


import cva
import numpy as np




def       a__						(		snake_case__				:       np.ndarray	,      snake_case__				:       int	,      snake_case__				:       int	,      snake_case__				:       int    ):

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




def       a__						(		snake_case__				:       np.ndarray	,      snake_case__				:       int	,      snake_case__				:       int    ):
  _UpperCAmelCase		:       Tuple       =    image[x_coordinate][y_coordinate]
  _UpperCAmelCase		:       int       =    [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
  _UpperCAmelCase		:       Optional[Any]       =    [
      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.
 SCREAMING_SNAKE_CASE__				:    Dict         =    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
 SCREAMING_SNAKE_CASE__				:    str         =    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]):
   SCREAMING_SNAKE_CASE__				:    str         =    local_binary_value(image, i, j)

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









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




def       a__						(		snake_case__				:       float    ):
  return (
      (1 - 2 * point) * sqrt(point - point**2    ) + 2 * point + asin(sqrt(1 - point    )    )
  ) / 4




def       a__						(		snake_case__				:       int    ):
  _UpperCAmelCase		:       int       =    (circles_number + 1 - sqrt(2 * circles_number    )) / (
      2 * (circles_number**2 + 1)
  )
  _UpperCAmelCase		:       Union[str, Any]       =    circles_number * intersection_y

  _UpperCAmelCase		:       Tuple       =    intersection_x * intersection_y / 2
  _UpperCAmelCase		:       str       =    circle_bottom_arc_integral(
      1 / 2    ) - circle_bottom_arc_integral(snake_case__    )

  return triangle_area + concave_region_area




def       a__						(		snake_case__				:       float = 1 / 1000    ):
  _UpperCAmelCase		:       Dict       =    (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() = }')





