Source code for torchio.transforms.augmentation.intensity.random_gamma

from collections import defaultdict
from typing import Tuple

import torch

from ....utils import to_tuple
from ....typing import TypeRangeFloat
from ....data.subject import Subject
from ... import IntensityTransform
from .. import RandomTransform


[docs]class RandomGamma(RandomTransform, IntensityTransform): r"""Randomly change contrast of an image by raising its values to the power :math:`\gamma`. Args: log_gamma: Tuple :math:`(a, b)` to compute the exponent :math:`\gamma = e ^ \beta`, where :math:`\beta \sim \mathcal{U}(a, b)`. If a single value :math:`d` is provided, then :math:`\beta \sim \mathcal{U}(-d, d)`. Negative and positive values for this argument perform gamma compression and expansion, respectively. See the `Gamma correction`_ Wikipedia entry for more information. **kwargs: See :class:`~torchio.transforms.Transform` for additional keyword arguments. .. _Gamma correction: https://en.wikipedia.org/wiki/Gamma_correction .. note:: Fractional exponentiation of negative values is generally not well-defined for non-complex numbers. If negative values are found in the input image :math:`I`, the applied transform is :math:`\text{sign}(I) |I|^\gamma`, instead of the usual :math:`I^\gamma`. The :class:`~torchio.transforms.RescaleIntensity` transform may be used to ensure that all values are positive. This is generally not problematic, but it is recommended to visualize results on image with negative values. More information can be found on `this StackExchange question`_. .. _this StackExchange question: https://math.stackexchange.com/questions/317528/how-do-you-compute-negative-numbers-to-fractional-powers Example: >>> import torchio as tio >>> subject = tio.datasets.FPG() >>> transform = tio.RandomGamma(log_gamma=(-0.3, 0.3)) # gamma between 0.74 and 1.34 >>> transformed = transform(subject) """ # noqa: E501 def __init__( self, log_gamma: TypeRangeFloat = (-0.3, 0.3), **kwargs ): super().__init__(**kwargs) self.log_gamma_range = self._parse_range(log_gamma, 'log_gamma') def apply_transform(self, subject: Subject) -> Subject: arguments = defaultdict(dict) for name, image in self.get_images_dict(subject).items(): gammas = [ self.get_params(self.log_gamma_range) for _ in image.data ] arguments['gamma'][name] = gammas transform = Gamma(**self.add_include_exclude(arguments)) transformed = transform(subject) return transformed def get_params(self, log_gamma_range: Tuple[float, float]) -> float: gamma = self.sample_uniform(*log_gamma_range).exp().item() return gamma
class Gamma(IntensityTransform): r"""Change contrast of an image by raising its values to the power :math:`\gamma`. Args: gamma: Exponent to which values in the image will be raised. Negative and positive values for this argument perform gamma compression and expansion, respectively. See the `Gamma correction`_ Wikipedia entry for more information. **kwargs: See :class:`~torchio.transforms.Transform` for additional keyword arguments. .. _Gamma correction: https://en.wikipedia.org/wiki/Gamma_correction .. note:: Fractional exponentiation of negative values is generally not well-defined for non-complex numbers. If negative values are found in the input image :math:`I`, the applied transform is :math:`\text{sign}(I) |I|^\gamma`, instead of the usual :math:`I^\gamma`. The :class:`~torchio.transforms.preprocessing.intensity.rescale.RescaleIntensity` transform may be used to ensure that all values are positive. This is generally not problematic, but it is recommended to visualize results on image with negative values. More information can be found on `this StackExchange question`_. .. _this StackExchange question: https://math.stackexchange.com/questions/317528/how-do-you-compute-negative-numbers-to-fractional-powers Example: >>> import torchio as tio >>> subject = tio.datasets.FPG() >>> transform = tio.Gamma(0.8) >>> transformed = transform(subject) """ # noqa: E501 def __init__( self, gamma: float, **kwargs ): super().__init__(**kwargs) self.gamma = gamma self.args_names = ('gamma',) self.invert_transform = False def apply_transform(self, subject: Subject) -> Subject: gamma = self.gamma for name, image in self.get_images_dict(subject).items(): if self.arguments_are_dict(): gamma = self.gamma[name] gammas = to_tuple(gamma, length=len(image.data)) transformed_tensors = [] image.set_data(image.data.float()) for gamma, tensor in zip(gammas, image.data): if self.invert_transform: correction = power(tensor, 1 - gamma) transformed_tensor = tensor * correction else: transformed_tensor = power(tensor, gamma) transformed_tensors.append(transformed_tensor) image.set_data(torch.stack(transformed_tensors)) return subject def power(tensor, gamma): if tensor.min() < 0: output = tensor.sign() * tensor.abs() ** gamma else: output = tensor ** gamma return output