one_similar = False
for _ in range(100):
image_after = ia.augment_batch(images)[0]
if np.allclose(image_target, image_after):
one_similar = True
break
self.assertTrue(one_similar)
def test_two_channels(self):
Tests augmentation of images with two channels (either first or last
After Change
// all must be similar
for _ in range(100):
image_after = ia.augment_batch(images)[0]
self.assertTrue(np.allclose(image_target, image_after))
def test_two_channels(self):
Tests augmentation of images with two channels (either first or last
axis of each image). Tested using x-translation.