# Source code for kornia.augmentation._2d.intensity.equalize

from typing import Any, Dict, Optional

from torch import Tensor

from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
from kornia.enhance import equalize

[docs]class RandomEqualize(IntensityAugmentationBase2D): r"""Equalize given tensor image or a batch of tensor images randomly. .. image:: _static/img/RandomEqualize.png Args: p: Probability to equalize an image. 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.equalize`. Examples: >>> rng = torch.manual_seed(0) >>> input = torch.rand(1, 1, 5, 5) >>> equalize = RandomEqualize(p=1.) >>> equalize(input) tensor([[[[0.4963, 0.7682, 0.0885, 0.1320, 0.3074], [0.6341, 0.4901, 0.8964, 0.4556, 0.6323], [0.3489, 0.4017, 0.0223, 0.1689, 0.2939], [0.5185, 0.6977, 0.8000, 0.1610, 0.2823], [0.6816, 0.9152, 0.3971, 0.8742, 0.4194]]]]) To apply the exact augmenation again, you may take the advantage of the previous parameter state: >>> input = torch.rand(1, 3, 32, 32) >>> aug = RandomEqualize(p=1.) >>> (aug(input) == aug(input, params=aug._params)).all() tensor(True) """ def __init__(self, same_on_batch: bool = False, p: float = 0.5, keepdim: bool = False) -> None: super().__init__(p=p, 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: return equalize(input)