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

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)