def save_load_save_load(self, dimensions):
path = self.dir / f'img{dimensions}d.nii'
shape = [4]
for _ in range(dimensions - 1):
shape.append(6)
shape = *shape[1:4], shape[0] # save channels in last dim
nii = nib.Nifti1Image(np.random.rand(*shape), np.eye(4))
nii.to_filename(path)
read_nii_tensor, _ = io.read_image(path)
assert read_nii_tensor.shape == shape
assert read_nii_tensor.ndim == dimensions
image = ScalarImage(path)
image.save(path)
read_tio_tensor, _ = io.read_image(path)
assert tuple(read_tio_tensor.shape) == shape
assert read_tio_tensor.ndim == dimensions, read_tio_tensor.shape
def apply_transform(self, subject):
corrupted_query = subject.filename.split(".")[0] + "*"
files = glob(self.path_corrupt + "/**/" + corrupted_query,
recursive=True)
corrupt_path = files[random.randint(0, len(files) - 1)]
transformed, _ = read_image(corrupt_path)
vol = subject['im'][tio.DATA].float()
transformed = transformed.float()
if self.norm_mode == 1:
vol = vol / vol.max()
transformed = transformed / transformed.max()
elif self.norm_mode == 2:
vol = (vol - vol.min()) / (vol.max() - vol.min())
transformed = (transformed - transformed.min()) / (
transformed.max() - transformed.min())
subject['im'][tio.DATA] = torch.cat([vol, transformed], 0)
return subject
def test_4d_not_supported(self):
path = self.dir / 'img4d.nii'
nii = nib.Nifti1Image(np.random.rand(4, 5, 6, 7), np.eye(4))
nii.to_filename(str(path))
with self.assertRaises(ValueError):
io.read_image(path)
def test_read_dicom_dir(self):
io.read_image(self.dicom_dir)
def test_read_dicom_file(self):
io.read_image(self.dicom_path)
def test_read_image(self):
# I need to find something readable by nib but not sitk
io.read_image(self.nii_path)
io.read_image(self.nii_path, itk_first=True)
def test_read_dicom_dir(self):
tensor, _ = io.read_image(self.dicom_dir)
self.assertEqual(tuple(tensor.shape), (1, 88, 128, 17))
