OpenCV之5种滤波方法

5种滤波方法分别为：

3种线性滤波：方框滤波、均值滤波、高斯滤波

2种非线性滤波：中值滤波、双边滤波

简单来说，方框滤波就是邻域内取个平均值，均值滤波就是方框滤波再归一化一下，高斯滤波就是用正态分布去和图像做卷积。

中值滤波是用像素点邻域灰度值的中值来代替这个像素点的灰度值，而双边滤波像素点的灰度值是邻域像素值的加权组合，是一个基于空间分布的高斯滤波函数，可以很好的保留边缘附近的像素值。

从书上抄的代码：

#include <opencv2/opencv.hpp>#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/imgproc/imgproc.hpp>#include <cstdio>using namespace cv;Mat g_srcImage,g_dstImage1,g_dstImage2,g_dstImage3,g_dstImage4,g_dstImage5;int g_nBoxFilterValue=6;int g_nMeanBlurValue=10;int g_nGaussianBlurValue=6;int g_nMedianBlurValue=10;int g_nBilateralFilterValue=10;static void on_BoxFilter(int,void *);static void on_MeanBlur(int,void *);static void on_GaussianBlur(int,void *);static void on_MedianBlur(int,void *);static void on_BilateralFilter(int,void *);int main(){    //====================载入原图====================    g_srcImage=imread("/Users/hxh/Desktop/2.jpg",0);    if (!g_srcImage.data){        printf("no source image!\n");        return false;    }    //==============复制原图到5个Mat类型中==============    g_dstImage1=g_srcImage.clone();    g_dstImage2=g_srcImage.clone();    g_dstImage3=g_srcImage.clone();    g_dstImage4=g_srcImage.clone();    g_dstImage5=g_srcImage.clone();    //===================显示原图=====================    namedWindow("0:source image",WINDOW_AUTOSIZE);    imshow("0:source image",g_srcImage);    //================【<1>方框滤波】==================    namedWindow("1:image after box filter",WINDOW_AUTOSIZE);    createTrackbar("内核值：", "1:image after box filter", &g_nBoxFilterValue, 50,on_BoxFilter);    on_MeanBlur(g_nBoxFilterValue, 0);    //================【<2>均值滤波】==================    namedWindow("2:image after mean blur",WINDOW_AUTOSIZE);    createTrackbar("内核值：", "2:image after mean blur", &g_nMeanBlurValue, 50,on_MeanBlur);    on_MeanBlur(g_nMeanBlurValue,0);    //================【<3>高斯滤波】==================    namedWindow("3:image after gaussian blur",WINDOW_AUTOSIZE);    createTrackbar("内核值：", "3:image after gaussian blur", &g_nGaussianBlurValue, 50,on_GaussianBlur);    on_GaussianBlur(g_nGaussianBlurValue,0);    //================【<4>中值滤波】==================    namedWindow("4:image after median filter",WINDOW_AUTOSIZE);    createTrackbar("参数值：", "4:image after median filter", &g_nMedianBlurValue, 50,on_MedianBlur);    on_MedianBlur(g_nMedianBlurValue,0);    //================【<5>双边滤波】==================    namedWindow("5:image after bilateral filter",WINDOW_AUTOSIZE);    createTrackbar("参数值：","5:image after bilateral filter",&g_nBilateralFilterValue,50,on_BilateralFilter);    on_BilateralFilter(g_nBilateralFilterValue, 0);    //==================输入'q'结束===================    while (char(waitKey(1))!='q'){}    return 0;}//==============【on_BoxFilter()函数】================static void on_BoxFilter(int,void *){    boxFilter(g_srcImage, g_dstImage1, -1, Size(g_nBoxFilterValue+1,g_nBoxFilterValue+1));    imshow("1:image after box filter",g_dstImage1);}//==============【on_MeanBlur()函数】=================static void on_MeanBlur(int,void *){    blur(g_srcImage,g_dstImage2,Size(g_nMeanBlurValue+1,g_nMeanBlurValue+1),Point(-1,-1));    imshow("2:image after mean blur",g_dstImage2);}//============【on_GaussianBlur()函数】===============static void on_GaussianBlur(int,void *){    GaussianBlur(g_srcImage, g_dstImage3, Size(g_nGaussianBlurValue*2+1,g_nGaussianBlurValue*2+1), 0, 0);    imshow("3:image after gaussian blur",g_dstImage3);}//==============【on_MedianBlur()函数】===============static void on_MedianBlur(int,void *){    medianBlur(g_srcImage, g_dstImage4, g_nMedianBlurValue*2+1);    imshow("4:image after median filter",g_dstImage4);}//============【on_BilateralFilter()函数】============static void on_BilateralFilter(int,void *){    bilateralFilter(g_srcImage, g_dstImage5, g_nBilateralFilterValue, g_nBilateralFilterValue*2, g_nBilateralFilterValue/2);    imshow("5:image after bilateral filter",g_dstImage5);}