Source code for kornia.augmentation._3d.intensity.equalize

from typing import Any, Dict, Optional

from torch import Tensor

from kornia.augmentation._3d.base import AugmentationBase3D
from kornia.enhance import equalize3d


[docs]class RandomEqualize3D(AugmentationBase3D): r"""Apply random equalization to 3D volumes (5D tensor). Args: p: probability of the image being equalized. 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, D, H, W)` or :math:`(B, C, D, H, W)`, Optional: :math:`(B, 4, 4)` - Output: :math:`(B, C, D, H, W)` Note: Input tensor must be float and normalized into [0, 1] for the best differentiability support. Additionally, this function accepts another transformation tensor (:math:`(B, 4, 4)`), then the applied transformation will be merged int to the input transformation tensor and returned. Examples: >>> import torch >>> rng = torch.manual_seed(0) >>> input = torch.rand(1, 1, 3, 3, 3) >>> aug = RandomEqualize3D(p=1.0) >>> aug(input) tensor([[[[[0.4963, 0.7682, 0.0885], [0.1320, 0.3074, 0.6341], [0.4901, 0.8964, 0.4556]], <BLANKLINE> [[0.6323, 0.3489, 0.4017], [0.0223, 0.1689, 0.2939], [0.5185, 0.6977, 0.8000]], <BLANKLINE> [[0.1610, 0.2823, 0.6816], [0.9152, 0.3971, 0.8742], [0.4194, 0.5529, 0.9527]]]]]) To apply the exact augmenation again, you may take the advantage of the previous parameter state: >>> input = torch.rand(1, 3, 32, 32, 32) >>> aug = RandomEqualize3D(p=1.) >>> (aug(input) == aug(input, params=aug._params)).all() tensor(True) """ def __init__( self, p: float = 0.5, same_on_batch: bool = False, 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 compute_transformation(self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any]) -> Tensor: return self.identity_matrix(input) def apply_transform( self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None ) -> Tensor: return equalize3d(input)