'''simple docstring'''





import cva
import numpy as np






def SCREAMING_SNAKE_CASE__  (				snake_case       :				np.ndarray				,	snake_case       :				int				,	snake_case       :				int				,	snake_case       :				int  )   ->				int:







     """simple docstring"""

     try:
          return int(image[x_coordinate][y_coordinate] >= center  )
     except (IndexError, TypeError):
          return 0






def SCREAMING_SNAKE_CASE__  (				snake_case       :				np.ndarray				,	snake_case       :				int				,	snake_case       :				int  )   ->				int:







     """simple docstring"""
     a					:							List[Any]							= image[x_coordinate][y_coordinate]
     a					:							Any							= [1, 2, 4, 8, 16, 32, 64, 128]

     # skip get_neighbors_pixel if center is null
     if center is None:
          return 0

     # Starting from the top right, assigning value to pixels clockwise
     a					:							Tuple							= [
         get_neighbors_pixel(snake_case				,	x_coordinate - 1				,	y_coordinate + 1				,	snake_case  ),
         get_neighbors_pixel(snake_case				,	snake_case				,	y_coordinate + 1				,	snake_case  ),
         get_neighbors_pixel(snake_case				,	x_coordinate - 1				,	snake_case				,	snake_case  ),
         get_neighbors_pixel(snake_case				,	x_coordinate + 1				,	y_coordinate + 1				,	snake_case  ),
         get_neighbors_pixel(snake_case				,	x_coordinate + 1				,	snake_case				,	snake_case  ),
         get_neighbors_pixel(snake_case				,	x_coordinate + 1				,	y_coordinate - 1				,	snake_case  ),
         get_neighbors_pixel(snake_case				,	snake_case				,	y_coordinate - 1				,	snake_case  ),
         get_neighbors_pixel(snake_case				,	x_coordinate - 1				,	y_coordinate - 1				,	snake_case  ),
     ]

     # Converting the binary value to decimal.
     return sum(
         binary_value * power for binary_value, power in zip(snake_case				,	snake_case  )  )


if __name__ == "__main__":
      # Reading the image and converting it to grayscale.
      UpperCamelCase       :     Optional[Any]			=    cva.imread(
          """digital_image_processing/image_data/lena.jpg""", cva.IMREAD_GRAYSCALE
      )

      # Create a numpy array as the same height and width of read image
      UpperCamelCase       :     Any			=    np.zeros((image.shape[0], image.shape[1]))

      # Iterating through the image and calculating the
      # local binary pattern value for each pixel.
      for i in range(0, image.shape[0]):
            for j in range(0, image.shape[1]):
                  UpperCamelCase       :     Union[str, Any]			=    local_binary_value(image, i, j)

      cva.imshow("""local binary pattern""", lbp_image)
      cva.waitKey(0)
      cva.destroyAllWindows()


'''simple docstring'''





from itertools import count
from math import asin, pi, sqrt






def SCREAMING_SNAKE_CASE__  (				snake_case       :				float  )   ->				float:







     """simple docstring"""
     return (
         (1 - 2 * point) * sqrt(point - point**2  ) + 2 * point + asin(sqrt(1 - point  )  )
     ) / 4






def SCREAMING_SNAKE_CASE__  (				snake_case       :				int  )   ->				float:







     """simple docstring"""
     a					:							List[str]							= (circles_number + 1 - sqrt(2 * circles_number  )) / (
         2 * (circles_number**2 + 1)
     )
     a					:							int							= circles_number * intersection_y

     a					:							List[Any]							= intersection_x * intersection_y / 2
     a					:							List[str]							= circle_bottom_arc_integral(
         1 / 2  ) - circle_bottom_arc_integral(snake_case  )

     return triangle_area + concave_region_area






def SCREAMING_SNAKE_CASE__  (				snake_case       :				float = 1 / 1_000  )   ->				int:







     """simple docstring"""
     a					:							str							= (1 - pi / 4) / 4

     for n in count(1  ):
          if concave_triangle_area(snake_case  ) / l_section_area < fraction:
               return n

     return -1


if __name__ == "__main__":
      print(f'''{solution() = }''')

