





import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve

def      __snake_case						(					_lowerCAmelCase : Tuple	) ->						int:
   A_					:				Union[str, Any]          =			np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]	)
   A_					:				Tuple          =			np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]	)

   A_					:				Optional[int]          =			np.abs(img_convolve(_lowerCAmelCase				,				_lowerCAmelCase	)	)
   A_					:				Dict          =			np.abs(img_convolve(_lowerCAmelCase				,				_lowerCAmelCase	)	)
   # modify the pix within [0, 255]
   A_					:				List[Any]          =			dst_x * 255 / np.max(_lowerCAmelCase	)
   A_					:				int          =			dst_y * 255 / np.max(_lowerCAmelCase	)

   A_					:				Union[str, Any]          =			np.sqrt((np.square(_lowerCAmelCase	)) + (np.square(_lowerCAmelCase	))	)
   A_					:				Dict          =			dst_xy * 255 / np.max(_lowerCAmelCase	)
   A_					:				List[Any]          =			dst_xy.astype(np.uinta	)

   A_					:				List[Any]          =			np.arctana(_lowerCAmelCase				,				_lowerCAmelCase	)
   return dst, theta


if __name__ == "__main__":
 # read original image
 _lowerCAmelCase							:      Tuple						=	imread('''../image_data/lena.jpg''')
 # turn image in gray scale value
 _lowerCAmelCase							:      Dict						=	cvtColor(img, COLOR_BGR2GRAY)

 _lowerCAmelCase							:      Union[str, Any]						=	sobel_filter(gray)

 # show result images
 imshow('''sobel filter''', sobel_grad)
 imshow('''sobel theta''', sobel_theta)
 waitKey(0)







import torch
from torch import autograd


class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Any			,      snake_case       :torch.tensor			,      snake_case       :float			,      snake_case       :bool  ):





        '''simple docstring'''




        A_					:				int          =			inputs.numel()
        A_					:				str          =			int(0.005 * nb_elems  ) + 1
        if sigmoid:
           A_					:				List[Any]          =			(torch.sigmoid(snake_case  ) > threshold).type(inputs.type()  )
        else:
           A_					:				Optional[Any]          =			(inputs > threshold).type(inputs.type()  )
        if mask.sum() < nb_min:
           # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
           A_					:				Tuple          =			inputs.flatten().kthvalue(max(nb_elems - nb_min			,      1  )  ).values
           A_					:				Optional[int]          =			(inputs > k_threshold).type(inputs.type()  )
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Optional[int]			,      snake_case       :int  ):





        '''simple docstring'''




        return gradOutput, None, None




class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Tuple			,      snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				Tuple          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.flatten().sort(descending=snake_case  )
        A_					:				Dict          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				str          =			mask.flatten()
        A_					:				List[Any]          =			0
        A_					:				List[str]          =			1
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :List[Any]			,      snake_case       :Optional[Any]  ):





        '''simple docstring'''




        return gradOutput, None




class 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				str          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.abs().flatten().sort(descending=snake_case  )
        A_					:				int          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				List[str]          =			mask.flatten()
        A_					:				List[str]          =			0
        A_					:				Union[str, Any]          =			1
        return mask
