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OpenCv中实现了三种立体匹配算法：
BM算法
SGBM算法
GC算法
参考：http://blog.csdn.net/wqvbjhc/article/details/6260844
首先介绍：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
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; }
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原文地址：http://www.cnblogs.com/adong7639/p/4267326.html
