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- """
- Implementation of median filter algorithm
- """
- from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
- from numpy import zeros_like, ravel, sort, multiply, divide, int8
- def median_filter(gray_img, mask=3):
- """
- :param gray_img: gray image
- :param mask: mask size
- :return: image with median filter
- """
- # set image borders
- bd = int(mask / 2)
- # copy image size
- median_img = zeros_like(gray_img)
- for i in range(bd, gray_img.shape[0] - bd):
- for j in range(bd, gray_img.shape[1] - bd):
- # get mask according with mask
- kernel = ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1])
- # calculate mask median
- median = sort(kernel)[int8(divide((multiply(mask, mask)), 2) + 1)]
- median_img[i, j] = median
- return median_img
- if __name__ == "__main__":
- # read original image
- img = imread("../image_data/lena.jpg")
- # turn image in gray scale value
- gray = cvtColor(img, COLOR_BGR2GRAY)
- # get values with two different mask size
- median3x3 = median_filter(gray, 3)
- median5x5 = median_filter(gray, 5)
- # show result images
- imshow("median filter with 3x3 mask", median3x3)
- imshow("median filter with 5x5 mask", median5x5)
- waitKey(0)
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