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        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       Tuple:



			"""simple docstring"""







			lowercase							=		k_size // 2
			lowercase		,lowercase							=		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
			lowercase							=		1 / (2 * pi * sigma) * exp(-(square(UpperCAmelCase       ) + square(UpperCAmelCase       )) / (2 * square(UpperCAmelCase       ))       )
			return g






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Optional[int]:



			"""simple docstring"""







			lowercase		,lowercase							=		image.shape[0], image.shape[1]
			# dst image height and width
			lowercase							=		height - k_size + 1
			lowercase							=		width - k_size + 1

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

			#  turn the kernel into shape(k*k, 1)
			lowercase							=		gen_gaussian_kernel(UpperCAmelCase,		UpperCAmelCase       )
			lowercase							=		ravel(UpperCAmelCase       )

			# reshape and get the dst image
			lowercase							=		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()






import cmath
import math






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       complex:



			"""simple docstring"""







			lowercase							=		math.radians(UpperCAmelCase       )
			lowercase							=		math.radians(UpperCAmelCase       )

			# Convert voltage and current to rectangular form
			lowercase							=		cmath.rect(UpperCAmelCase,		UpperCAmelCase       )
			lowercase							=		cmath.rect(UpperCAmelCase,		UpperCAmelCase       )

			# Calculate apparent power
			return voltage_rect * current_rect


if __name__ == "__main__":
						import doctest

						doctest.testmod()





