code: 
  
  
  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()
  
  
  
  
  
  
style_context: 
  
  
  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() = }')
  
  
  
  
  
  