





'''simple docstring'''






import math
import sys

import cva
import numpy as np






def      __A						(							a_						:  np.ndarray      ,a_						:  float	):
						# For applying gaussian function for each element in matrix.
						lowerCAmelCase     :    List[str]						=   math.sqrt(a_	)
						lowerCAmelCase     :    Dict						=   1 / (sigma * math.sqrt(2 * math.pi	))
						return cons * np.exp(-((img / sigma) ** 2) * 0.5	)






def      __A						(							a_						:  np.ndarray      ,a_						:  int      ,a_						:  int      ,a_						:  int	):
						lowerCAmelCase     :    Tuple						=   kernel_size // 2
						return img[x - half : x + half + 1, y - half : y + half + 1]






def      __A						(							a_						:  int      ,a_						:  float	):
						# Creates a gaussian kernel of given dimension.
						lowerCAmelCase     :    List[str]						=   np.zeros((kernel_size, kernel_size)	)
						for i in range(0      ,a_	):
												for j in range(0      ,a_	):
																		lowerCAmelCase     :    Optional[int]						=   math.sqrt(
																		    abs(i - kernel_size // 2	) ** 2 + abs(j - kernel_size // 2	) ** 2	)
						return vec_gaussian(a_      ,a_	)






def      __A						(							a_						:  np.ndarray      ,a_						:  float      ,a_						:  float      ,a_						:  int      ,):
						lowerCAmelCase     :    List[str]						=   np.zeros(img.shape	)
						lowerCAmelCase     :    Dict						=   get_gauss_kernel(a_      ,a_	)
						lowerCAmelCase     :    List[str]						=   img.shape
						for i in range(kernel_size // 2      ,size_x - kernel_size // 2	):
												for j in range(kernel_size // 2      ,size_y - kernel_size // 2	):
																		lowerCAmelCase     :    Tuple						=   get_slice(a_      ,a_      ,a_      ,a_	)
																		lowerCAmelCase     :    int						=   img_s - img_s[kernel_size // 2, kernel_size // 2]
																		lowerCAmelCase     :    Optional[Any]						=   vec_gaussian(a_      ,a_	)
																		lowerCAmelCase     :    int						=   np.multiply(a_      ,a_	)
																		lowerCAmelCase     :    int						=   np.multiply(a_      ,a_	)
																		lowerCAmelCase     :    List[Any]						=   np.sum(a_	) / np.sum(a_	)
																		lowerCAmelCase     :    Optional[Any]						=   val
						return imga






def      __A						(							a_						:  list	):
						lowerCAmelCase     :    Optional[Any]						=   args[1] if args[1:] else "../image_data/lena.jpg"
						lowerCAmelCase     :    Tuple						=   float(args[2]	) if args[2:] else 1.0
						lowerCAmelCase     :    Optional[int]						=   float(args[3]	) if args[3:] else 1.0
						if args[4:]:
												lowerCAmelCase     :    Union[str, Any]						=   int(args[4]	)
												lowerCAmelCase     :    Optional[Any]						=   kernel_size + abs(kernel_size % 2 - 1	)
						else:
												lowerCAmelCase     :    Any						=   5
						return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
					lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase       		=      parse_args(sys.argv)
					lowerCAmelCase       		=      cva.imread(filename, 0)
					cva.imshow("""input image""", img)

					lowerCAmelCase       		=      img / 2_55
					lowerCAmelCase       		=      out.astype("""float32""")
					lowerCAmelCase       		=      bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
					lowerCAmelCase       		=      out * 2_55
					lowerCAmelCase       		=      np.uinta(out)
					cva.imshow("""output image""", out)
					cva.waitKey(0)
					cva.destroyAllWindows()










'''simple docstring'''






import numpy as np






def      __A						(							a_						:  np.array	):
		return (2 / (1 + np.exp(-2 * vector	))) - 1


if __name__ == "__main__":
					import doctest

					doctest.testmod()



