
import math
import sys

import cva
import numpy as np


def     __lowerCAmelCase			(			a__						,    a__  )       ->  np.ndarray:
				# For applying gaussian function for each element in matrix.
				__a										=    math.sqrt(a__  )
				__a										=    1 / (sigma * math.sqrt(2 * math.pi  ))
				return cons * np.exp(-((img / sigma) ** 2) * 0.5  )


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__  )       ->  np.ndarray:
				__a										=    kernel_size // 2
				return img[x - half : x + half + 1, y - half : y + half + 1]


def     __lowerCAmelCase			(			a__						,    a__  )       ->  np.ndarray:
				# Creates a gaussian kernel of given dimension.
				__a										=    np.zeros((kernel_size, kernel_size)  )
				for i in range(0						,    a__  ):
								for j in range(0						,    a__  ):
												__a										=    math.sqrt(
												    abs(i - kernel_size // 2  ) ** 2 + abs(j - kernel_size // 2  ) ** 2  )
				return vec_gaussian(a__						,    a__  )


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__						,    )       ->  np.ndarray:
				__a										=    np.zeros(img.shape  )
				__a										=    get_gauss_kernel(a__						,    a__  )
				__a							,     __a										=    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  ):
												__a										=    get_slice(a__						,    a__						,    a__						,    a__  )
												__a										=    img_s - img_s[kernel_size // 2, kernel_size // 2]
												__a										=    vec_gaussian(a__						,    a__  )
												__a										=    np.multiply(a__						,    a__  )
												__a										=    np.multiply(a__						,    a__  )
												__a										=    np.sum(a__  ) / np.sum(a__  )
												__a										=    val
				return imga


def     __lowerCAmelCase			(			a__  )       ->  tuple:
				__a										=    args[1] if args[1:] else '''../image_data/lena.jpg'''
				__a										=    float(args[2]  ) if args[2:] else 1.0
				__a										=    float(args[3]  ) if args[3:] else 1.0
				if args[4:]:
								__a										=    int(args[4]  )
								__a										=    kernel_size + abs(kernel_size % 2 - 1  )
				else:
								__a										=    5
				return filename, spatial_variance, intensity_variance, kernel_size


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

					A     :	str    = img / 2_5_5
					A     :	Dict    = out.astype('float32')
					A     :	Union[str, Any]    = bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
					A     :	Union[str, Any]    = out * 2_5_5
					A     :	Any    = np.uinta(out)
					cva.imshow('output image', out)
					cva.waitKey(0)
					cva.destroyAllWindows()

def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  list:
				__a										=    len(a__  )
				__a										=    [[0] * n for i in range(a__  )]
				for i in range(a__  ):
								__a										=    y_points[i]

				for i in range(2						,    a__  ):
								for j in range(a__						,    a__  ):
												__a										=    (
												    (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()