





'''simple docstring'''






from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass




@dataclass
class  _A :
		lowercase__:   int
		lowercase__:   int





class  _A :

		def __init__(   self					:   Optional[int]  ,      __magic_name__					:   int			)					->    Optional[int]:


					"""simple docstring"""

					__snake_case					:      list[list[Edge]]							      =     [[] for _ in range(__magic_name__			)]
					__snake_case					:      Tuple							      =     size

		def __getitem__(   self					:   Optional[int]  ,      __magic_name__					:   int			)					->    Iterator[Edge]:


					"""simple docstring"""

					return iter(self._graph[vertex]			)

		@property
		def 	lowercase__     (   self					:   List[str]			)					->    Dict:


					"""simple docstring"""

					return self._size

		def 	lowercase__     (   self					:   Optional[int]  ,      __magic_name__					:   int  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    str:


					"""simple docstring"""

					if weight not in (0, 1):
								raise ValueError("""Edge weight must be either 0 or 1."""			)

					if to_vertex < 0 or to_vertex >= self.size:
								raise ValueError("""Vertex indexes must be in [0; size)."""			)

					self._graph[from_vertex].append(Edge(__magic_name__  ,      __magic_name__			)			)

		def 	lowercase__     (   self					:   Optional[Any]  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    int | None:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     deque([start_vertex]			)
					__snake_case					:      list[int | None]							      =     [None] * self.size
					__snake_case					:      int							      =     0

					while queue:
								__snake_case					:      str							      =     queue.popleft()
								__snake_case					:      Union[str, Any]							      =     distances[current_vertex]
								if current_distance is None:
											continue

								for edge in self[current_vertex]:
											__snake_case					:      int							      =     current_distance + edge.weight
											__snake_case					:      Optional[int]							      =     distances[edge.destination_vertex]
											if (
											    isinstance(__magic_name__  ,      __magic_name__			)
											    and new_distance >= dest_vertex_distance
											):
														continue
											__snake_case					:      Optional[Any]							      =     new_distance
											if edge.weight == 0:
														queue.appendleft(edge.destination_vertex			)
											else:
														queue.append(edge.destination_vertex			)

					if distances[finish_vertex] is None:
								raise ValueError("""No path from start_vertex to finish_vertex."""			)

					return distances[finish_vertex]


if __name__ == "__main__":
	import doctest

	doctest.testmod()









'''simple docstring'''






import cva
import numpy as np




class  _A :

		def __init__(   self					:   Any  ,      __magic_name__					:   float  ,      __magic_name__					:   int			)					->    Optional[int]:


					"""simple docstring"""

					if k in (0.04, 0.06):
								__snake_case					:      List[str]							      =     k
								__snake_case					:      int							      =     window_size
					else:
								raise ValueError("""invalid k value"""			)

		def __str__(   self					:   Union[str, Any]			)					->    str:


					"""simple docstring"""

					return str(self.k			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   str			)					->    tuple[cva.Mat, list[list[int]]]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     cva.imread(__magic_name__  ,      0			)
					__snake_case      ,     __snake_case					:      List[str]							      =     img.shape
					__snake_case					:      list[list[int]]							      =     []
					__snake_case					:      str							      =     img.copy()
					__snake_case					:      Tuple							      =     cva.cvtColor(__magic_name__  ,      cva.COLOR_GRAY2RGB			)
					__snake_case      ,     __snake_case					:      List[Any]							      =     np.gradient(__magic_name__			)
					__snake_case					:      Optional[Any]							      =     dx**2
					__snake_case					:      Tuple							      =     dy**2
					__snake_case					:      List[Any]							      =     dx * dy
					__snake_case					:      List[Any]							      =     0.04
					__snake_case					:      Tuple							      =     self.window_size // 2
					for y in range(__magic_name__  ,      h - offset			):
								for x in range(__magic_name__  ,      w - offset			):
											__snake_case					:      Dict							      =     ixx[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()
											__snake_case					:      Optional[int]							      =     iyy[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()
											__snake_case					:      str							      =     ixy[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()

											__snake_case					:      List[str]							      =     (wxx * wyy) - (wxy**2)
											__snake_case					:      Dict							      =     wxx + wyy
											__snake_case					:      List[str]							      =     det - k * (trace**2)
											# Can change the value
											if r > 0.5:
														corner_list.append([x, y, r]			)
														color_img.itemset((y, x, 0)  ,      0			)
														color_img.itemset((y, x, 1)  ,      0			)
														color_img.itemset((y, x, 2)  ,      2_55			)
					return color_img, corner_list


if __name__ == "__main__":
	__UpperCamelCase					    =	HarrisCorner(0.04, 3)
	__UpperCamelCase    ,		__UpperCamelCase					    =	edge_detect.detect("path_to_image")
	cva.imwrite("detect.png", color_img)


