def test_resize(self):
for origsize in [(4,5), (4,4), (7,5)]:
image = np.random.uniform(size=(4,5))
for ny in [3,4,5,7,9,15]:
for nx in [3,5,7,19]:
shape = (ny, nx)
x = io._resize(image, shape)
self.assertEqual(x.shape, shape)
#- sub- and super- selection should remain centered on original image
image = np.random.uniform(size=(4,5))
tmp = io._resize(image, (4,3))
self.assertTrue(np.all(tmp == image[:, 1:-1]))
tmp = io._resize(image, (4,7))
self.assertTrue(np.all(tmp[:,1:-1] == image))
def test_resize(self):
for origsize in [(4, 5), (4, 4), (7, 5)]:
image = np.random.uniform(size=(4, 5))
for ny in [3, 4, 5, 7, 9, 15]:
for nx in [3, 5, 7, 19]:
shape = (ny, nx)
x = io._resize(image, shape)
self.assertEqual(x.shape, shape)
#- sub- and super- selection should remain centered on original image
image = np.random.uniform(size=(4, 5))
tmp = io._resize(image, (4, 3))
self.assertTrue(np.all(tmp == image[:, 1:-1]))
tmp = io._resize(image, (4, 7))
self.assertTrue(np.all(tmp[:, 1:-1] == image))
def test_resize(self):
image = np.random.uniform(size=(4,5))
for shape in [(3,4), (4,5), (3,6), (5,4), (5,6)]:
x = io._resize(image, shape)
self.assertEqual(x.shape, shape)
def test_resize(self):
image = np.random.uniform(size=(4, 5))
for shape in [(3, 4), (4, 5), (3, 6), (5, 4), (5, 6)]:
x = io._resize(image, shape)
self.assertEqual(x.shape, shape)