opencv学习（十一）：高斯模糊

代码如下：

# 导入cv模块
import cv2 as cv
import numpy as np


# 确保在0-255之间
def clamp(pv):
    if pv > 255:
        return 255
    if pv < 0:
        return 0
    else:
        return pv

def gaussian_noise(image):  # 加噪声
    h, w, c = image.shape
    for row in range(h):
        for col in range(w):
            s = np.random.normal(0, 20, 3)
            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("------------Hi,Python!-------------")
# 读取图像，支持 bmp、jpg、png、tiff 等常用格式
src = cv.imread("F:/Projects/images/2.jpg")
# 创建窗口并显示图像
cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
cv.imshow("input image", src)  # 显示原图
t1 = cv.getCPUTickCount()
gaussian_noise(src)   #加噪声
t2 = cv.getCPUTickCount()
time = (t2 - t1)/cv.getTickFrequency()
print("time consume:%s ms"%(time*1000))
dst=cv.GaussianBlur(src,(0,0),15)
cv.imshow("Gaussian Blur",src) #高斯模糊

dst1=cv.GaussianBlur(src,(5,5),0)
cv.imshow("Gaussian_Blur",dst1)

cv.waitKey(0)
# 释放窗口
cv.destroyAllWindows()

效果如下：