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

from typing import Any, Dict, Optional

import torch
from torch import Tensor

from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
from kornia.color import rgb_to_grayscale


[docs]class RandomGrayscale(IntensityAugmentationBase2D): r"""Apply random transformation to Grayscale according to a probability p value. .. image:: _static/img/RandomGrayscale.png Args: rgb_weights: Weights that will be applied on each channel (RGB). The sum of the weights should add up to one. p: probability of the image to be transformed to grayscale. 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.color.rgb_to_grayscale`. Examples: >>> rng = torch.manual_seed(0) >>> inputs = torch.randn((1, 3, 3, 3)) >>> aug = RandomGrayscale(p=1.0) >>> aug(inputs) tensor([[[[-1.1344, -0.1330, 0.1517], [-0.0791, 0.6711, -0.1413], [-0.1717, -0.9023, 0.0819]], <BLANKLINE> [[-1.1344, -0.1330, 0.1517], [-0.0791, 0.6711, -0.1413], [-0.1717, -0.9023, 0.0819]], <BLANKLINE> [[-1.1344, -0.1330, 0.1517], [-0.0791, 0.6711, -0.1413], [-0.1717, -0.9023, 0.0819]]]]) To apply the exact augmenation again, you may take the advantage of the previous parameter state: >>> input = torch.randn(1, 3, 32, 32) >>> aug = RandomGrayscale(p=1.0) >>> (aug(input) == aug(input, params=aug._params)).all() tensor(True) """ def __init__( self, same_on_batch: bool = False, p: float = 0.1, 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) def apply_transform( self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None ) -> Tensor: # Make sure it returns (*, 3, H, W) grayscale = torch.ones_like(input) grayscale[:] = rgb_to_grayscale(input) return grayscale