|
| 1 | +""" |
| 2 | +Implementation of median filter algorithm |
| 3 | +""" |
| 4 | + |
| 5 | +from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey |
| 6 | +from numpy import zeros_like, ravel, sort, multiply, divide, int8 |
| 7 | + |
| 8 | + |
| 9 | +def median_filter(gray_img, mask=3): |
| 10 | + """ |
| 11 | + :param gray_img: gray image |
| 12 | + :param mask: mask size |
| 13 | + :return: image with median filter |
| 14 | + """ |
| 15 | + # set image borders |
| 16 | + bd = int(mask / 2) |
| 17 | + # copy image size |
| 18 | + median_img = zeros_like(gray) |
| 19 | + for i in range(bd, gray_img.shape[0] - bd): |
| 20 | + for j in range(bd, gray_img.shape[1] - bd): |
| 21 | + # get mask according with mask |
| 22 | + kernel = ravel(gray_img[i - bd:i + bd + 1, j - bd:j + bd + 1]) |
| 23 | + # calculate mask median |
| 24 | + median = sort(kernel)[int8(divide((multiply(mask, mask)), 2) + 1)] |
| 25 | + median_img[i, j] = median |
| 26 | + return median_img |
| 27 | + |
| 28 | + |
| 29 | +if __name__ == '__main__': |
| 30 | + # read original image |
| 31 | + img = imread('lena.jpg') |
| 32 | + # turn image in gray scale value |
| 33 | + gray = cvtColor(img, COLOR_BGR2GRAY) |
| 34 | + |
| 35 | + # get values with two different mask size |
| 36 | + median3x3 = median_filter(gray, 3) |
| 37 | + median5x5 = median_filter(gray, 5) |
| 38 | + |
| 39 | + # show result images |
| 40 | + imshow('median filter with 3x3 mask', median3x3) |
| 41 | + imshow('median filter with 5x5 mask', median5x5) |
| 42 | + waitKey(0) |
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