def test_parse_stds(self):
def do_assert(transform):
self.assertEqual(transform.std_ranges, 3 * (0, 1))
do_assert(RandomBlur(std=1))
do_assert(RandomBlur(std=(0, 1)))
do_assert(RandomBlur(std=3 * (1, )))
do_assert(RandomBlur(std=3 * [0, 1]))
def test_no_blurring(self):
transform = RandomBlur(std=0)
transformed = transform(self.sample_subject)
self.assertTensorAlmostEqual(
self.sample_subject.t1.data,
transformed.t1.data,
)
def test_with_blurring(self):
transform = RandomBlur(std=(1, 3))
transformed = transform(self.sample_subject)
self.assertTensorNotEqual(
self.sample_subject.t1.data,
transformed.t1.data,
)
def simulate_artefacts(sample, artefacts=(0, 0, 0, 0)):
transforms = []
if artefacts[0] == 1:
if random.random() < 0.5:
degrees = (5, random.random() * 10 + 5)
else:
degrees = (-(random.random() * 10 + 5), -5)
if random.random() < 0.5:
translation = (5, random.random() * 10 + 5)
else:
translation = (-(random.random() * 10 + 5), -5)
transforms.append(
RandomMotion(degrees=degrees,
translation=translation,
num_transforms=random.randint(2, 15)))
elif artefacts[1] == 1:
transforms.append(
RandomGhosting(num_ghosts=(2, 10), intensity=(0.5, 0.75)))
elif artefacts[2] == 1:
transforms.append(RandomBlur(std=(0.5, 2.)))
elif artefacts[3] == 1:
transforms.append(RandomNoise(std=(0.01, 0.05)))
transforms = Compose(transforms)
return transforms(sample)
def test_no_blurring(self):
transform = RandomBlur(std=0)
transformed = transform(self.sample)
assert_array_equal(self.sample.t1.data, transformed.t1.data)
def test_wrong_std_type(self):
with self.assertRaises(ValueError):
RandomBlur(std='wrong')
def test_std_range_with_negative_min(self):
with self.assertRaises(ValueError):
RandomBlur(std=(-0.5, 4))
def test_negative_std(self):
with self.assertRaises(ValueError):
RandomBlur(std=-2)
def test_with_blurring(self):
transform = RandomBlur(std=(1, 3))
transformed = transform(self.sample)
with self.assertRaises(AssertionError):
assert_array_equal(self.sample.t1.data, transformed.t1.data)