remove matrix to transform and back. add axis for last_index

src/torchio/transforms/augmentation/intensity/random_motion.py

@@ -224,35 +224,18 @@ def get_rigid_transforms(
  ) -> List[sitk.Euler3DTransform]:
  center_ijk = np.array(image.GetSize()) / 2
  center_lps = image.TransformContinuousIndexToPhysicalPoint(center_ijk)
- identity = np.eye(4)
- matrices = [identity]
+ ident_transform = sitk.Euler3DTransform()
+ ident_transform.SetCenter(center_lps)
+ transforms = [ident_transform]
  for degrees, translation in zip(degrees_params, translation_params):
  radians = np.radians(degrees).tolist()
  motion = sitk.Euler3DTransform()
  motion.SetCenter(center_lps)
  motion.SetRotation(*radians)
  motion.SetTranslation(translation.tolist())
- motion_matrix = self.transform_to_matrix(motion)
- matrices.append(motion_matrix)
- transforms = [self.matrix_to_transform(m) for m in matrices]
+ transforms.append(motion)
  return transforms
 
- @staticmethod
- def transform_to_matrix(transform: sitk.Euler3DTransform) -> np.ndarray:
- matrix = np.eye(4)
- rotation = np.array(transform.GetMatrix()).reshape(3, 3)
- matrix[:3, :3] = rotation
- matrix[:3, 3] = transform.GetTranslation()
- return matrix
-
- @staticmethod
- def matrix_to_transform(matrix: np.ndarray) -> sitk.Euler3DTransform:
- transform = sitk.Euler3DTransform()
- rotation = matrix[:3, :3].flatten().tolist()
- transform.SetMatrix(rotation)
- transform.SetTranslation(matrix[:3, 3])
- return transform
-
  def resample_images(
  self,
  image: sitk.Image,
@@ -261,10 +244,10 @@ def resample_images(
  ) -> List[sitk.Image]:
  floating = reference = image
  default_value = np.float64(sitk.GetArrayViewFromImage(image).min())
+ interpolator = self.get_sitk_interpolator(interpolation)
  transforms = transforms[1:] # first is identity
  images = [image] # first is identity
  for transform in transforms:
- interpolator = self.get_sitk_interpolator(interpolation)
  resampler = sitk.ResampleImageFilter()
  resampler.SetInterpolator(interpolator)
  resampler.SetReferenceImage(reference)
@@ -301,7 +284,7 @@ def add_artifact(
  spectra.append(spectrum)
  self.sort_spectra(spectra, times)
  result_spectrum = torch.empty_like(spectra[0])
- last_index = result_spectrum.shape[2]
+ last_index = result_spectrum.shape[axis]
  indices = (last_index * times).astype(int).tolist()
  indices.append(last_index)
  ini = 0