





from __future__ import annotations

_lowerCamelCase	:    Any          =      [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]






def 	a_		(	__lowercase       :     list[list[int]]	, __lowercase       :     list[int]	, __lowercase       :     list[int]	, __lowercase       :     int	, __lowercase       :     list[list[int]]	, )			->  List[Any]:
			_snake_case          =      [
			    [0 for col in range(len(grid[0]			)			)] for row in range(len(__lowerCamelCase			)			)
			]  # the reference grid
			_snake_case          =      1
			_snake_case          =      [
			    [0 for col in range(len(grid[0]			)			)] for row in range(len(__lowerCamelCase			)			)
			]  # the action grid

			_snake_case          =      init[0]
			_snake_case          =      init[1]
			_snake_case          =      0
			_snake_case          =      g + heuristic[x][y]  # cost from starting cell to destination cell
			_snake_case          =      [[f, g, x, y]]

			_snake_case          =      False  # flag that is set when search is complete
			_snake_case          =      False  # flag set if we can't find expand

			while not found and not resign:
						if len(__lowerCamelCase			) == 0:
									raise ValueError('Algorithm is unable to find solution'			)
						else:  # to choose the least costliest action so as to move closer to the goal
									cell.sort()
									cell.reverse()
									_snake_case          =      cell.pop()
									_snake_case          =      next_cell[2]
									_snake_case          =      next_cell[3]
									_snake_case          =      next_cell[1]

									if x == goal[0] and y == goal[1]:
												_snake_case          =      True
									else:
												for i in range(len(__lowerCamelCase			)			):  # to try out different valid actions
															_snake_case          =      x + DIRECTIONS[i][0]
															_snake_case          =      y + DIRECTIONS[i][1]
															if xa >= 0 and xa < len(__lowerCamelCase			) and ya >= 0 and ya < len(grid[0]			):
																		if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																					_snake_case          =      g + cost
																					_snake_case          =      ga + heuristic[xa][ya]
																					cell.append([fa, ga, xa, ya]			)
																					_snake_case          =      1
																					_snake_case          =      i
			_snake_case          =      []
			_snake_case          =      goal[0]
			_snake_case          =      goal[1]
			invpath.append([x, y]			)  # we get the reverse path from here
			while x != init[0] or y != init[1]:
						_snake_case          =      x - DIRECTIONS[action[x][y]][0]
						_snake_case          =      y - DIRECTIONS[action[x][y]][1]
						_snake_case          =      xa
						_snake_case          =      ya
						invpath.append([x, y]			)

			_snake_case          =      []
			for i in range(len(__lowerCamelCase			)			):
						path.append(invpath[len(__lowerCamelCase			) - 1 - i]			)
			return path, action


if __name__ == "__main__":
			_lowerCamelCase	:    int          =      [
			    [0, 1, 0, 0, 0, 0],
			    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
			    [0, 1, 0, 0, 0, 0],
			    [0, 1, 0, 0, 1, 0],
			    [0, 0, 0, 0, 1, 0],
			]

			_lowerCamelCase	:    Union[str, Any]          =      [0, 0]
			# all coordinates are given in format [y,x]
			_lowerCamelCase	:    int          =      [len(grid) - 1, len(grid[0]) - 1]
			_lowerCamelCase	:    int          =      1

			# the cost map which pushes the path closer to the goal
			_lowerCamelCase	:    List[str]          =      [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
			for i in range(len(grid)):
						for j in range(len(grid[0])):
									_lowerCamelCase	:    int          =      abs(i - goal[0]) + abs(j - goal[1])
									if grid[i][j] == 1:
												# added extra penalty in the heuristic map
												_lowerCamelCase	:    Any          =      99

			_lowerCamelCase   ,					_lowerCamelCase	:    Union[str, Any]          =      search(grid, init, goal, cost, heuristic)

			print('''ACTION MAP''')
			for i in range(len(action)):
						print(action[i])

			for i in range(len(path)):
						print(path[i])

from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def 	a_		(	__lowercase       :     np.ndarray	, __lowercase       :     np.ndarray	, __lowercase       :     np.ndarray	, __lowercase       :     int	, __lowercase       :     int			)			->  np.ndarray:
			_snake_case          =      cva.getAffineTransform(__lowercase	, __lowercase			)
			return cva.warpAffine(__lowercase	, __lowercase	, (rows, cols)			)


if __name__ == "__main__":
			# read original image
			_lowerCamelCase	:    Optional[Any]          =      cva.imread(
			    str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
			)
			# turn image in gray scale value
			_lowerCamelCase	:    List[str]          =      cva.cvtColor(image, cva.COLOR_BGR2GRAY)
			# get image shape
			_lowerCamelCase   ,					_lowerCamelCase	:    List[Any]          =      gray_img.shape

			# set different points to rotate image
			_lowerCamelCase	:    str          =      np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
			_lowerCamelCase	:    Optional[Any]          =      np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
			_lowerCamelCase	:    List[str]          =      np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
			_lowerCamelCase	:    Dict          =      np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

			# add all rotated images in a list
			_lowerCamelCase	:    int          =      [
			    gray_img,
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			]

			# plot different image rotations
			_lowerCamelCase	:    Any          =      plt.figure(1)
			_lowerCamelCase	:    List[Any]          =      ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
			for i, image in enumerate(images):
						plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
						plt.title(titles[i])
						plt.axis('''off''')
						plt.subplots_adjust(left=0.0, bottom=0.0_5, right=1.0, top=0.9_5)
			plt.show()