我正在尝试校准我的两个 Point Grey (Blackfly) 相机以获得立体视觉效果。我正在使用 OpenCV 附带的教程 stereo_calib.cpp(下面的代码)。出于某种原因，我得到了非常糟糕的结果(RMS 误差 = 4.49756 和平均重投影误差 = 8.06533)并且我所有的校正图像都变成灰色。我认为我的问题是我没有为 stereoCalibrate() 函数选择正确的标志，但我尝试了许多不同的组合，充其量矫正后的图像会变形。

这是我使用的图像的链接和一个校正后的样本对:https://www.dropbox.com/sh/5wp31o8xcn6vmjl/AAADAfGiaT_NyXEB3zMpcEvVa#/

任何帮助将不胜感激!!

static void
StereoCalib(const vector<string>& imagelist, Size boardSize, bool useCalibrated=true, bool showRectified=true)
{
    if( imagelist.size() % 2 != 0 )
    {
    cout << "Error: the image list contains odd (non-even) number of elements\n";
    return;
    }

    bool displayCorners = true;//false;//true;
    const int maxScale = 1;//2;
    const float squareSize = 1.8;
    //const float squareSize = 1.f;  // Set this to your actual square size

    // ARRAY AND VECTOR STORAGE:

    vector<vector<Point2f> > imagePoints[2];
    vector<vector<Point3f> > objectPoints;
    Size imageSize;

    //int i, j, k, nimages = (int)imagelist.size()/2;
    int i, j, k, nimages = (int)imagelist.size();

    cout << "nimages: " << nimages << "\n";

    imagePoints[0].resize(nimages);
    imagePoints[1].resize(nimages);
    vector<string> goodImageList;

    for( i = j = 0; i < nimages; i++ )
    {
    for( k = 0; k < 2; k++ )
    {
        const string& filename = imagelist[i*2+k];
        Mat img = imread(filename, 0);
        if(img.empty()) {
            break;
        }
        if( imageSize == Size() ) {
            imageSize = img.size();
        } else if( img.size() != imageSize )
        {
            cout << "The image " << filename << " has the size different from the first image size. Skipping the pair\n";
            break;
        }
        bool found = false;
        vector<Point2f>& corners = imagePoints[k][j];
        for( int scale = 1; scale <= maxScale; scale++ )
        {
            Mat timg;
            if( scale == 1 )
                timg = img;
            else
                resize(img, timg, Size(), scale, scale);
            found = findChessboardCorners(timg, boardSize, corners,
                CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_NORMALIZE_IMAGE);
            if( found )
            {
                if( scale > 1 )
                {
                    Mat cornersMat(corners);
                    cornersMat *= 1./scale;
                }
                break;
            }
        }
        if( displayCorners )
        {
            cout << filename << endl;
            Mat cimg, cimg1;
            cvtColor(img, cimg, COLOR_GRAY2BGR);
            drawChessboardCorners(cimg, boardSize, corners, found);
            double sf = 1280./MAX(img.rows, img.cols);
            resize(cimg, cimg1, Size(), sf, sf);
            imshow("corners", cimg1);
            char c = (char)waitKey(500);
            if( c == 27 || c == 'q' || c == 'Q' ) //Allow ESC to quit
                exit(-1);
        }
        else
            putchar('.');
        if( !found ) {
        cout << "!found\n";
            break;
        }
        cornerSubPix(img, corners, Size(11,11), Size(-1,-1),
                     TermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,
                                  30, 0.01));
    }
    if( k == 2 )
    {
        goodImageList.push_back(imagelist[i*2]);
        goodImageList.push_back(imagelist[i*2+1]);
        j++;
    }
    }
    cout << j << " pairs have been successfully detected.\n";
    nimages = j;
    if( nimages < 2 )
    {
    cout << "Error: too little pairs to run the calibration\n";
    return;
    }

    imagePoints[0].resize(nimages);
    imagePoints[1].resize(nimages);
    objectPoints.resize(nimages);

    for( i = 0; i < nimages; i++ )
    {
    for( j = 0; j < boardSize.height; j++ )
        for( k = 0; k < boardSize.width; k++ )
            objectPoints[i].push_back(Point3f(j*squareSize, k*squareSize, 0));
    }

    cout << "Running stereo calibration ...\n";

    Mat cameraMatrix[2], distCoeffs[2];
    cameraMatrix[0] = Mat::eye(3, 3, CV_64F);
    cameraMatrix[1] = Mat::eye(3, 3, CV_64F);
    Mat R, T, E, F;

    double rms = stereoCalibrate(objectPoints, imagePoints[0], imagePoints[1],
                cameraMatrix[0], distCoeffs[0],
                cameraMatrix[1], distCoeffs[1],
                imageSize, R, T, E, F,
                //TermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, 1e-5));

                TermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, 1e-5),
                CV_CALIB_FIX_ASPECT_RATIO +
                //CV_CALIB_ZERO_TANGENT_DIST +
                CV_CALIB_SAME_FOCAL_LENGTH +
                CV_CALIB_RATIONAL_MODEL +
                //CV_CALIB_FIX_K3);
                //CV_CALIB_FIX_K2);
                CV_CALIB_FIX_K3 + CV_CALIB_FIX_K4 + CV_CALIB_FIX_K5);
                //CV_CALIB_FIX_K1 + CV_CALIB_FIX_K2 + CV_CALIB_FIX_K3 + CV_CALIB_FIX_K4 + CV_CALIB_FIX_K5);
    cout << "done with RMS error=" << rms << endl;

    double err = 0;
    int npoints = 0;
    vector<Vec3f> lines[2];
    for( i = 0; i < nimages; i++ )
    {
    int npt = (int)imagePoints[0][i].size();
    Mat imgpt[2];
    for( k = 0; k < 2; k++ )
    {
        imgpt[k] = Mat(imagePoints[k][i]);
        undistortPoints(imgpt[k], imgpt[k], cameraMatrix[k], distCoeffs[k], Mat(), cameraMatrix[k]);
        computeCorrespondEpilines(imgpt[k], k+1, F, lines[k]);
    }
    for( j = 0; j < npt; j++ )
    {
        double errij = fabs(imagePoints[0][i][j].x*lines[1][j][0] +
                            imagePoints[0][i][j].y*lines[1][j][1] + lines[1][j][2]) +
                       fabs(imagePoints[1][i][j].x*lines[0][j][0] +
                            imagePoints[1][i][j].y*lines[0][j][1] + lines[0][j][2]);
        err += errij;
    }
    npoints += npt;
    }
    cout << "average reprojection err = " <<  err/npoints << endl;

    // save intrinsic parameters
    FileStorage fs("intrinsics.yml", CV_STORAGE_WRITE);
    if( fs.isOpened() )
    {
    fs << "M1" << cameraMatrix[0] << "D1" << distCoeffs[0] <<
        "M2" << cameraMatrix[1] << "D2" << distCoeffs[1];
    fs.release();
    }
    else
    cout << "Error: can not save the intrinsic parameters\n";

    Mat R1, R2, P1, P2, Q;
    Rect validRoi[2];

    stereoRectify(cameraMatrix[0], distCoeffs[0],
              cameraMatrix[1], distCoeffs[1],
              imageSize, R, T, R1, R2, P1, P2, Q,
              //CALIB_ZERO_DISPARITY, 1, imageSize, &validRoi[0], &validRoi[1]);
              CALIB_ZERO_DISPARITY, 0, imageSize, &validRoi[0], &validRoi[1]);

    fs.open("extrinsics.yml", CV_STORAGE_WRITE);
    if( fs.isOpened() )
    {
    fs << "R" << R << "T" << T << "R1" << R1 << "R2" << R2 << "P1" << P1 << "P2" << P2 << "Q" << Q;
    fs.release();
    }
    else
    cout << "Error: can not save the intrinsic parameters\n";

    // OpenCV can handle left-right
    // or up-down camera arrangements
    //bool isVerticalStereo = fabs(P2.at<double>(1, 3)) > fabs(P2.at<double>(0, 3));
    bool isVerticalStereo = false;

// COMPUTE AND DISPLAY RECTIFICATION
    if( !showRectified )
    return;

    Mat rmap[2][2];
// IF BY CALIBRATED (BOUGUET'S METHOD)
    if( useCalibrated )
    {
    // we already computed everything
    }
// OR ELSE HARTLEY'S METHOD
    else
 // use intrinsic parameters of each camera, but
 // compute the rectification transformation directly
 // from the fundamental matrix
    {
    vector<Point2f> allimgpt[2];
    for( k = 0; k < 2; k++ )
    {
        for( i = 0; i < nimages; i++ )
            std::copy(imagePoints[k][i].begin(), imagePoints[k][i].end(), back_inserter(allimgpt[k]));
    }
    F = findFundamentalMat(Mat(allimgpt[0]), Mat(allimgpt[1]), FM_8POINT, 0, 0);
    Mat H1, H2;
    stereoRectifyUncalibrated(Mat(allimgpt[0]), Mat(allimgpt[1]), F, imageSize, H1, H2, 3);

    R1 = cameraMatrix[0].inv()*H1*cameraMatrix[0];
    R2 = cameraMatrix[1].inv()*H2*cameraMatrix[1];
    P1 = cameraMatrix[0];
    P2 = cameraMatrix[1];
    }

    //Precompute maps for cv::remap()
    initUndistortRectifyMap(cameraMatrix[0], distCoeffs[0], R1, P1, imageSize, CV_16SC2, rmap[0][0], rmap[0][1]);
    initUndistortRectifyMap(cameraMatrix[1], distCoeffs[1], R2, P2, imageSize, CV_16SC2, rmap[1][0], rmap[1][1]);

    Mat canvas;
    double sf;
    int w, h;
    if( !isVerticalStereo )
    {
    sf = 600./MAX(imageSize.width, imageSize.height);
    w = cvRound(imageSize.width*sf);
    h = cvRound(imageSize.height*sf);
    canvas.create(h, w*2, CV_8UC3);
    }
    else
    {
    sf = 600./MAX(imageSize.width, imageSize.height);
    w = cvRound(imageSize.width*sf);
    h = cvRound(imageSize.height*sf);
    canvas.create(h*2, w, CV_8UC3);
    }

    for( i = 0; i < nimages; i++ )
    {
    for( k = 0; k < 2; k++ )
    {
        Mat img = imread(goodImageList[i*2+k], 0), rimg, cimg;
        remap(img, rimg, rmap[k][0], rmap[k][1], CV_INTER_LINEAR);
        cvtColor(rimg, cimg, COLOR_GRAY2BGR);
        Mat canvasPart = !isVerticalStereo ? canvas(Rect(w*k, 0, w, h)) : canvas(Rect(0, h*k, w, h));
        resize(cimg, canvasPart, canvasPart.size(), 0, 0, CV_INTER_AREA);
        if( useCalibrated )
        {
            Rect vroi(cvRound(validRoi[k].x*sf), cvRound(validRoi[k].y*sf),
                      cvRound(validRoi[k].width*sf), cvRound(validRoi[k].height*sf));
            rectangle(canvasPart, vroi, Scalar(0,0,255), 3, 8);
        }
    }

    if( !isVerticalStereo )
        for( j = 0; j < canvas.rows; j += 16 )
            line(canvas, Point(0, j), Point(canvas.cols, j), Scalar(0, 255, 0), 1, 8);
    else
        for( j = 0; j < canvas.cols; j += 16 )
            line(canvas, Point(j, 0), Point(j, canvas.rows), Scalar(0, 255, 0), 1, 8);
    imshow("rectified", canvas);
    char c = (char)waitKey();
    if( c == 27 || c == 'q' || c == 'Q' )
        break;
    }
}

最佳答案

首先，关于您的校准图像。我看到一些可以导致更好校准的要点:

• 使用更稳定的图像。您的大部分图像都有些模糊，导致角点检测的准确性不佳
• 改变规模。您使用的大多数图像大约呈现棋盘。与摄像头的距离相同。
• 小心你的棋盘本身。它似乎非常依赖它的支持。如果您想获得良好的校准效果，则必须确保您的棋盘牢固地贴在平坦的表面上。

关于如何进行良好校准，您在 this SO answer 中有更详细的建议。

现在，关于立体校准本身。我发现实现良好校准的最佳方法是分别校准每个相机内部参数(使用 calibrateCamera 函数)，然后使用内部参数作为猜测来校准外部参数(使用 stereoCalibrate)。查看 stereoCalibrate 标志以了解如何执行此操作。

除此之外，您在 stereoCalibrate 函数中的标志如下:

1. CV_CALIB_FIX_ASPECT_RATIO :强制固定宽高比 fx/fy
2. CV_CALIB_SAME_FOCAL_LENGTH:看起来不错，因为您有两个相同的相机。您可以通过独立校准每个相机来检查它是否准确
3. CV_CALIB_RATIONAL_MODEL:启用 K3、k4 和 k5 失真参数
4. CV_CALIB_FIX_K3 + CV_CALIB_FIX_K4 + CV_CALIB_FIX_K5:修复这 3 个参数。由于您不使用任何 uess，实际上您在这里将它们设置为 0，因此带有这些标志的 CV_CALIB_RATIONAL_MODEL 选项在您的代码中没有用处

请注意，如果您独立校准每个相机并使用内在函数，则您对这些数据的使用程度不同:

1. 使用 CV_CALIB_FIX_INTRINSIcflags，内在函数将按原样使用，并且仅优化外在参数
2. 使用 CV_CALIB_USE_INTRINSIC_GUESS，内在函数将被用作猜测但再次优化
3. 结合使用 CV_CALIB_FIX_PRINCIPAL_POINT、CV_CALIB_FIX_FOCAL_LENGTH 和 CV_CALIB_FIX_K1,...,CV_CALIB_FIX_K6，您可以了解哪些参数是固定的，哪些参数再次优化

关于c++ - OpenCV 立体相机校准/图像校正，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/24130884/