





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						)	->						Any:







  """simple docstring"""





  lowerCamelCase__: Dict         =k_size // 2
  lowerCamelCase__    ,   lowerCamelCase__: Any         =mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  lowerCamelCase__: Dict         =1 / (2 * pi * sigma) * exp(-(square(__a						) + square(__a						)) / (2 * square(__a						))						)
  return g





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a						)	->						Tuple:







  """simple docstring"""





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

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

  #  turn the kernel into shape(k*k, 1)
  lowerCamelCase__: List[Any]         =gen_gaussian_kernel(__a      ,       __a						)
  lowerCamelCase__: str         =ravel(__a						)

  # reshape and get the dst image
  lowerCamelCase__: Dict         =dot(__a      ,       __a						).reshape(__a      ,       __a						).astype(__a						)

  return dst


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

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



from abc import ABC, abstractmethod
from argparse import ArgumentParser







class 							lowerCAmelCase__   ( __lowercase						):

						@staticmethod
						@abstractmethod
						def 			A_					(      a					)       ->   Union[str, Any]:


								'''simple docstring'''

								raise NotImplementedError()







						@abstractmethod
						def 			A_					(      self					)       ->   List[Any]:


								'''simple docstring'''

								raise NotImplementedError()



