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- """
- 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()
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