


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

__lowerCamelCase        =     imread(R'''digital_image_processing/image_data/lena_small.jpg''')
__lowerCamelCase        =     cvtColor(img, COLOR_BGR2GRAY)



def  _a			(						):
     a_    :     List[Any]										=    cn.convert_to_negative(__UpperCamelCase )
     # assert negative_img array for at least one True
     assert negative_img.any()



def  _a			(						):
     with Image.open("""digital_image_processing/image_data/lena_small.jpg""" ) as img:
          # Work around assertion for response
          assert str(cc.change_contrast(__UpperCamelCase  ,  1_1_0 ) ).startswith(
              """<PIL.Image.Image image mode=RGB size=100x100 at""" )



def  _a			(						):
     a_    :     int										=    canny.gen_gaussian_kernel(9  ,  sigma=1.4 )
     # Assert ambiguous array
     assert resp.all()



def  _a			(						):
     a_    :     Any										=    imread("""digital_image_processing/image_data/lena_small.jpg"""  ,  0 )
     # assert ambiguous array for all == True
     assert canny_img.all()
     a_    :     str										=    canny.canny(__UpperCamelCase )
     # assert canny array for at least one True
     assert canny_array.any()



def  _a			(						):
     assert gg.gaussian_filter(__UpperCamelCase  ,  5  ,  sigma=0.9 ).all()



def  _a			(						):
     # laplace diagonals
     a_    :     int										=    array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
     a_    :     List[str]										=    conv.img_convolve(__UpperCamelCase  ,  __UpperCamelCase ).astype(__UpperCamelCase )
     assert res.any()



def  _a			(						):
     assert med.median_filter(__UpperCamelCase  ,  3 ).any()



def  _a			(						):
     a_    :     List[str]										=    sob.sobel_filter(__UpperCamelCase )
     assert grad.any() and theta.any()



def  _a			(						):
     a_    :     str										=    sp.make_sepia(__UpperCamelCase  ,  2_0 )
     assert sepia.all()



def  _a			(						__UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg" ):
     a_    :     List[Any]										=    bs.Burkes(imread(__UpperCamelCase  ,  1 )  ,  1_2_0 )
     burkes.process()
     assert burkes.output_img.any()



def  _a			(						__UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"  ,  ):
     a_    :     Optional[Any]										=    rs.NearestNeighbour(imread(__UpperCamelCase  ,  1 )  ,  4_0_0  ,  2_0_0 )
     nn.process()
     assert nn.output.any()



def  _a			(						):
     a_    :     List[str]										=    """digital_image_processing/image_data/lena.jpg"""

     # Reading the image and converting it to grayscale.
     a_    :     Optional[int]										=    imread(__UpperCamelCase  ,  0 )

     # Test for get_neighbors_pixel function() return not None
     a_    :     Tuple										=    0
     a_    :     Union[str, Any]										=    0
     a_    :     Tuple										=    image[x_coordinate][y_coordinate]

     a_    :     Optional[int]										=    lbp.get_neighbors_pixel(
         __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )

     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
     a_    :     int										=    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] ):
               a_    :     Tuple										=    lbp.local_binary_value(__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )

     assert lbp_image.any()







from heapq import heappop, heappush

import numpy as np



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  ):
     a_			,			a_    :     Any										=    grid.shape
     a_    :     Dict										=    [-1, 1, 0, 0]
     a_    :     List[Any]										=    [0, 0, -1, 1]
     if allow_diagonal:
          dx += [-1, -1, 1, 1]
          dy += [-1, 1, -1, 1]

     a_			,			a_    :     int										=    [(0, source)], set()
     a_    :     Optional[Any]										=    np.full((rows, cols)  ,  np.inf )
     a_    :     Tuple										=    0
     a_    :     Optional[Any]										=    np.empty((rows, cols)  ,  dtype=__UpperCamelCase )
     a_    :     Optional[Any]										=    None

     while queue:
          ((a_)			,			(a_))    :     Any										=    heappop(__UpperCamelCase )
          if (x, y) in visited:
               continue
          visited.add((x, y) )

          if (x, y) == destination:
               a_    :     Tuple										=    []
               while (x, y) != source:
                    path.append((x, y) )
                    a_			,			a_    :     Any										=    predecessors[x, y]
               path.append(__UpperCamelCase )  # add the source manually
               path.reverse()
               return matrix[destination], path

          for i in range(len(__UpperCamelCase ) ):
               a_			,			a_    :     Dict										=    x + dx[i], y + dy[i]
               if 0 <= nx < rows and 0 <= ny < cols:
                    a_    :     List[Any]										=    grid[nx][ny]
                    if next_node == 1 and matrix[nx, ny] > dist + 1:
                         heappush(__UpperCamelCase  ,  (dist + 1, (nx, ny)) )
                         a_    :     List[str]										=    dist + 1
                         a_    :     Union[str, Any]										=    (x, y)

     return np.inf, []


if __name__ == "__main__":
       import doctest

       doctest.testmod()



