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

import warnings
from typing import Any, Dict, List, Optional, Tuple, Union

from kornia.augmentation import random_generator as rg
from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
from kornia.constants import pi
from kornia.core import Tensor
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. However, it does not follow the color theory and is not be actively maintained. Prefer using :func:`kornia.augmentation.ColorJiggle` .. 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 = 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