opencv学习(二十三)之方框、均值、高斯、中值、双边滤波综合比较

/*
*本程序的目的旨在对前面介绍的滤波器进行一个比较
*对比每种滤波器的滤波效果和耗时
*除双边滤波外其他滤波器kernel均是5*5或5
*对于双边滤波器取其直径典型值30
*sigmaColor和sigmaSpace的值均由直径计算而来
*/

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

using namespace std;
using namespace cv;

int main()
{
Mat srcImage = imread("lena.jpg");

if(srcImage.empty())
{
cout << "图片加载失败!" << endl;
return -1;
}
else
cout << "图片加载成功!" << endl << endl;

namedWindow("原图像", WINDOW_AUTOSIZE);
imshow("原图像", srcImage);

/********************方框滤波*******************/
Mat dstImage_boxFilter;
double time_boxFilter = static_cast<double>(getTickCount());
boxFilter(srcImage, dstImage_boxFilter, -1, Size(5, 5));
time_boxFilter = ((double)getTickCount()-time_boxFilter)/getTickFrequency();
cout << "方框滤波耗时: " << time_boxFilter << " s" << endl;
namedWindow("方框滤波图像", WINDOW_AUTOSIZE);
imshow("方框滤波图像", dstImage_boxFilter);

/********************均值滤波*****************/
Mat dstImage_blur;
double time_blur = static_cast<double>(getTickCount());
blur(srcImage, dstImage_blur, Size(5, 5));
time_blur = ((double)getTickCount()-time_blur)/getTickFrequency();
cout << "均值滤波耗时: " << time_blur << " s" << endl;
namedWindow("均值滤波图像", WINDOW_AUTOSIZE);
imshow("均值滤波图像", dstImage_blur);

/********************高斯滤波******************/
Mat dstImage_GaussianBlur;
double time_GaussianBlur = static_cast<double>(getTickCount());
GaussianBlur(srcImage, dstImage_GaussianBlur, Size(5, 5), 0, 0);
time_GaussianBlur = ((double)getTickCount()-time_GaussianBlur)/getTickFrequency();
cout << "高斯滤波耗时: " << time_GaussianBlur << " s" << endl;
namedWindow("高斯滤波图像", WINDOW_AUTOSIZE);
imshow("高斯滤波图像", dstImage_GaussianBlur);

/*******************中值滤波*******************/
Mat dstImage_medianBlur;
double time_medianBlur=static_cast<double>(getTickCount());
medianBlur(srcImage, dstImage_medianBlur, 5);
time_medianBlur = ((double)getTickCount()-time_medianBlur)/getTickFrequency();
cout << "中值滤波耗时: " << time_medianBlur << " s" << endl;
namedWindow("中值滤波图像", WINDOW_AUTOSIZE);
imshow("中值滤波图像", dstImage_medianBlur);

/******************双边滤波******************/
Mat dstImage_bilateralFilter;
double time_bilateralFilter = static_cast<double>(getTickCount());
bilateralFilter(srcImage, dstImage_bilateralFilter, 30, 60, 15);
time_bilateralFilter=((double)getTickCount()-time_bilateralFilter)/getTickFrequency();
cout << "双边滤波耗时: " << time_bilateralFilter << " s" << endl;
namedWindow("双边滤波图像", WINDOW_AUTOSIZE);
imshow("双边滤波图像", dstImage_bilateralFilter);

waitKey(0);

return 0;
}