


'''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    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   _a					:  Optional[Any]						=  k_size // 2
   _a		,						_a					:  List[Any]						=  mgrid[0 - center : k_size - center, 0 - center : k_size - center]
   _a					:  Union[str, Any]						=  1 / (2 * pi * sigma) * exp(-(square(A      ) + square(A      )) / (2 * square(A      ))      )
   return g


def    UpperCAmelCase_  (       A		,	A		,	A      ):
   '''simple docstring'''





   _a		,						_a					:  Dict						=  image.shape[0], image.shape[1]
   # dst image height and width
   _a					:  Any						=  height - k_size + 1
   _a					:  List[str]						=  width - k_size + 1

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

   #  turn the kernel into shape(k*k, 1)
   _a					:  Dict						=  gen_gaussian_kernel(A		,	A      )
   _a					:  List[Any]						=  ravel(A      )

   # reshape and get the dst image
   _a					:  str						=  dot(A		,	A      ).reshape(A		,	A      ).astype(A      )

   return dst


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

 # get values with two different mask size
 UpperCAmelCase_      :		Dict          =		gaussian_filter(gray, 3, sigma=1)
 UpperCAmelCase_      :		Tuple          =		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'''




from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass







@dataclass
class 					a			:
 '''simple docstring'''




 __lowerCAmelCase						: int
 __lowerCAmelCase						: TreeNode | None							  =		None
 __lowerCAmelCase						: TreeNode | None							  =		None





UpperCAmelCase_      :		Dict          =		namedtuple("CoinsDistribResult", "moves excess")


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   if root is None:
      return 0

   # Validation
   def count_nodes(A      ) -> int:
      if node is None:
         return 0

      return count_nodes(node.left      ) + count_nodes(node.right      ) + 1

   def count_coins(A      ) -> int:
      if node is None:
         return 0

      return count_coins(node.left      ) + count_coins(node.right      ) + node.data

   if count_nodes(A      ) != count_coins(A      ):
      raise ValueError('The nodes number should be same as the number of coins'      )

   # Main calculation
   def get_distrib(A      ) -> CoinsDistribResult:

      if node is None:
         return CoinsDistribResult(0		,	1      )

      _a		,						_a					:  Optional[Any]						=  get_distrib(node.left      )
      _a		,						_a					:  Tuple						=  get_distrib(node.right      )

      _a					:  List[str]						=  1 - left_distrib_excess
      _a					:  str						=  1 - right_distrib_excess

      _a					:  Any						=  (
          left_distrib_moves
          + right_distrib_moves
          + abs(A      )
          + abs(A      )
      )
      _a					:  Optional[int]						=  node.data - coins_to_left - coins_to_right

      return CoinsDistribResult(A		,	A      )

   return get_distrib(A      )[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()


