old_contents: """
  Implementation of gaussian filter algorithm
  """
  from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
  from numpy import pi, mgrid, exp, square, zeros, ravel, dot, uint8
  
  
  def gen_gaussian_kernel(k_size, sigma):
      center = k_size // 2
      x, y = mgrid[0 - center : k_size - center, 0 - center : k_size - center]
      g = 1 / (2 * pi * sigma) * exp(-(square(x) + square(y)) / (2 * square(sigma)))
      return g
  
  
  def gaussian_filter(image, k_size, sigma):
      height, width = image.shape[0], image.shape[1]
      # dst image height and width
      dst_height = height - k_size + 1
      dst_width = width - k_size + 1
  
      # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
      image_array = zeros((dst_height * dst_width, k_size * k_size))
      row = 0
      for i in range(0, dst_height):
          for j in range(0, dst_width):
              window = ravel(image[i : i + k_size, j : j + k_size])
              image_array[row, :] = window
              row += 1
  
      #  turn the kernel into shape(k*k, 1)
      gaussian_kernel = gen_gaussian_kernel(k_size, sigma)
      filter_array = ravel(gaussian_kernel)
  
      # reshape and get the dst image
      dst = dot(image_array, filter_array).reshape(dst_height, dst_width).astype(uint8)
  
      return dst
  
  
  if __name__ == "__main__":
      # read original image
      img = imread(r"../image_data/lena.jpg")
      # turn image in gray scale value
      gray = cvtColor(img, COLOR_BGR2GRAY)
  
      # get values with two different mask size
      gaussian3x3 = gaussian_filter(gray, 3, sigma=1)
      gaussian5x5 = gaussian_filter(gray, 5, sigma=0.8)
  
      # show result images
      imshow("gaussian filter with 3x3 mask", gaussian3x3)
      imshow("gaussian filter with 5x5 mask", gaussian5x5)
      waitKey()
  
new_contents: """
  Implementation of gaussian filter algorithm
  """
  from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
  from numpy import pi, mgrid, exp, square, zeros, ravel, dot, uint8
  from itertools import product
  
  
  def gen_gaussian_kernel(k_size, sigma):
      center = k_size // 2
      x, y = mgrid[0 - center : k_size - center, 0 - center : k_size - center]
      g = 1 / (2 * pi * sigma) * exp(-(square(x) + square(y)) / (2 * square(sigma)))
      return g
  
  
  def gaussian_filter(image, k_size, sigma):
      height, width = image.shape[0], image.shape[1]
      # dst image height and width
      dst_height = height - k_size + 1
      dst_width = width - k_size + 1
  
      # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
      image_array = zeros((dst_height * dst_width, k_size * k_size))
      row = 0
      for i, j in product(range(dst_height), range(dst_width)): 
              window = ravel(image[i : i + k_size, j : j + k_size])
              image_array[row, :] = window
              row += 1
  
      #  turn the kernel into shape(k*k, 1)
      gaussian_kernel = gen_gaussian_kernel(k_size, sigma)
      filter_array = ravel(gaussian_kernel)
  
      # reshape and get the dst image
      dst = dot(image_array, filter_array).reshape(dst_height, dst_width).astype(uint8)
  
      return dst
  
  
  if __name__ == "__main__":
      # read original image
      img = imread(r"../image_data/lena.jpg")
      # turn image in gray scale value
      gray = cvtColor(img, COLOR_BGR2GRAY)
  
      # get values with two different mask size
      gaussian3x3 = gaussian_filter(gray, 3, sigma=1)
      gaussian5x5 = gaussian_filter(gray, 5, sigma=0.8)
  
      # show result images
      imshow("gaussian filter with 3x3 mask", gaussian3x3)
      imshow("gaussian filter with 5x5 mask", gaussian5x5)
      waitKey()
  
subject: Update gaussian_filter.py (#1548)