def test_low_pass_2_3(self):
r = np.array([[[0.3088114, 0.24855971, 0.39614979],
[0.33504927, 0.36201988, 0.55967795],
[0.22154193, 0.35337206, 0.46181069],
[0.13350745, 0.23712374, 0.2895637]],
[[0.30984611, 0.25329434, 0.39856696],
[0.34230614, 0.35664168, 0.56242281],
[0.23367623, 0.3610497, 0.46558965],
[0.14036547, 0.23883435, 0.29052476]]])
# alternate result, which is based on more exact numeric integral
r_alternate = np.array([[[0.30842096, 0.24838884, 0.39577196],
[0.33501527, 0.36180561, 0.55950431],
[0.22166263, 0.3532943, 0.46158449],
[0.13340038, 0.23694009, 0.28940603]],
[[0.30944436, 0.25303936, 0.39814189],
[0.34211247, 0.35650942, 0.56218549],
[0.23353816, 0.36085143, 0.46528782],
[0.14011613, 0.23858205, 0.29034499]]])
self.assertTrue(
np.allclose(
hybrid.low_pass(self.img1, 2, 3), r, rtol=1e-4, atol=1e-08)
or np.allclose(hybrid.low_pass(self.img1, 2, 3),
r_alternate,
rtol=1e-4,
atol=1e-08))
def test_low_pass_9_7(self):
r = np.array([[[0.17963478, 0.17124501, 0.12221388],
[0.21933258, 0.20746511, 0.16113371],
[0.22114507, 0.20886138, 0.16244521],
[0.22029549, 0.20774176, 0.16180487],
[0.18792763, 0.17151596, 0.13658299]],
[[0.18170777, 0.17314406, 0.12382304],
[0.22168257, 0.20972768, 0.1634043],
[0.2235093, 0.21113553, 0.1647422],
[0.22264545, 0.2100001, 0.16410069],
[0.18989056, 0.17328373, 0.13859635]],
[[0.18158356, 0.17294561, 0.12393794],
[0.22135196, 0.20945285, 0.1637038],
[0.22317083, 0.21085531, 0.16505207],
[0.22230311, 0.20971788, 0.16441722],
[0.18955739, 0.17295608, 0.13893827]],
[[0.17926668, 0.17065694, 0.12255439],
[0.2183528, 0.20665068, 0.16202127],
[0.22014196, 0.20803099, 0.16336348],
[0.21928093, 0.20690543, 0.16274287],
[0.18694027, 0.17054497, 0.13759625]]])
# alternate result, which is based on more exact numeric integral
r_alternate = np.array([[[0.17963458, 0.17124395, 0.12221343],
[0.2193303, 0.20746254, 0.16113291],
[0.22114092, 0.20885738, 0.16244308],
[0.2202922, 0.2077389, 0.1618034],
[0.18792575, 0.17151434, 0.13658246]],
[[0.18170545, 0.17314105, 0.12382096],
[0.22167787, 0.20972279, 0.16340117],
[0.2235027, 0.21112917, 0.16473771],
[0.22263973, 0.20999491, 0.16409686],
[0.18988667, 0.17328031, 0.13859375]],
[[0.18158137, 0.17294281, 0.12393574],
[0.22134759, 0.20944824, 0.16370037],
[0.22316458, 0.21084925, 0.16504725],
[0.22229775, 0.209713, 0.16441304],
[0.18955385, 0.17295299, 0.1389353]],
[[0.17926684, 0.17065647, 0.12255362],
[0.2183515, 0.20664894, 0.16201958],
[0.22013885, 0.20802785, 0.16336041],
[0.21927869, 0.20690345, 0.16274041],
[0.18693941, 0.17054435, 0.13759465]]])
self.assertTrue(
np.allclose(
hybrid.low_pass(self.img2, 9, 7), r, rtol=1e-4, atol=1e-08)
or np.allclose(hybrid.low_pass(self.img2, 9, 7),
r_alternate,
rtol=1e-4,
atol=1e-08))
def test_low_pass_9_7(self):
r = np.array([[[ 0.17963478, 0.17124501, 0.12221388],
[ 0.21933258, 0.20746511, 0.16113371],
[ 0.22114507, 0.20886138, 0.16244521],
[ 0.22029549, 0.20774176, 0.16180487],
[ 0.18792763, 0.17151596, 0.13658299]],
[[ 0.18170777, 0.17314406, 0.12382304],
[ 0.22168257, 0.20972768, 0.1634043 ],
[ 0.2235093 , 0.21113553, 0.1647422 ],
[ 0.22264545, 0.2100001 , 0.16410069],
[ 0.18989056, 0.17328373, 0.13859635]],
[[ 0.18158356, 0.17294561, 0.12393794],
[ 0.22135196, 0.20945285, 0.1637038 ],
[ 0.22317083, 0.21085531, 0.16505207],
[ 0.22230311, 0.20971788, 0.16441722],
[ 0.18955739, 0.17295608, 0.13893827]],
[[ 0.17926668, 0.17065694, 0.12255439],
[ 0.2183528 , 0.20665068, 0.16202127],
[ 0.22014196, 0.20803099, 0.16336348],
[ 0.21928093, 0.20690543, 0.16274287],
[ 0.18694027, 0.17054497, 0.13759625]]])
self.assertTrue(np.allclose(hybrid.low_pass(self.img2, 9, 7), r,
atol=1e-08))
def test_low_pass_2_3(self):
r = np.array([[[0.3088114, 0.24855971, 0.39614979],
[0.33504927, 0.36201988, 0.55967795],
[0.22154193, 0.35337206, 0.46181069],
[0.13350745, 0.23712374, 0.2895637]],
[[0.30984611, 0.25329434, 0.39856696],
[0.34230614, 0.35664168, 0.56242281],
[0.23367623, 0.3610497, 0.46558965],
[0.14036547, 0.23883435, 0.29052476]]])
self.assertTrue(
np.allclose(hybrid.low_pass(self.img1, 2, 3), r, atol=1e-08))