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import warnings |
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from collections import defaultdict |
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from typing import Tuple, Optional, Sequence |
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import torch |
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from ....utils import to_tuple |
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from ....torchio import DATA, TypeRangeFloat |
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from ....data.subject import Subject |
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from ... import IntensityTransform |
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from .. import RandomTransform |
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class RandomGamma(RandomTransform, IntensityTransform): |
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r"""Randomly change contrast of an image by raising its values to the power |
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:math:`\gamma`. |
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Args: |
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log_gamma: Tuple :math:`(a, b)` to compute the exponent |
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:math:`\gamma = e ^ \beta`, |
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where :math:`\beta \sim \mathcal{U}(a, b)`. |
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If a single value :math:`d` is provided, then |
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:math:`\beta \sim \mathcal{U}(-d, d)`. |
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Negative and positive values for this argument perform gamma |
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compression and expansion, respectively. |
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See the `Gamma correction`_ Wikipedia entry for more information. |
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p: Probability that this transform will be applied. |
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keys: See :py:class:`~torchio.transforms.Transform`. |
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.. _Gamma correction: https://en.wikipedia.org/wiki/Gamma_correction |
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.. warning:: Fractional exponentiation of negative values is generally not |
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well-defined for non-complex numbers. |
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If negative values are found in the input image :math:`I`, |
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the applied transform is :math:`\text{sign}(I) |I|^\gamma`, |
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instead of the usual :math:`I^\gamma`. The |
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:py:class:`~torchio.transforms.preprocessing.intensity.rescale.RescaleIntensity` |
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transform may be used to ensure that all values are positive. |
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Example: |
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>>> import torchio as tio |
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>>> from torchio import RandomGamma |
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>>> from tio.datasets import FPG |
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>>> subject = FPG() |
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>>> transform = RandomGamma(log_gamma=(-0.3, 0.3)) # gamma between 0.74 and 1.34 |
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>>> transformed = transform(subject) |
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""" |
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def __init__( |
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self, |
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log_gamma: TypeRangeFloat = (-0.3, 0.3), |
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p: float = 1, |
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keys: Optional[Sequence[str]] = None, |
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): |
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super().__init__(p=p, keys=keys) |
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self.log_gamma_range = self.parse_range(log_gamma, 'log_gamma') |
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def apply_transform(self, subject: Subject) -> Subject: |
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arguments = defaultdict(dict) |
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for name, image in self.get_images_dict(subject).items(): |
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gammas = [self.get_params(self.log_gamma_range) for _ in image.data] |
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arguments['gamma'][name] = gammas |
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transform = Gamma(**arguments) |
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transformed = transform(subject) |
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return transformed |
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def get_params(self, log_gamma_range: Tuple[float, float]) -> float: |
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gamma = self.sample_uniform(*log_gamma_range).exp().item() |
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return gamma |
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class Gamma(IntensityTransform): |
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r"""Change contrast of an image by raising its values to the power |
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:math:`\gamma`. |
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Args: |
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gamma: Exponent to which values in the image will be raised. |
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Negative and positive values for this argument perform gamma |
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compression and expansion, respectively. |
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See the `Gamma correction`_ Wikipedia entry for more information. |
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keys: See :py:class:`~torchio.transforms.Transform`. |
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.. _Gamma correction: https://en.wikipedia.org/wiki/Gamma_correction |
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.. warning:: Fractional exponentiation of negative values is generally not |
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well-defined for non-complex numbers. |
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If negative values are found in the input image :math:`I`, |
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the applied transform is :math:`\text{sign}(I) |I|^\gamma`, |
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instead of the usual :math:`I^\gamma`. The |
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:py:class:`~torchio.transforms.preprocessing.intensity.rescale.RescaleIntensity` |
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transform may be used to ensure that all values are positive. |
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Example: |
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>>> import torchio as tio |
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>>> from torchio import Gamma |
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>>> from tio.datasets import FPG |
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>>> subject = FPG() |
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>>> transform = Gamma(0.8) |
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>>> transformed = transform(subject) |
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""" |
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def __init__( |
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self, |
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gamma: float, |
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keys: Optional[Sequence[str]] = None, |
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): |
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super().__init__(keys=keys) |
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self.gamma = gamma |
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self.args_names = ('gamma',) |
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self.invert_transform = False |
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def apply_transform(self, subject: Subject) -> Subject: |
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gamma = self.gamma |
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for name, image in self.get_images_dict(subject).items(): |
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if self.arguments_are_dict(): |
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gamma = self.gamma[name] |
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gammas = to_tuple(gamma, length=len(image.data)) |
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transformed_tensors = [] |
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for gamma, tensor in zip(gammas, image.data): |
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if self.invert_transform: |
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correction = power(tensor, 1 - gamma) |
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transformed_tensor = tensor * correction |
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else: |
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transformed_tensor = power(tensor, gamma) |
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transformed_tensors.append(transformed_tensor) |
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image[DATA] = torch.stack(transformed_tensors) |
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return subject |
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def power(tensor, gamma): |
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if tensor.min() < 0: |
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message = ( |
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'Negative values found in input tensor. See the documentation for' |
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' more details on the implemented workaround:' |
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' https://torchio.readthedocs.io/transforms/augmentation.html#randomgamma' |
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) |
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warnings.warn(message) |
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output = tensor.sign() * tensor.abs() ** gamma |
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else: |
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output = tensor ** gamma |
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return output |
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fepegar /
torchio
