






import numpy as np
from PIL import Image
def       a__						(  A					):
   lowercase__						:	Any         = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
   return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b
def       a__						(  A					):
   return (gray > 127) & (gray <= 255)
def       a__						(  A		,				A					):
   lowercase__						:	int         = np.zeros_like(A					)
   lowercase__						:	List[Any]         = np.zeros(
       (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)					)

   # Copy image to padded image
   lowercase__						:	Dict         = image

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


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

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










def       a__						(  A					):

   if not isinstance(A		,				A					) or number < 0:
      raise ValueError("""Input must be a non-negative integer"""					)

   lowercase__						:	str         = number & ~(number - 1)
   lowercase__						:	Optional[int]         = 0
   while intermediate:
      intermediate >>= 1
      index += 1
   return index - 1


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)


