Source code for kornia.augmentation._2d.intensity.saturation

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)