import numpy as np
from PIL import Image



def 						__snake_case (       lowerCAmelCase       :     np.array			):
 __UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase         =					rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
 return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b



def 						__snake_case (       lowerCAmelCase       :     np.array			):
 return (gray > 127) & (gray <= 255)



def 						__snake_case (       lowerCAmelCase       :     np.array      ,  lowerCAmelCase       :     np.array			):
 __UpperCAmelCase         =					np.zeros_like(lowerCAmelCase			)
 __UpperCAmelCase         =					np.zeros(
     (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)			)

 # Copy image to padded image
 __UpperCAmelCase         =					image

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


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

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




'''simple docstring'''




def 						__snake_case (       lowerCAmelCase       :     str			):
 __UpperCAmelCase         =					5381
 for x in s:
  __UpperCAmelCase         =					((hash_value << 5) + hash_value) + ord(lowerCAmelCase			)
 return hash_value & 0Xffff_ffff



