"""simple docstring"""







import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

_SCREAMING_SNAKE_CASE	:		List[str]   =   1_8_0



def       A__    (	lowercase_     :	List[Any]      , lowercase_     :	Optional[int]						)  ->							List[Any]:
   __lowerCamelCase    		=					k_size // 2
   __lowerCamelCase    		=					np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
   __lowerCamelCase    		=					(
       1
       / (2 * np.pi * sigma)
       * np.exp(-(np.square(UpperCAmelCase__						) + np.square(UpperCAmelCase__						)) / (2 * np.square(UpperCAmelCase__						))						)
   )
   return g



def       A__    (	lowercase_     :	Dict      , lowercase_     :	Union[str, Any]      , lowercase_     :	int						)  ->							List[Any]:
   __lowerCamelCase    		=					np.zeros(UpperCAmelCase__						)

   for row in range(1      , image_shape[0] - 1						):
      for col in range(1      , image_shape[1] - 1						):
         __lowerCamelCase    		=					gradient_direction[row, col]

         if (
             0 <= direction < PI / 8
             or 15 * PI / 8 <= direction <= 2 * PI
             or 7 * PI / 8 <= direction <= 9 * PI / 8
         ):
            __lowerCamelCase    		=					sobel_grad[row, col - 1]
            __lowerCamelCase    		=					sobel_grad[row, col + 1]
            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
               __lowerCamelCase    		=					sobel_grad[row, col]

         elif (
             PI / 8 <= direction < 3 * PI / 8
             or 9 * PI / 8 <= direction < 11 * PI / 8
         ):
            __lowerCamelCase    		=					sobel_grad[row + 1, col - 1]
            __lowerCamelCase    		=					sobel_grad[row - 1, col + 1]
            if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
               __lowerCamelCase    		=					sobel_grad[row, col]

         elif (
             3 * PI / 8 <= direction < 5 * PI / 8
             or 11 * PI / 8 <= direction < 13 * PI / 8
         ):
            __lowerCamelCase    		=					sobel_grad[row - 1, col]
            __lowerCamelCase    		=					sobel_grad[row + 1, col]
            if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
               __lowerCamelCase    		=					sobel_grad[row, col]

         elif (
             5 * PI / 8 <= direction < 7 * PI / 8
             or 13 * PI / 8 <= direction < 15 * PI / 8
         ):
            __lowerCamelCase    		=					sobel_grad[row - 1, col - 1]
            __lowerCamelCase    		=					sobel_grad[row + 1, col + 1]
            if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
               __lowerCamelCase    		=					sobel_grad[row, col]

   return destination



def       A__    (	lowercase_     :	List[str]      , lowercase_     :	Optional[int]      , lowercase_     :	Any      , lowercase_     :	Any      , lowercase_     :	List[Any]      , lowercase_     :	int						)  ->							Optional[Any]:
   for row in range(1      , image_shape[0] - 1						):
      for col in range(1      , image_shape[1] - 1						):
         if destination[row, col] >= threshold_high:
            __lowerCamelCase    		=					strong
         elif destination[row, col] <= threshold_low:
            __lowerCamelCase    		=					0
         else:
            __lowerCamelCase    		=					weak



def       A__    (	lowercase_     :	Any      , lowercase_     :	Tuple      , lowercase_     :	Tuple      , lowercase_     :	Optional[int]						)  ->							Optional[Any]:
   for row in range(1      , image_shape[0]						):
      for col in range(1      , image_shape[1]						):
         if destination[row, col] == weak:
            if 255 in (
                destination[row, col + 1],
                destination[row, col - 1],
                destination[row - 1, col],
                destination[row + 1, col],
                destination[row - 1, col - 1],
                destination[row + 1, col - 1],
                destination[row - 1, col + 1],
                destination[row + 1, col + 1],
            ):
               __lowerCamelCase    		=					strong
            else:
               __lowerCamelCase    		=					0



def       A__    (	lowercase_     :	List[Any]      , lowercase_     :	Dict=15      , lowercase_     :	str=30      , lowercase_     :	Optional[Any]=128      , lowercase_     :	List[Any]=255						)  ->							Optional[int]:
   # gaussian_filter
   __lowerCamelCase    		=					img_convolve(UpperCAmelCase__      , gen_gaussian_kernel(9      , sigma=1.4						)						)
   # get the gradient and degree by sobel_filter
   __lowerCamelCase    		=					sobel_filter(UpperCAmelCase__						)
   __lowerCamelCase    		=					PI + np.radadeg(UpperCAmelCase__						)

   __lowerCamelCase    		=					suppress_non_maximum(image.shape      , UpperCAmelCase__      , UpperCAmelCase__						)

   detect_high_low_threshold(
       image.shape      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__						)

   track_edge(image.shape      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__						)

   return destination


if __name__ == "__main__":
     # read original image in gray mode
     _SCREAMING_SNAKE_CASE	:		Dict   =   cva.imread(R'''../image_data/lena.jpg''', 0)
     # canny edge detection
     _SCREAMING_SNAKE_CASE	:		Any   =   canny(lena)
     cva.imshow('''canny''', canny_destination)
     cva.waitKey(0)







'''simple docstring'''




from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments




def 				lowerCamelCase      ( )     -> List[Any]:
							lowercase_   :     Tuple									=			HfArgumentParser(UpperCAmelCase__					)
							try:
														lowercase_   :     List[Any]									=			parser.parse_args_into_dataclasses()[0]
							except ValueError as e:
														lowercase_   :     int									=			"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
														lowercase_   :     Any									=			""" """.join(str(UpperCAmelCase__					).split(""" """					)[:-1]					)
														lowercase_   :     Optional[Any]									=			""""""
														lowercase_   :     List[str]									=			eval(str(UpperCAmelCase__					).split(""" """					)[-1]					)
														lowercase_   :     Dict									=			[]
														for arg in depreciated_args:
																					# arg[2:] removes '--'
																					if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
																												# arg[5:] removes '--no_'
																												full_error_msg += arg_error_msg.format(arg[5:]					)
																					else:
																												wrong_args.append(UpperCAmelCase__					)
														if len(UpperCAmelCase__					) > 0:
																					lowercase_   :     Dict									=			full_error_msg + begin_error_msg + str(UpperCAmelCase__					)
														raise ValueError(UpperCAmelCase__					)

							lowercase_   :     Optional[int]									=			PyTorchBenchmark(args=UpperCAmelCase__					)
							benchmark.run()


if __name__ == "__main__":
						main()





