





import cva
import numpy as np



def _a							(      lowerCamelCase:  np.ndarray   ,							lowerCamelCase:  int   ,							lowerCamelCase:  int   ,							lowerCamelCase:  int					)      ->		int:







      '''simple docstring'''






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



def _a							(      lowerCamelCase:  np.ndarray   ,							lowerCamelCase:  int   ,							lowerCamelCase:  int					)      ->		int:







      '''simple docstring'''






      __A			  =							image[x_coordinate][y_coordinate]
      __A			  =							[1, 2, 4, 8, 16, 32, 64, 1_28]

      # 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			  =							[
          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.
       snake_case__   :				str						=	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
       snake_case__   :				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]):
                     snake_case__   :				int						=	local_binary_value(image, i, j)

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












import os



def _a							(      )      ->		int:







      '''simple docstring'''






      __A			  =							os.path.dirname(os.path.realpath(lowerCamelCase					)					)
      __A			  =							os.path.join(lowerCamelCase   ,							'''triangle.txt'''					)

      with open(lowerCamelCase					) as in_file:
            __A			  =							[[int(lowerCamelCase					) for i in line.split()] for line in in_file]

      while len(lowerCamelCase					) != 1:
            __A			  =							triangle.pop()
            __A			  =							triangle[-1]
            for j in range(len(lowerCamelCase					) - 1					):
                  curr_row[j] += max(last_row[j]   ,							last_row[j + 1]					)
      return triangle[0][0]


if __name__ == "__main__":
       print(solution())





