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