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import warnings |
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import numpy as np |
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from ....utils import to_tuple |
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from ....data.subject import Subject |
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from ....typing import TypeSpatialShape |
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from ... import SpatialTransform |
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from .resample import Resample |
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from .crop_or_pad import CropOrPad |
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class Resize(SpatialTransform): |
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"""Resample images so the output shape matches the given target shape. |
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The field of view remains the same. |
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Args: |
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target_shape: Tuple :math:`(W, H, D)`. If a single value :math:`N` is |
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provided, then :math:`W = H = D = N`. |
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image_interpolation: See :ref:`Interpolation`. |
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""" |
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def __init__( |
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self, |
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target_shape: TypeSpatialShape, |
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image_interpolation: str = 'linear', |
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**kwargs |
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): |
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super().__init__(**kwargs) |
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self.target_shape = np.asarray(to_tuple(target_shape, length=3)) |
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self.image_interpolation = self.parse_interpolation( |
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image_interpolation) |
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self.args_names = ( |
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'target_shape', |
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'image_interpolation', |
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) |
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def apply_transform(self, subject: Subject) -> Subject: |
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shape_in = np.asarray(subject.spatial_shape) |
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shape_out = self.target_shape |
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spacing_in = np.asarray(subject.spacing) |
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spacing_out = shape_in / shape_out * spacing_in |
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resample = Resample( |
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spacing_out, |
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image_interpolation=self.image_interpolation, |
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) |
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resampled = resample(subject) |
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# Sometimes, the output shape is one voxel too large |
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# Probably because Resample uses np.ceil to compute the shape |
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if not resampled.spatial_shape == tuple(shape_out): |
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message = ( |
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f'Output shape {resampled.spatial_shape}' |
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f' != target shape {tuple(shape_out)}. Fixing with CropOrPad' |
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) |
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warnings.warn(message) |
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crop_pad = CropOrPad(shape_out) |
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resampled = crop_pad(resampled) |
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return resampled |
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fepegar /
torchio
