基于OPENCV的双目重建

xiaoxiao2021-02-28  7

接上一篇,得到了stero.xml之后,我们就可以进行双目重建了。

其实也没啥,都是直接调用库函数的。

首先打开两个相机,读出参数,然后计算视差,然后计算坐标,就完了。

完整代码如下:

#include <cv.h> #include <highgui.h> #include <iostream> #include<opencv2\opencv.hpp> using namespace std; using namespace cv; int i = 0; Mat frame; Mat Left, Right,pointClouds; Mat mx0, my0, mx1, my1,Q; Rect rect0, rect1; cv::Ptr<cv::StereoBM> m_BM = cv::StereoBM::create(); void onMouse(int event, int x, int y, int flags, void* userdata) { if (event == 1) imwrite(to_string(i++) + ".jpg", frame); } void LoadData(void) { FileStorage fs("stero.xml", cv::FileStorage::READ); fs ["mx0"] >> mx0; fs [ "my0"] >> my0; fs [ "mx1"] >> mx1; fs [ "my1"] >> my1; fs["Q"] >> Q; fs [ "rect0"] >> rect0; fs [ "rect1"] >> rect1; fs.release(); //m_BM.state->roi1 = rect0; //m_BM.state->roi2 = rect1; //m_BM.state->preFilterCap = 31; //m_BM.state->SADWindowSize = 9; //m_BM.state->minDisparity = 0; //m_BM.state->numberOfDisparities = 16; //m_BM.state->textureThreshold = 10; //m_BM.state->uniquenessRatio = 25; //m_BM.state->speckleWindowSize = 100; //m_BM.state->speckleRange = 200; //m_BM.state->disp12MaxDiff = -1; int unitDisparity = 15;//40 int numberOfDisparities = unitDisparity * 16; m_BM->setROI1(rect0); m_BM->setROI2(rect1); m_BM->setPreFilterCap(31); m_BM->setBlockSize(9); //SAD窗口大小 m_BM->setMinDisparity(0); //确定匹配搜索从哪里开始 默认值是0 m_BM->setNumDisparities(16); //在该数值确定的视差范围内进行搜索,视差窗口 // 即最大视差值与最小视差值之差, 大小必须是16的整数倍 m_BM->setTextureThreshold(10); //保证有足够的纹理以克服噪声 m_BM->setUniquenessRatio(25); //使用匹配功能模式 m_BM->setSpeckleWindowSize(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 m_BM->setSpeckleRange(200); // 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 m_BM->setDisp12MaxDiff(-1); } void saveDisp(Mat mat) { FILE* fp = fopen("a.txt", "wt"); fprintf(fp, "d\n", mat.rows); fprintf(fp, "d\n", mat.cols); for(int y = 0; y < mat.rows; y++) { for(int x = 0; x < mat.cols; x++) { int disp = (int)mat.at<float>(y, x); // 这里视差矩阵是CV_16S 格式的,故用 short 类型读取 fprintf(fp, "%d\n", disp); // 若视差矩阵是 CV_32F 格式,则用 float 类型读取 } //fprintf(fp, "\n"); } fclose(fp); } int main() { char key; VideoCapture cap_left(0); if (!cap_left.isOpened()){ cout << "left unopen"; return -1; } VideoCapture cap_right(1); if (!cap_right.isOpened()){ cout << "right unopen"; return -1; } cap_left.set(CV_CAP_PROP_FRAME_WIDTH, 1920); cap_left.set(CV_CAP_PROP_FRAME_HEIGHT, 1080); cap_right.set(CV_CAP_PROP_FRAME_WIDTH, 1920); cap_right.set(CV_CAP_PROP_FRAME_HEIGHT, 1080); LoadData(); namedWindow("points", 0); namedWindow("left", 0); namedWindow("right", 0); while (true) { cap_left.grab(); cap_right.grab(); cap_left.retrieve(Left); cap_right.retrieve(Right); cv::Mat img1proc, img2proc; cvtColor(Left, img1proc, CV_BGR2GRAY); cvtColor(Right, img2proc, CV_BGR2GRAY); // 校正图像,使左右视图行对齐 cv::Mat img1remap, img2remap; // remap(img1proc, img1remap, mx0, my0, cv::INTER_LINEAR); // 对用于视差计算的画面进行校正 // remap(img2proc, img2remap, mx1, my1, cv::INTER_LINEAR); // 对左右视图的左边进行边界延拓,以获取与原始视图相同大小的有效视差区域 cv::Mat img1border, img2border; copyMakeBorder(img1proc, img1border, 0, 0, m_BM->getNumDisparities(), 0, IPL_BORDER_REPLICATE); copyMakeBorder(img1proc, img2border, 0, 0, m_BM->getNumDisparities(), 0, IPL_BORDER_REPLICATE); // 计算视差 cv::Mat dispBorder,disp; m_BM->compute(img1border, img2border, dispBorder); dispBorder.convertTo(dispBorder, CV_32F); dispBorder.convertTo(disp, CV_8U, 255 / (16 * 16.)); imshow("disp", dispBorder); saveDisp(dispBorder); waitKey(20); // remap(Left, Left, mx0, my0, cv::INTER_LINEAR); rectangle(Left, rect0, CV_RGB(0, 0, 255), 3); // remap(Right, Right, mx1, my1, cv::INTER_LINEAR); rectangle(Right, rect1, CV_RGB(0, 0, 255), 3); reprojectImageTo3D(dispBorder, pointClouds, Q, true); for (int y = 0; y < pointClouds.rows; ++y) { for (int x = 0; x < pointClouds.cols; ++x) { cv::Point3f point = pointClouds.at<cv::Point3f>(y, x); point.y = -point.y; pointClouds.at<cv::Point3f>(y, x) = point; } } pointClouds.convertTo(disp, CV_8U); frame = Mat::zeros(pointClouds.rows, pointClouds.cols, CV_8UC1); for (int y = 0; y<pointClouds.rows; y++) { for (int x = 0; x<pointClouds.cols; x++) { frame.at<uchar>(y, x) = pointClouds.at<cv::Point3f>(y, x).z; } } imshow("left", Left); imshow("right", Right); imshow("points", frame); waitKey(20); cout << i++ << endl; //Mat temp; frame.copyTo(temp); //if (i==3) //for (int y = 0; y<pointClouds.rows; y+=10) //{ // for (int x = 0; x<pointClouds.cols; x+=10) // { // cout << (float)temp.at<uchar>(y, x)<<endl; // waitKey(); // } //} } return 0; }

但是,精度受到立体匹配的影响,误差很大,上面的匹配方法是BM法,参数有待改进。同时全局匹配代价很大,比较慢。

下面我使用了一种简单的方法,对空间中的棋盘进行识别,只匹配棋盘上的一点,计算其视差和坐标,得到的效果还是比较好的。

#include <cv.h> #include <highgui.h> #include <iostream> using namespace std; using namespace cv; #define A 1 #define B 1 Mat frame(1080, 1920 * 2, CV_8UC3); Size grids(9, 6); Mat Left, Right; vector<Point2f> corner1, corner2; int main() { Mat Q, out, dist, P0, P1,out2; //Q.create(4, 4,CV_32FC1); out.create(4, 1, CV_32FC1); dist.create(4, 1, CV_32FC1); FileStorage q("stero.xml", FileStorage::READ); q["Q"] >> Q; q["P0"] >> P0; q["P1"] >> P1; cout << Q<<endl; cout << P0 << endl; cout << P1 << endl; //Q.at<double>(3, 2) = -Q.at<double>(3, 2); Q.convertTo(Q, CV_32FC1); P0.convertTo(P0, CV_32FC1); P1.convertTo(P1, CV_32FC1); q.release(); Mat show(1080/2,1920,CV_8UC3); VideoCapture cap_left(2); if (!cap_left.isOpened()){ cout << "left unopen"; return -1; } VideoCapture cap_right(0); if (!cap_right.isOpened()){ cout << "right unopen"; return -1; } cap_left.set(CV_CAP_PROP_FRAME_WIDTH, 1920); cap_left.set(CV_CAP_PROP_FRAME_HEIGHT, 1080); cap_right.set(CV_CAP_PROP_FRAME_WIDTH, 1920); cap_right.set(CV_CAP_PROP_FRAME_HEIGHT, 1080); char key; namedWindow("frame", 0); while (1) { cap_left.grab(); cap_right.grab(); cap_left.retrieve(Left); cap_right.retrieve(Right); bool a=findChessboardCorners(Left, grids, corner1, CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE + CALIB_CB_FAST_CHECK); bool b=findChessboardCorners(Right, grids, corner2, CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE + CALIB_CB_FAST_CHECK); if (a&&b) { circle(Left, corner1[0], 50, { 255, 0, 0 }, 2); circle(Right, corner2[0], 50, { 255, 0, 0 }, 2); if(A) {   triangulatePoints(P0, P1, corner1, corner2, out2); //out = out2.colRange(0, 0); putText(Left, "X:" + to_string(out2.at<float>(0, 0) / out2.at<float>(3, 0)), { 10, 50 }, 0, 1.0f, CV_RGB(255, 0, 0), 2); putText(Left, "Y:" + to_string(out2.at<float>(1, 0) / out2.at<float>(3, 0)), { 10, 100 }, 0, 1.0f, CV_RGB(0, 255, 0), 2); putText(Left, "Z:" + to_string(out2.at<float>(2, 0) / out2.at<float>(3, 0)), { 10, 150 }, 0, 1.0f, CV_RGB(0, 0, 255), 2); } if (B) { dist.at<float>(0, 0) = corner1[0].x; dist.at<float>(1, 0) = corner1[0].y; dist.at<float>(2, 0) = corner1[0].x - corner2[0].x; dist.at<float>(3, 0) = 1; cout << dist << endl; out = Q*dist; cout << out << endl; out = out / out.at<float>(3,0); cout << out << endl; if (out.isContinuous()) { putText(Left, "X:" + to_string(((float*)out.data)[0]), { 10, 200 }, 0, 1.0f, CV_RGB(255, 0, 0), 2); putText(Left, "Y:" + to_string(((float*)out.data)[1]), { 10, 250 }, 0, 1.0f, CV_RGB(0, 255, 0), 2); putText(Left, "Z:" + to_string(((float*)out.data)[2]), { 10, 300 }, 0, 1.0f, CV_RGB(0, 0, 255), 2); } } if (C) { dist.at<float>(0, 0) = corner1[0].x; dist.at<float>(1, 0) = corner1[0].y; dist.at<float>(2, 0) = corner1[0].x - corner2[0].x; dist.at<float>(3, 0) = 1; //cout << dist << endl; tempQ.at<uchar>(0, 0) = (uchar)dist.at<float>(2, 0); reprojectImageTo3D(tempQ, tempQ2, Q); //out = out / out.data[3]; putText(Left, "X:" + to_string(tempQ2.at<Vec3f>(0, 0)[0]), { 10, 200 + 150 }, 0, 1.0f, CV_RGB(255, 0, 0), 2); putText(Left, "Y:" + to_string(tempQ2.at<Vec3f>(0, 0)[1]), { 10, 250 + 150 }, 0, 1.0f, CV_RGB(0, 255, 0), 2); putText(Left, "Z:" + to_string(tempQ2.at<Vec3f>(0, 0)[2]), { 10, 300 + 150 }, 0, 1.0f, CV_RGB(0, 0, 255), 2); } } Left.copyTo(frame(Rect(0, 0, 1920, 1080))); Right.copyTo(frame(Rect(1920, 0, 1920, 1080))); resize(frame,show,Size(1920 , 1080/2 )); key = waitKey(20); if (key == ' ')return 0; imshow("frame", show); // imshow("frame", Left); // imshow("frame2", Right); } return 0; }

上面使用了ABC三种方法对坐标进行结算,其根本原理都是B所示的操作。这里就不介绍了。

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