def test_DATAAUGMENTATION_parameter_classification(self):
data_aug = Data_Augmentation()
data_aug.config_p_per_sample = 1
data_aug.seg_augmentation = False
img_aug, seg_aug = data_aug.run(self.img3D, self.seg3D)
self.assertFalse(np.array_equal(img_aug, self.img3D))
self.assertTrue(np.array_equal(seg_aug, self.seg3D))
def test_DATAAUGMENTATION_parameter_gaussiannoise(self):
data_aug = Data_Augmentation(cycles=1,
scaling=False,
rotations=False,
elastic_deform=False,
mirror=False,
brightness=False,
contrast=False,
gamma=False,
gaussian_noise=True)
data_aug.config_p_per_sample = 1
img_aug, seg_aug = data_aug.run(self.img3D, self.seg3D)
self.assertFalse(np.array_equal(img_aug, self.img3D))
self.assertTrue(np.array_equal(seg_aug, self.seg3D))
def test_DATAAUGMENTATION_parameter_percentage(self):
data_aug = Data_Augmentation(cycles=100,
scaling=True,
rotations=False,
elastic_deform=False,
mirror=False,
brightness=False,
contrast=False,
gamma=False,
gaussian_noise=False)
data_aug.config_p_per_sample = 0.3
img_aug, seg_aug = data_aug.run(self.img3D, self.seg3D)
counter_equal = 0
for i in range(0, 100):
is_equal = np.array_equal(img_aug[i], self.img3D[0])
if is_equal: counter_equal += 1
ratio = counter_equal / 100
self.assertTrue(ratio >= 0.5 and ratio <= 0.9)
def test_DATAAUGMENTATION_BASE_run2D(self):
data_aug = Data_Augmentation()
data_aug.config_p_per_sample = 1
img_aug, seg_aug = data_aug.run(self.img2D, self.seg2D)
self.assertEqual(img_aug.shape, self.img2D.shape)
self.assertFalse(np.array_equal(img_aug, self.img2D))
self.assertEqual(seg_aug.shape, self.seg2D.shape)
self.assertFalse(np.array_equal(seg_aug, self.seg2D))
data_aug = Data_Augmentation(cycles=1,
scaling=False,
rotations=False,
elastic_deform=False,
mirror=False,
brightness=False,
contrast=False,
gamma=False,
gaussian_noise=False)
img_aug, seg_aug = data_aug.run(self.img2D, self.seg2D)
self.assertTrue(np.array_equal(img_aug, self.img2D))