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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
TorchIO-project /
torchio
