


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 	_a     ( SCREAMING_SNAKE_CASE		: Union[str, Any]			,       SCREAMING_SNAKE_CASE		: Optional[Any]   ):







 """simple docstring"""



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

def 	_a     ( SCREAMING_SNAKE_CASE		: Union[str, Any]			,       SCREAMING_SNAKE_CASE		: str			,       SCREAMING_SNAKE_CASE		: List[Any]   ):







 """simple docstring"""



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

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

 #  turn the kernel into shape(k*k, 1)
 UpperCamelCase__    :			List[str]      				=      gen_gaussian_kernel(SCREAMING_SNAKE_CASE			,       SCREAMING_SNAKE_CASE   )
 UpperCamelCase__    :			Dict      				=      ravel(SCREAMING_SNAKE_CASE   )

 # reshape and get the dst image
 UpperCamelCase__    :			Optional[int]      				=      dot(SCREAMING_SNAKE_CASE			,       SCREAMING_SNAKE_CASE   ).reshape(SCREAMING_SNAKE_CASE			,       SCREAMING_SNAKE_CASE   ).astype(SCREAMING_SNAKE_CASE   )

 return dst


if __name__ == "__main__":
       # read original image
       __UpperCamelCase				:				Dict  				= 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				:				Optional[Any]  				= gaussian_filter(gray, 3, sigma=1)
       __UpperCamelCase				:				Optional[int]  				= 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'''

def 			__UpperCamelCase       (   UpperCAmelCase = 1		,     UpperCAmelCase = 1000     ):
 lowercase__				:    Dict  =					1
 lowercase__				:    Dict  =					0

 for divide_by_number in range(UpperCAmelCase		,     digit + 1     ):
  lowercase__				:    list[int]  =					[]
  lowercase__				:    Union[str, Any]  =					numerator
  for _ in range(1		,     digit + 1     ):
   if now_divide in has_been_divided:
    if longest_list_length < len(UpperCAmelCase     ):
     lowercase__				:    Dict  =					len(UpperCAmelCase     )
     lowercase__				:    Optional[Any]  =					divide_by_number
   else:
    has_been_divided.append(UpperCAmelCase     )
    lowercase__				:    int  =					now_divide * 10 % divide_by_number

 return the_digit


# Tests
if __name__ == "__main__":
  import doctest

  doctest.testmod()

