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)