import warnings
import numpy as np
from ... import SpatialTransform
from ....data.subject import Subject
from ....typing import TypeSpatialShape
from ....utils import to_tuple
from .crop_or_pad import CropOrPad
from .resample import Resample
[docs]
class Resize(SpatialTransform):
"""Resample images so the output shape matches the given target shape.
The field of view remains the same.
.. warning:: In most medical image applications, this transform should not
be used as it will deform the physical object by scaling anistropically
along the different dimensions. The solution to change an image size is
typically applying :class:`~torchio.transforms.Resample` and
:class:`~torchio.transforms.CropOrPad`.
Args:
target_shape: Tuple :math:`(W, H, D)`. If a single value :math:`N` is
provided, then :math:`W = H = D = N`. The size of dimensions set to
-1 will be kept.
image_interpolation: See :ref:`Interpolation`.
label_interpolation: See :ref:`Interpolation`.
"""
def __init__(
self,
target_shape: TypeSpatialShape,
image_interpolation: str = 'linear',
label_interpolation: str = 'nearest',
**kwargs,
):
super().__init__(**kwargs)
self.target_shape = np.asarray(to_tuple(target_shape, length=3))
self.image_interpolation = self.parse_interpolation(
image_interpolation,
)
self.label_interpolation = self.parse_interpolation(
label_interpolation,
)
self.args_names = [
'target_shape',
'image_interpolation',
'label_interpolation',
]
def apply_transform(self, subject: Subject) -> Subject:
shape_in = np.asarray(subject.spatial_shape)
shape_out = self.target_shape
negative_mask = shape_out == -1
shape_out[negative_mask] = shape_in[negative_mask]
spacing_in = np.asarray(subject.spacing)
spacing_out = shape_in / shape_out * spacing_in
resample = Resample(
spacing_out,
image_interpolation=self.image_interpolation,
label_interpolation=self.label_interpolation,
)
resampled = resample(subject)
assert isinstance(resampled, Subject)
# Sometimes, the output shape is one voxel too large
# Probably because Resample uses np.ceil to compute the shape
if not resampled.spatial_shape == tuple(shape_out):
message = (
f'Output shape {resampled.spatial_shape}'
f' != target shape {tuple(shape_out)}. Fixing with CropOrPad'
)
warnings.warn(message, RuntimeWarning, stacklevel=2)
crop_pad = CropOrPad(shape_out) # type: ignore[arg-type]
resampled = crop_pad(resampled)
assert isinstance(resampled, Subject)
return resampled