《基于Opencv的几种立体匹配算法+ELAS.docx》由会员分享，可在线阅读，更多相关《基于Opencv的几种立体匹配算法+ELAS.docx（12页珍藏版）》请在三一办公上搜索。
1、基于Opencv的几种立体匹配算法+ELAS基于Opencv的几种立体匹配算法+ELAS 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
2、 / generate disparity image using LIBELAS int bd = 0; const int32_t dims3 = 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 par
3、am; param.postprocess_only_left = false; Elas elas(param); elas.process(leftImage.data,rightImage.data,leftdpf.ptr(0),rightdpf.ptr(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); /- Che
4、ck its extreme values cv:minMaxLoc( disp_l, &minVal, &maxVal ); coutMin disp: Max value minValmaxVal; /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 );
5、 coutMin disp: Max value minValmaxVal; /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(dis
6、p8u_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); coutendlOverendl; cv:waitKey(0); ret
7、urn 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; /* 0 ? numberOfDisparities : (
8、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.
9、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
10、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-s
11、peckleRange = 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;/左视差图和右视差图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 ); coutMin disp: Max va
12、lue minValmaxVal; /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
13、,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 numberO
14、fDisparities =16*2; /* 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。P2P1，可取或
15、者 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, rightIm
16、age, disp); /- Check its extreme values cv:minMaxLoc( disp, &minVal, &maxVal ); coutMin disp: Max value minValmaxVal; /numberOfDisparities.= (maxVal - minVal) /- 4. Display it as a CV_8UC1 image disp.convertTo(disp8u, CV_8U, 255/(maxVal - minVal);/(numberOfDisparities*16.) cv:normalize(disp8u, disp8
17、u, 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:imrea
18、d(./test_images/leftr.png,0); cv:Mat rightImage = cv:imread(./test_images/rightr.png,0); int numberOfDisparities =16*2; /* 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.min
19、Disp = -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_PAR
20、AMS | 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 ); coutMin disp: Max value minValendldepth,1); IplImage * right_disp_ =cvCreateImage(cvGetSize(leftImage),leftImage-depth,1); cvFindS
21、tereoCorrespondenceGC( 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_,
22、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_); coutendlOverendl; cvWaitKey(0); cvDestroyAllWindows; cvRe
23、leaseImage(&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
24、 = 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 =
25、 &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); c
26、v:imshow(Left,leftImage); cv:imshow(Right,rightImage); /* /- Check its extreme values cv:minMaxLoc(right_disp_, &minVal, &maxVal ); coutMin disp: Max value minValmaxVal; /numberOfDisparities.= (maxVal - minVal) /- Display it as a CV_8UC1 image right_disp_.convertTo(disp8u_r, CV_8U, 255/(maxVal - min
27、Val);/(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_); coutendlOverendl; cv:waitKey(0); return 0;
