import cv2 as cv
import numpy as np

def clamp(pv):
    if pv > 255:
        return 255
    if pv < 0:
        return 0
    else:
        return pv

def gaussian_noise(image):
    h, w, c = image.shape
    for row in range(h):
        for col in range(w):
            s = np.random.normal(0, 20, 3)       ##高斯概率分佈u,σ,n
            b = image[row, col, 0]  # blue
            g = image[row, col, 1]  # green
            r = image[row, col, 2]  # red
            image[row, col, 0] = clamp(b + s[0])
            image[row, col, 1] = clamp(g + s[1])
            image[row, col, 2] = clamp(r + s[2])
    cv.imshow("noise image", image)


print("--------- Python OpenCV Tutorial ---------")
src = cv.imread("C:/Users/weiqiangwen/Desktop/sest/contours.png")
cv.namedWindow("input contours",cv.WINDOW_AUTOSIZE)
cv.imshow("contours", src)
t1 = cv.getTickCount()
#gaussian_noise(src)
dst = cv.GaussianBlur(src, (0, 0), 15)          #高斯模糊
cv.imshow("Gaussian Blur", dst)
t2 = cv.getTickCount()
time = (t2 - t1)/cv.getTickFrequency()
print("time consume : %s"%(time*1000))
cv.waitKey(0)

cv.destroyAllWindows()