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from typing import Union, Tuple, Optional, List |
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import torch |
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from ....torchio import TypeRangeFloat |
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from ....data.subject import Subject |
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from ....utils import to_tuple |
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from .. import RandomTransform |
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from ...preprocessing import Resample |
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class RandomDownsample(RandomTransform): |
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"""Downsample an image along an axis. |
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This transform simulates an image that has been acquired using anisotropic |
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spacing, using downsampling with nearest neighbor interpolation. |
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Args: |
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axes: Axis or tuple of axes along which the image will be downsampled. |
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downsampling: Downsampling factor :math:`m \gt 1`. If a tuple |
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:math:`(a, b)` is provided then :math:`m \sim \mathcal{U}(a, b)`. |
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p: Probability that this transform will be applied. |
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seed: See :py:class:`~torchio.transforms.augmentation.RandomTransform`. |
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Example: |
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>>> from torchio import RandomDownsample |
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>>> from torchio.datasets import Colin27 |
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>>> transform = RandomDownsample(axes=1, downsampling=2.) # Multiply spacing of second axis by 2 |
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>>> transform = RandomDownsample( |
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... axes=(0, 1, 2), downsampling=(2, 5) |
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... ) # Multiply spacing of one of the 3 axes by a factor randomly chosen in [2, 5] |
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>>> colin = Colin27 |
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>>> transformed = transform(colin) # images have now anisotropic spacing |
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""" |
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def __init__( |
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self, |
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axes: Union[int, Tuple[int, ...]] = (0, 1, 2), |
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downsampling: TypeRangeFloat = (1.5, 5), |
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p: float = 1, |
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seed: Optional[int] = None, |
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): |
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super().__init__(p=p, seed=seed) |
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self.axes = self.parse_axes(axes) |
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self.downsampling_range = self.parse_range( |
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downsampling, 'downsampling', min_constraint=1) |
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@staticmethod |
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def get_params( |
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axes: Tuple[int, ...], |
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downsampling_range: Tuple[float, float], |
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) -> List[bool]: |
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axis = axes[torch.randint(0, len(axes), (1,))] |
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downsampling = torch.FloatTensor(1).uniform_(*downsampling_range).item() |
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return axis, downsampling |
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@staticmethod |
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def parse_axes(axes: Union[int, Tuple[int, ...]]): |
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axes_tuple = to_tuple(axes) |
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for axis in axes_tuple: |
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is_int = isinstance(axis, int) |
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if not is_int or axis not in (0, 1, 2): |
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raise ValueError('All axes must be 0, 1 or 2') |
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return axes_tuple |
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def apply_transform(self, sample: Subject) -> Subject: |
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axis, downsampling = self.get_params(self.axes, self.downsampling_range) |
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random_parameters_dict = {'axis': axis, 'downsampling': downsampling} |
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target_spacing = list(sample.spacing) |
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target_spacing[axis] *= downsampling |
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transform = Resample( |
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tuple(target_spacing), |
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image_interpolation='nearest', |
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copy=False, # already copied in super().__init__ |
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) |
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sample = transform(sample) |
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sample.add_transform(self, random_parameters_dict) |
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return sample |
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fepegar /
torchio
