Add Transpose transform

docs/source/transforms/preprocessing.rst

@@ -68,26 +68,36 @@ Spatial
 
 .. currentmodule:: torchio.transforms
 
+
 :class:`CropOrPad`
 ~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: CropOrPad
     :show-inheritance:
     :members: _get_six_bounds_parameters
 
+
 :class:`ToOrientation`
 ~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: ToOrientation
     :show-inheritance:
 
+
 :class:`ToCanonical`
 ~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: ToCanonical
     :show-inheritance:
 
 
+:class:`Transpose`
+~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: Transpose
+    :show-inheritance:
+
+
 :class:`Resample`
 ~~~~~~~~~~~~~~~~~
 

src/torchio/data/image.py

@@ -190,7 +190,7 @@ def __repr__(self):
             [
                 f'shape: {self.shape}',
                 f'spacing: {self.get_spacing_string()}',
-                f'orientation: {"".join(self.orientation)}+',
+                f'orientation: {self.orientation_str}+',
             ]
         )
         if self._loaded:
@@ -253,6 +253,7 @@ def set_data(self, tensor: TypeData):
             tensor: 4D tensor with dimensions :math:`(C, W, H, D)`.
         """
         self[DATA] = self._parse_tensor(tensor, none_ok=False)
+        self._loaded = True
 
     @property
     def tensor(self) -> torch.Tensor:
@@ -324,7 +325,12 @@ def width(self) -> int:
     @property
     def orientation(self) -> tuple[str, str, str]:
         """Orientation codes."""
-        return nib.aff2axcodes(self.affine)
+        return nib.orientations.aff2axcodes(self.affine)
+
+    @property
+    def orientation_str(self) -> str:
+        """Orientation as a string."""
+        return ''.join(self.orientation)
 
     @property
     def direction(self) -> TypeDirection3D:
@@ -339,7 +345,7 @@ def spacing(self) -> tuple[float, float, float]:
         """Voxel spacing in mm."""
         _, spacing = get_rotation_and_spacing_from_affine(self.affine)
         sx, sy, sz = spacing
-        return sx, sy, sz
+        return float(sx), float(sy), float(sz)
 
     @property
     def origin(self) -> tuple[float, float, float]:
@@ -743,7 +749,7 @@ def to_gif(
         )
 
     def to_ras(self) -> Image:
-        if self.orientation != tuple('RAS'):
+        if self.orientation_str != 'RAS':
             from ..transforms.preprocessing.spatial.to_canonical import ToCanonical
 
             return ToCanonical()(self)

src/torchio/transforms/__init__.py

@@ -56,6 +56,7 @@
 from .preprocessing import To
 from .preprocessing import ToCanonical
 from .preprocessing import ToOrientation
+from .preprocessing import Transpose
 from .preprocessing import ZNormalization
 from .preprocessing.intensity.histogram_standardization import train_histogram
 from .preprocessing.label.label_transform import LabelTransform
@@ -105,6 +106,7 @@
     'To',
     'ToCanonical',
     'ToOrientation',
+    'Transpose',
     'ZNormalization',
     'HistogramStandardization',
     'RescaleIntensity',

src/torchio/transforms/augmentation/spatial/random_flip.py

@@ -86,16 +86,15 @@ class Flip(SpatialTransform):
     def __init__(self, axes, **kwargs):
         super().__init__(**kwargs)
         self.axes = _parse_axes(axes)
-        self.args_names = ('axes',)
+        self.args_names = ['axes']
 
     def apply_transform(self, subject: Subject) -> Subject:
         axes = _ensure_axes_indices(subject, self.axes)
         for image in self.get_images(subject):
             _flip_image(image, axes)
         return subject
 
-    @staticmethod
-    def is_invertible():
+    def is_invertible(self):
         return True
 
     def inverse(self):

src/torchio/transforms/preprocessing/__init__.py

@@ -19,6 +19,7 @@
 from .spatial.resize import Resize
 from .spatial.to_canonical import ToCanonical
 from .spatial.to_orientation import ToOrientation
+from .spatial.transpose import Transpose
 
 __all__ = [
     'Pad',
@@ -27,6 +28,7 @@
     'Resample',
     'ToCanonical',
     'ToOrientation',
+    'Transpose',
     'CropOrPad',
     'CopyAffine',
     'EnsureShapeMultiple',

src/torchio/transforms/preprocessing/spatial/to_orientation.py

@@ -71,7 +71,7 @@ def __init__(self, orientation: str = 'RAS', **kwargs):
 
     def apply_transform(self, subject: Subject) -> Subject:
         for image in subject.get_images(intensity_only=False):
-            current_orientation = ''.join(nib.aff2axcodes(image.affine))
+            current_orientation = ''.join(nib.orientations.aff2axcodes(image.affine))
 
             # If the image is already in the target orientation, skip it
             if current_orientation == self.orientation:
@@ -80,7 +80,7 @@ def apply_transform(self, subject: Subject) -> Subject:
             # NIfTI images should have channels in 5th dimension
             array = rearrange(image.numpy(), 'C W H D -> W H D 1 C')
 
-            nii = nib.Nifti1Image(array, image.affine)
+            nii = nib.nifti1.Nifti1Image(array, image.affine)
 
             # Compute transform from current orientation to target orientation
             current_orientation = orientations.io_orientation(nii.affine)

src/torchio/transforms/preprocessing/spatial/transpose.py

@@ -0,0 +1,38 @@
+from ....data.subject import Subject
+from ...spatial_transform import SpatialTransform
+from .to_orientation import ToOrientation
+
+
+class Transpose(SpatialTransform):
+    """Swap the first and last spatial dimensions of the image.
+
+    The spatial metadata is updated accordingly, so the world coordinates of
+    all voxels in the input and output spaces match.
+
+    Example:
+
+    >>> import torchio as tio
+    >>> image = tio.datasets.FPG().t1
+    >>> image
+    ScalarImage(shape: (1, 256, 256, 176); spacing: (1.00, 1.00, 1.00); orientation: PIR+; path: "/home/fernando/.cache/torchio/fpg/t1.nii.gz")
+    >>> transpose = tio.Transpose()
+    >>> transposed = transpose(image)
+    >>> transposed
+    ScalarImage(shape: (1, 176, 256, 256); spacing: (1.00, 1.00, 1.00); orientation: RIP+; dtype: torch.IntTensor; memory: 44.0 MiB)
+    """
+
+    def apply_transform(self, subject: Subject) -> Subject:
+        for image in self.get_images(subject):
+            old_orientation = image.orientation_str
+            new_orientation = old_orientation[::-1]
+            transform = ToOrientation(new_orientation)
+            transposed = transform(image)
+            image.set_data(transposed.data)
+            image.affine = transposed.affine
+        return subject
+
+    def is_invertible(self):
+        return True
+
+    def inverse(self):
+        return self

tests/transforms/preprocessing/test_transpose.py

@@ -0,0 +1,21 @@
+import SimpleITK as sitk
+
+import torchio as tio
+
+from ...utils import TorchioTestCase
+
+
+class TestTranspose(TorchioTestCase):
+    def test_transpose(self):
+        transform = tio.Transpose()
+        image = tio.ScalarImage(self.get_image_path('image'))
+        transformed = transform(image)
+        sitk_image = sitk.GetImageFromArray(image.numpy()[0])
+        from_sitk = tio.ScalarImage.from_sitk(sitk_image)
+        self.assert_tensor_equal(transformed.data, from_sitk.data)
+
+    def test_orientation_reversed(self):
+        transform = tio.Transpose()
+        image = tio.ScalarImage(self.get_image_path('image'))
+        transformed = transform(image)
+        self.assertEqual(transformed.orientation_str, image.orientation_str[::-1])