


'''simple docstring'''




import math





def      _a		(lowercase__ :      int	) -> float:






     """simple docstring"""
     __snake_case    			=	math.log(2       ,      1_0	) * number
     __snake_case    			=	round((log_number - int(lowercase__	))       ,      1_5	)

     return difference





def      _a		(lowercase__ :      int = 6_7_8_9_1_0	) -> int:






     """simple docstring"""
     __snake_case    			=	9_0
     __snake_case    			=	0

     __snake_case    			=	math.log(1.23       ,      1_0	)
     __snake_case    			=	math.log(1.24       ,      1_0	)
     __snake_case    			=	0

     while position < number:
          __snake_case    			=	log_difference(lowercase__	)

          if difference >= upper_limit:
               power_iterator += 9_3

          elif difference < lower_limit:
               power_iterator += 1_9_6

          else:
               __snake_case    			=	power_iterator
               power_iterator += 1_9_6
               position += 1

     return previous_power


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       print(f'''{solution() = }''')








'''simple docstring'''




import numpy as np
from PIL import Image





def      _a		(lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     __snake_case			,					__snake_case			,					__snake_case    			=	rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
     return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b





def      _a		(lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     return (gray > 1_2_7) & (gray <= 2_5_5)





def      _a		(lowercase__ :      np.array       ,      lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     __snake_case    			=	np.zeros_like(lowercase__	)
     __snake_case    			=	np.zeros(
         (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

     # Copy image to padded image
     __snake_case    			=	image

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


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

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




