


"""simple docstring"""


import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

_snake_case          =							imread(R"digital_image_processing/image_data/lena_small.jpg")
_snake_case          =							cvtColor(img, COLOR_BGR2GRAY)



def   snake_case						(				)->		List[str]:
      '''simple docstring'''


      lowerCamelCase__           =		cn.convert_to_negative(a_							)
      # assert negative_img array for at least one True
      assert negative_img.any()



def   snake_case						(				)->		Union[str, Any]:
      '''simple docstring'''


      with Image.open('digital_image_processing/image_data/lena_small.jpg'							) as img:
            # Work around assertion for response
            assert str(cc.change_contrast(a_		,   110							)							).startswith(
                '<PIL.Image.Image image mode=RGB size=100x100 at'							)



def   snake_case						(				)->		Union[str, Any]:
      '''simple docstring'''


      lowerCamelCase__           =		canny.gen_gaussian_kernel(9		,   sigma=1.4							)
      # Assert ambiguous array
      assert resp.all()



def   snake_case						(				)->		Any:
      '''simple docstring'''


      lowerCamelCase__           =		imread('digital_image_processing/image_data/lena_small.jpg'		,   0							)
      # assert ambiguous array for all == True
      assert canny_img.all()
      lowerCamelCase__           =		canny.canny(a_							)
      # assert canny array for at least one True
      assert canny_array.any()



def   snake_case						(				)->		Dict:
      '''simple docstring'''


      assert gg.gaussian_filter(a_		,   5		,   sigma=0.9							).all()



def   snake_case						(				)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]							)
      lowerCamelCase__           =		conv.img_convolve(a_		,   a_							).astype(a_							)
      assert res.any()



def   snake_case						(				)->		Optional[int]:
      '''simple docstring'''


      assert med.median_filter(a_		,   3							).any()



def   snake_case						(				)->		Any:
      '''simple docstring'''


      lowerCamelCase__           =		sob.sobel_filter(a_							)
      assert grad.any() and theta.any()



def   snake_case						(				)->		Union[str, Any]:
      '''simple docstring'''


      lowerCamelCase__           =		sp.make_sepia(a_		,   20							)
      assert sepia.all()



def   snake_case						(				_a:     str = "digital_image_processing/image_data/lena_small.jpg"							)->		List[str]:
      '''simple docstring'''


      lowerCamelCase__           =		bs.Burkes(imread(a_		,   1							)		,   120							)
      burkes.process()
      assert burkes.output_img.any()



def   snake_case						(				_a:     str = "digital_image_processing/image_data/lena_small.jpg"		,   )->		Any:
      '''simple docstring'''


      lowerCamelCase__           =		rs.NearestNeighbour(imread(a_		,   1							)		,   400		,   200							)
      nn.process()
      assert nn.output.any()






def   snake_case						(				)->		List[str]:
      '''simple docstring'''


      lowerCamelCase__           =		'''digital_image_processing/image_data/lena.jpg'''

      # Reading the image and converting it to grayscale.
      lowerCamelCase__           =		imread(a_		,   0							)

      # Test for get_neighbors_pixel function() return not None
      lowerCamelCase__           =		0
      lowerCamelCase__           =		0
      lowerCamelCase__           =		image[x_coordinate][y_coordinate]

      lowerCamelCase__           =		lbp.get_neighbors_pixel(
          a_		,   a_		,   a_		,   a_							)

      assert neighbors_pixels is not None

      # Test for local_binary_pattern function()
      # Create a numpy array as the same height and width of read image
      lowerCamelCase__           =		np.zeros((image.shape[0], image.shape[1])							)

      # Iterating through the image and calculating the local binary pattern value
      # for each pixel.
      for i in range(0		,   image.shape[0]							):
            for j in range(0		,   image.shape[1]							):
                  lowerCamelCase__           =		lbp.local_binary_value(a_		,   a_		,   a_							)

      assert lbp_image.any()






"""simple docstring"""


from __future__ import annotations

_snake_case          =							[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]



def   snake_case						(				_a:     list[list[int]]		,   _a:     list[int]		,   _a:     list[int]		,   _a:     int		,   _a:     list[list[int]]		,   )->		tuple[list[list[int]], list[list[int]]]:
      '''simple docstring'''


      lowerCamelCase__           =		[
          [0 for col in range(len(grid[0]							)							)] for row in range(len(_a							)							)
      ]  # the reference grid
      lowerCamelCase__           =		1
      lowerCamelCase__           =		[
          [0 for col in range(len(grid[0]							)							)] for row in range(len(_a							)							)
      ]  # the action grid

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

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

      while not found and not resign:
            if len(_a							) == 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()
                  lowerCamelCase__           =		cell.pop()
                  lowerCamelCase__           =		next_cell[2]
                  lowerCamelCase__           =		next_cell[3]
                  lowerCamelCase__           =		next_cell[1]

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

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


if __name__ == "__main__":
   _snake_case          =							[
       [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],
   ]

   _snake_case          =							[0, 0]
   # all coordinates are given in format [y,x]
   _snake_case          =							[len(grid) - 1, len(grid[0]) - 1]
   _snake_case          =							1

   # the cost map which pushes the path closer to the goal
   _snake_case          =							[[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])):
         _snake_case          =							abs(i - goal[0]) + abs(j - goal[1])
         if grid[i][j] == 1:
            # added extra penalty in the heuristic map
            _snake_case          =							99

   _snake_case      ,					_snake_case          =							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])



