import warnings
from typing import Any, Dict, List, Optional, Tuple, Union, cast
from torch import Tensor
from kornia.augmentation import random_generator as rg
from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
from kornia.constants import pi
from kornia.enhance import (
adjust_brightness_accumulative,
adjust_contrast_with_mean_subtraction,
adjust_hue,
adjust_saturation_with_gray_subtraction,
)
[docs]class ColorJitter(IntensityAugmentationBase2D):
r"""Apply a random transformation to the brightness, contrast, saturation and hue of a tensor image.
This implementation aligns PIL. Hence, the output is close to TorchVision.
.. image:: _static/img/ColorJitter.png
Args:
p: probability of applying the transformation.
brightness: The brightness factor to apply.
contrast: The contrast factor to apply.
saturation: The saturation factor to apply.
hue: The hue 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_brightness_accumulative`,
:func:`kornia.enhance.adjust_contrast_with_mean_subtraction`,
:func:`kornia.enhance.adjust_saturation_with_gray_subtraction`,
:func:`kornia.enhance.adjust_hue`.
Examples:
>>> rng = torch.manual_seed(0)
>>> inputs = torch.ones(1, 3, 3, 3)
>>> aug = ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.)
>>> aug(inputs)
tensor([[[[0.9993, 0.9993, 0.9993],
[0.9993, 0.9993, 0.9993],
[0.9993, 0.9993, 0.9993]],
<BLANKLINE>
[[0.9993, 0.9993, 0.9993],
[0.9993, 0.9993, 0.9993],
[0.9993, 0.9993, 0.9993]],
<BLANKLINE>
[[0.9993, 0.9993, 0.9993],
[0.9993, 0.9993, 0.9993],
[0.9993, 0.9993, 0.9993]]]])
To apply the exact augmenation again, you may take the advantage of the previous parameter state:
>>> input = torch.randn(1, 3, 32, 32)
>>> aug = ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.)
>>> (aug(input) == aug(input, params=aug._params)).all()
tensor(True)
"""
def __init__(
self,
brightness: Union[Tensor, float, Tuple[float, float], List[float]] = 0.0,
contrast: Union[Tensor, float, Tuple[float, float], List[float]] = 0.0,
saturation: Union[Tensor, float, Tuple[float, float], List[float]] = 0.0,
hue: Union[Tensor, float, Tuple[float, float], List[float]] = 0.0,
same_on_batch: bool = False,
p: float = 1.0,
keepdim: bool = False,
return_transform: Optional[bool] = None,
silence_instantiation_warning: bool = False,
) -> None:
super().__init__(p=p, return_transform=return_transform, same_on_batch=same_on_batch, keepdim=keepdim)
if not silence_instantiation_warning:
warnings.warn(
"`ColorJitter` is now following Torchvision implementation. Old "
"behavior can be retrieved by instantiating `ColorJiggle`.",
category=DeprecationWarning,
)
self.brightness = brightness
self.contrast = contrast
self.saturation = saturation
self.hue = hue
self._param_generator = cast(
rg.ColorJitterGenerator, rg.ColorJitterGenerator(brightness, contrast, saturation, hue)
)
def apply_transform(
self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None
) -> Tensor:
transforms = [
lambda img: adjust_brightness_accumulative(img, params["brightness_factor"]),
lambda img: adjust_contrast_with_mean_subtraction(img, params["contrast_factor"]),
lambda img: adjust_saturation_with_gray_subtraction(img, params["saturation_factor"]),
lambda img: adjust_hue(img, params["hue_factor"] * 2 * pi),
]
jittered = input
for idx in params["order"].tolist():
t = transforms[idx]
jittered = t(jittered)
return jittered