o
    oi                     @   sT   d dl mZmZmZ d dl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OptionalN)Tensor)IntensityAugmentationBase2D)rgb_to_grayscalec                       st   e Zd ZdZ	dde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 )RandomGrayscalea  Apply random transformation to Grayscale according to a probability p value.

    .. image:: _static/img/RandomGrayscale.png

    Args:
        rgb_weights: Weights that will be applied on each channel (RGB).
            The sum of the weights should add up to one.
        p: probability of the image to be transformed to grayscale.
        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.color.rgb_to_grayscale`.

    Examples:
        >>> rng = torch.manual_seed(0)
        >>> inputs = torch.randn((1, 3, 3, 3))
        >>> aug = RandomGrayscale(p=1.0)
        >>> aug(inputs)
        tensor([[[[-1.1344, -0.1330,  0.1517],
                  [-0.0791,  0.6711, -0.1413],
                  [-0.1717, -0.9023,  0.0819]],
        <BLANKLINE>
                 [[-1.1344, -0.1330,  0.1517],
                  [-0.0791,  0.6711, -0.1413],
                  [-0.1717, -0.9023,  0.0819]],
        <BLANKLINE>
                 [[-1.1344, -0.1330,  0.1517],
                  [-0.0791,  0.6711, -0.1413],
                  [-0.1717, -0.9023,  0.0819]]]])

    To apply the exact augmenation again, you may take the advantage of the previous parameter state:
        >>> input = torch.randn(1, 3, 32, 32)
        >>> aug = RandomGrayscale(p=1.0)
        >>> (aug(input) == aug(input, params=aug._params)).all()
        tensor(True)

    NF皙?rgb_weightssame_on_batchpkeepdimreturnc                    s   t  j|||d || _d S )N)r   r   r   )super__init__r
   )selfr
   r   r   r   	__class__ _/home/ubuntu/.local/lib/python3.10/site-packages/kornia/augmentation/_2d/intensity/grayscale.pyr   H   s   
zRandomGrayscale.__init__inputparamsflags	transformc                 C   s$   t |}t|| jd|d d < |S )N)r
   )torch	ones_liker   r
   )r   r   r   r   r   	grayscaler   r   r   apply_transformN   s   
zRandomGrayscale.apply_transform)NFr	   F)N)__name__
__module____qualname____doc__r   r   boolfloatr   r   strr   r   __classcell__r   r   r   r   r      s4    -

r   )typingr   r   r   r   r   &kornia.augmentation._2d.intensity.baser   kornia.colorr   r   r   r   r   r   <module>   s   