VS实现双目校准.docx-资料库

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结合 VS 实现原始数据 Focal Length: Principal point: Skew: 90.00000 ? 0.00000 degrees Distortion: [ 0.07297 0.35585 fc_left = [ 688.30717 cc_left = [ 315.31975 683.13738 ] ? [ 40.80430 264.40537 ] ? [ 9.36113 31.31605 ] 12.80300 ] alpha_c_left = [ 0.00000 ] ? [ 0.00000 ] => angle of pixel axes = kc_left = [ 0.27153 -1.00396 0.01107 0.00364 0.00000 ] ? 0.00958 0.00576 0.00000 ] Intrinsic parameters of right camera: Focal Length: Principal point: Skew: 90.00000 ? 0.00000 degrees Distortion: [ 0.10409 0.49537 fc_right = [ 665.32773 cc_right = [ 345.54178 alpha_c_right = [ 0.00000 ] ? [ 0.00000 660.17940 ] ? [ 50.31995 249.92143 ] ? [ 10.80196 39.53371 ] 16.45595 ] ] => angle of pixel axes = kc_right = [ 0.22386 0.01260 0.00630 0.00000 ] -0.93591 0.00725 0.00018 0.00000 ] ? Extrinsic parameters (position of right camera wrt left camera): Rotation vector: Translation vector: om = [ -0.02302 T = [ -39.78384 -0.00735 0.00089 ] -40.44029 ] 1.21230 获取校准参数环境 VS2013+opencv-3.1.0 /******************************/ /* 立体匹配和测距 */ /******************************/ #include #include #include using namespace std; using namespace cv; const int imageWidth = 640; const int imageHeight = 480; Size imageSize = Size(imageWidth, imageHeight); //摄像头的分辨率 Mat rgbImageL, grayImageL; Mat rgbImageR, grayImageR; Mat rectifyImageL, rectifyImageR;

Rect validROIL;//图像校正之后，会对图像进行裁剪，这里的validROI就是指裁剪之后的区域 Rect validROIR; Mat mapLx, mapLy, mapRx, mapRy; //映射表 Mat Rl, Rr, Pl, Pr, Q; //校正旋转矩阵R，投影矩阵P 重投影矩阵Q Mat xyz; //三维坐标 Point origin; //鼠标按下的起始点 Rect selection; //定义矩形选框 bool selectObject = false; //是否选择对象 int blockSize = 0, uniquenessRatio = 0, numDisparities = 0; Ptr bm = StereoBM::create(16, 9); /* 事先标定好的相机的参数 fx 0 cx 0 fy cy 0 0 1 */ Mat cameraMatrixL = (Mat_(3, 3) << 688.30717, 0, 315.31975, 0, 683.13738, 264.40537, 0, 0, 1); Mat distCoeffL = (Mat_(5, 1) << 0.27153, -1.00396, 0.01107, 0.00364, 0.00000); Mat cameraMatrixR = (Mat_(3, 3) << 665.32773, 0, 345.54178, 0, 660.17940, 249.92143, 0, 0, 1); Mat distCoeffR = (Mat_(5, 1) << 0.22386, -0.93591, 0.00725, 0.00018, 0.00000); Mat T = (Mat_(3, 1) << -39.78384, 1.21230, -40.44029);//T平移向量 Mat rec = (Mat_(3, 1) << -0.02302, -0.00735, 0.00089);//rec旋转向量 Mat R;//R 旋转矩阵 /*****立体匹配*****/ void stereo_match(int, void*) { bm->setBlockSize(2 * blockSize + 5); //SAD窗口大小，5~21之间为宜 bm->setROI1(validROIL); bm->setROI2(validROIR); bm->setPreFilterCap(31); bm->setMinDisparity(0); //最小视差，默认值为0, 可以是负值，int型

bm->setNumDisparities(numDisparities * 16 + 16);//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，int型 bm->setTextureThreshold(10); bm->setUniquenessRatio(uniquenessRatio);//uniquenessRatio主要可以防止误匹配 bm->setSpeckleWindowSize(100); bm->setSpeckleRange(32); bm->setDisp12MaxDiff(-1); Mat disp, disp8; bm->compute(rectifyImageL, rectifyImageR, disp);//输入图像必须为灰度图 disp.convertTo(disp8, CV_8U, 255 / ((numDisparities * 16 + 16)*16.));//计算出的视差是CV_16S格式 reprojectImageTo3D(disp, xyz, Q, true); //在实际求距离时，ReprojectTo3D出来的X / W, Y / W, Z / W都要乘以16(也就是W除以16)，才能得到正确的三维坐标信息。 xyz = xyz * 16; imshow("disparity", disp8); } /*****描述：鼠标操作回调*****/ static void onMouse(int event, int x, int y, int, void*) { if (selectObject) { } selection.x = MIN(x, origin.x); selection.y = MIN(y, origin.y); selection.width = std::abs(x - origin.x); selection.height = std::abs(y - origin.y); switch (event) { case EVENT_LBUTTONDOWN: //鼠标左按钮按下的事件 origin = Point(x, y); selection = Rect(x, y, 0, 0); selectObject = true; cout << origin << "in world coordinate is: " << xyz.at(origin) << endl; break; case EVENT_LBUTTONUP: //鼠标左按钮释放的事件 selectObject = false; if (selection.width > 0 && selection.height > 0) break; } }

/*****主函数*****/ int main() { std::ofstream Fs_mapLx("path\\mapLx.txt"); std::ofstream Fs_mapLy("path\\mapLy.txt"); std::ofstream Fs_mapRx("path\\mapRx.txt"); std::ofstream Fs_mapRy("path\\mapRy.txt"); /* 立体校正 */ Rodrigues(rec, R); //Rodrigues变换 stereoRectify(cameraMatrixL, distCoeffL, cameraMatrixR, distCoeffR, imageSize, R, T, Rl, Rr, Pl, Pr, Q, CALIB_ZERO_DISPARITY, 0, imageSize, &validROIL, &validROIR); //std::cout << Rl << std::endl; //std::cout << Rr << std::endl; initUndistortRectifyMap(cameraMatrixL, distCoeffL, Rl, Pr, imageSize, CV_32FC1, mapLx, mapLy); initUndistortRectifyMap(cameraMatrixR, distCoeffR, Rr, Pr, imageSize, CV_32FC1, mapRx, mapRy); int he = mapLx.rows; int wi = mapLx.cols; // 保存校准数据 for (int i = 0; i < he; i++) for (int j = 0; j < wi; j++) { } Fs_mapLx << (float)mapLx.ptr(i)[j] << "\t"; Fs_mapLy << (float)mapLy.ptr(i)[j] << "\t"; Fs_mapRx << (float)mapRx.ptr(i)[j] << "\t"; Fs_mapRy << (float)mapRy.ptr(i)[j] << "\t"; Fs_mapLx << endl; Fs_mapLy << endl; Fs_mapRx << endl; Fs_mapRy << endl; { }

Fs_mapLx.close(); Fs_mapLy.close(); Fs_mapRx.close(); Fs_mapRy.close(); // 保存校准数据 /* 读取图片 */ rgbImageL = imread("校准图片\\L.jpg", CV_LOAD_IMAGE_COLOR); cvtColor(rgbImageL, grayImageL, CV_BGR2GRAY); rgbImageR = imread("校准图片\\R.jpg", CV_LOAD_IMAGE_COLOR); cvtColor(rgbImageR, grayImageR, CV_BGR2GRAY); imshow("ImageL Before Rectify", grayImageL); imshow("ImageR Before Rectify", grayImageR); /* 经过remap之后，左右相机的图像已经共面并且行对准了 */ remap(grayImageL, rectifyImageL, mapLx, mapLy, INTER_LINEAR); remap(grayImageR, rectifyImageR, mapRx, mapRy, INTER_LINEAR); /* 把校正结果显示出来 */ Mat rgbRectifyImageL, rgbRectifyImageR; cvtColor(rectifyImageL, rgbRectifyImageL, CV_GRAY2BGR); //伪彩色图 cvtColor(rectifyImageR, rgbRectifyImageR, CV_GRAY2BGR); //单独显示 //rectangle(rgbRectifyImageL, validROIL, Scalar(0, 0, 255), 3, 8); //rectangle(rgbRectifyImageR, validROIR, Scalar(0, 0, 255), 3, 8); imshow("ImageL After Rectify", rgbRectifyImageL); imshow("ImageR After Rectify", rgbRectifyImageR); //显示在同一张图上 Mat canvas; double sf; int w, h; sf = 480. / MAX(imageSize.width, imageSize.height); w = cvRound(imageSize.width * sf); h = cvRound(imageSize.height * sf); canvas.create(h, w * 2, CV_8UC3); //注意通道

//左图像画到画布上 Mat canvasPart = canvas(Rect(w * 0, 0, w, h)); //得到画布的一部分 resize(rgbRectifyImageL, canvasPart, canvasPart.size(), 0, 0, INTER_AREA); //把图像缩放到跟canvasPart一样大小 Rect vroiL(cvRound(validROIL.x*sf), cvRound(validROIL.y*sf), //获得被截取的区域 cvRound(validROIL.width*sf), cvRound(validROIL.height*sf)); //rectangle(canvasPart, vroiL, Scalar(0, 0, 255), 3, 8); //画上一个矩形 cout << "Painted ImageL" << endl; //右图像画到画布上 canvasPart = canvas(Rect(w, 0, w, h)); //获得画布的另一部分 resize(rgbRectifyImageR, canvasPart, canvasPart.size(), 0, 0, INTER_LINEAR); Rect vroiR(cvRound(validROIR.x * sf), cvRound(validROIR.y*sf), cvRound(validROIR.width * sf), cvRound(validROIR.height * sf)); //rectangle(canvasPart, vroiR, Scalar(0, 0, 255), 3, 8); cout << "Painted ImageR" << endl; //画上对应的线条 for (int i = 0; i < canvas.rows; i += 16) line(canvas, Point(0, i), Point(canvas.cols, i), Scalar(0, 255, 0), 1, 8); imshow("rectified", canvas); /* 立体匹配 */ namedWindow("disparity", CV_WINDOW_AUTOSIZE); // 创建SAD窗口 Trackbar createTrackbar("BlockSize:\n", "disparity", &blockSize, 8, stereo_match); // 创建视差唯一性百分比窗口 Trackbar createTrackbar("UniquenessRatio:\n", "disparity", &uniquenessRatio, 50, stereo_match); // 创建视差窗口 Trackbar createTrackbar("NumDisparities:\n", "disparity", &numDisparities, 16, stereo_match); //鼠标响应函数setMouseCallback(窗口名称, 鼠标回调函数, 传给回调函数的参数，一般取0) setMouseCallback("disparity", onMouse, 0); stereo_match(0, 0); waitKey(0); return 0;

} C++校准程序： #include #include #include #include using namespace std; int main() { CString path_L = "校准图像\\L.jpg"; CString path_R = "校准图像\\R.jpg"; ifstream fin_mapLx("path\\mapLx.txt"); //获取校准数据 ifstream fin_mapLy("path\\mapLy.txt"); //获取校准数据 ifstream fin_mapRx("path\\mapRx.txt"); //获取校准数据 ifstream fin_mapRy("path\\mapRy.txt"); //获取校准数据 CImage m_Image_L; m_Image_L.Load(path_L); CImage m_Image_R; m_Image_R.Load(path_R); int nimgWidth = m_Image_L.GetWidth(); int nimgheigt = m_Image_L.GetHeight(); vector> img_L(nimgheigt, vector(nimgWidth)); // 校准前图像矩阵 vector> img_R(nimgheigt, vector(nimgWidth)); // 校准前图像矩阵 vector> img_L1(nimgheigt, vector(nimgWidth)); // 校准后图像矩阵 vector> img_R1(nimgheigt, vector(nimgWidth)); // 校准后图像矩阵 vector> mapLx(nimgheigt, vector(nimgWidth)); // 校准数据数据数据数据 vector> mapLy(nimgheigt, vector(nimgWidth)); // 校准 vector> mapRx(nimgheigt, vector(nimgWidth)); // 校准 vector> mapRy(nimgheigt, vector(nimgWidth)); // 校准

byte* pRealData_L = (byte*)m_Image_L.GetBits(); int pit_L = m_Image_L.GetPitch(); int bitCount_L = m_Image_L.GetBPP() / 8; byte* pRealData_R = (byte*)m_Image_R.GetBits(); int pit_R = m_Image_R.GetPitch(); int bitCount_R = m_Image_R.GetBPP() / 8; for (int y = 0; y> 16; // 将RGB转灰度 img_R[y][x] = (int)(((int)(int)(*(pRealData_R + pit_R*y + x*bitCount_R)) * 19595 + (int)(int)(*(pRealData_R + pit_R*y + x*bitCount_R + 1)) * 38469 + (int)(int)(*(pRealData_R + pit_R*y + x*bitCount_R + 2)) * 7472)) >> 16; // 将RGB转灰度 fin_mapLx >> mapLx[y][x]; fin_mapLy >> mapLy[y][x]; fin_mapRx >> mapRx[y][x]; fin_mapRy >> mapRy[y][x]; img_L1[y][x] = 0.0; img_R1[y][x] = 0.0; } } fin_mapLx.clear(); fin_mapLy.clear(); fin_mapRx.clear(); fin_mapRy.clear(); // 将校准后的数据保存，在MATLAB中进行显示 ofstream Fs_img_L("path\\img_L.txt"); ofstream Fs_img_R("path\\img_R.txt"); for (int i = 0; i < nimgheigt; i++)

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