





import cva
import numpy as np

def      __snake_case						(					_lowerCAmelCase : np.ndarray				,				_lowerCAmelCase : int				,				_lowerCAmelCase : int				,				_lowerCAmelCase : int	) ->						int:

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

def      __snake_case						(					_lowerCAmelCase : np.ndarray				,				_lowerCAmelCase : int				,				_lowerCAmelCase : int	) ->						int:
   A_					:				Optional[int]          =			image[x_coordinate][y_coordinate]
   A_					:				Optional[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_					:				List[Any]          =			[
       get_neighbors_pixel(_lowerCAmelCase				,				x_coordinate - 1				,				y_coordinate + 1				,				_lowerCAmelCase	),
       get_neighbors_pixel(_lowerCAmelCase				,				_lowerCAmelCase				,				y_coordinate + 1				,				_lowerCAmelCase	),
       get_neighbors_pixel(_lowerCAmelCase				,				x_coordinate - 1				,				_lowerCAmelCase				,				_lowerCAmelCase	),
       get_neighbors_pixel(_lowerCAmelCase				,				x_coordinate + 1				,				y_coordinate + 1				,				_lowerCAmelCase	),
       get_neighbors_pixel(_lowerCAmelCase				,				x_coordinate + 1				,				_lowerCAmelCase				,				_lowerCAmelCase	),
       get_neighbors_pixel(_lowerCAmelCase				,				x_coordinate + 1				,				y_coordinate - 1				,				_lowerCAmelCase	),
       get_neighbors_pixel(_lowerCAmelCase				,				_lowerCAmelCase				,				y_coordinate - 1				,				_lowerCAmelCase	),
       get_neighbors_pixel(_lowerCAmelCase				,				x_coordinate - 1				,				y_coordinate - 1				,				_lowerCAmelCase	),
   ]

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


if __name__ == "__main__":
 # Reading the image and converting it to grayscale.
 _lowerCAmelCase							:      Union[str, 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
 _lowerCAmelCase							:      Dict						=	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]):
   _lowerCAmelCase							:      int						=	local_binary_value(image, i, j)

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







def      __snake_case						(					_lowerCAmelCase : float				,				_lowerCAmelCase : float				,				_lowerCAmelCase : int	) ->						float:
   A_					:				List[str]          =			[1 / first_term]
   A_					:				Tuple          =			1 / first_term
   for _ in range(number_of_terms - 1	):
      first_term += common_difference
      arithmetic_progression.append(_lowerCAmelCase	)
   A_					:				List[str]          =			[1 / step for step in arithmetic_progression]
   return sum(_lowerCAmelCase	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(sum_of_harmonic_progression(1 / 2, 2, 2))
