



"""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 				SCREAMING_SNAKE_CASE__   (		SCREAMING_SNAKE_CASE__			:	Dict					,     SCREAMING_SNAKE_CASE__			:	Union[str, Any]      ):



					"""simple docstring"""





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







def 				SCREAMING_SNAKE_CASE__   (		SCREAMING_SNAKE_CASE__			:	Tuple					,     SCREAMING_SNAKE_CASE__			:	Dict					,     SCREAMING_SNAKE_CASE__			:	Tuple      ):



					"""simple docstring"""





					snake_case_       :		int			       =       image.shape[0], image.shape[1]
					# dst image height and width
					snake_case_       :		Optional[Any]			       =       height - k_size + 1
					snake_case_       :		Dict			       =       width - k_size + 1

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

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

					# reshape and get the dst image
					snake_case_       :		Optional[Any]			       =       dot(__UpperCAmelCase					,     __UpperCAmelCase      ).reshape(__UpperCAmelCase					,     __UpperCAmelCase      ).astype(__UpperCAmelCase      )

					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()









'''simple docstring'''

def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					list:





			"""simple docstring"""







			snake_case:    Optional[int]      							=len(__UpperCAmelCase					)
			snake_case:    Optional[int]      							=[[0] * n for i in range(__UpperCAmelCase					)]
			for i in range(__UpperCAmelCase					):
						snake_case:    Optional[int]      							=y_points[i]

			for i in range(2				,							__UpperCAmelCase					):
						for j in range(__UpperCAmelCase				,							__UpperCAmelCase					):
									snake_case:    List[str]      							=(
									    (xa - x_points[j - i + 1]) * q[j][i - 1]
									    - (xa - x_points[j]) * q[j - 1][i - 1]
									) / (x_points[j] - x_points[j - i + 1])

			return [q[n - 1][n - 1], q]


if __name__ == "__main__":
	import doctest

	doctest.testmod()




