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_saturation
[docs]class RandomSaturation(IntensityAugmentationBase2D):
r"""Apply a random transformation to the saturation of a tensor image.
This implementation aligns PIL. Hence, the output is close to TorchVision.
.. image:: _static/img/RandomSaturation.png
Args:
p: probability of applying the transformation.
saturation: the saturation 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_saturation
Examples:
>>> rng = torch.manual_seed(0)
>>> inputs = torch.rand(1, 3, 3, 3)
>>> aug = RandomSaturation(saturation = (0.5,2.),p=1.)
>>> aug(inputs)
tensor([[[[0.5569, 0.7682, 0.3529],
[0.4811, 0.3474, 0.7411],
[0.5028, 0.8964, 0.6772]],
<BLANKLINE>
[[0.6323, 0.5358, 0.5265],
[0.4203, 0.2706, 0.5525],
[0.5185, 0.7863, 0.8681]],
<BLANKLINE>
[[0.3711, 0.4989, 0.6816],
[0.9152, 0.3971, 0.8742],
[0.4636, 0.7060, 0.9527]]]])
To apply the exact augmenation again, you may take the advantage of the previous parameter state:
>>> input = torch.rand(1, 3, 32, 32)
>>> aug = RandomSaturation((0.8,1.2), p=1.)
>>> (aug(input) == aug(input, params=aug._params)).all()
tensor(True)
"""
def __init__(
self,
saturation: 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.saturation: Tensor = _range_bound(saturation, 'saturation', center=1.0)
self._param_generator = rg.PlainUniformGenerator((self.saturation, "saturation_factor", None, None))
def apply_transform(
self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None
) -> Tensor:
saturation_factor = params["saturation_factor"].to(input)
return adjust_saturation(input, saturation_factor)