opencv—数据增强处理2（噪声计算）

接上篇数据增强

代码6：高斯噪声、椒盐噪声

import cv2
import random
from numpy import *
def PepperandSalt(src,percetage):
    NoiseImg=src
    NoiseNum=int(percetage*src.shape[0]*src.shape[1])
    for i in range(NoiseNum):
        randX=random.random_integers(0,src.shape[0]-1)
        randY=random.random_integers(0,src.shape[1]-1)
        if random.random_integers(0,1)<=0.5:
            NoiseImg[randX,randY]=0
        else:
            NoiseImg[randX,randY]=255          
    return NoiseImg
    
def GaussianNoise(src,means,sigma):
    NoiseImg=src
    rows=NoiseImg.shape[0]
    cols=NoiseImg.shape[1]
    for i in range(rows):
        for j in range(cols):
            NoiseImg[i,j]=NoiseImg[i,j]+random.gauss(means,sigma)
            if  NoiseImg[i,j]< 0:
                 NoiseImg[i,j]=0
            elif  NoiseImg[i,j]>255:
                 NoiseImg[i,j]=255
    return NoiseImg 
img=cv2.imread('img/a.jpg',0)
img1=PepperandSalt(img,0.2)
cv2.imwrite('img2/a2.jpg',img1)
cv2.imshow('PepperandSalt',img1)
cv2.waitKey(0)

---

关于噪声的具体像素计算过程如下：

#1、生成高斯噪声
fn = "img/a.jpg"
if __name__ == '__main__':
    img = cv2.imread(fn)
    coutn = 10000
    for k in range(0,coutn):
        #get the random point
        xi = int(np.random.uniform(0,img.shape[1]))
        xj = int(np.random.uniform(0,img.shape[0]))
        #add noise
        if img.ndim == 2:
            img[xj,xi] = 255
        elif img.ndim == 3:
            img[xj,xi,0] = 25
            img[xj,xi,1] = 20
            img[xj,xi,2] = 20
    cv2.namedWindow('img')
    cv2.imshow('img',img)
    cv2.waitKey()
    cv2.destroyAllWindows()

#2、椒盐噪声
filename = "img/a.jpg"
winname = "figure"
img = cv2.imread(filename)

def addSaltNoise():
    # 指定信噪比
    SNR = 0.99
    # 获取总共像素个数
    size = img.size
    # 因为信噪比是 SNR ，所以噪声占据百分之10，所以需要对这百分之10加噪声
    noiseSize = int(size * (1 - SNR))
    # 对这些点加噪声
    for k in range(0, noiseSize):
        # 随机获取 某个点
        xi = int(np.random.uniform(0, img.shape[1]))
        xj = int(np.random.uniform(0, img.shape[0]))
        # 增加噪声
        if img.ndim == 2:
            img[xj, xi] = 255
        elif img.ndim == 3:
            img[xj, xi] = 0
    cv2.imshow(winname, img)
    # cv2.waitKey(0)
    cv2.imwrite('img2/a1.jpg',img)

def main():
    addSaltNoise()

if __name__ == '__main__':
    main()

3、随机椒盐
img=cv2.imread('img/a.jpg')
height,weight,channel = img.shape

#随机生成5000个椒盐噪声
for i in range(5000):
    x = np.random.randint(0,height)
    y = np.random.randint(0,weight)
    img[x ,y ,:] = 255

cv2.imshow("123.jpg",img)
cv2.waitKey(0)