同http://blog.csdn.net/chuhang_zhqr/article/details/51179881类似，采用
这两个经典的图片进行测试。
关于BM和SGBM以及VAR的参数设置请参考 晨宇思远
本文代码基于opencv2.4.9 本文源码地址在我的CSDN代码资源： http://download.csdn.net/detail/chuhang_zhqr/9703763
0：ELAS 这里要去下载Elas的开源库函数。
int StereoMatch::ElasMatch(){ cv::Mat disp_l,disp_r,disp8u_l,disp8u_r; double minVal; double maxVal; //视差图的极值 cv::Mat leftImage = cv::imread("../test_images/leftr31.png",0); cv::Mat rightImage = cv::imread("../test_images/rightr31.png",0); // 计算视差 // generate disparity image using LIBELAS int bd = 0; const int32_t dims[3] = {leftImage.cols,leftImage.rows,leftImage.cols}; cv::Mat leftdpf = cv::Mat::zeros(cv::Size(leftImage.cols,leftImage.rows), CV_32F); cv::Mat rightdpf = cv::Mat::zeros(cv::Size(leftImage.cols,leftImage.rows), CV_32F); Elas::parameters param; param.postprocess_only_left = false; Elas elas(param); elas.process(leftImage.data,rightImage.data,leftdpf.ptr<float>(0),rightdpf.ptr<float>(0),dims); cv::Mat(leftdpf(cv::Rect(bd,0,leftImage.cols,leftImage.rows))).copyTo(disp_l); cv::Mat(rightdpf(cv::Rect(bd,0,rightImage.cols,rightImage.rows))).copyTo(disp_r); //-- Check its extreme values cv::minMaxLoc( disp_l, &minVal, &maxVal ); cout<<"Min disp: Max value"<< minVal<<maxVal; //numberOfDisparities.= (maxVal - minVal) //-- Display it as a CV_8UC1 image disp_l.convertTo(disp8u_l, CV_8U, 255/(maxVal - minVal));//(numberOfDisparities*16.) cv::minMaxLoc( disp_r, &minVal, &maxVal ); cout<<"Min disp: Max value"<< minVal<<maxVal; //numberOfDisparities.= (maxVal - minVal) //-- Display it as a CV_8UC1 image disp_r.convertTo(disp8u_r, CV_8U, 255/(maxVal - minVal));//(numberOfDisparities*16.) cv::normalize(disp8u_l, disp8u_l, 0, 255, CV_MINMAX, CV_8UC1); // obtain normalized image cv::normalize(disp8u_r, disp8u_r, 0, 255, CV_MINMAX, CV_8UC1); // obtain normalized image cv::imshow("Left",leftImage); cv::imshow("Right",rightImage); cv::imshow("Elas_left",disp8u_l); cv::imshow("Elas_right",disp8u_r); cv::imwrite("Elas_left.png",disp8u_l); cv::imwrite("Elas_right.png",disp8u_r); cout<<endl<<"Over"<<endl; cv::waitKey(0); return 0;}1：BM算法
int StereoMatch::BMMatching(){ cv::Mat disp,disp8u; double minVal; double maxVal; //视差图的极值 cv::Mat leftImage = cv::imread("../test_images/leftr.png",0); cv::Mat rightImage = cv::imread("../test_images/rightr.png",0); int SADWindowSize = 19; int numberOfDisparities =16*3; /**< Range of disparity */ numberOfDisparities = numberOfDisparities > 0 ? numberOfDisparities : ((leftImage.cols/8) + 15) & -16; //bm.state->roi1 = remapMat.Calib_Roi_L;//左右视图的有效像素区域，一般由双目校正阶段的 cvStereoRectify 函数传递，也可以自行设定。 //bm.state->roi2 = remapMat.Calib_Roi_R;//一旦在状态参数中设定了 roi1 和 roi2，OpenCV 会通过cvGetValidDisparityROI 函数计算出视差图的有效区域，在有效区域外的视差值将被清零。 //bm.State->preFilterSize=41;//预处理滤波器窗口大小,5-21,odd bm.state->preFilterCap = 31; //63,1-31//预处理滤波器的截断值，预处理的输出值仅保留[-preFilterCap, preFilterCap]范围内的值, bm.state->SADWindowSize = SADWindowSize > 0 ? SADWindowSize : 9; //SAD窗口大小5-21 bm.state->minDisparity = 0; //64 最小视差，默认值为 0 bm.state->numberOfDisparities = numberOfDisparities; //128视差窗口，即最大视差值与最小视差值之差, 窗口大小必须是 16 的整数倍 bm.state->textureThreshold = 10;//低纹理区域的判断阈值。如果当前SAD窗口内所有邻居像素点的x导数绝对值之和小于指定阈值，则该窗口对应的像素点的视差值为 0 bm.state->uniquenessRatio = 15;//5-15 视差唯一性百分比， 视差窗口范围内最低代价是次低代价的(1 + uniquenessRatio/100)倍时，最低代价对应的视差值才是该像素点的视差，否则该像素点的视差为 0 bm.state->speckleWindowSize = 100;//检查视差连通区域变化度的窗口大小, 值为 0 时取消 speckle 检查 bm.state->speckleRange = 32;//视差变化阈值，当窗口内视差变化大于阈值时，该窗口内的视差清零 bm.state->disp12MaxDiff = 1;//左视差图（直接计算得出）和右视差图（通过cvValidateDisparity计算得出）之间的最大容许差异。超过该阈值的视差值将被清零。该参数默认为 -1，即不执行左右视差检查。 //注意在程序调试阶段最好保持该值为 -1，以便查看不同视差窗口生成的视差效果。 // 计算视差 bm(leftImage, rightImage, disp); //-- Check its extreme values cv::minMaxLoc( disp, &minVal, &maxVal ); cout<<"Min disp: Max value"<< minVal<<maxVal; //numberOfDisparities.= (maxVal - minVal) //-- 4. Display it as a CV_8UC1 image disp.convertTo(disp8u, CV_8U, 255/(maxVal - minVal));//(numberOfDisparities*16.) cv::normalize(disp8u, disp8u, 0, 255, CV_MINMAX, CV_8UC1); // obtain normalized image cv::imshow("left",leftImage); cv::imshow("right",leftImage); cv::imshow("Disp",disp8u); cv::imwrite("bm.png",disp8u); cv::waitKey(0);}2：SGBM
int StereoMatch::SGBMMatching(){ cv::Mat disp,disp8u; double minVal; double maxVal; //视差图的极值 cv::Mat leftImage = cv::imread("../test_images/leftr.png",0); cv::Mat rightImage = cv::imread("../test_images/rightr.png",0); int numberOfDisparities =16*2; /**< Range of disparity */ numberOfDisparities = numberOfDisparities > 0 ? numberOfDisparities : ((leftImage.cols/8) + 15) & -16; int SADWindowSize = 11; sgbm.preFilterCap = 63; sgbm.SADWindowSize = SADWindowSize > 0 ? SADWindowSize : 3; //3-11 int cn = leftImage.channels(); sgbm.P1 = 8*cn*sgbm.SADWindowSize*sgbm.SADWindowSize;//P1、P2的值越大，视差越平滑。P2>P1，可取（50，800）或者（40，2500） sgbm.P2 = 32*cn*sgbm.SADWindowSize*sgbm.SADWindowSize; sgbm.minDisparity = 0; sgbm.numberOfDisparities = numberOfDisparities; //128,256 sgbm.uniquenessRatio = 10; //10,0 sgbm.speckleWindowSize = 100; //200 sgbm.speckleRange = 32; sgbm.disp12MaxDiff = 1; sgbm.fullDP = 1; // 计算视差 sgbm(leftImage, rightImage, disp); //-- Check its extreme values cv::minMaxLoc( disp, &minVal, &maxVal ); cout<<"Min disp: Max value"<< minVal<<maxVal; //numberOfDisparities.= (maxVal - minVal) //-- 4. Display it as a CV_8UC1 image disp.convertTo(disp8u, CV_8U, 255/(maxVal - minVal));//(numberOfDisparities*16.) cv::normalize(disp8u, disp8u, 0, 255, CV_MINMAX, CV_8UC1); // obtain normalized image cv::imshow("left",leftImage); cv::imshow("right",leftImage); cv::imshow("Disp",disp8u); cv::imwrite("sgbm.png",disp8u); cv::waitKey(0);}3：VAR
int StereoMatch::VARMatching(){ cv::Mat disp,disp8u; double minVal; double maxVal; //视差图的极值 cv::Mat leftImage = cv::imread("../test_images/leftr.png",0); cv::Mat rightImage = cv::imread("../test_images/rightr.png",0); int numberOfDisparities =16*2; /**< Range of disparity */ numberOfDisparities = numberOfDisparities > 0 ? numberOfDisparities : ((leftImage.cols/8) + 15) & -16; var.levels = 3; // ignored with USE_AUTO_PARAMS var.pyrScale = 0.5; // ignored with USE_AUTO_PARAMS var.nIt = 25; var.minDisp = -numberOfDisparities; var.maxDisp = 0; var.poly_n = 3; var.poly_sigma = 0.0; var.fi = 15.0f; var.lambda = 0.03f; var.penalization = var.PENALIZATION_TICHONOV; // ignored with USE_AUTO_PARAMS var.cycle = var.CYCLE_V; // ignored with USE_AUTO_PARAMS var.flags = var.USE_SMART_ID | var.USE_AUTO_PARAMS | var.USE_INITIAL_DISPARITY | var.USE_MEDIAN_FILTERING ; // 计算视差 var(leftImage, rightImage, disp); //-- Check its extreme values cv::minMaxLoc( disp, &minVal, &maxVal ); cout<<"Min disp: Max value"<< minVal<<endl<<maxVal; //numberOfDisparities.= (maxVal - minVal) //-- 4. Display it as a CV_8UC1 image disp.convertTo(disp8u, CV_8U, 255/(maxVal - minVal));//(numberOfDisparities*16.) cv::normalize(disp8u, disp8u, 0, 255, CV_MINMAX, CV_8UC1); // obtain normalized image cv::imshow("left",leftImage); cv::imshow("right",leftImage); cv::imshow("Disp",disp8u); cv::imwrite("var.png",disp8u); cv::waitKey(0);}4：GC GC好像只有在c版本的，我这里做了两个，一个是C的，一个是改了接口的C++版本的。
int StereoMatch::GCMatching(){ IplImage * leftImage = cvLoadImage("../test_images/leftr31.png",0); IplImage * rightImage = cvLoadImage("../test_images/rightr31.png",0); CvStereoGCState* state = cvCreateStereoGCState( 16, 4 ); IplImage * left_disp_ =cvCreateImage(cvGetSize(leftImage),leftImage->depth,1); IplImage * right_disp_ =cvCreateImage(cvGetSize(leftImage),leftImage->depth,1); cvFindStereoCorrespondenceGC( leftImage, rightImage, left_disp_, right_disp_, state, 0 ); cvReleaseStereoGCState( &state ); cvNamedWindow("Left",1); cvNamedWindow("Right",1); cvNamedWindow("GC_left",1); cvNamedWindow("GC_right",1); cvShowImage("Left",leftImage); cvShowImage("Right",rightImage); cvNormalize(left_disp_,left_disp_,0,255,CV_MINMAX,CV_8UC1); cvNormalize(right_disp_,right_disp_,0,255,CV_MINMAX,CV_8UC1); cvShowImage("GC_left",left_disp_); cvShowImage("GC_right",right_disp_); cvSaveImage("GC_left.png",left_disp_); cvSaveImage("GC_right.png",right_disp_); cout<<endl<<"Over"<<endl; cvWaitKey(0); cvDestroyAllWindows(); cvReleaseImage(&leftImage); cvReleaseImage(&rightImage); return 0;}int StereoMatch::GCMatching_Mat(){ double minVal; double maxVal; //视差图的极值 cv::Mat disp8u_l,disp8u_r; cv::Mat leftImage = cv::imread("../test_images/leftr31.png",0); cv::Mat rightImage = cv::imread("../test_images/rightr31.png",0); CvStereoGCState* state = cvCreateStereoGCState( 16, 5 ); cv::Mat left_disp_ =leftImage.clone(); cv::Mat right_disp_ =rightImage.clone(); IplImage temp = (IplImage)leftImage; IplImage* leftimg = &temp; IplImage temp1 = (IplImage)rightImage; IplImage* rightimg = &temp1; IplImage temp2 = (IplImage)left_disp_; IplImage* leftdisp = &temp2; IplImage temp3 = (IplImage)right_disp_; IplImage* rightdisp = &temp3; cvFindStereoCorrespondenceGC( leftimg, rightimg, leftdisp, rightdisp, state, 0 ); cvReleaseStereoGCState( &state ); cv::namedWindow("Left",1); cv::namedWindow("Right",1); cv::namedWindow("GC_left",1); cv::namedWindow("GC_right",1); cv::imshow("Left",leftImage); cv::imshow("Right",rightImage);/* //-- Check its extreme values cv::minMaxLoc(right_disp_, &minVal, &maxVal ); cout<<"Min disp: Max value"<< minVal<<maxVal; //numberOfDisparities.= (maxVal - minVal) //-- Display it as a CV_8UC1 image right_disp_.convertTo(disp8u_r, CV_8U, 255/(maxVal - minVal));//(numberOfDisparities*16.) cv::normalize(left_disp_,left_disp_,0,255,CV_MINMAX,CV_8UC1); cv::normalize(disp8u_r,disp8u_r,0,255,CV_MINMAX,CV_8UC1);*/ cv::normalize(left_disp_,left_disp_,0,255,CV_MINMAX,CV_8UC1); cv::normalize(right_disp_,right_disp_,0,255,CV_MINMAX,CV_8UC1); cv::imshow("GC_left",left_disp_); cv::imshow("GC_right",right_disp_); cv::imwrite("GC_left.png",left_disp_); cv::imwrite("GC_right.png",right_disp_); cout<<endl<<"Over"<<endl; cv::waitKey(0); return 0;}所有的胜利，与征服自己的胜利比起来，都是微不足道
