




'''simple docstring'''






def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->list[tuple[int, int, int]]:






       '''simple docstring'''




       lowercase  :		Dict						=      sorted(lowerCamelCase   ,				key=lambda lowerCamelCase      : edge[2]      )

       lowercase  :		str						=      list(range(lowerCamelCase      )      )

       def find_parent(lowerCamelCase      ):
              if i != parent[i]:
                     lowercase  :		Optional[int]						=      find_parent(parent[i]      )
              return parent[i]

       lowercase  :		Union[str, Any]						=      0
       lowercase  :		Any						=      []

       for edge in edges:
              lowercase  :		Optional[Any]						=      find_parent(edge[0]      )
              lowercase  :		Any						=      find_parent(edge[1]      )
              if parent_a != parent_b:
                     minimum_spanning_tree_cost += edge[2]
                     minimum_spanning_tree.append(lowerCamelCase      )
                     lowercase  :		Optional[int]						=      parent_b

       return minimum_spanning_tree


if __name__ == "__main__":  # pragma: no cover
      lowercase	:      List[Any]				=       list(map(int, input().strip().split()))
      lowercase	:      List[Any]				=       []

      for _ in range(num_edges):
            lowercase	:      Optional[Any]				=       (int(x) for x in input().strip().split())
            edges.append((nodea, nodea, cost))

      kruskal(num_nodes, edges)











'''simple docstring'''






import numpy as np
from PIL import Image




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase,     lowercase,     lowercase  :		Union[str, Any]						=      rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       return (gray > 127) & (gray <= 255)




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      np.zeros_like(lowerCamelCase      )
       lowercase  :		Any						=      np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)      )

       # Copy image to padded image
       lowercase  :		List[str]						=      image

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


# kernel to be applied
lowercase	:      List[Any]				=       np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

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




