OpenCv中实现了三种立体匹配算法:
BM算法
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
BM算法:速度很快,效果一般
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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");
}
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left.png right.png disparity.jpg
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
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#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;
}
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left.png right.png disparity.jpg
GC算法 效果最好,速度最慢
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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);
}
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left.png right.png disparity.jpg
如何设置BM、SGBM和GC算法的状态参数?
参看:http://blog.csdn.net/chenyusiyuan/article/details/5967291
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