滤波【OpenCV&Python】

0 引言

整理了一下常见的滤波方法，效果图就不放了。

一、加噪

1 添加椒盐噪声

import cv2 as cv
import numpy as np

def rgb2gray(img):
    h=img.shape[0]
    w=img.shape[1]
    img1=np.zeros((h,w),np.uint8)
    for i in range(h):
        for j in range(w):
            img1[i,j]=0.144*img[i,j,0]+0.587*img[i,j,1]+0.299*img[i,j,2]
    return img1

def noise(img,snr):
    h=img.shape[0]
    w=img.shape[1]
    img1=img.copy()
    sp=h*w   # 计算图像像素点个数
    NP=int(sp*(1-snr))   # 计算图像椒盐噪声点个数
    for i in range (NP):
        randx=np.random.randint(1,h-1)   # 生成一个 1 至 h-1 之间的随机整数
        randy=np.random.randint(1,w-1)   # 生成一个 1 至 w-1 之间的随机整数
        if np.random.random()<=0.5:   # np.random.random()生成一个 0 至 1 之间的浮点数
            img1[randx,randy]=0
        else:
            img1[randx,randy]=255
    return img1

image=cv.imread("C:\\Users\\Kris\\Desktop\\lena.jpg")
grayimage=rgb2gray(image)
jiaoyannoiseimage=noise(grayimage,0.6)   # 将信噪比设定为0.6
cv.imshow("image",image)
cv.imshow("grayimage",grayimage)
cv.imshow("jiaoyannoiseimage",jiaoyannoiseimage)
image =cv.imwrite('C:\\Users\\Kris\\Desktop\\quzao\\image.png',image,[int( cv.IMWRITE_JPEG_QUALITY), 95])
grayimage =cv.imwrite('C:\\Users\\Kris\\Desktop\\quzao\\grayimage.png',grayimage,[int( cv.IMWRITE_JPEG_QUALITY), 95])
jiaoyannoiseimage =cv.imwrite('C:\\Users\\Kris\\Desktop\\quzao\\jiaoyannoiseimage.png',jiaoyannoiseimage,[int( cv.IMWRITE_JPEG_QUALITY), 95])
cv.waitKey(0)
cv.destroyAllWindows()

2 添加高斯噪声

import cv2 as cv
import numpy as np

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

#给图片增加高斯噪声，计算花费很长时间
def gaussian_noise(src):
    image = src
    h, w, c = image.shape
    for row in range(h):
        for col in range(w):
            #获取三个高斯随机数
            #第一个参数：概率分布的均值，对应着整个分布的中心
            #第二个参数：概率分布的标准差，对应于分布的宽度
            #第三个参数：生成高斯随机数数量
            s = np.random.normal(0, 20, 3)
            #获取每个像素点的bgr值
            b = image[row, col, 0]  #blue
            g = image[row, col, 1]  #green
            r = image[row, col, 2]  #red\
            #给每个像素值设置新的bgr值
            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.namedWindow('Gaussian_noise', 0)
    cv.resizeWindow('Gaussian_noise', 600, 600)
    cv.imshow('Gaussian_noise', image)

def gaussian_blur(src): # 定义高斯模糊
    image = src
    dst = cv.GaussianBlur(image, (0, 0), 15)

    cv.namedWindow('Gaussian_blur', 0)
    cv.resizeWindow('Gaussian_blur', 600, 600)
    cv.imshow('Gaussian_blur', dst)

src = cv.imread('C:\\Users\\Kris\\Desktop\\lena.jpg')
cv.namedWindow('inputimage', 0)
cv.resizeWindow('inputimage', 600, 600)
cv.imshow('inputimage', src)

#gaussiannoise = gaussian_noise(src) # 调用上述定义的高斯噪声
# gaussian_blur = gaussian_blur(src) # 调用高斯模糊函数

inputimage =cv.imwrite('C:\\Users\\Kris\\Desktop\\quzao\\inputimage.png',src,[int(cv.IMWRITE_JPEG_QUALITY), 95])
Gaussian_noise =cv.imwrite('C:\\Users\\Kris\\Desktop\\quzao\\Gaussian_noise.png',src,[int(cv.IMWRITE_JPEG_QUALITY), 95])
#Gaussian_blur =cv.imwrite('C:\\Users\\Kris\\Desktop\\quzao\\Gaussian_blur.png',src,[int(cv.IMWRITE_JPEG_QUALITY), 95])


cv.waitKey(0)

cv.destroyAllWindows()

二、滤波

1 均值滤波

import cv2
o = cv2.imread("C:\\Users\\Kris\\Desktop\\quzao\\jiaoyannoiseimage.png") # 读取噪声图像
r5 = cv2.blur(o,(5,5))
r30 = cv2.blur(o,(30,30))
cv2.imshow("original",o)
cv2.imshow("result5",r5)
cv2.imshow("result30",r30)
r5 = cv2.imwrite('C:\\Users\\Kris\\Desktop\\quzao\\jiaoyanjunzhi5.png',r5,[int(cv2.IMWRITE_JPEG_QUALITY), 95])
r30 = cv2.imwrite('C:\\Users\\Kris\\Desktop\\quzao\\jiaoyanjunzhi30.png',r30,[int(cv2.IMWRITE_JPEG_QUALITY), 95])
cv2.waitKey(0)
cv2.destroyALLWindows()

2 方框滤波

import cv2
o = cv2.imread("C:\\Users\\Kris\\Desktop\\quzao\\Gaussian_noise.png") # 读取噪声图像
# r = cv2.boxFilter(o,-1,(5,5)) # normalize=1进行归一化，相当于均值滤波
r = cv2.boxFilter(o,-1,(5,5),normalize=0) # normalize=0不进行归一化
cv2.imshow("original",o)
cv2.imshow("result",r)
r = cv2.imwrite('C:\\Users\\Kris\\Desktop\\quzao\\gaosibox0.png',r,[int(cv2.IMWRITE_JPEG_QUALITY), 95])
cv2.waitKey(0)
cv2.destroyALLWindows()

3 中值滤波

import cv2
o = cv2.imread("C:\\Users\\Kris\\Desktop\\quzao\\jiaoyannoiseimage.png") # 读取噪声图像
r = cv2.medianBlur(o,7)
cv2.imshow("original",o)
cv2.imshow("result",r)
r = cv2.imwrite('C:\\Users\\Kris\\Desktop\\quzao\\jiaoyanmedianBlur7.png',r,[int(cv2.IMWRITE_JPEG_QUALITY), 95])
cv2.waitKey(0)
cv2.destroyALLWindows()

4 高斯滤波

import cv2
o = cv2.imread("C:\\Users\\Kris\\Desktop\\quzao\\jiaoyannoiseimage.png") # 读取噪声图像
r5 = cv2.GaussianBlur(o,(5,5),0,0)
cv2.imshow("original",o)
cv2.imshow("result",r5)
#r = cv2.imwrite('C:\\Users\\Kris\\Desktop\\quzao\\jiaoyanmedianBlur7.png',r5,[int(cv2.IMWRITE_JPEG_QUALITY), 95])
cv2.waitKey(0)
cv2.destroyALLWindows()

5 双边滤波

import cv2
o = cv2.imread("C:\\Users\\Kris\\Desktop\\quzao\\jiaoyannoiseimage.png") # 读取噪声图像
r5 = cv2.bilateralFilter(o,25,200,200)
cv2.imshow("original",o)
cv2.imshow("result",r5)
r = cv2.imwrite('C:\\Users\\Kris\\Desktop\\quzao\\jiaoyanbilateralFilter200.png',r5,[int(cv2.IMWRITE_JPEG_QUALITY), 95])
cv2.waitKey(0)
cv2.destroyALLWindows()