from typing import Any, Dict, Optional, Tuple
from kornia.augmentation import random_generator as rg
from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
from kornia.core import Tensor
from kornia.enhance.adjust import adjust_gamma
[docs]class RandomGamma(IntensityAugmentationBase2D):
r"""Apply a random transformation to the gamma of a tensor image.
This implementation aligns PIL. Hence, the output is close to TorchVision.
.. image:: _static/img/RandomGamma.png
Args:
p: probability of applying the transformation.
gamma: the gamma factor to apply
gain: the gain factor to apply
silence_instantiation_warning: if True, silence the warning at instantiation.
same_on_batch: apply the same transformation across the batch.
keepdim: whether to keep the output shape the same as input (True) or broadcast it
to the batch form (False).
Shape:
- Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`, Optional: :math:`(B, 3, 3)`
- Output: :math:`(B, C, H, W)`
.. note::
This function internally uses :func:`kornia.enhance.adjust_gamma`
Examples:
>>> rng = torch.manual_seed(0)
>>> inputs = torch.rand(1, 3, 3, 3)
>>> aug = RandomGamma((0.5,2.),(1.5,1.5),p=1.)
>>> aug(inputs)
tensor([[[[1.0000, 1.0000, 0.3912],
[0.4883, 0.7801, 1.0000],
[1.0000, 1.0000, 0.9702]],
<BLANKLINE>
[[1.0000, 0.8368, 0.9048],
[0.1824, 0.5597, 0.7609],
[1.0000, 1.0000, 1.0000]],
<BLANKLINE>
[[0.5452, 0.7441, 1.0000],
[1.0000, 0.8990, 1.0000],
[0.9267, 1.0000, 1.0000]]]])
To apply the exact augmenation again, you may take the advantage of the previous parameter state:
>>> input = torch.rand(1, 3, 32, 32)
>>> aug = RandomGamma((0.8,1.2), p=1.)
>>> (aug(input) == aug(input, params=aug._params)).all()
tensor(True)
"""
def __init__(
self,
gamma: Tuple[float, float] = (1.0, 1.0),
gain: Tuple[float, float] = (1.0, 1.0),
same_on_batch: bool = False,
p: float = 1.0,
keepdim: bool = False,
return_transform: Optional[bool] = None,
) -> None:
super().__init__(p=p, return_transform=return_transform, same_on_batch=same_on_batch, keepdim=keepdim)
self._param_generator = rg.PlainUniformGenerator(
(gamma, "gamma_factor", None, None), (gain, "gain_factor", None, None)
)
def apply_transform(
self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None
) -> Tensor:
gamma_factor = params["gamma_factor"].to(input)
gain_factor = params["gain_factor"].to(input)
return adjust_gamma(input, gamma_factor, gain_factor)