o
    oiR                     @   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_brightnessc                       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 )RandomBrightnessa  Apply a random transformation to the brightness of a tensor image.

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

    .. image:: _static/img/RandomBrightness.png

    Args:
        brightness: the brightness factor to apply
        clip_output: if true clip output
        silence_instantiation_warning: if True, silence the warning at instantiation.
        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_brightness`

    Examples:
        >>> rng = torch.manual_seed(0)
        >>> inputs = torch.rand(1, 3, 3, 3)
        >>> aug = RandomBrightness(brightness = (0.5,2.),p=1.)
        >>> aug(inputs)
        tensor([[[[0.0505, 0.3225, 0.0000],
                  [0.0000, 0.0000, 0.1883],
                  [0.0443, 0.4507, 0.0099]],
        <BLANKLINE>
                 [[0.1866, 0.0000, 0.0000],
                  [0.0000, 0.0000, 0.0000],
                  [0.0728, 0.2519, 0.3543]],
        <BLANKLINE>
                 [[0.0000, 0.0000, 0.2359],
                  [0.4694, 0.0000, 0.4284],
                  [0.0000, 0.1072, 0.5070]]]])

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

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

          ?r   TFr   
brightnessclip_outputsame_on_batchpkeepdimreturnNc                    sD   t  j|||d t|dddd| _t| jdd d f| _|| _d S )N)r   r   r   r   r   )g        g       @)centerboundsbrightness_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/brightness.pyr   K   s   
zRandomBrightness.__init__inputparamsflags	transformc                 C   s    |d  |}t||d | jS )Nr      )tor
   r   )r   r!   r"   r#   r$   r   r   r   r    apply_transformY   s   z RandomBrightness.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@    1


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   