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





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 lowerCamelCase			,    lowerCamelCase		:	str							=      image.shape[0], image.shape[1]
 # dst image height and width
 lowerCamelCase		:	str							=      height - k_size + 1
 lowerCamelCase		:	int							=      width - k_size + 1

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

 #  turn the kernel into shape(k*k, 1)
 lowerCamelCase		:	Union[str, Any]							=      gen_gaussian_kernel(lowerCamelCase,	lowerCamelCase							)
 lowerCamelCase		:	List[Any]							=      ravel(lowerCamelCase							)

 # reshape and get the dst image
 lowerCamelCase		:	Dict							=      dot(lowerCamelCase,	lowerCamelCase							).reshape(lowerCamelCase,	lowerCamelCase							).astype(lowerCamelCase							)

 return dst


if __name__ == "__main__":
      # read original image
      _lowerCamelCase					   =imread(R"""../image_data/lena.jpg""")
      # turn image in gray scale value
      _lowerCamelCase					   =cvtColor(img, COLOR_BGR2GRAY)

      # get values with two different mask size
      _lowerCamelCase					   =gaussian_filter(gray, 3, sigma=1)
      _lowerCamelCase					   =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()






_lowerCamelCase					   ={
    "joule": 1.0,
    "kilojoule": 1_0_0_0,
    "megajoule": 1_0_0_0_0_0_0,
    "gigajoule": 1_0_0_0_0_0_0_0_0_0,
    "wattsecond": 1.0,
    "watthour": 3_6_0_0,
    "kilowatthour": 3_6_0_0_0_0_0,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_1_8_6.8,
    "kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4E-1_9,
    "britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
    "footpound": 1.355818,
}





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
  lowerCamelCase		:	Dict							=      (
      F'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
      F'''Valid values are: {", ".join(lowerCamelCase							)}'''
  )
  raise ValueError(lowerCamelCase							)
 return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
      import doctest

      doctest.testmod()




