



'''simple docstring'''





from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  k_size // 2
     _a  ,    _a      =  mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     _a      =  1 / (2 * pi * sigma) * exp(-(square(__A) + square(__A)) / (2 * square(__A)))
     return g




def lowerCAmelCase  (__A  ,	__A  ,	__A):



     """simple docstring"""



     _a  ,    _a      =  image.shape[0], image.shape[1]
     # dst image height and width
     _a      =  height - k_size + 1
     _a      =  width - k_size + 1

     # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
     _a      =  zeros((dst_height * dst_width, k_size * k_size))
     _a      =  0
     for i, j in product(range(__A)  ,	range(__A)):
          _a      =  ravel(image[i : i + k_size, j : j + k_size])
          _a      =  window
          row += 1

     #  turn the kernel into shape(k*k, 1)
     _a      =  gen_gaussian_kernel(__A  ,	__A)
     _a      =  ravel(__A)

     # reshape and get the dst image
     _a      =  dot(__A  ,	__A).reshape(__A  ,	__A).astype(__A)

     return dst


if __name__ == "__main__":
  # read original image
  lowercase_		       =  imread(R"../image_data/lena.jpg")
  # turn image in gray scale value
  lowercase_		       =  cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  lowercase_		       =  gaussian_filter(gray, 3, sigma=1)
  lowercase_		       =  gaussian_filter(gray, 5, sigma=0.8)

  # show result images
  imshow("gaussian filter with 3x3 mask", gaussianaxa)
  imshow("gaussian filter with 5x5 mask", gaussianaxa)
  waitKey()








'''simple docstring'''





import math






class __A			:



 '''simple docstring'''







 def __init__(self ,   A=0       )    ->     Dict:  # a graph with Node 0,1,...,N-1


      """simple docstring"""



      _a      =  n
      _a      =  [
          [math.inf for j in range(0 ,   A       )] for i in range(0 ,   A       )
      ]  # adjacency matrix for weight
      _a      =  [
          [math.inf for j in range(0 ,   A       )] for i in range(0 ,   A       )
      ]  # dp[i][j] stores minimum distance from i to j



 def 					a__  (self ,   A ,   A ,   A       )    ->     Tuple:


      """simple docstring"""



      _a      =  w



 def 					a__  (self       )    ->     List[Any]:


      """simple docstring"""



      for k in range(0 ,   self.n       ):
           for i in range(0 ,   self.n       ):
                for j in range(0 ,   self.n       ):
                     _a      =  min(self.dp[i][j] ,   self.dp[i][k] + self.dp[k][j]       )



 def 					a__  (self ,   A ,   A       )    ->     str:


      """simple docstring"""



      return self.dp[u][v]


if __name__ == "__main__":
  lowercase_		       =  Graph(5)
  graph.add_edge(0, 2, 9)
  graph.add_edge(0, 4, 10)
  graph.add_edge(1, 3, 5)
  graph.add_edge(2, 3, 7)
  graph.add_edge(3, 0, 10)
  graph.add_edge(3, 1, 2)
  graph.add_edge(3, 2, 1)
  graph.add_edge(3, 4, 6)
  graph.add_edge(4, 1, 3)
  graph.add_edge(4, 2, 4)
  graph.add_edge(4, 3, 9)
  graph.floyd_warshall()
  graph.show_min(1, 4)
  graph.show_min(0, 3)



