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.augmentation.utils import _range_bound
from kornia.core import Tensor
from kornia.enhance.adjust import adjust_contrast
[docs]class RandomContrast(IntensityAugmentationBase2D):
r"""Apply a random transformation to the contrast of a tensor image.
This implementation aligns PIL. Hence, the output is close to TorchVision.
.. image:: _static/img/RandomContrast.png
Args:
p: probability of applying the transformation.
contrast: the contrast factor to apply
clip_output: if true clip output
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_contrast
Examples:
>>> rng = torch.manual_seed(0)
>>> inputs = torch.rand(1, 3, 3, 3)
>>> aug = RandomContrast(contrast = (0.5,2.),p=1.)
>>> aug(inputs)
tensor([[[[0.2750, 0.4258, 0.0490],
[0.0732, 0.1704, 0.3514],
[0.2716, 0.4969, 0.2525]],
<BLANKLINE>
[[0.3505, 0.1934, 0.2227],
[0.0124, 0.0936, 0.1629],
[0.2874, 0.3867, 0.4434]],
<BLANKLINE>
[[0.0893, 0.1564, 0.3778],
[0.5072, 0.2201, 0.4845],
[0.2325, 0.3064, 0.5281]]]])
To apply the exact augmenation again, you may take the advantage of the previous parameter state:
>>> input = torch.rand(1, 3, 32, 32)
>>> aug = RandomContrast((0.8,1.2), p=1.)
>>> (aug(input) == aug(input, params=aug._params)).all()
tensor(True)
"""
def __init__(
self,
contrast: Tuple[float, float] = (1.0, 1.0),
clip_output: bool = True,
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.contrast: Tensor = _range_bound(contrast, 'contrast', center=1.0)
self._param_generator = rg.PlainUniformGenerator((self.contrast, "contrast_factor", None, None))
self.clip_output = clip_output
def apply_transform(
self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None
) -> Tensor:
contrast_factor = params["contrast_factor"].to(input)
return adjust_contrast(input, contrast_factor, self.clip_output)