


"""simple docstring"""






from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

UpperCamelCase__							:Dict       =  {
    """A""": ["""B""", """C"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E"""],
}





def 							A_					(   snake_case__					,				snake_case__		)				->     list[str]:
			_UpperCamelCase				:Optional[int]				    =  {start}
			_UpperCamelCase				:Dict				    =  [start]
			_UpperCamelCase				:Queue				    =  Queue()
			queue.put(snake_case__		)
			while not queue.empty():
						_UpperCamelCase				:List[str]				    =  queue.get()
						for w in graph[v]:
									if w not in explored:
												explored.add(snake_case__		)
												result.append(snake_case__		)
												queue.put(snake_case__		)
			return result





def 							A_					(   snake_case__					,				snake_case__		)				->     list[str]:
			_UpperCamelCase				:Any				    =  {start}
			_UpperCamelCase				:Union[str, Any]				    =  [start]
			_UpperCamelCase				:Optional[int]				    =  deque([start]		)
			while queue:
						_UpperCamelCase				:Dict				    =  queue.popleft()
						for child in graph[v]:
									if child not in visited:
												visited.add(snake_case__		)
												result.append(snake_case__		)
												queue.append(snake_case__		)
			return result





def 							A_					(   snake_case__		)				->     None:
			_UpperCamelCase				:Union[str, Any]				    =  f"from __main__ import G, {name}"
			_UpperCamelCase				:Dict				    =  1_00_00
			_UpperCamelCase				:List[str]				    =  timeit(f"{name}(G, 'A')"					,				setup=snake_case__					,				number=snake_case__		)
			print(f"{name:<35} finished {number} runs in {res:.5f} seconds"		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					benchmark_function("""breadth_first_search""")
					benchmark_function("""breadth_first_search_with_deque""")
					# breadth_first_search                finished 10000 runs in 0.20999 seconds
					# breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds





"""simple docstring"""






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve





def 							A_					(   snake_case__		)				->     str:
			_UpperCamelCase				:List[Any]				    =  np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]		)
			_UpperCamelCase				:Dict				    =  np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]		)

			_UpperCamelCase				:List[str]				    =  np.abs(img_convolve(snake_case__					,				snake_case__		)		)
			_UpperCamelCase				:Dict				    =  np.abs(img_convolve(snake_case__					,				snake_case__		)		)
			# modify the pix within [0, 255]
			_UpperCamelCase				:List[Any]				    =  dst_x * 2_55 / np.max(snake_case__		)
			_UpperCamelCase				:Tuple				    =  dst_y * 2_55 / np.max(snake_case__		)

			_UpperCamelCase				:Tuple				    =  np.sqrt((np.square(snake_case__		)) + (np.square(snake_case__		))		)
			_UpperCamelCase				:Any				    =  dst_xy * 2_55 / np.max(snake_case__		)
			_UpperCamelCase				:Optional[int]				    =  dst_xy.astype(np.uinta		)

			_UpperCamelCase				:str				    =  np.arctana(snake_case__					,				snake_case__		)
			return dst, theta


if __name__ == "__main__":
					# read original image
					UpperCamelCase__							:List[str]       =  imread("""../image_data/lena.jpg""")
					# turn image in gray scale value
					UpperCamelCase__							:Union[str, Any]       =  cvtColor(img, COLOR_BGR2GRAY)

					UpperCamelCase__      , UpperCamelCase__							:Optional[int]       =  sobel_filter(gray)

					# show result images
					imshow("""sobel filter""", sobel_grad)
					imshow("""sobel theta""", sobel_theta)
					waitKey(0)

