

import numpy as np
from PIL import Image

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     np.array       )      ->				np.array:
				_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase				:						int       						=			rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
				return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b

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

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     np.array     ,					lowerCAmelCase:     np.array       )      ->				np.array:
				_UpperCAmelCase				:						List[Any]       						=			np.zeros_like(lowerCAmelCase       )
				_UpperCAmelCase				:						Any       						=			np.zeros(
				    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)       )

				# Copy image to padded image
				_UpperCAmelCase				:						List[Any]       						=			image

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


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

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





from __future__ import annotations

class  a     :


						def __init__(				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			vertices
										_UpperCAmelCase				:						List[str]       						=			[[0] * vertices for _ in range(A_		)]


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										print("Vertex \t Distance from Source"		)
										for vertex in range(self.vertices		):
														print(A_  ,    "\t\t"  ,    distances_from_source[vertex]		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Tuple       						=			1e7
										_UpperCAmelCase				:						Any       						=			0

										# Search not nearest vertex not in the shortest path tree
										for vertex in range(self.vertices		):
														if distances_from_source[vertex] < minimum and visited[vertex] is False:
																		_UpperCAmelCase				:						List[Any]       						=			distances_from_source[vertex]
																		_UpperCAmelCase				:						Tuple       						=			vertex
										return min_index





						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			[int(1e7		)] * self.vertices  # distances from the source
										_UpperCAmelCase				:						Optional[Any]       						=			0
										_UpperCAmelCase				:						List[str]       						=			[False] * self.vertices

										for _ in range(self.vertices		):
														_UpperCAmelCase				:						str       						=			self.minimum_distance(A_  ,    A_		)
														_UpperCAmelCase				:						str       						=			True

														# Update dist value of the adjacent vertices
														# of the picked vertex only if the current
														# distance is greater than new distance and
														# the vertex in not in the shortest path tree
														for v in range(self.vertices		):
																		if (
																		    self.graph[u][v] > 0
																		    and visited[v] is False
																		    and distances[v] > distances[u] + self.graph[u][v]
																		):
																						_UpperCAmelCase				:						str       						=			distances[u] + self.graph[u][v]

										self.print_solution(A_		)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_						=					Graph(9)
					SCREAMING_SNAKE_CASE_						=					[
					    [0, 4, 0, 0, 0, 0, 0, 8, 0],
					    [4, 0, 8, 0, 0, 0, 0, 11, 0],
					    [0, 8, 0, 7, 0, 4, 0, 0, 2],
					    [0, 0, 7, 0, 9, 14, 0, 0, 0],
					    [0, 0, 0, 9, 0, 10, 0, 0, 0],
					    [0, 0, 4, 14, 10, 0, 2, 0, 0],
					    [0, 0, 0, 0, 0, 2, 0, 1, 6],
					    [8, 11, 0, 0, 0, 0, 1, 0, 7],
					    [0, 0, 2, 0, 0, 0, 6, 7, 0],
					]
					graph.dijkstra(0)


