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 <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <iostream> 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<Mat> 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
