torchio.transforms.preprocessing.spatial.transpose

Complexity

Total Complexity

4

Size/Duplication

Total Lines	39
Duplicated Lines	0 %

Importance

Changes

0

3 Methods

Rating	Name	Duplication	Size	Complexity
A	Transpose.inverse()	0	2	1
A	Transpose.is_invertible()	0	2	1
A	Transpose.apply_transform()	0	9	2

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