from __future__ import annotations

UpperCAmelCase_    		=				'''Alexander Joslin'''

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    int:




       '''simple docstring'''






       if number <= 0:
              _A=						F"n must be greater than 0. Got n = {number}"
              raise ValueError(lowerCAmelCase_ )

       _A=						[number + 1] * (number + 1)

       # starting position
       _A=						0
       for i in range(1							,       lowerCAmelCase_ ):
              _A=						min(table[i + 1]							,       table[i] + 1 )
              # check if out of bounds
              if i * 2 <= number:
                     _A=						min(table[i * 2]							,       table[i] + 1 )
              # check if out of bounds
              if i * 3 <= number:
                     _A=						min(table[i * 3]							,       table[i] + 1 )
       return table[number]


if __name__ == "__main__":
      import doctest

      doctest.testmod()
import numpy as np
from PIL import Image

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       _A,      _A,      _A=						rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       return (gray > 1_27) & (gray <= 2_55)

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       _A=						np.zeros_like(lowerCAmelCase_ )
       _A=						np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )

       # Copy image to padded image
       _A=						image

       # Iterate over image & apply kernel
       for x in range(image.shape[1] ):
              for y in range(image.shape[0] ):
                     _A=						(
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     _A=						int(summation == 5 )
       return output


# kernel to be applied
UpperCAmelCase_    		=				np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      UpperCAmelCase_    		=				np.array(Image.open(R'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      UpperCAmelCase_    		=				erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      UpperCAmelCase_    		=				Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')