





"""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

a__					:     str							=				1_8_0





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Optional[int]:

 '''simple docstring'''






 snake_case__    							=				k_size // 2
 snake_case__							,   snake_case__    							=				np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
 snake_case__    							=				(
     1
     / (2 * np.pi * sigma)
     * np.exp(-(np.square(_UpperCAmelCase	) + np.square(_UpperCAmelCase	)) / (2 * np.square(_UpperCAmelCase	))	)
 )
 return g





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Optional[Any]:

 '''simple docstring'''






 snake_case__    							=				np.zeros(_UpperCAmelCase	)

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

   if (
       0 <= direction < PI / 8
       or 1_5 * PI / 8 <= direction <= 2 * PI
       or 7 * PI / 8 <= direction <= 9 * PI / 8
   ):
    snake_case__    							=				sobel_grad[row, col - 1]
    snake_case__    							=				sobel_grad[row, col + 1]
    if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
     snake_case__    							=				sobel_grad[row, col]

   elif (
       PI / 8 <= direction < 3 * PI / 8
       or 9 * PI / 8 <= direction < 1_1 * PI / 8
   ):
    snake_case__    							=				sobel_grad[row + 1, col - 1]
    snake_case__    							=				sobel_grad[row - 1, col + 1]
    if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
     snake_case__    							=				sobel_grad[row, col]

   elif (
       3 * PI / 8 <= direction < 5 * PI / 8
       or 1_1 * PI / 8 <= direction < 1_3 * PI / 8
   ):
    snake_case__    							=				sobel_grad[row - 1, col]
    snake_case__    							=				sobel_grad[row + 1, col]
    if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
     snake_case__    							=				sobel_grad[row, col]

   elif (
       5 * PI / 8 <= direction < 7 * PI / 8
       or 1_3 * PI / 8 <= direction < 1_5 * PI / 8
   ):
    snake_case__    							=				sobel_grad[row - 1, col - 1]
    snake_case__    							=				sobel_grad[row + 1, col + 1]
    if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
     snake_case__    							=				sobel_grad[row, col]

 return destination





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	)    ->  List[Any]:

 '''simple docstring'''






 for row in range(1	,     image_shape[0] - 1	):
  for col in range(1	,     image_shape[1] - 1	):
   if destination[row, col] >= threshold_high:
    snake_case__    							=				strong
   elif destination[row, col] <= threshold_low:
    snake_case__    							=				0
   else:
    snake_case__    							=				weak





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Optional[Any]:

 '''simple docstring'''






 for row in range(1	,     image_shape[0]	):
  for col in range(1	,     image_shape[1]	):
   if destination[row, col] == weak:
    if 2_5_5 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],
    ):
     snake_case__    							=				strong
    else:
     snake_case__    							=				0





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase=1_5	,     _UpperCAmelCase=3_0	,     _UpperCAmelCase=1_2_8	,     _UpperCAmelCase=2_5_5	)    ->  Any:

 '''simple docstring'''






 snake_case__    							=				img_convolve(_UpperCAmelCase	,     gen_gaussian_kernel(9	,     sigma=1.4	)	)
 # get the gradient and degree by sobel_filter
 snake_case__							,   snake_case__    							=				sobel_filter(_UpperCAmelCase	)
 snake_case__    							=				PI + np.radadeg(_UpperCAmelCase	)

 snake_case__    							=				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
   a__					:     List[Any]							=				cva.imread(r"""../image_data/lena.jpg""", 0)
   # canny edge detection
   a__					:     Optional[Any]							=				canny(lena)
   cva.imshow("""canny""", canny_destination)
   cva.waitKey(0)








"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__					:     Tuple							=				logging.get_logger(__name__)

a__					:     str							=				{
    """facebook/regnet-y-040""": """https://huggingface.co/facebook/regnet-y-040/blob/main/config.json""",
}



class      lowercase_				(     __lowerCAmelCase							):
       snake_case__				:							Tuple											=     """regnet"""
       snake_case__				:							List[Any]											=     ["""x""", """y"""]

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE=3   ,					__SCREAMING_SNAKE_CASE=32   ,					__SCREAMING_SNAKE_CASE=[128, 192, 512, 1088]   ,					__SCREAMING_SNAKE_CASE=[2, 6, 12, 2]   ,					__SCREAMING_SNAKE_CASE=64   ,					__SCREAMING_SNAKE_CASE="y"   ,					__SCREAMING_SNAKE_CASE="relu"   ,					**__SCREAMING_SNAKE_CASE   ,					):






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE     )
        if layer_type not in self.layer_types:
         raise ValueError(F"layer_type={layer_type} is not one of {','.join(self.layer_types     )}"     )
        snake_case__    							=				num_channels
        snake_case__    							=				embedding_size
        snake_case__    							=				hidden_sizes
        snake_case__    							=				depths
        snake_case__    							=				groups_width
        snake_case__    							=				layer_type
        snake_case__    							=				hidden_act
        # always downsample in the first stage
        snake_case__    							=				True

