基于opencv的几种图像滤波

一、介绍

    盒式滤波、均值滤波、高斯滤波、中值滤波、双边滤波、导向滤波。

    boxFilter()

    blur()

    GaussianBlur()

    medianBlur()

    bilateralFilter()

二、代码

#include                       
#include           
#include       
#include  

using namespace std;
using namespace cv;

#define WINDOWNAME "【滤波处理结果窗口】"  

//---------------【全局变量声明部分】-------------------------  
Mat g_srcIamge, g_dstImage1, g_dstImage2, g_dstImage3, g_dstImage4, g_dstImage5, g_dstImage6;
int g_nBoxFilterValue = 1;       //盒式滤波内核值  
int g_nMeanBlurValue = 1;        //均值滤波内核值  
int g_nGaussianBlurValue = 1;    //高斯滤波内核值  
int g_nMedianBlurValue = 1;      //中值滤波内核值  
int g_nBilateralFilterValue = 1; //双边滤波内核值  
int g_nGuidedFilterValue = 1;    //导向滤波内核值  
const int g_nMaxVal = 20;        //预设滑动条最大值 

//--------------【全局函数声明部分】-------------------------   
static void on_BoxFilter(int, void*);       //盒式滤波器  
static void on_MeanBlur(int, void*);        //均值滤波器  
static void on_GaussianBlur(int, void*);    //高斯滤波器  
static void on_MedianBlur(int, void*);      //中值滤波器  
static void on_BilateralFilter(int, void*); //双边滤波器  
static void on_GuidedFilter(int, void*);    //导向滤波器  
void guidedFilter(Mat& srcMat, Mat& guidedMat, Mat& dstImage, int radius, double eps); //导向滤波器  

//----------------------------【主函数】---------------------------  
int main()
{
    //------------【1】读取源图像并检查图像是否读取成功------------    
    g_srcIamge = imread("D:\\opencv4_1_2\\opencv\\sources\\samples\\data\\lena.jpg");
    if (!g_srcIamge.data)
    {
        cout << "读取图片错误，请重新输入正确路径！\n";
        system("pause");
        return -1;
    }
    namedWindow("【源图像】", 1);     //创建窗口  
    imshow("【源图像】", g_srcIamge); //显示窗口  
    
    //------------【2】在WINDOWNAME窗口上分别创建滤波6个滑动条------------         
    namedWindow(WINDOWNAME); //创建窗口    
    createTrackbar("方框滤波", WINDOWNAME, &g_nBoxFilterValue, g_nMaxVal, on_BoxFilter); //创建方框滤波轨迹条  
    on_BoxFilter(g_nBoxFilterValue, 0);
    createTrackbar("均值滤波", WINDOWNAME, &g_nMeanBlurValue, g_nMaxVal, on_MeanBlur); //创建均值滤波轨迹条  
    on_MeanBlur(g_nMeanBlurValue, 0);
    createTrackbar("高斯滤波", WINDOWNAME, &g_nGaussianBlurValue, g_nMaxVal, on_GaussianBlur); //创建高斯滤波轨迹条  
    on_GaussianBlur(g_nGaussianBlurValue, 0);
    createTrackbar("中值滤波", WINDOWNAME, &g_nMedianBlurValue, g_nMaxVal, on_MedianBlur); //创建中值滤波轨迹条  
    on_MedianBlur(g_nMedianBlurValue, 0);
    createTrackbar("双边滤波", WINDOWNAME, &g_nBilateralFilterValue, g_nMaxVal, on_BilateralFilter); //创建双边滤波轨迹条  
    on_BilateralFilter(g_nBilateralFilterValue, 0);
    createTrackbar("导向滤波", WINDOWNAME, &g_nGuidedFilterValue, g_nMaxVal, on_GuidedFilter); //创建导向滤波轨迹条  
    on_GuidedFilter(g_nGuidedFilterValue, 0);

    //------------【3】退出程序------------    
    cout << "\t按下'q'键，退出程序~！\n" << endl;
    while (char(waitKey(1)) != 'q') {}
    return 0;
}

//----------------------【on_BoxFilter()函数】------------------------  
static void on_BoxFilter(int, void*)
{
    boxFilter(g_srcIamge, g_dstImage1, -1, Size(g_nBoxFilterValue * 2 + 1, g_nBoxFilterValue * 2 + 1));
    cout << "\n当前为【盒式滤波】处理效果，其内核大小为：" << g_nBoxFilterValue * 2 + 1 << endl;
    imshow(WINDOWNAME, g_dstImage1);
}

//----------------------【on_MeanBlur()函数】------------------------  
static void on_MeanBlur(int, void*)
{
    blur(g_srcIamge, g_dstImage2, Size(g_nMeanBlurValue * 2 + 1, g_nMeanBlurValue * 2 + 1), Point(-1, -1));
    cout << "\n当前为【均值滤波】处理效果，其内核大小为：" << g_nMeanBlurValue * 2 + 1 << endl;
    imshow(WINDOWNAME, g_dstImage2);
}

//----------------------【on_GaussianBlur()函数】------------------------  
static void on_GaussianBlur(int, void*)
{
    GaussianBlur(g_srcIamge, g_dstImage3, Size(g_nGaussianBlurValue * 2 + 1, g_nGaussianBlurValue * 2 + 1), 0, 0);
    cout << "\n当前为【高斯滤波】处理效果，其内核大小为：" << g_nGaussianBlurValue * 2 + 1 << endl;
    imshow(WINDOWNAME, g_dstImage3);
}

//----------------------【on_MedianBlur()函数】------------------------  
static void on_MedianBlur(int, void*)
{
    medianBlur(g_srcIamge, g_dstImage4, g_nMedianBlurValue * 2 + 1);
    cout << "\n当前为【中值滤波】处理效果，其内核大小为：" << g_nMedianBlurValue * 2 + 1 << endl;
    imshow(WINDOWNAME, g_dstImage4);
}

//----------------------【on_BilateralFilter()函数】------------------------  
static void on_BilateralFilter(int, void*)
{
    bilateralFilter(g_srcIamge, g_dstImage5, g_nBilateralFilterValue, g_nBilateralFilterValue * 2, g_nBilateralFilterValue / 2);
    cout << "\n当前为【双边滤波】处理效果，其内核大小为：" << g_nBilateralFilterValue << endl;
    imshow(WINDOWNAME, g_dstImage5);
}

//----------------------【on_GuidedFilter()函数】------------------------  
static void on_GuidedFilter(int, void*)
{
    vector vSrcImage, vResultImage;
    //【1】对源图像进行通道分离，并对每个分通道进行导向滤波操作  
    split(g_srcIamge, vSrcImage);
    for (int i = 0; i < 3; i++)
    {
        Mat tempImage;
        vSrcImage[i].convertTo(tempImage, CV_64FC1, 1.0 / 255.0); //将分通道转换成浮点型数据  
        Mat cloneImage = tempImage.clone(); //将tempImage复制一份到cloneImage  
        Mat resultImage;
        guidedFilter(tempImage, cloneImage, resultImage, g_nGuidedFilterValue * 2 + 1, 0.01); //对分通道分别进行导向滤波  
        vResultImage.push_back(resultImage); //将分通道导向滤波后的结果存放到vResultImage中  
    }
    //【2】将分通道导向滤波后结果合并  
    merge(vResultImage, g_dstImage6);
    cout << "\n当前处理为【导向滤波】，其内核大小为：" << g_nGuidedFilterValue * 2 + 1 << endl;
    imshow(WINDOWNAME, g_dstImage6);
}

//-------------------【实现导向滤波器函数部分】-------------------------  
void guidedFilter(Mat& srcMat, Mat& guidedMat, Mat& dstImage, int radius, double eps)
{
    //------------【0】转换源图像信息，将输入扩展为64位浮点型，以便以后做乘法------------  
    srcMat.convertTo(srcMat, CV_64FC1);
    guidedMat.convertTo(guidedMat, CV_64FC1);
    //--------------【1】各种均值计算----------------------------------  
    Mat mean_p, mean_I, mean_Ip, mean_II;
    boxFilter(srcMat, mean_p, CV_64FC1, Size(radius, radius)); //生成待滤波图像均值mean_p   
    boxFilter(guidedMat, mean_I, CV_64FC1, Size(radius, radius)); //生成导向图像均值mean_I     
    boxFilter(srcMat.mul(guidedMat), mean_Ip, CV_64FC1, Size(radius, radius)); //生成互相关均值mean_Ip  
    boxFilter(guidedMat.mul(guidedMat), mean_II, CV_64FC1, Size(radius, radius)); //生成导向图像自相关均值mean_II  
    //--------------【2】计算相关系数，计算Ip的协方差cov和I的方差var------------------  
    Mat cov_Ip = mean_Ip - mean_I.mul(mean_p);
    Mat var_I = mean_II - mean_I.mul(mean_I);
    //---------------【3】计算参数系数a、b-------------------  
    Mat a = cov_Ip / (var_I + eps);
    Mat b = mean_p - a.mul(mean_I);
    //--------------【4】计算系数a、b的均值-----------------  
    Mat mean_a, mean_b;
    boxFilter(a, mean_a, CV_64FC1, Size(radius, radius));
    boxFilter(b, mean_b, CV_64FC1, Size(radius, radius));
    //---------------【5】生成输出矩阵------------------  
    dstImage = mean_a.mul(srcMat) + mean_b;
}

三、显示