


from __future__ import annotations
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)     ->			None:







 """simple docstring"""


 _lowercase							      =len(__snake_case						)

 # If row is equal to the size of the board it means there are a queen in each row in
 # the current board (possible_board)
 if row == n:
  # We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
  # this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
  boards.append(['''. ''' * i + '''Q ''' + '''. ''' * (n - 1 - i) for i in possible_board]						)
  return

 # We iterate each column in the row to find all possible results in each row
 for col in range(__snake_case						):
  # We apply that we learned previously. First we check that in the current board
  # (possible_board) there are not other same value because if there is it means
  # that there are a collision in vertical. Then we apply the two formulas we
  # learned before:
  #
  # 45º: y - x = b or 45: row - col = b
  # 135º: y + x = b or row + col = b.
  #
  # And we verify if the results of this two formulas not exist in their variables
  # respectively.  (diagonal_right_collisions, diagonal_left_collisions)
  #
  # If any or these are True it means there is a collision so we continue to the
  # next value in the for loop.
  if (
      col in possible_board
      or row - col in diagonal_right_collisions
      or row + col in diagonal_left_collisions
  ):
   continue

  # If it is False we call dfs function again and we update the inputs
  depth_first_search(
      [*possible_board, col]     ,						[*diagonal_right_collisions, row - col]     ,						[*diagonal_left_collisions, row + col]     ,						__snake_case     ,						__snake_case     ,						)
def 				UpperCAmelCase_					(							__snake_case						)     ->			None:







 """simple docstring"""


 _lowercase							      =[]
 depth_first_search([]     ,						[]     ,						[]     ,						__snake_case     ,						__snake_case						)

 # Print all the boards
 for board in boards:
  for column in board:
   print(__snake_case						)
  print(''''''						)

 print(len(__snake_case						)     ,						'''solutions were found.'''						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  n_queens_solution(4)








import cva
import numpy as np

class 	A_       :
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				if k in (0.04, 0.06):
							UpperCAmelCase   		=				k
							UpperCAmelCase   		=				window_size
				else:
							raise ValueError('''invalid k value'''		)
	def __str__(    self		):






				'''simple docstring'''







				return str(self.k		)







	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				cva.imread(_A							,					0		)
				UpperCAmelCase		,			UpperCAmelCase   		=				img.shape
				UpperCAmelCase   		=				[]
				UpperCAmelCase   		=				img.copy()
				UpperCAmelCase   		=				cva.cvtColor(_A							,					cva.COLOR_GRAY2RGB		)
				UpperCAmelCase		,			UpperCAmelCase   		=				np.gradient(_A		)
				UpperCAmelCase   		=				dx**2
				UpperCAmelCase   		=				dy**2
				UpperCAmelCase   		=				dx * dy
				UpperCAmelCase   		=				0.04
				UpperCAmelCase   		=				self.window_size // 2
				for y in range(_A							,					h - offset		):
							for x in range(_A							,					w - offset		):
										UpperCAmelCase   		=				ixx[
										    y - offset : y + offset + 1, x - offset : x + offset + 1
										].sum()
										UpperCAmelCase   		=				iyy[
										    y - offset : y + offset + 1, x - offset : x + offset + 1
										].sum()
										UpperCAmelCase   		=				ixy[
										    y - offset : y + offset + 1, x - offset : x + offset + 1
										].sum()

										UpperCAmelCase   		=				(wxx * wyy) - (wxy**2)
										UpperCAmelCase   		=				wxx + wyy
										UpperCAmelCase   		=				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_5_5		)
				return color_img, corner_list


if __name__ == "__main__":
				__A				:    Tuple       =   HarrisCorner(0.04, 3)
				__A    , __A				:    List[Any]       =   edge_detect.detect("path_to_image")
				cva.imwrite("detect.png", color_img)



