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基于Opencv的几种立体匹配算法+ELAS
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;
}
基于Opencv的几种立体匹配算法+ELAS
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