o
    oi#                     @   sL   d dl mZmZmZ d dlmZ d dlmZ d dlm	Z	 G dd deZ
dS )    )AnyDictOptional)Tensor)IntensityAugmentationBase3D
equalize3dc                       s   e Zd ZdZddedededdf fd	d
Zdedee	ef dee	e
f defddZ	ddedee	ef dee	e
f dee def
ddZ  ZS )RandomEqualize3Da  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)

          ?Fpsame_on_batchkeepdimreturnNc                    s   t  j|||d d S )N)r   r   r   )super__init__)selfr   r   r   	__class__ ^/home/ubuntu/.local/lib/python3.10/site-packages/kornia/augmentation/_3d/intensity/equalize.pyr   F   s   zRandomEqualize3D.__init__inputparamsflagsc                 C   s
   |  |S N)identity_matrix)r   r   r   r   r   r   r   compute_transformationI   s   
z'RandomEqualize3D.compute_transformation	transformc                 C   s   t |S r   r   )r   r   r   r   r   r   r   r   apply_transformL   s   z RandomEqualize3D.apply_transform)r
   FFr   )__name__
__module____qualname____doc__floatboolr   r   r   strr   r   r   r   __classcell__r   r   r   r   r	      s      +*

r	   N)typingr   r   r   torchr   &kornia.augmentation._3d.intensity.baser   kornia.enhancer   r	   r   r   r   r   <module>   s
   