354],
[0.46900319, 0.43362333, 0.1204375],
[0.0763568, 0.34369899, 0.34342089],
[0.74037127, 0.45551524, -0.04270779],
[0.7577536, 0.56546852, 0.16100508]],
[[-0.07602759, 0.62244632, 0.01915834],
[0.37805956, 0.04694523, 0.50508651],
[0.12438509, 0.66055904, 0.09461593],
[0.74389594, 0.55968301, 0.35314264],
[-0.0023768, 0.11131836, -0.00425025]],
[[0.5287591, 0.22472023, 0.22278178],
[0.0902746, 0.41083376, 0.27969932],
[0.29012127, 0.15634734, 0.21812224],
[0.65651199, 0.3670653, -0.01746895],
[0.19367974, -0.11212831, 0.81590791]],
[[0.05740742, 0.22434851, 0.16189004],
[0.39432443, -0.07318179, -0.05993402],
[0.25907931, 0.75856707, 0.48519183],
[0.39822248, -0.00237734, 0.53796371],
[0.14244353, 0.4504209, 0.3658293]]])
self.assertTrue(
np.allclose(
hybrid.high_pass(self.img2, 9, 7), r, rtol=1e-4, atol=1e-08)
or np.allclose(hybrid.high_pass(self.img2, 9, 7),
r_alternate,
rtol=1e-4,
atol=1e-08))
def test_high_pass_9_7(self):
r = np.array([[[ 0.2862107 , -0.04295958, 0.29505309],
[ 0.46900091, 0.43362076, 0.12043671],
[ 0.07635266, 0.34369499, 0.34341876],
[ 0.74036798, 0.45551239, -0.04270926],
[ 0.75775172, 0.5654669 , 0.16100456]],
[[-0.07602991, 0.62244331, 0.01915625],
[ 0.37805486, 0.04694034, 0.50508338],
[ 0.12437849, 0.66055268, 0.09461144],
[ 0.74389022, 0.55967782, 0.35313881],
[-0.00238069, 0.11131494, -0.00425285]],
[[ 0.52875691, 0.22471744, 0.22277958],
[ 0.09027023, 0.41082914, 0.27969589],
[ 0.29011503, 0.15634128, 0.21811742],
[ 0.65650664, 0.36706042, -0.01747312],
[ 0.1936762 , -0.1121314 , 0.81590494]],
[[ 0.05740758, 0.22434804, 0.16188927],
[ 0.39432313, -0.07318352, -0.05993572],
[ 0.2590762 , 0.75856393, 0.48518876],
[ 0.39822023, -0.00237932, 0.53796125],
[ 0.14244267, 0.45042029, 0.36582769]]])
self.assertTrue(np.allclose(hybrid.high_pass(self.img2, 9, 7), r,
atol=1e-08))
def test_high_pass_2_3(self):
r = np.array([[[0.67840929, 0.42564602, 0.56367731],
[-0.06834775, -0.14577867, 0.09220944],
[-0.21991411, 0.57926609, 0.32041161],
[0.19807773, -0.14934361, 0.16678943]],
[[0.43283425, 0.13444749, 0.52570046],
[0.27778178, 0.53327464, 0.30796907],
[0.14707917, -0.3018394, 0.30696447],
[0.30284919, 0.73104188, 0.23689022]]])
self.assertTrue(
np.allclose(hybrid.high_pass(self.img1, 2, 3), r, atol=1e-08))