def test_no_noise(self):
transform = RandomNoise(mean=0, std=0)
transformed = transform(self.sample_subject)
self.assertTensorAlmostEqual(
self.sample_subject.t1.data,
transformed.t1.data,
)
def test_with_noise(self):
transform = RandomNoise()
transformed = transform(self.sample_subject)
self.assertTensorNotEqual(
self.sample_subject.t1.data,
transformed.t1.data,
)
def test_constant_noise(self):
transform = RandomNoise(mean=(5., 5.), std=0.)
transformed = transform(self.sample)
self.assertTensorAlmostEqual(
self.sample.t1.data + 5,
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_noise(self):
transform = RandomNoise(mean=0., std=0.)
transformed = transform(self.sample)
assert_array_equal(self.sample.t1.data, transformed.t1.data)
def test_wrong_mean_type(self):
with self.assertRaises(ValueError):
RandomNoise(mean='wrong')
def test_wrong_std_type(self):
with self.assertRaises(ValueError):
RandomNoise(std='wrong')
def test_std_range_with_negative_min(self):
with self.assertRaises(ValueError):
RandomNoise(std=(-0.5, 4))
def test_negative_std(self):
with self.assertRaises(ValueError):
RandomNoise(std=-2)
def test_constant_noise(self):
transform = RandomNoise(mean=(5., 5.), std=0.)
transformed = transform(self.sample)
assert_array_equal(self.sample.t1.data + 5, transformed.t1.data)
def test_with_noise(self):
transform = RandomNoise()
transformed = transform(self.sample)
with self.assertRaises(AssertionError):
assert_array_equal(self.sample.t1.data, transformed.t1.data)
def test_no_noise(self):
transform = RandomNoise(mean=0., std=0.)
transformed = transform(self.sample)
self.assertTensorAlmostEqual(self.sample.t1.data, transformed.t1.data)