一、 SAD算法
1.算法原理
SAD(Sum of absolute differences)是一种图像匹配算法。基本思想：差的绝对值之和。此算法常用于图像块匹配，将每个像素对应数值之差的绝对值求和，据此评估两个图像块的相似度。该算法快速、但并不精确，通常用于多级处理的初步筛选。
2.基本流程
输入：两幅图像，一幅Left-Image，一幅Right-Image
对左图，依次扫描，选定一个锚点：
（1）构造一个小窗口,类似于卷积核；
（2）用窗口覆盖左边的图像,选择出窗口覆盖区域内的所有像素点；
（3）同样用窗口覆盖右边的图像并选择出覆盖区域的像素点；
（4）左边覆盖区域减去右边覆盖区域，并求出所有像素点灰度差的绝对值之和；
（5）移动右边图像的窗口，重复（3）-（4）的处理（这里有个搜索范围,超过这个范围跳出）；
（6）找到这个范围内SAD值最小的窗口,即找到了左图锚点的最佳匹配的像素块。
参考代码：SAD.h
#include"iostream"
#include"opencv2/opencv.hpp"
#include"iomanip"
using namespace std;
using namespace cv;
class SAD
{
public:
SAD():winSize(7),DSR(30){}
SAD(int _winSize,int _DSR):winSize(_winSize),DSR(_DSR){}
Mat computerSAD(Mat &L,Mat &R); //计算SAD
private:
int winSize; //卷积核的尺寸
int DSR; //视差搜索范围
};
Mat SAD::computerSAD(Mat &L,Mat &R)
{
int Height=L.rows;
int Width=L.cols;
Mat Kernel_L(Size(winSize,winSize),CV_8U,Scalar::all(0));
Mat Kernel_R(Size(winSize,winSize),CV_8U,Scalar::all(0));
Mat Disparity(Height,Width,CV_8U,Scalar(0)); //视差图
for(int i=0;i<Width-winSize;i++) //左图从DSR开始遍历
{
for(int j=0;j<Height-winSize;j++)
{
Kernel_L=L(Rect(i,j,winSize,winSize));
Mat MM(1,DSR,CV_32F,Scalar(0)); //
for(int k=0;k<DSR;k++)
{
int x=i-k;
if(x>=0)
{
Kernel_R=R(Rect(x,j,winSize,winSize));
Mat Dif;
absdiff(Kernel_L, Kernel_R, Dif);//
Scalar ADD=sum(Dif);
float a=ADD[0];
MM.at<float>(k)=a;
}
}
Point minLoc;
minMaxLoc(MM, NULL, NULL,&minLoc,NULL);
int loc=minLoc.x;
//int loc=DSR-loc;
Disparity.at<char>(j,i)=loc*16;
}
double rate=double(i)/(Width);
cout<<"已完成"<<setprecision(2)<<rate*100<<"%"<<endl; //处理进度
}
return Disparity;
}// MySAD.cpp : 定义控制台应用程序的入口点。
//
#include "stdafx.h"
#include"SAD.h"
int _tmain(int argc, _TCHAR* argv[])
{
Mat Img_L=imread("imL.png",0);
Mat Img_R=imread("imR.png",0);
Mat Disparity; //视差图
//SAD mySAD;
SAD mySAD(7,30);
Disparity=mySAD.computerSAD(Img_L,Img_R);
imshow("Img_L",Img_L);
imshow("Img_R",Img_R);
imshow("Disparity",Disparity);
waitKey();
return 0;
}
二、BM算法：速度很快，效果一般
SGBM算法 Stereo Processing by Semiglobal Matching and Mutual Information
GC算法 算法文献：Realistic CG Stereo Image Dataset with Ground Truth Disparity Maps
参考：http://blog.csdn.net/wqvbjhc/article/details/6260844
[cpp] view plain copy
- <code class="language-cpp">void BM()
- {
- IplImage * img1 = cvLoadImage("left.png",0);
- IplImage * img2 = cvLoadImage("right.png",0);
- CvStereoBMState* BMState=cvCreateStereoBMState();
- assert(BMState);
- BMState->preFilterSize=9;
- BMState->preFilterCap=31;
- BMState->SADWindowSize=15;
- BMState->minDisparity=0;
- BMState->numberOfDisparities=64;
- BMState->textureThreshold=10;
- BMState->uniquenessRatio=15;
- BMState->speckleWindowSize=100;
- BMState->speckleRange=32;
- BMState->disp12MaxDiff=1;
- CvMat* disp=cvCreateMat(img1->height,img1->width,CV_16S);
- CvMat* vdisp=cvCreateMat(img1->height,img1->width,CV_8U);
- int64 t=getTickCount();
- cvFindStereoCorrespondenceBM(img1,img2,disp,BMState);
- t=getTickCount()-t;
- cout<<"Time elapsed:"<<t*1000/getTickFrequency()<<endl;
- cvSave("disp.xml",disp);
- cvNormalize(disp,vdisp,0,255,CV_MINMAX);
- cvNamedWindow("BM_disparity",0);
- cvShowImage("BM_disparity",vdisp);
- cvWaitKey(0);
- //cvSaveImage("cones\\BM_disparity.png",vdisp);
- cvReleaseMat(&disp);
- cvReleaseMat(&vdisp);
- cvDestroyWindow("BM_disparity");
- }</code>
三、SGBM算法
作为一种全局匹配算法，立体匹配的效果明显好于局部匹配算法，但是同时复杂度上也要远远大于局部匹配算法。算法主要是参考Stereo Processing by Semiglobal Matching and Mutual Information。
opencv中实现的SGBM算法计算匹配代价没有按照原始论文的互信息作为代价，而是按照块匹配的代价。
参考：http://www.opencv.org.cn/forum.php?mod=viewthread&tid=23854
#include <highgui.h>
#include <cv.h>
#include <cxcore.h>
#include <iostream>
using namespace std;
using namespace cv;
int main()
{
IplImage * img1 = cvLoadImage("left.png",0);
IplImage * img2 = cvLoadImage("right.png",0);
cv::StereoSGBM sgbm;
int SADWindowSize = 9;
sgbm.preFilterCap = 63;
sgbm.SADWindowSize = SADWindowSize > 0 ? SADWindowSize : 3;
int cn = img1->nChannels;
int numberOfDisparities=64;
sgbm.P1 = 8*cn*sgbm.SADWindowSize*sgbm.SADWindowSize;
sgbm.P2 = 32*cn*sgbm.SADWindowSize*sgbm.SADWindowSize;
sgbm.minDisparity = 0;
sgbm.numberOfDisparities = numberOfDisparities;
sgbm.uniquenessRatio = 10;
sgbm.speckleWindowSize = 100;
sgbm.speckleRange = 32;
sgbm.disp12MaxDiff = 1;
Mat disp, disp8;
int64 t = getTickCount();
sgbm((Mat)img1, (Mat)img2, disp);
t = getTickCount() - t;
cout<<"Time elapsed:"<<t*1000/getTickFrequency()<<endl;
disp.convertTo(disp8, CV_8U, 255/(numberOfDisparities*16.));
namedWindow("left", 1);
cvShowImage("left", img1);
namedWindow("right", 1);
cvShowImage("right", img2);
namedWindow("disparity", 1);
imshow("disparity", disp8);
waitKey();
imwrite("sgbm_disparity.png", disp8);
cvDestroyAllWindows();
return 0;
}
四、GC算法 效果最好，速度最慢
void GC()
{
IplImage * img1 = cvLoadImage("left.png",0);
IplImage * img2 = cvLoadImage("right.png",0);
CvStereoGCState* GCState=cvCreateStereoGCState(64,3);
assert(GCState);
cout<<"start matching using GC"<<endl;
CvMat* gcdispleft=cvCreateMat(img1->height,img1->width,CV_16S);
CvMat* gcdispright=cvCreateMat(img2->height,img2->width,CV_16S);
CvMat* gcvdisp=cvCreateMat(img1->height,img1->width,CV_8U);
int64 t=getTickCount();
cvFindStereoCorrespondenceGC(img1,img2,gcdispleft,gcdispright,GCState);
t=getTickCount()-t;
cout<<"Time elapsed:"<<t*1000/getTickFrequency()<<endl;
//cvNormalize(gcdispleft,gcvdisp,0,255,CV_MINMAX);
//cvSaveImage("GC_left_disparity.png",gcvdisp);
cvNormalize(gcdispright,gcvdisp,0,255,CV_MINMAX);
cvSaveImage("GC_right_disparity.png",gcvdisp);
cvNamedWindow("GC_disparity",0);
cvShowImage("GC_disparity",gcvdisp);
cvWaitKey(0);
cvReleaseMat(&gcdispleft);
cvReleaseMat(&gcdispright);
cvReleaseMat(&gcvdisp);
}