高斯噪点
#!/usr/bin/env python
# _*_ coding:utf-8 _*_
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
#对每一行每一列每一个像素点三个通道加上一个随机数的值就产生了一个随机高斯的随机噪声的图片
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)
src = cv.imread("F:\miao.jpg")
cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
cv.imshow("input image", src)
gaussian_noise(src)
cv.waitKey(0)
cv.destroyAllWindows()
运行结果,如图:
高斯模糊处理
#!/usr/bin/env python
# _*_ coding:utf-8 _*_
import cv2 as cv
import numpy as np
def clamp(pv):
if pv > 255:
return 255
if pv < 0:
return 0
else:
return pv
src = cv.imread("F:\miao.jpg")
cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
cv.imshow("input image", src)
t1 = cv.getTickCount()
t2 = cv.getTickCount()
time = (t2 - t1)/cv.getTickFrequency()
print("time consume : %s"%(time*1000))
dst = cv.GaussianBlur(src, (0, 0), 15)
cv.imshow("Gaussian Blur", dst)
cv.waitKey(0)
cv.destroyAllWindows()
运行结果,如下图: