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

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_brightness


[docs]class RandomBrightness(IntensityAugmentationBase2D): r"""Apply a random transformation to the brightness of a tensor image. This implementation aligns PIL. Hence, the output is close to TorchVision. .. image:: _static/img/RandomBrighness.png Args: p: probability of applying the transformation. brightness: the brightness 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_brightness` Examples: >>> rng = torch.manual_seed(0) >>> inputs = torch.rand(1, 3, 3, 3) >>> aug = RandomBrightness(brightness = (0.5,2.),p=1.) >>> aug(inputs) tensor([[[[0.0505, 0.3225, 0.0000], [0.0000, 0.0000, 0.1883], [0.0443, 0.4507, 0.0099]], <BLANKLINE> [[0.1866, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000], [0.0728, 0.2519, 0.3543]], <BLANKLINE> [[0.0000, 0.0000, 0.2359], [0.4694, 0.0000, 0.4284], [0.0000, 0.1072, 0.5070]]]]) To apply the exact augmenation again, you may take the advantage of the previous parameter state: >>> input = torch.rand(1, 3, 32, 32) >>> aug = RandomBrightness((0.8,1.2), p=1.) >>> (aug(input) == aug(input, params=aug._params)).all() tensor(True) """ def __init__( self, brightness: 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.brightness: Tensor = _range_bound(brightness, 'brightness', center=1.0, bounds=(0.0, 2.0)) self._param_generator = rg.PlainUniformGenerator((self.brightness, "brightness_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: brightness_factor = params["brightness_factor"].to(input) return adjust_brightness(input, brightness_factor - 1, self.clip_output)