o
    oi                     @   sh   d dl mZmZmZmZ d dlmZ d dl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Tuple)random_generator)IntensityAugmentationBase2D)_range_bound)Tensor)adjust_contrastc                       s   e Zd ZdZ					ddeeef deded	ed
eddf 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 )RandomContrasta  Apply a random transformation to the contrast of a tensor image.

    This implementation aligns PIL. Hence, the output is close to TorchVision.

    .. image:: _static/img/RandomContrast.png

    Args:
        contrast: the contrast factor to apply.
        clip_output: if true clip output.
        same_on_batch: apply the same transformation across the batch.
        p: probability of applying the transformation.
        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_contrast`

    Examples:
        >>> rng = torch.manual_seed(0)
        >>> inputs = torch.rand(1, 3, 3, 3)
        >>> aug = RandomContrast(contrast = (0.5, 2.), p = 1.)
        >>> aug(inputs)
        tensor([[[[0.2750, 0.4258, 0.0490],
                  [0.0732, 0.1704, 0.3514],
                  [0.2716, 0.4969, 0.2525]],
        <BLANKLINE>
                 [[0.3505, 0.1934, 0.2227],
                  [0.0124, 0.0936, 0.1629],
                  [0.2874, 0.3867, 0.4434]],
        <BLANKLINE>
                 [[0.0893, 0.1564, 0.3778],
                  [0.5072, 0.2201, 0.4845],
                  [0.2325, 0.3064, 0.5281]]]])

    To apply the exact augmenation again, you may take the advantage of the previous parameter state:

        >>> input = torch.rand(1, 3, 32, 32)
        >>> aug = RandomContrast((0.8,1.2), p=1.)
        >>> (aug(input) == aug(input, params=aug._params)).all()
        tensor(True)

          ?r   TFr   contrastclip_outputsame_on_batchpkeepdimreturnNc                    sB   t  j|||d t|ddd| _t| jdd d f| _|| _d S )N)r   r   r   r   r   )centercontrast_factor)super__init__r   r   rgPlainUniformGenerator_param_generatorr   )selfr   r   r   r   r   	__class__ ^/home/ubuntu/.local/lib/python3.10/site-packages/kornia/augmentation/_2d/intensity/contrast.pyr   J   s   
zRandomContrast.__init__inputparamsflags	transformc                 C   s   |d  |}t||| jS )Nr   )tor
   r   )r   r    r!   r"   r#   r   r   r   r   apply_transformX   s   zRandomContrast.apply_transform)r   TFr   F)N)__name__
__module____qualname____doc__r   floatboolr   r	   r   strr   r   r%   __classcell__r   r   r   r   r      s@    0


r   N)typingr   r   r   r   kornia.augmentationr   r   &kornia.augmentation._2d.intensity.baser   kornia.augmentation.utilsr   kornia.corer	   kornia.enhance.adjustr
   r   r   r   r   r   <module>   s   