OpenCV/opencv/modules/calib3d/test/test_cameracalibration.cpp

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#include "test_precomp.hpp"
#include "opencv2/calib3d/calib3d_c.h"

namespace opencv_test { namespace {

#if 0
class CV_ProjectPointsTest : public cvtest::ArrayTest
{
public:
    CV_ProjectPointsTest();

protected:
    int read_params( const cv::FileStorage& fs );
    void fill_array( int test_case_idx, int i, int j, Mat& arr );
    int prepare_test_case( int test_case_idx );
    void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
    double get_success_error_level( int test_case_idx, int i, int j );
    void run_func();
    void prepare_to_validation( int );

    bool calc_jacobians;
};


CV_ProjectPointsTest::CV_ProjectPointsTest()
    : cvtest::ArrayTest( "3d-ProjectPoints", "cvProjectPoints2", "" )
{
    test_array[INPUT].push_back(NULL);  // rotation vector
    test_array[OUTPUT].push_back(NULL); // rotation matrix
    test_array[OUTPUT].push_back(NULL); // jacobian (J)
    test_array[OUTPUT].push_back(NULL); // rotation vector (backward transform result)
    test_array[OUTPUT].push_back(NULL); // inverse transform jacobian (J1)
    test_array[OUTPUT].push_back(NULL); // J*J1 (or J1*J) == I(3x3)
    test_array[REF_OUTPUT].push_back(NULL);
    test_array[REF_OUTPUT].push_back(NULL);
    test_array[REF_OUTPUT].push_back(NULL);
    test_array[REF_OUTPUT].push_back(NULL);
    test_array[REF_OUTPUT].push_back(NULL);

    element_wise_relative_error = false;
    calc_jacobians = false;
}


int CV_ProjectPointsTest::read_params( const cv::FileStorage& fs )
{
    int code = cvtest::ArrayTest::read_params( fs );
    return code;
}


void CV_ProjectPointsTest::get_test_array_types_and_sizes(
    int /*test_case_idx*/, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
    RNG& rng = ts->get_rng();
    int depth = cvtest::randInt(rng) % 2 == 0 ? CV_32F : CV_64F;
    int i, code;

    code = cvtest::randInt(rng) % 3;
    types[INPUT][0] = CV_MAKETYPE(depth, 1);

    if( code == 0 )
    {
        sizes[INPUT][0] = cvSize(1,1);
        types[INPUT][0] = CV_MAKETYPE(depth, 3);
    }
    else if( code == 1 )
        sizes[INPUT][0] = cvSize(3,1);
    else
        sizes[INPUT][0] = cvSize(1,3);

    sizes[OUTPUT][0] = cvSize(3, 3);
    types[OUTPUT][0] = CV_MAKETYPE(depth, 1);

    types[OUTPUT][1] = CV_MAKETYPE(depth, 1);

    if( cvtest::randInt(rng) % 2 )
        sizes[OUTPUT][1] = cvSize(3,9);
    else
        sizes[OUTPUT][1] = cvSize(9,3);

    types[OUTPUT][2] = types[INPUT][0];
    sizes[OUTPUT][2] = sizes[INPUT][0];

    types[OUTPUT][3] = types[OUTPUT][1];
    sizes[OUTPUT][3] = cvSize(sizes[OUTPUT][1].height, sizes[OUTPUT][1].width);

    types[OUTPUT][4] = types[OUTPUT][1];
    sizes[OUTPUT][4] = cvSize(3,3);

    calc_jacobians = 1;//cvtest::randInt(rng) % 3 != 0;
    if( !calc_jacobians )
        sizes[OUTPUT][1] = sizes[OUTPUT][3] = sizes[OUTPUT][4] = cvSize(0,0);

    for( i = 0; i < 5; i++ )
    {
        types[REF_OUTPUT][i] = types[OUTPUT][i];
        sizes[REF_OUTPUT][i] = sizes[OUTPUT][i];
    }
}


double CV_ProjectPointsTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int j )
{
    return j == 4 ? 1e-2 : 1e-2;
}


void CV_ProjectPointsTest::fill_array( int /*test_case_idx*/, int /*i*/, int /*j*/, CvMat* arr )
{
    double r[3], theta0, theta1, f;
    CvMat _r = cvMat( arr->rows, arr->cols, CV_MAKETYPE(CV_64F,CV_MAT_CN(arr->type)), r );
    RNG& rng = ts->get_rng();

    r[0] = cvtest::randReal(rng)*CV_PI*2;
    r[1] = cvtest::randReal(rng)*CV_PI*2;
    r[2] = cvtest::randReal(rng)*CV_PI*2;

    theta0 = sqrt(r[0]*r[0] + r[1]*r[1] + r[2]*r[2]);
    theta1 = fmod(theta0, CV_PI*2);

    if( theta1 > CV_PI )
        theta1 = -(CV_PI*2 - theta1);

    f = theta1/(theta0 ? theta0 : 1);
    r[0] *= f;
    r[1] *= f;
    r[2] *= f;

    cvTsConvert( &_r, arr );
}


int CV_ProjectPointsTest::prepare_test_case( int test_case_idx )
{
    int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
    return code;
}


void CV_ProjectPointsTest::run_func()
{
    CvMat *v2m_jac = 0, *m2v_jac = 0;
    if( calc_jacobians )
    {
        v2m_jac = &test_mat[OUTPUT][1];
        m2v_jac = &test_mat[OUTPUT][3];
    }

    cvProjectPoints2( &test_mat[INPUT][0], &test_mat[OUTPUT][0], v2m_jac );
    cvProjectPoints2( &test_mat[OUTPUT][0], &test_mat[OUTPUT][2], m2v_jac );
}


void CV_ProjectPointsTest::prepare_to_validation( int /*test_case_idx*/ )
{
    const CvMat* vec = &test_mat[INPUT][0];
    CvMat* m = &test_mat[REF_OUTPUT][0];
    CvMat* vec2 = &test_mat[REF_OUTPUT][2];
    CvMat* v2m_jac = 0, *m2v_jac = 0;
    double theta0, theta1;

    if( calc_jacobians )
    {
        v2m_jac = &test_mat[REF_OUTPUT][1];
        m2v_jac = &test_mat[REF_OUTPUT][3];
    }


    cvTsProjectPoints( vec, m, v2m_jac );
    cvTsProjectPoints( m, vec2, m2v_jac );
    cvTsCopy( vec, vec2 );

    theta0 = cvtest::norm( cvarrtomat(vec2), 0, CV_L2 );
    theta1 = fmod( theta0, CV_PI*2 );

    if( theta1 > CV_PI )
        theta1 = -(CV_PI*2 - theta1);
    cvScale( vec2, vec2, theta1/(theta0 ? theta0 : 1) );

    if( calc_jacobians )
    {
        //cvInvert( v2m_jac, m2v_jac, CV_SVD );
        if( cvtest::norm(cvarrtomat(&test_mat[OUTPUT][3]), 0, CV_C) < 1000 )
        {
            cvTsGEMM( &test_mat[OUTPUT][1], &test_mat[OUTPUT][3],
                      1, 0, 0, &test_mat[OUTPUT][4],
                      v2m_jac->rows == 3 ? 0 : CV_GEMM_A_T + CV_GEMM_B_T );
        }
        else
        {
            cvTsSetIdentity( &test_mat[OUTPUT][4], cvScalarAll(1.) );
            cvTsCopy( &test_mat[REF_OUTPUT][2], &test_mat[OUTPUT][2] );
        }
        cvTsSetIdentity( &test_mat[REF_OUTPUT][4], cvScalarAll(1.) );
    }
}


CV_ProjectPointsTest ProjectPoints_test;

#endif

// --------------------------------- CV_CameraCalibrationTest --------------------------------------------

typedef Matx33d RotMat;

class CV_CameraCalibrationTest : public cvtest::BaseTest
{
public:
    CV_CameraCalibrationTest();
    ~CV_CameraCalibrationTest();
    void clear();
protected:
    int compare(double* val, double* refVal, int len,
                double eps, const char* paramName);
    virtual void calibrate(Size imageSize,
        const std::vector<std::vector<Point2d> >& imagePoints,
        const std::vector<std::vector<Point3d> >& objectPoints,
        int iFixedPoint, Mat& distortionCoeffs, Mat& cameraMatrix, std::vector<Vec3d>& translationVectors,
        std::vector<RotMat>& rotationMatrices, std::vector<Point3d>& newObjPoints,
        std::vector<double>& stdDevs, std::vector<double>& perViewErrors,
        int flags ) = 0;
    virtual void project( const std::vector<Point3d>& objectPoints,
        const RotMat& rotationMatrix, const Vec3d& translationVector,
        const Mat& cameraMatrix, const Mat& distortion,
        std::vector<Point2d>& imagePoints ) = 0;

    void run(int);
};

CV_CameraCalibrationTest::CV_CameraCalibrationTest()
{
}

CV_CameraCalibrationTest::~CV_CameraCalibrationTest()
{
    clear();
}

void CV_CameraCalibrationTest::clear()
{
    cvtest::BaseTest::clear();
}

int CV_CameraCalibrationTest::compare(double* val, double* ref_val, int len,
                                      double eps, const char* param_name )
{
    return cvtest::cmpEps2_64f( ts, val, ref_val, len, eps, param_name );
}

void CV_CameraCalibrationTest::run( int start_from )
{
    int code = cvtest::TS::OK;
    cv::String            filepath;
    cv::String            filename;

    std::vector<std::vector<Point2d> >  imagePoints;
    std::vector<std::vector<Point3d> >  objectPoints;
    std::vector<std::vector<Point2d> >  reprojectPoints;

    std::vector<Vec3d>        transVects;
    std::vector<RotMat>       rotMatrs;
    std::vector<Point3d>      newObjPoints;
    std::vector<double>       stdDevs;
    std::vector<double>       perViewErrors;

    std::vector<Vec3d>        goodTransVects;
    std::vector<RotMat>       goodRotMatrs;
    std::vector<Point3d>      goodObjPoints;
    std::vector<double>       goodPerViewErrors;
    std::vector<double>       goodStdDevs;

    Mat             cameraMatrix;
    Mat             distortion = Mat::zeros(1, 5, CV_64F);
    Mat             goodDistortion = Mat::zeros(1, 5, CV_64F);

    FILE*           file = 0;
    FILE*           datafile = 0;
    int             i,j;
    int             currImage;
    int             currPoint;
    char            i_dat_file[100];

    int progress = 0;
    int values_read = -1;

    filepath = cv::format("%scv/cameracalibration/", ts->get_data_path().c_str() );
    filename = cv::format("%sdatafiles.txt", filepath.c_str() );
    datafile = fopen( filename.c_str(), "r" );
    if( datafile == 0 )
    {
        ts->printf( cvtest::TS::LOG, "Could not open file with list of test files: %s\n", filename.c_str() );
        code = cvtest::TS::FAIL_MISSING_TEST_DATA;
        ts->set_failed_test_info( code );
        return;
    }

    int numTests = 0;
    values_read = fscanf(datafile,"%d",&numTests);
    CV_Assert(values_read == 1);

    for( int currTest = start_from; currTest < numTests; currTest++ )
    {
        values_read = fscanf(datafile,"%s",i_dat_file);
        CV_Assert(values_read == 1);
        filename = cv::format("%s%s", filepath.c_str(), i_dat_file);
        file = fopen(filename.c_str(),"r");

        ts->update_context( this, currTest, true );

        if( file == 0 )
        {
            ts->printf( cvtest::TS::LOG,
                "Can't open current test file: %s\n",filename.c_str());
            if( numTests == 1 )
            {
                code = cvtest::TS::FAIL_MISSING_TEST_DATA;
                break;
            }
            continue; // if there is more than one test, just skip the test
        }

        Size imageSize;
        values_read = fscanf(file,"%d %d\n",&(imageSize.width),&(imageSize.height));
        CV_Assert(values_read == 2);
        if( imageSize.width <= 0 || imageSize.height <= 0 )
        {
            ts->printf( cvtest::TS::LOG, "Image size in test file is incorrect\n" );
            code = cvtest::TS::FAIL_INVALID_TEST_DATA;
            break;
        }

        /* Read etalon size */
        Size etalonSize;
        values_read = fscanf(file,"%d %d\n",&(etalonSize.width),&(etalonSize.height));
        CV_Assert(values_read == 2);
        if( etalonSize.width <= 0 || etalonSize.height <= 0 )
        {
            ts->printf( cvtest::TS::LOG, "Pattern size in test file is incorrect\n" );
            code = cvtest::TS::FAIL_INVALID_TEST_DATA;
            break;
        }

        int numPoints = etalonSize.width * etalonSize.height;

        /* Read number of images */
        int numImages = 0;
        values_read = fscanf(file,"%d\n",&numImages);
        CV_Assert(values_read == 1);
        if( numImages <=0 )
        {
            ts->printf( cvtest::TS::LOG, "Number of images in test file is incorrect\n");
            code = cvtest::TS::FAIL_INVALID_TEST_DATA;
            break;
        }

        /* Read calibration flags */
        int calibFlags = 0;
        values_read = fscanf(file,"%d\n",&calibFlags);
        CV_Assert(values_read == 1);

        /* Read index of the fixed point */
        int iFixedPoint;
        values_read = fscanf(file,"%d\n",&iFixedPoint);
        CV_Assert(values_read == 1);

        /* Need to allocate memory */
        imagePoints.resize(numImages);
        objectPoints.resize(numImages);
        reprojectPoints.resize(numImages);
        for( currImage = 0; currImage < numImages; currImage++ )
        {
            imagePoints[currImage].resize(numPoints);
            objectPoints[currImage].resize(numPoints);
            reprojectPoints[currImage].resize(numPoints);
        }

        transVects.resize(numImages);
        rotMatrs.resize(numImages);
        newObjPoints.resize(numPoints);
        stdDevs.resize(CALIB_NINTRINSIC + 6*numImages + 3*numPoints);
        perViewErrors.resize(numImages);

        goodTransVects.resize(numImages);
        goodRotMatrs.resize(numImages);
        goodObjPoints.resize(numPoints);
        goodPerViewErrors.resize(numImages);

        int nstddev = CALIB_NINTRINSIC + 6*numImages + 3*numPoints;
        goodStdDevs.resize(nstddev);

        for( currImage = 0; currImage < numImages; currImage++ )
        {
            for( currPoint = 0; currPoint < numPoints; currPoint++ )
            {
                double x,y,z;
                values_read = fscanf(file,"%lf %lf %lf\n",&x,&y,&z);
                CV_Assert(values_read == 3);

                objectPoints[currImage][currPoint].x = x;
                objectPoints[currImage][currPoint].y = y;
                objectPoints[currImage][currPoint].z = z;
            }
        }

        /* Read image points */
        for( currImage = 0; currImage < numImages; currImage++ )
        {
            for( currPoint = 0; currPoint < numPoints; currPoint++ )
            {
                double x,y;
                values_read = fscanf(file,"%lf %lf\n",&x,&y);
                CV_Assert(values_read == 2);

                imagePoints[currImage][currPoint].x = x;
                imagePoints[currImage][currPoint].y = y;
            }
        }

        /* Read good data computed before */

        /* Focal lengths */
        double goodFcx,goodFcy;
        values_read = fscanf(file,"%lf %lf",&goodFcx,&goodFcy);
        CV_Assert(values_read == 2);

        /* Principal points */
        double goodCx,goodCy;
        values_read = fscanf(file,"%lf %lf",&goodCx,&goodCy);
        CV_Assert(values_read == 2);

        /* Read distortion */

        for( i = 0; i < 4; i++ )
        {
            values_read = fscanf(file,"%lf",&goodDistortion.at<double>(i)); CV_Assert(values_read == 1);
        }

        /* Read good Rot matrices */
        for( currImage = 0; currImage < numImages; currImage++ )
        {
            for( i = 0; i < 3; i++ )
                for( j = 0; j < 3; j++ )
                {
                    values_read = fscanf(file, "%lf", &goodRotMatrs[currImage].val[i*3+j]);
                    CV_Assert(values_read == 1);
                }
        }

        /* Read good Trans vectors */
        for( currImage = 0; currImage < numImages; currImage++ )
        {
            for( i = 0; i < 3; i++ )
            {
                values_read = fscanf(file, "%lf", &goodTransVects[currImage].val[i]);
                CV_Assert(values_read == 1);
            }
        }

        bool releaseObject = iFixedPoint > 0 && iFixedPoint < numPoints - 1;
        /* Read good refined 3D object points */
        if( releaseObject )
        {
            for( i = 0; i < numPoints; i++ )
            {
                for( j = 0; j < 3; j++ )
                {
                    values_read = fscanf(file, "%lf", &goodObjPoints[i].x + j);
                    CV_Assert(values_read == 1);
                }
            }
        }

        /* Read good stdDeviations */
        for (i = 0; i < CALIB_NINTRINSIC + numImages*6; i++)
        {
            values_read = fscanf(file, "%lf", &goodStdDevs[i]);
            CV_Assert(values_read == 1);
        }
        for( ; i < nstddev; i++ )
        {
            if( releaseObject )
            {
                values_read = fscanf(file, "%lf", &goodStdDevs[i]);
                CV_Assert(values_read == 1);
            }
            else
                goodStdDevs[i] = 0.0;
        }

        cameraMatrix = Mat::zeros(3, 3, CV_64F);
        cameraMatrix.at<double>(0, 0) = cameraMatrix.at<double>(1, 1) = 807.;
        cameraMatrix.at<double>(0, 2) = (imageSize.width - 1)*0.5;
        cameraMatrix.at<double>(1, 2) = (imageSize.height - 1)*0.5;
        cameraMatrix.at<double>(2, 2) = 1.;

        /* Now we can calibrate camera */
        calibrate(  imageSize,
                    imagePoints,
                    objectPoints,
                    iFixedPoint,
                    distortion,
                    cameraMatrix,
                    transVects,
                    rotMatrs,
                    newObjPoints,
                    stdDevs,
                    perViewErrors,
                    calibFlags );

        /* ---- Reproject points to the image ---- */
        for( currImage = 0; currImage < numImages; currImage++ )
        {
            if( releaseObject )
            {
                objectPoints[currImage] = newObjPoints;
            }
            project(  objectPoints[currImage],
                      rotMatrs[currImage],
                      transVects[currImage],
                      cameraMatrix,
                      distortion,
                      reprojectPoints[currImage]);
        }

        /* ----- Compute reprojection error ----- */
        double dx,dy;
        double rx,ry;
        double meanDx,meanDy;
        double maxDx = 0.0;
        double maxDy = 0.0;

        meanDx = 0;
        meanDy = 0;
        for( currImage = 0; currImage < numImages; currImage++ )
        {
            double imageMeanDx = 0;
            double imageMeanDy = 0;
            for( currPoint = 0; currPoint < etalonSize.width * etalonSize.height; currPoint++ )
            {
                rx = reprojectPoints[currImage][currPoint].x;
                ry = reprojectPoints[currImage][currPoint].y;
                dx = rx - imagePoints[currImage][currPoint].x;
                dy = ry - imagePoints[currImage][currPoint].y;

                meanDx += dx;
                meanDy += dy;

                imageMeanDx += dx*dx;
                imageMeanDy += dy*dy;

                dx = fabs(dx);
                dy = fabs(dy);

                if( dx > maxDx )
                    maxDx = dx;

                if( dy > maxDy )
                    maxDy = dy;
            }
            goodPerViewErrors[currImage] = sqrt( (imageMeanDx + imageMeanDy) /
                                           (etalonSize.width * etalonSize.height));

            //only for c-version of test (it does not provides evaluation of perViewErrors
            //and returns zeros)
            if(perViewErrors[currImage] == 0.0)
                perViewErrors[currImage] = goodPerViewErrors[currImage];
        }

        meanDx /= numImages * etalonSize.width * etalonSize.height;
        meanDy /= numImages * etalonSize.width * etalonSize.height;

        /* ========= Compare parameters ========= */
        CV_Assert(cameraMatrix.type() == CV_64F && cameraMatrix.size() == Size(3, 3));
        CV_Assert(distortion.type() == CV_64F);

        Size dsz = distortion.size();
        CV_Assert(dsz == Size(4, 1) || dsz == Size(1, 4) || dsz == Size(5, 1) || dsz == Size(1, 5));

        /*std::cout << "cameraMatrix: " << cameraMatrix << "\n";
        std::cout << "curr distCoeffs: " << distortion << "\n";
        std::cout << "good distCoeffs: " << goodDistortion << "\n";*/

        /* ----- Compare focal lengths ----- */
        code = compare(&cameraMatrix.at<double>(0, 0), &goodFcx, 1, 0.1, "fx");
        if( code < 0 )
            break;

        code = compare(&cameraMatrix.at<double>(1, 1),&goodFcy, 1, 0.1, "fy");
        if( code < 0 )
            break;

        /* ----- Compare principal points ----- */
        code = compare(&cameraMatrix.at<double>(0,2), &goodCx, 1, 0.1, "cx");
        if( code < 0 )
            break;

        code = compare(&cameraMatrix.at<double>(1,2), &goodCy, 1, 0.1, "cy");
        if( code < 0 )
            break;

        /* ----- Compare distortion ----- */
        code = compare(&distortion.at<double>(0), &goodDistortion.at<double>(0), 4, 0.1, "[k1,k2,p1,p2]");
        if( code < 0 )
            break;

        /* ----- Compare rot matrixs ----- */
        CV_Assert(rotMatrs.size() == (size_t)numImages);
        CV_Assert(transVects.size() == (size_t)numImages);

        //code = compare(rotMatrs[0].val, goodRotMatrs[0].val, 9*numImages, 0.05, "rotation matrices");
        for( i = 0; i < numImages; i++ )
        {
            if( cv::norm(rotMatrs[i], goodRotMatrs[i], NORM_INF) > 0.05 )
            {
                printf("rot mats for frame #%d are very different\n", i);
                std::cout << "curr:\n" << rotMatrs[i] << std::endl;
                std::cout << "good:\n" << goodRotMatrs[i] << std::endl;

                code = TS::FAIL_BAD_ACCURACY;
                break;
            }
        }
        if( code < 0 )
            break;

        /* ----- Compare rot matrixs ----- */
        code = compare(transVects[0].val, goodTransVects[0].val, 3*numImages, 0.1, "translation vectors");
        if( code < 0 )
            break;

        /* ----- Compare refined 3D object points ----- */
        if( releaseObject )
        {
            code = compare(&newObjPoints[0].x, &goodObjPoints[0].x, 3*numPoints, 0.1, "refined 3D object points");
            if( code < 0 )
                break;
        }

        /* ----- Compare per view re-projection errors ----- */
        CV_Assert(perViewErrors.size() == (size_t)numImages);
        code = compare(&perViewErrors[0], &goodPerViewErrors[0], numImages, 1.1, "per view errors vector");
        if( code < 0 )
            break;

        /* ----- Compare standard deviations of parameters ----- */
        if( stdDevs.size() < (size_t)nstddev )
            stdDevs.resize(nstddev);
        for ( i = 0; i < nstddev; i++)
        {
            if(stdDevs[i] == 0.0)
                stdDevs[i] = goodStdDevs[i];
        }
        code = compare(&stdDevs[0], &goodStdDevs[0], nstddev, .5,
                       "stdDevs vector");
        if( code < 0 )
            break;

        /*if( maxDx > 1.0 )
        {
            ts->printf( cvtest::TS::LOG,
                      "Error in reprojection maxDx=%f > 1.0\n",maxDx);
            code = cvtest::TS::FAIL_BAD_ACCURACY; break;
        }

        if( maxDy > 1.0 )
        {
            ts->printf( cvtest::TS::LOG,
                      "Error in reprojection maxDy=%f > 1.0\n",maxDy);
            code = cvtest::TS::FAIL_BAD_ACCURACY; break;
        }*/

        progress = update_progress( progress, currTest, numTests, 0 );

        fclose(file);
        file = 0;
    }

    if( file )
        fclose(file);

    if( datafile )
        fclose(datafile);

    if( code < 0 )
        ts->set_failed_test_info( code );
}

// --------------------------------- CV_CameraCalibrationTest_CPP --------------------------------------------

class CV_CameraCalibrationTest_CPP : public CV_CameraCalibrationTest
{
public:
    CV_CameraCalibrationTest_CPP(){}
protected:
    virtual void calibrate(Size imageSize,
                           const std::vector<std::vector<Point2d> >& imagePoints,
                           const std::vector<std::vector<Point3d> >& objectPoints,
                           int iFixedPoint, Mat& distortionCoeffs, Mat& cameraMatrix, std::vector<Vec3d>& translationVectors,
                           std::vector<RotMat>& rotationMatrices, std::vector<Point3d>& newObjPoints,
                           std::vector<double>& stdDevs, std::vector<double>& perViewErrors,
                           int flags );
    virtual void project( const std::vector<Point3d>& objectPoints,
                         const RotMat& rotationMatrix, const Vec3d& translationVector,
                         const Mat& cameraMatrix, const Mat& distortion,
                         std::vector<Point2d>& imagePoints );
};

void CV_CameraCalibrationTest_CPP::calibrate(Size imageSize,
    const std::vector<std::vector<Point2d> >& _imagePoints,
    const std::vector<std::vector<Point3d> >& _objectPoints,
    int iFixedPoint, Mat& _distCoeffs, Mat& _cameraMatrix, std::vector<Vec3d>& translationVectors,
    std::vector<RotMat>& rotationMatrices, std::vector<Point3d>& newObjPoints,
    std::vector<double>& stdDevs, std::vector<double>& perViewErrors,
    int flags )
{
    int pointCount = (int)_imagePoints[0].size();
    size_t i, imageCount = _imagePoints.size();
    vector<vector<Point3f> > objectPoints( imageCount );
    vector<vector<Point2f> > imagePoints( imageCount );
    Mat cameraMatrix, distCoeffs(1,4,CV_64F,Scalar::all(0));
    vector<Mat> rvecs, tvecs;
    Mat newObjMat;
    Mat stdDevsMatInt, stdDevsMatExt;
    Mat stdDevsMatObj;
    Mat perViewErrorsMat;

    for( i = 0; i < imageCount; i++ )
    {
        Mat(_imagePoints[i]).convertTo(imagePoints[i], CV_32F);
        Mat(_objectPoints[i]).convertTo(objectPoints[i], CV_32F);
    }

    size_t nstddev0 = CV_CALIB_NINTRINSIC + imageCount*6, nstddev1 = nstddev0 + _imagePoints[0].size()*3;
    for( i = nstddev0; i < nstddev1; i++ )
    {
        stdDevs[i] = 0.0;
    }

    calibrateCameraRO( objectPoints,
                       imagePoints,
                       imageSize,
                       iFixedPoint,
                       cameraMatrix,
                       distCoeffs,
                       rvecs,
                       tvecs,
                       newObjMat,
                       stdDevsMatInt,
                       stdDevsMatExt,
                       stdDevsMatObj,
                       perViewErrorsMat,
                       flags );

    bool releaseObject = iFixedPoint > 0 && iFixedPoint < pointCount - 1;
    if( releaseObject )
    {
        newObjMat.convertTo( newObjPoints, CV_64F );
    }

    Mat stdDevMats[] = {stdDevsMatInt, stdDevsMatExt, stdDevsMatObj}, stdDevsMat;
    vconcat(stdDevMats, releaseObject ? 3 : 2, stdDevsMat);
    stdDevsMat.convertTo(stdDevs, CV_64F);

    perViewErrorsMat.convertTo(perViewErrors, CV_64F);
    cameraMatrix.convertTo(_cameraMatrix, CV_64F);
    distCoeffs.convertTo(_distCoeffs, CV_64F);

    for( i = 0; i < imageCount; i++ )
    {
        Mat r9;
        cvtest::Rodrigues( rvecs[i], r9 );
        cv::transpose(r9, r9);
        r9.convertTo(rotationMatrices[i], CV_64F);
        tvecs[i].convertTo(translationVectors[i], CV_64F);
    }
}


void CV_CameraCalibrationTest_CPP::project( const std::vector<Point3d>& objectPoints,
                         const RotMat& rotationMatrix, const Vec3d& translationVector,
                         const Mat& cameraMatrix, const Mat& distortion,
                         std::vector<Point2d>& imagePoints )
{
    projectPoints(objectPoints, rotationMatrix, translationVector, cameraMatrix, distortion, imagePoints );
    /*Mat objectPoints( pointCount, 3, CV_64FC1, _objectPoints );
    Mat rmat( 3, 3, CV_64FC1, rotationMatrix ),
        rvec( 1, 3, CV_64FC1 ),
        tvec( 1, 3, CV_64FC1, translationVector );
    Mat cameraMatrix( 3, 3, CV_64FC1, _cameraMatrix );
    Mat distCoeffs( 1, 4, CV_64FC1, distortion );
    vector<Point2f> imagePoints;
    cvtest::Rodrigues( rmat, rvec );

    objectPoints.convertTo( objectPoints, CV_32FC1 );
    projectPoints( objectPoints, rvec, tvec,
                   cameraMatrix, distCoeffs, imagePoints );
    vector<Point2f>::const_iterator it = imagePoints.begin();
    for( int i = 0; it != imagePoints.end(); ++it, i++ )
    {
        _imagePoints[i] = cvPoint2D64f( it->x, it->y );
    }*/
}


//----------------------------------------- CV_CalibrationMatrixValuesTest --------------------------------

class CV_CalibrationMatrixValuesTest : public cvtest::BaseTest
{
public:
    CV_CalibrationMatrixValuesTest() {}
protected:
    void run(int);
    virtual void calibMatrixValues( const Mat& cameraMatrix, Size imageSize,
        double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength,
        Point2d& principalPoint, double& aspectRatio ) = 0;
};

void CV_CalibrationMatrixValuesTest::run(int)
{
    int code = cvtest::TS::OK;
    const double fcMinVal = 1e-5;
    const double fcMaxVal = 1000;
    const double apertureMaxVal = 0.01;

    RNG rng = ts->get_rng();

    double fx, fy, cx, cy, nx, ny;
    Mat cameraMatrix( 3, 3, CV_64FC1 );
    cameraMatrix.setTo( Scalar(0) );
    fx = cameraMatrix.at<double>(0,0) = rng.uniform( fcMinVal, fcMaxVal );
    fy = cameraMatrix.at<double>(1,1) = rng.uniform( fcMinVal, fcMaxVal );
    cx = cameraMatrix.at<double>(0,2) = rng.uniform( fcMinVal, fcMaxVal );
    cy = cameraMatrix.at<double>(1,2) = rng.uniform( fcMinVal, fcMaxVal );
    cameraMatrix.at<double>(2,2) = 1;

    Size imageSize( 600, 400 );

    double apertureWidth = (double)rng * apertureMaxVal,
           apertureHeight = (double)rng * apertureMaxVal;

    double fovx, fovy, focalLength, aspectRatio,
           goodFovx, goodFovy, goodFocalLength, goodAspectRatio;
    Point2d principalPoint, goodPrincipalPoint;


    calibMatrixValues( cameraMatrix, imageSize, apertureWidth, apertureHeight,
        fovx, fovy, focalLength, principalPoint, aspectRatio );

    // calculate calibration matrix values
    goodAspectRatio = fy / fx;

    if( apertureWidth != 0.0 && apertureHeight != 0.0 )
    {
        nx = imageSize.width / apertureWidth;
        ny = imageSize.height / apertureHeight;
    }
    else
    {
        nx = 1.0;
        ny = goodAspectRatio;
    }

    goodFovx = (atan2(cx, fx) + atan2(imageSize.width  - cx, fx)) * 180.0 / CV_PI;
    goodFovy = (atan2(cy, fy) + atan2(imageSize.height - cy, fy)) * 180.0 / CV_PI;

    goodFocalLength = fx / nx;

    goodPrincipalPoint.x = cx / nx;
    goodPrincipalPoint.y = cy / ny;

    // check results
    if( fabs(fovx - goodFovx) > FLT_EPSILON )
    {
        ts->printf( cvtest::TS::LOG, "bad fovx (real=%f, good = %f\n", fovx, goodFovx );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
        goto _exit_;
    }
    if( fabs(fovy - goodFovy) > FLT_EPSILON )
    {
        ts->printf( cvtest::TS::LOG, "bad fovy (real=%f, good = %f\n", fovy, goodFovy );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
        goto _exit_;
    }
    if( fabs(focalLength - goodFocalLength) > FLT_EPSILON )
    {
        ts->printf( cvtest::TS::LOG, "bad focalLength (real=%f, good = %f\n", focalLength, goodFocalLength );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
        goto _exit_;
    }
    if( fabs(aspectRatio - goodAspectRatio) > FLT_EPSILON )
    {
        ts->printf( cvtest::TS::LOG, "bad aspectRatio (real=%f, good = %f\n", aspectRatio, goodAspectRatio );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
        goto _exit_;
    }
    if( cv::norm(principalPoint - goodPrincipalPoint) > FLT_EPSILON ) // Point2d
    {
        ts->printf( cvtest::TS::LOG, "bad principalPoint\n" );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
        goto _exit_;
    }

_exit_:
    RNG& _rng = ts->get_rng();
    _rng = rng;
    ts->set_failed_test_info( code );
}

//----------------------------------------- CV_CalibrationMatrixValuesTest_CPP --------------------------------

class CV_CalibrationMatrixValuesTest_CPP : public CV_CalibrationMatrixValuesTest
{
public:
    CV_CalibrationMatrixValuesTest_CPP() {}
protected:
    virtual void calibMatrixValues( const Mat& cameraMatrix, Size imageSize,
        double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength,
        Point2d& principalPoint, double& aspectRatio );
};

void CV_CalibrationMatrixValuesTest_CPP::calibMatrixValues( const Mat& cameraMatrix, Size imageSize,
                                                         double apertureWidth, double apertureHeight,
                                                         double& fovx, double& fovy, double& focalLength,
                                                         Point2d& principalPoint, double& aspectRatio )
{
    calibrationMatrixValues( cameraMatrix, imageSize, apertureWidth, apertureHeight,
        fovx, fovy, focalLength, principalPoint, aspectRatio );
}


//----------------------------------------- CV_ProjectPointsTest --------------------------------
void calcdfdx( const vector<vector<Point2f> >& leftF, const vector<vector<Point2f> >& rightF, double eps, Mat& dfdx )
{
    const int fdim = 2;
    CV_Assert( !leftF.empty() && !rightF.empty() && !leftF[0].empty() && !rightF[0].empty() );
    CV_Assert( leftF[0].size() ==  rightF[0].size() );
    CV_Assert( fabs(eps) > std::numeric_limits<double>::epsilon() );
    int fcount = (int)leftF[0].size(), xdim = (int)leftF.size();

    dfdx.create( fcount*fdim, xdim, CV_64FC1 );

    vector<vector<Point2f> >::const_iterator arrLeftIt = leftF.begin();
    vector<vector<Point2f> >::const_iterator arrRightIt = rightF.begin();
    for( int xi = 0; xi < xdim; xi++, ++arrLeftIt, ++arrRightIt )
    {
        CV_Assert( (int)arrLeftIt->size() ==  fcount );
        CV_Assert( (int)arrRightIt->size() ==  fcount );
        vector<Point2f>::const_iterator lIt = arrLeftIt->begin();
        vector<Point2f>::const_iterator rIt = arrRightIt->begin();
        for( int fi = 0; fi < dfdx.rows; fi+=fdim, ++lIt, ++rIt )
        {
            dfdx.at<double>(fi, xi )   = 0.5 * ((double)(rIt->x - lIt->x)) / eps;
            dfdx.at<double>(fi+1, xi ) = 0.5 * ((double)(rIt->y - lIt->y)) / eps;
        }
    }
}

class CV_ProjectPointsTest : public cvtest::BaseTest
{
public:
    CV_ProjectPointsTest() {}
protected:
    void run(int);
    virtual void project( const Mat& objectPoints,
        const Mat& rvec, const Mat& tvec,
        const Mat& cameraMatrix,
        const Mat& distCoeffs,
        vector<Point2f>& imagePoints,
        Mat& dpdrot, Mat& dpdt, Mat& dpdf,
        Mat& dpdc, Mat& dpddist,
        double aspectRatio=0 ) = 0;
};

void CV_ProjectPointsTest::run(int)
{
    //typedef float matType;

    int code = cvtest::TS::OK;
    const int pointCount = 100;

    const float zMinVal = 10.0f, zMaxVal = 100.0f,
                rMinVal = -0.3f, rMaxVal = 0.3f,
                tMinVal = -2.0f, tMaxVal = 2.0f;

    const float imgPointErr = 1e-3f,
                dEps = 1e-3f;

    double err;

    Size imgSize( 600, 800 );
    Mat_<float> objPoints( pointCount, 3), rvec( 1, 3), rmat, tvec( 1, 3 ), cameraMatrix( 3, 3 ), distCoeffs( 1, 4 ),
      leftRvec, rightRvec, leftTvec, rightTvec, leftCameraMatrix, rightCameraMatrix, leftDistCoeffs, rightDistCoeffs;

    RNG rng = ts->get_rng();

    // generate data
    cameraMatrix << 300.f,  0.f,    imgSize.width/2.f,
                    0.f,    300.f,  imgSize.height/2.f,
                    0.f,    0.f,    1.f;
    distCoeffs << 0.1, 0.01, 0.001, 0.001;

    rvec(0,0) = rng.uniform( rMinVal, rMaxVal );
    rvec(0,1) = rng.uniform( rMinVal, rMaxVal );
    rvec(0,2) = rng.uniform( rMinVal, rMaxVal );
    rmat = cv::Mat_<float>::zeros(3, 3);
    cvtest::Rodrigues( rvec, rmat );

    tvec(0,0) = rng.uniform( tMinVal, tMaxVal );
    tvec(0,1) = rng.uniform( tMinVal, tMaxVal );
    tvec(0,2) = rng.uniform( tMinVal, tMaxVal );

    for( int y = 0; y < objPoints.rows; y++ )
    {
        Mat point(1, 3, CV_32FC1, objPoints.ptr(y) );
        float z = rng.uniform( zMinVal, zMaxVal );
        point.at<float>(0,2) = z;
        point.at<float>(0,0) = (rng.uniform(2.f,(float)(imgSize.width-2)) - cameraMatrix(0,2)) / cameraMatrix(0,0) * z;
        point.at<float>(0,1) = (rng.uniform(2.f,(float)(imgSize.height-2)) - cameraMatrix(1,2)) / cameraMatrix(1,1) * z;
        point = (point - tvec) * rmat;
    }

    vector<Point2f> imgPoints;
    vector<vector<Point2f> > leftImgPoints;
    vector<vector<Point2f> > rightImgPoints;
    Mat dpdrot, dpdt, dpdf, dpdc, dpddist,
        valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist;

    project( objPoints, rvec, tvec, cameraMatrix, distCoeffs,
        imgPoints, dpdrot, dpdt, dpdf, dpdc, dpddist, 0 );

    // calculate and check image points
    CV_Assert( (int)imgPoints.size() == pointCount );
    vector<Point2f>::const_iterator it = imgPoints.begin();
    for( int i = 0; i < pointCount; i++, ++it )
    {
        Point3d p( objPoints(i,0), objPoints(i,1), objPoints(i,2) );
        double z = p.x*rmat(2,0) + p.y*rmat(2,1) + p.z*rmat(2,2) + tvec(0,2),
               x = (p.x*rmat(0,0) + p.y*rmat(0,1) + p.z*rmat(0,2) + tvec(0,0)) / z,
               y = (p.x*rmat(1,0) + p.y*rmat(1,1) + p.z*rmat(1,2) + tvec(0,1)) / z,
               r2 = x*x + y*y,
               r4 = r2*r2;
        Point2f validImgPoint;
        double a1 = 2*x*y,
               a2 = r2 + 2*x*x,
               a3 = r2 + 2*y*y,
               cdist = 1+distCoeffs(0,0)*r2+distCoeffs(0,1)*r4;
        validImgPoint.x = static_cast<float>((double)cameraMatrix(0,0)*(x*cdist + (double)distCoeffs(0,2)*a1 + (double)distCoeffs(0,3)*a2)
            + (double)cameraMatrix(0,2));
        validImgPoint.y = static_cast<float>((double)cameraMatrix(1,1)*(y*cdist + (double)distCoeffs(0,2)*a3 + distCoeffs(0,3)*a1)
            + (double)cameraMatrix(1,2));

        if( fabs(it->x - validImgPoint.x) > imgPointErr ||
            fabs(it->y - validImgPoint.y) > imgPointErr )
        {
            ts->printf( cvtest::TS::LOG, "bad image point\n" );
            code = cvtest::TS::FAIL_BAD_ACCURACY;
            goto _exit_;
        }
    }

    // check derivatives
    // 1. rotation
    leftImgPoints.resize(3);
    rightImgPoints.resize(3);
    for( int i = 0; i < 3; i++ )
    {
        rvec.copyTo( leftRvec ); leftRvec(0,i) -= dEps;
        project( objPoints, leftRvec, tvec, cameraMatrix, distCoeffs,
            leftImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
        rvec.copyTo( rightRvec ); rightRvec(0,i) += dEps;
        project( objPoints, rightRvec, tvec, cameraMatrix, distCoeffs,
            rightImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
    }
    calcdfdx( leftImgPoints, rightImgPoints, dEps, valDpdrot );
    err = cvtest::norm( dpdrot, valDpdrot, NORM_INF );
    if( err > 3 )
    {
        ts->printf( cvtest::TS::LOG, "bad dpdrot: too big difference = %g\n", err );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
    }

    // 2. translation
    for( int i = 0; i < 3; i++ )
    {
        tvec.copyTo( leftTvec ); leftTvec(0,i) -= dEps;
        project( objPoints, rvec, leftTvec, cameraMatrix, distCoeffs,
            leftImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
        tvec.copyTo( rightTvec ); rightTvec(0,i) += dEps;
        project( objPoints, rvec, rightTvec, cameraMatrix, distCoeffs,
            rightImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
    }
    calcdfdx( leftImgPoints, rightImgPoints, dEps, valDpdt );
    if( cvtest::norm( dpdt, valDpdt, NORM_INF ) > 0.2 )
    {
        ts->printf( cvtest::TS::LOG, "bad dpdtvec\n" );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
    }

    // 3. camera matrix
    // 3.1. focus
    leftImgPoints.resize(2);
    rightImgPoints.resize(2);
    cameraMatrix.copyTo( leftCameraMatrix ); leftCameraMatrix(0,0) -= dEps;
    project( objPoints, rvec, tvec, leftCameraMatrix, distCoeffs,
        leftImgPoints[0], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
    cameraMatrix.copyTo( leftCameraMatrix ); leftCameraMatrix(1,1) -= dEps;
    project( objPoints, rvec, tvec, leftCameraMatrix, distCoeffs,
        leftImgPoints[1], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
    cameraMatrix.copyTo( rightCameraMatrix ); rightCameraMatrix(0,0) += dEps;
    project( objPoints, rvec, tvec, rightCameraMatrix, distCoeffs,
        rightImgPoints[0], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
    cameraMatrix.copyTo( rightCameraMatrix ); rightCameraMatrix(1,1) += dEps;
    project( objPoints, rvec, tvec, rightCameraMatrix, distCoeffs,
        rightImgPoints[1], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
    calcdfdx( leftImgPoints, rightImgPoints, dEps, valDpdf );
    if ( cvtest::norm( dpdf, valDpdf, NORM_L2 ) > 0.2 )
    {
        ts->printf( cvtest::TS::LOG, "bad dpdf\n" );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
    }
    // 3.2. principal point
    leftImgPoints.resize(2);
    rightImgPoints.resize(2);
    cameraMatrix.copyTo( leftCameraMatrix ); leftCameraMatrix(0,2) -= dEps;
    project( objPoints, rvec, tvec, leftCameraMatrix, distCoeffs,
        leftImgPoints[0], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
    cameraMatrix.copyTo( leftCameraMatrix ); leftCameraMatrix(1,2) -= dEps;
    project( objPoints, rvec, tvec, leftCameraMatrix, distCoeffs,
        leftImgPoints[1], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
    cameraMatrix.copyTo( rightCameraMatrix ); rightCameraMatrix(0,2) += dEps;
    project( objPoints, rvec, tvec, rightCameraMatrix, distCoeffs,
        rightImgPoints[0], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
    cameraMatrix.copyTo( rightCameraMatrix ); rightCameraMatrix(1,2) += dEps;
    project( objPoints, rvec, tvec, rightCameraMatrix, distCoeffs,
        rightImgPoints[1], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
    calcdfdx( leftImgPoints, rightImgPoints, dEps, valDpdc );
    if ( cvtest::norm( dpdc, valDpdc, NORM_L2 ) > 0.2 )
    {
        ts->printf( cvtest::TS::LOG, "bad dpdc\n" );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
    }

    // 4. distortion
    leftImgPoints.resize(distCoeffs.cols);
    rightImgPoints.resize(distCoeffs.cols);
    for( int i = 0; i < distCoeffs.cols; i++ )
    {
        distCoeffs.copyTo( leftDistCoeffs ); leftDistCoeffs(0,i) -= dEps;
        project( objPoints, rvec, tvec, cameraMatrix, leftDistCoeffs,
            leftImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
        distCoeffs.copyTo( rightDistCoeffs ); rightDistCoeffs(0,i) += dEps;
        project( objPoints, rvec, tvec, cameraMatrix, rightDistCoeffs,
            rightImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
    }
    calcdfdx( leftImgPoints, rightImgPoints, dEps, valDpddist );
    if( cvtest::norm( dpddist, valDpddist, NORM_L2 ) > 0.3 )
    {
        ts->printf( cvtest::TS::LOG, "bad dpddist\n" );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
    }

_exit_:
    RNG& _rng = ts->get_rng();
    _rng = rng;
    ts->set_failed_test_info( code );
}

//----------------------------------------- CV_ProjectPointsTest_CPP --------------------------------
class CV_ProjectPointsTest_CPP : public CV_ProjectPointsTest
{
public:
    CV_ProjectPointsTest_CPP() {}
protected:
    virtual void project( const Mat& objectPoints,
        const Mat& rvec, const Mat& tvec,
        const Mat& cameraMatrix,
        const Mat& distCoeffs,
        vector<Point2f>& imagePoints,
        Mat& dpdrot, Mat& dpdt, Mat& dpdf,
        Mat& dpdc, Mat& dpddist,
        double aspectRatio=0 );
};

void CV_ProjectPointsTest_CPP::project( const Mat& objectPoints, const Mat& rvec, const Mat& tvec,
                                       const Mat& cameraMatrix, const Mat& distCoeffs, vector<Point2f>& imagePoints,
                                       Mat& dpdrot, Mat& dpdt, Mat& dpdf, Mat& dpdc, Mat& dpddist, double aspectRatio)
{
    Mat J;
    projectPoints( objectPoints, rvec, tvec, cameraMatrix, distCoeffs, imagePoints, J, aspectRatio);
    J.colRange(0, 3).copyTo(dpdrot);
    J.colRange(3, 6).copyTo(dpdt);
    J.colRange(6, 8).copyTo(dpdf);
    J.colRange(8, 10).copyTo(dpdc);
    J.colRange(10, J.cols).copyTo(dpddist);
}

///////////////////////////////// Stereo Calibration /////////////////////////////////////

class CV_StereoCalibrationTest : public cvtest::BaseTest
{
public:
    CV_StereoCalibrationTest();
    ~CV_StereoCalibrationTest();
    void clear();
protected:
    bool checkPandROI( int test_case_idx,
        const Mat& M, const Mat& D, const Mat& R,
        const Mat& P, Size imgsize, Rect roi );

    // covers of tested functions
    virtual double calibrateStereoCamera( const vector<vector<Point3f> >& objectPoints,
        const vector<vector<Point2f> >& imagePoints1,
        const vector<vector<Point2f> >& imagePoints2,
        Mat& cameraMatrix1, Mat& distCoeffs1,
        Mat& cameraMatrix2, Mat& distCoeffs2,
        Size imageSize, Mat& R, Mat& T,
        Mat& E, Mat& F, TermCriteria criteria, int flags ) = 0;
    virtual void rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1,
        const Mat& cameraMatrix2, const Mat& distCoeffs2,
        Size imageSize, const Mat& R, const Mat& T,
        Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q,
        double alpha, Size newImageSize,
        Rect* validPixROI1, Rect* validPixROI2, int flags ) = 0;
    virtual bool rectifyUncalibrated( const Mat& points1,
        const Mat& points2, const Mat& F, Size imgSize,
        Mat& H1, Mat& H2, double threshold=5 ) = 0;
    virtual void triangulate( const Mat& P1, const Mat& P2,
        const Mat &points1, const Mat &points2,
        Mat &points4D ) = 0;
    virtual void correct( const Mat& F,
        const Mat &points1, const Mat &points2,
        Mat &newPoints1, Mat &newPoints2 ) = 0;

    void run(int);
};


CV_StereoCalibrationTest::CV_StereoCalibrationTest()
{
}


CV_StereoCalibrationTest::~CV_StereoCalibrationTest()
{
    clear();
}

void CV_StereoCalibrationTest::clear()
{
    cvtest::BaseTest::clear();
}

bool CV_StereoCalibrationTest::checkPandROI( int test_case_idx, const Mat& M, const Mat& D, const Mat& R,
                                            const Mat& P, Size imgsize, Rect roi )
{
    const double eps = 0.05;
    const int N = 21;
    int x, y, k;
    vector<Point2f> pts, upts;

    // step 1. check that all the original points belong to the destination image
    for( y = 0; y < N; y++ )
        for( x = 0; x < N; x++ )
            pts.push_back(Point2f((float)x*imgsize.width/(N-1), (float)y*imgsize.height/(N-1)));

    undistortPoints(pts, upts, M, D, R, P );
    for( k = 0; k < N*N; k++ )
        if( upts[k].x < -imgsize.width*eps || upts[k].x > imgsize.width*(1+eps) ||
            upts[k].y < -imgsize.height*eps || upts[k].y > imgsize.height*(1+eps) )
        {
            ts->printf(cvtest::TS::LOG, "Test #%d. The point (%g, %g) was mapped to (%g, %g) which is out of image\n",
                test_case_idx, pts[k].x, pts[k].y, upts[k].x, upts[k].y);
            return false;
        }

    // step 2. check that all the points inside ROI belong to the original source image
    Mat temp(imgsize, CV_8U), utemp, map1, map2;
    temp = Scalar::all(1);
    initUndistortRectifyMap(M, D, R, P, imgsize, CV_16SC2, map1, map2);
    remap(temp, utemp, map1, map2, INTER_LINEAR);

    if(roi.x < 0 || roi.y < 0 || roi.x + roi.width > imgsize.width || roi.y + roi.height > imgsize.height)
    {
            ts->printf(cvtest::TS::LOG, "Test #%d. The ROI=(%d, %d, %d, %d) is outside of the imge rectangle\n",
                            test_case_idx, roi.x, roi.y, roi.width, roi.height);
            return false;
    }
    double s = sum(utemp(roi))[0];
    if( s > roi.area() || roi.area() - s > roi.area()*(1-eps) )
    {
            ts->printf(cvtest::TS::LOG, "Test #%d. The ratio of black pixels inside the valid ROI (~%g%%) is too large\n",
                            test_case_idx, s*100./roi.area());
            return false;
    }

    return true;
}

void CV_StereoCalibrationTest::run( int )
{
    const int ntests = 1;
    const double maxReprojErr = 2;
    const double maxScanlineDistErr_c = 3;
    const double maxScanlineDistErr_uc = 4;
    FILE* f = 0;

    for(int testcase = 1; testcase <= ntests; testcase++)
    {
        cv::String filepath;
        char buf[1000];
        filepath = cv::format("%scv/stereo/case%d/stereo_calib.txt", ts->get_data_path().c_str(), testcase );
        f = fopen(filepath.c_str(), "rt");
        Size patternSize;
        vector<string> imglist;

        if( !f || !fgets(buf, sizeof(buf)-3, f) || sscanf(buf, "%d%d", &patternSize.width, &patternSize.height) != 2 )
        {
            ts->printf( cvtest::TS::LOG, "The file %s can not be opened or has invalid content\n", filepath.c_str() );
            ts->set_failed_test_info( f ? cvtest::TS::FAIL_INVALID_TEST_DATA : cvtest::TS::FAIL_MISSING_TEST_DATA );
            if (f)
                fclose(f);
            return;
        }

        for(;;)
        {
            if( !fgets( buf, sizeof(buf)-3, f ))
                break;
            size_t len = strlen(buf);
            while( len > 0 && isspace(buf[len-1]))
                buf[--len] = '\0';
            if( buf[0] == '#')
                continue;
            filepath = cv::format("%scv/stereo/case%d/%s", ts->get_data_path().c_str(), testcase, buf );
            imglist.push_back(string(filepath));
        }
        fclose(f);

        if( imglist.size() == 0 || imglist.size() % 2 != 0 )
        {
            ts->printf( cvtest::TS::LOG, "The number of images is 0 or an odd number in the case #%d\n", testcase );
            ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
            return;
        }

        int nframes = (int)(imglist.size()/2);
        int npoints = patternSize.width*patternSize.height;
        vector<vector<Point3f> > objpt(nframes);
        vector<vector<Point2f> > imgpt1(nframes);
        vector<vector<Point2f> > imgpt2(nframes);
        Size imgsize;
        int total = 0;

        for( int i = 0; i < nframes; i++ )
        {
            Mat left = imread(imglist[i*2]);
            Mat right = imread(imglist[i*2+1]);
            if(left.empty() || right.empty())
            {
                ts->printf( cvtest::TS::LOG, "Can not load images %s and %s, testcase %d\n",
                    imglist[i*2].c_str(), imglist[i*2+1].c_str(), testcase );
                ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
                return;
            }
            imgsize = left.size();
            bool found1 = findChessboardCorners(left, patternSize, imgpt1[i]);
            bool found2 = findChessboardCorners(right, patternSize, imgpt2[i]);
            if(!found1 || !found2)
            {
                ts->printf( cvtest::TS::LOG, "The function could not detect boards (%d x %d) on the images %s and %s, testcase %d\n",
                    patternSize.width, patternSize.height,
                    imglist[i*2].c_str(), imglist[i*2+1].c_str(), testcase );
                ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
                return;
            }
            total += (int)imgpt1[i].size();
            for( int j = 0; j < npoints; j++ )
                objpt[i].push_back(Point3f((float)(j%patternSize.width), (float)(j/patternSize.width), 0.f));
        }

        // rectify (calibrated)
        Mat M1 = Mat::eye(3,3,CV_64F), M2 = Mat::eye(3,3,CV_64F), D1(5,1,CV_64F), D2(5,1,CV_64F), R, T, E, F;
        M1.at<double>(0,2) = M2.at<double>(0,2)=(imgsize.width-1)*0.5;
        M1.at<double>(1,2) = M2.at<double>(1,2)=(imgsize.height-1)*0.5;
        D1 = Scalar::all(0);
        D2 = Scalar::all(0);
        double err = calibrateStereoCamera(objpt, imgpt1, imgpt2, M1, D1, M2, D2, imgsize, R, T, E, F,
            TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 1e-6),
            CV_CALIB_SAME_FOCAL_LENGTH
            //+ CV_CALIB_FIX_ASPECT_RATIO
            + CV_CALIB_FIX_PRINCIPAL_POINT
            + CV_CALIB_ZERO_TANGENT_DIST
            + CV_CALIB_FIX_K3
            + CV_CALIB_FIX_K4 + CV_CALIB_FIX_K5 //+ CV_CALIB_FIX_K6
            );
        err /= nframes*npoints;
        if( err > maxReprojErr )
        {
            ts->printf( cvtest::TS::LOG, "The average reprojection error is too big (=%g), testcase %d\n", err, testcase);
            ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
            return;
        }

        Mat R1, R2, P1, P2, Q;
        Rect roi1, roi2;
        rectify(M1, D1, M2, D2, imgsize, R, T, R1, R2, P1, P2, Q, 1, imgsize, &roi1, &roi2, 0);
        Mat eye33 = Mat::eye(3,3,CV_64F);
        Mat R1t = R1.t(), R2t = R2.t();

        if( cvtest::norm(R1t*R1 - eye33, NORM_L2) > 0.01 ||
            cvtest::norm(R2t*R2 - eye33, NORM_L2) > 0.01 ||
            abs(determinant(F)) > 0.01)
        {
            ts->printf( cvtest::TS::LOG, "The computed (by rectify) R1 and R2 are not orthogonal,"
                "or the computed (by calibrate) F is not singular, testcase %d\n", testcase);
            ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
            return;
        }

        if(!checkPandROI(testcase, M1, D1, R1, P1, imgsize, roi1))
        {
            ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
            return;
        }

        if(!checkPandROI(testcase, M2, D2, R2, P2, imgsize, roi2))
        {
            ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
            return;
        }

        //check that Tx after rectification is equal to distance between cameras
        double tx = fabs(P2.at<double>(0, 3) / P2.at<double>(0, 0));
        if (fabs(tx - cvtest::norm(T, NORM_L2)) > 1e-5)
        {
            ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
            return;
        }

        //check that Q reprojects points before the camera
        double testPoint[4] = {0.0, 0.0, 100.0, 1.0};
        Mat reprojectedTestPoint = Q * Mat_<double>(4, 1, testPoint);
        CV_Assert(reprojectedTestPoint.type() == CV_64FC1);
        if( reprojectedTestPoint.at<double>(2) / reprojectedTestPoint.at<double>(3) < 0 )
        {
            ts->printf( cvtest::TS::LOG, "A point after rectification is reprojected behind the camera, testcase %d\n", testcase);
            ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
        }

        //check that Q reprojects the same points as reconstructed by triangulation
        const float minCoord = -300.0f;
        const float maxCoord = 300.0f;
        const float minDisparity = 0.1f;
        const float maxDisparity = 60.0f;
        const int pointsCount = 500;
        const float requiredAccuracy = 1e-3f;
        const float allowToFail = 0.2f; // 20%
        RNG& rng = ts->get_rng();

        Mat projectedPoints_1(2, pointsCount, CV_32FC1);
        Mat projectedPoints_2(2, pointsCount, CV_32FC1);
        Mat disparities(1, pointsCount, CV_32FC1);

        rng.fill(projectedPoints_1, RNG::UNIFORM, minCoord, maxCoord);
        rng.fill(disparities, RNG::UNIFORM, minDisparity, maxDisparity);
        projectedPoints_2.row(0) = projectedPoints_1.row(0) - disparities;
        Mat ys_2 = projectedPoints_2.row(1);
        projectedPoints_1.row(1).copyTo(ys_2);

        Mat points4d;
        triangulate(P1, P2, projectedPoints_1, projectedPoints_2, points4d);
        Mat homogeneousPoints4d = points4d.t();
        const int dimension = 4;
        homogeneousPoints4d = homogeneousPoints4d.reshape(dimension);
        Mat triangulatedPoints;
        convertPointsFromHomogeneous(homogeneousPoints4d, triangulatedPoints);

        Mat sparsePoints;
        sparsePoints.push_back(projectedPoints_1);
        sparsePoints.push_back(disparities);
        sparsePoints = sparsePoints.t();
        sparsePoints = sparsePoints.reshape(3);
        Mat reprojectedPoints;
        perspectiveTransform(sparsePoints, reprojectedPoints, Q);

        Mat diff;
        absdiff(triangulatedPoints, reprojectedPoints, diff);
        Mat mask = diff > requiredAccuracy;
        mask = mask.reshape(1);
        mask = mask.col(0) | mask.col(1) | mask.col(2);
        int numFailed = countNonZero(mask);
#if 0
        std::cout << "numFailed=" << numFailed << std::endl;
        for (int i = 0; i < triangulatedPoints.rows; i++)
        {
            if (mask.at<uchar>(i))
            {
                // failed points usually have 'w'~0 (points4d[3])
                std::cout << "i=" << i << " triangulatePoints=" << triangulatedPoints.row(i) << " reprojectedPoints=" << reprojectedPoints.row(i) << std::endl <<
                    "     points4d=" << points4d.col(i).t() << " projectedPoints_1=" << projectedPoints_1.col(i).t() << " disparities=" << disparities.col(i).t() << std::endl;
            }
        }
#endif

        if (numFailed >= allowToFail * pointsCount)
        {
            ts->printf( cvtest::TS::LOG, "Points reprojected with a matrix Q and points reconstructed by triangulation are different (tolerance=%g, failed=%d), testcase %d\n",
                requiredAccuracy, numFailed, testcase);
            ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
        }

        //check correctMatches
        const float constraintAccuracy = 1e-5f;
        Mat newPoints1, newPoints2;
        Mat points1 = projectedPoints_1.t();
        points1 = points1.reshape(2, 1);
        Mat points2 = projectedPoints_2.t();
        points2 = points2.reshape(2, 1);
        correctMatches(F, points1, points2, newPoints1, newPoints2);
        Mat newHomogeneousPoints1, newHomogeneousPoints2;
        convertPointsToHomogeneous(newPoints1, newHomogeneousPoints1);
        convertPointsToHomogeneous(newPoints2, newHomogeneousPoints2);
        newHomogeneousPoints1 = newHomogeneousPoints1.reshape(1);
        newHomogeneousPoints2 = newHomogeneousPoints2.reshape(1);
        Mat typedF;
        F.convertTo(typedF, newHomogeneousPoints1.type());
        for (int i = 0; i < newHomogeneousPoints1.rows; ++i)
        {
            Mat error = newHomogeneousPoints2.row(i) * typedF * newHomogeneousPoints1.row(i).t();
            CV_Assert(error.rows == 1 && error.cols == 1);
            if (cvtest::norm(error, NORM_L2) > constraintAccuracy)
            {
                ts->printf( cvtest::TS::LOG, "Epipolar constraint is violated after correctMatches, testcase %d\n", testcase);
                ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
            }
        }

        // rectifyUncalibrated
        CV_Assert( imgpt1.size() == imgpt2.size() );
        Mat _imgpt1( total, 1, CV_32FC2 ), _imgpt2( total, 1, CV_32FC2 );
        vector<vector<Point2f> >::const_iterator iit1 = imgpt1.begin();
        vector<vector<Point2f> >::const_iterator iit2 = imgpt2.begin();
        for( int pi = 0; iit1 != imgpt1.end(); ++iit1, ++iit2 )
        {
            vector<Point2f>::const_iterator pit1 = iit1->begin();
            vector<Point2f>::const_iterator pit2 = iit2->begin();
            CV_Assert( iit1->size() == iit2->size() );
            for( ; pit1 != iit1->end(); ++pit1, ++pit2, pi++ )
            {
                _imgpt1.at<Point2f>(pi,0) = Point2f( pit1->x, pit1->y );
                _imgpt2.at<Point2f>(pi,0) = Point2f( pit2->x, pit2->y );
            }
        }

        Mat _M1, _M2, _D1, _D2;
        vector<Mat> _R1, _R2, _T1, _T2;
        calibrateCamera( objpt, imgpt1, imgsize, _M1, _D1, _R1, _T1, 0 );
        calibrateCamera( objpt, imgpt2, imgsize, _M2, _D2, _R2, _T2, 0 );
        undistortPoints( _imgpt1, _imgpt1, _M1, _D1, Mat(), _M1 );
        undistortPoints( _imgpt2, _imgpt2, _M2, _D2, Mat(), _M2 );

        Mat matF, _H1, _H2;
        matF = findFundamentalMat( _imgpt1, _imgpt2 );
        rectifyUncalibrated( _imgpt1, _imgpt2, matF, imgsize, _H1, _H2 );

        Mat rectifPoints1, rectifPoints2;
        perspectiveTransform( _imgpt1, rectifPoints1, _H1 );
        perspectiveTransform( _imgpt2, rectifPoints2, _H2 );

        bool verticalStereo = abs(P2.at<double>(0,3)) < abs(P2.at<double>(1,3));
        double maxDiff_c = 0, maxDiff_uc = 0;
        for( int i = 0, k = 0; i < nframes; i++ )
        {
            vector<Point2f> temp[2];
            undistortPoints(imgpt1[i], temp[0], M1, D1, R1, P1);
            undistortPoints(imgpt2[i], temp[1], M2, D2, R2, P2);

            for( int j = 0; j < npoints; j++, k++ )
            {
                double diff_c = verticalStereo ? abs(temp[0][j].x - temp[1][j].x) : abs(temp[0][j].y - temp[1][j].y);
                Point2f d = rectifPoints1.at<Point2f>(k,0) - rectifPoints2.at<Point2f>(k,0);
                double diff_uc = verticalStereo ? abs(d.x) : abs(d.y);
                maxDiff_c = max(maxDiff_c, diff_c);
                maxDiff_uc = max(maxDiff_uc, diff_uc);
                if( maxDiff_c > maxScanlineDistErr_c )
                {
                    ts->printf( cvtest::TS::LOG, "The distance between %s coordinates is too big(=%g) (used calibrated stereo), testcase %d\n",
                        verticalStereo ? "x" : "y", diff_c, testcase);
                    ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
                    return;
                }
                if( maxDiff_uc > maxScanlineDistErr_uc )
                {
                    ts->printf( cvtest::TS::LOG, "The distance between %s coordinates is too big(=%g) (used uncalibrated stereo), testcase %d\n",
                        verticalStereo ? "x" : "y", diff_uc, testcase);
                    ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
                    return;
                }
            }
        }

        ts->printf( cvtest::TS::LOG, "Testcase %d. Max distance (calibrated) =%g\n"
            "Max distance (uncalibrated) =%g\n", testcase, maxDiff_c, maxDiff_uc );
    }
}

//-------------------------------- CV_StereoCalibrationTest_CPP ------------------------------

class CV_StereoCalibrationTest_CPP : public CV_StereoCalibrationTest
{
public:
    CV_StereoCalibrationTest_CPP() {}
protected:
    virtual double calibrateStereoCamera( const vector<vector<Point3f> >& objectPoints,
        const vector<vector<Point2f> >& imagePoints1,
        const vector<vector<Point2f> >& imagePoints2,
        Mat& cameraMatrix1, Mat& distCoeffs1,
        Mat& cameraMatrix2, Mat& distCoeffs2,
        Size imageSize, Mat& R, Mat& T,
        Mat& E, Mat& F, TermCriteria criteria, int flags );
    virtual void rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1,
        const Mat& cameraMatrix2, const Mat& distCoeffs2,
        Size imageSize, const Mat& R, const Mat& T,
        Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q,
        double alpha, Size newImageSize,
        Rect* validPixROI1, Rect* validPixROI2, int flags );
    virtual bool rectifyUncalibrated( const Mat& points1,
        const Mat& points2, const Mat& F, Size imgSize,
        Mat& H1, Mat& H2, double threshold=5 );
    virtual void triangulate( const Mat& P1, const Mat& P2,
        const Mat &points1, const Mat &points2,
        Mat &points4D );
    virtual void correct( const Mat& F,
        const Mat &points1, const Mat &points2,
        Mat &newPoints1, Mat &newPoints2 );
};

double CV_StereoCalibrationTest_CPP::calibrateStereoCamera( const vector<vector<Point3f> >& objectPoints,
                                             const vector<vector<Point2f> >& imagePoints1,
                                             const vector<vector<Point2f> >& imagePoints2,
                                             Mat& cameraMatrix1, Mat& distCoeffs1,
                                             Mat& cameraMatrix2, Mat& distCoeffs2,
                                             Size imageSize, Mat& R, Mat& T,
                                             Mat& E, Mat& F, TermCriteria criteria, int flags )
{
    return stereoCalibrate( objectPoints, imagePoints1, imagePoints2,
                    cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2,
                    imageSize, R, T, E, F, flags, criteria );
}

void CV_StereoCalibrationTest_CPP::rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1,
                                         const Mat& cameraMatrix2, const Mat& distCoeffs2,
                                         Size imageSize, const Mat& R, const Mat& T,
                                         Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q,
                                         double alpha, Size newImageSize,
                                         Rect* validPixROI1, Rect* validPixROI2, int flags )
{
    stereoRectify( cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2,
                imageSize, R, T, R1, R2, P1, P2, Q, flags, alpha, newImageSize,validPixROI1, validPixROI2 );
}

bool CV_StereoCalibrationTest_CPP::rectifyUncalibrated( const Mat& points1,
                       const Mat& points2, const Mat& F, Size imgSize, Mat& H1, Mat& H2, double threshold )
{
    return stereoRectifyUncalibrated( points1, points2, F, imgSize, H1, H2, threshold );
}

void CV_StereoCalibrationTest_CPP::triangulate( const Mat& P1, const Mat& P2,
        const Mat &points1, const Mat &points2,
        Mat &points4D )
{
    triangulatePoints(P1, P2, points1, points2, points4D);
}

void CV_StereoCalibrationTest_CPP::correct( const Mat& F,
        const Mat &points1, const Mat &points2,
        Mat &newPoints1, Mat &newPoints2 )
{
    correctMatches(F, points1, points2, newPoints1, newPoints2);
}

///////////////////////////////////////////////////////////////////////////////////////////////////

TEST(Calib3d_CalibrateCamera_CPP, regression) { CV_CameraCalibrationTest_CPP test; test.safe_run(); }
TEST(Calib3d_CalibrationMatrixValues_CPP, accuracy) { CV_CalibrationMatrixValuesTest_CPP test; test.safe_run(); }
TEST(Calib3d_ProjectPoints_CPP, regression) { CV_ProjectPointsTest_CPP test; test.safe_run(); }

TEST(Calib3d_ProjectPoints_CPP, inputShape)
{
    Matx31d rvec = Matx31d::zeros();
    Matx31d tvec(0, 0, 1);
    Matx33d cameraMatrix = Matx33d::eye();
    const float L = 0.1f;
    {
        //3xN 1-channel
        Mat objectPoints = (Mat_<float>(3, 2) << -L,  L,
                                                  L,  L,
                                                  0,  0);
        vector<Point2f> imagePoints;
        projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
        EXPECT_EQ(objectPoints.cols, static_cast<int>(imagePoints.size()));
        EXPECT_NEAR(imagePoints[0].x, -L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints[0].y,  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints[1].x,  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints[1].y,  L, std::numeric_limits<float>::epsilon());
    }
    {
        //Nx2 1-channel
        Mat objectPoints = (Mat_<float>(2, 3) << -L,  L, 0,
                                                  L,  L, 0);
        vector<Point2f> imagePoints;
        projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
        EXPECT_EQ(objectPoints.rows, static_cast<int>(imagePoints.size()));
        EXPECT_NEAR(imagePoints[0].x, -L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints[0].y,  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints[1].x,  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints[1].y,  L, std::numeric_limits<float>::epsilon());
    }
    {
        //1xN 3-channel
        Mat objectPoints(1, 2, CV_32FC3);
        objectPoints.at<Vec3f>(0,0) = Vec3f(-L, L, 0);
        objectPoints.at<Vec3f>(0,1) = Vec3f(L, L, 0);

        vector<Point2f> imagePoints;
        projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
        EXPECT_EQ(objectPoints.cols, static_cast<int>(imagePoints.size()));
        EXPECT_NEAR(imagePoints[0].x, -L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints[0].y,  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints[1].x,  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints[1].y,  L, std::numeric_limits<float>::epsilon());
    }
    {
        //Nx1 3-channel
        Mat objectPoints(2, 1, CV_32FC3);
        objectPoints.at<Vec3f>(0,0) = Vec3f(-L, L, 0);
        objectPoints.at<Vec3f>(1,0) = Vec3f(L, L, 0);

        vector<Point2f> imagePoints;
        projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
        EXPECT_EQ(objectPoints.rows, static_cast<int>(imagePoints.size()));
        EXPECT_NEAR(imagePoints[0].x, -L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints[0].y,  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints[1].x,  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints[1].y,  L, std::numeric_limits<float>::epsilon());
    }
    {
        //vector<Point3f>
        vector<Point3f> objectPoints;
        objectPoints.push_back(Point3f(-L, L, 0));
        objectPoints.push_back(Point3f(L, L, 0));

        vector<Point2f> imagePoints;
        projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
        EXPECT_EQ(objectPoints.size(), imagePoints.size());
        EXPECT_NEAR(imagePoints[0].x, -L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints[0].y,  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints[1].x,  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints[1].y,  L, std::numeric_limits<float>::epsilon());
    }
    {
        //vector<Point3d>
        vector<Point3d> objectPoints;
        objectPoints.push_back(Point3d(-L, L, 0));
        objectPoints.push_back(Point3d(L, L, 0));

        vector<Point2d> imagePoints;
        projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
        EXPECT_EQ(objectPoints.size(), imagePoints.size());
        EXPECT_NEAR(imagePoints[0].x, -L, std::numeric_limits<double>::epsilon());
        EXPECT_NEAR(imagePoints[0].y,  L, std::numeric_limits<double>::epsilon());
        EXPECT_NEAR(imagePoints[1].x,  L, std::numeric_limits<double>::epsilon());
        EXPECT_NEAR(imagePoints[1].y,  L, std::numeric_limits<double>::epsilon());
    }
}

TEST(Calib3d_ProjectPoints_CPP, outputShape)
{
    Matx31d rvec = Matx31d::zeros();
    Matx31d tvec(0, 0, 1);
    Matx33d cameraMatrix = Matx33d::eye();
    const float L = 0.1f;
    {
        vector<Point3f> objectPoints;
        objectPoints.push_back(Point3f(-L,  L, 0));
        objectPoints.push_back(Point3f( L,  L, 0));
        objectPoints.push_back(Point3f( L, -L, 0));

        //Mat --> will be Nx1 2-channel
        Mat imagePoints;
        projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
        EXPECT_EQ(static_cast<int>(objectPoints.size()), imagePoints.rows);
        EXPECT_NEAR(imagePoints.at<Vec2f>(0,0)(0), -L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints.at<Vec2f>(0,0)(1),  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints.at<Vec2f>(1,0)(0),  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints.at<Vec2f>(1,0)(1),  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints.at<Vec2f>(2,0)(0),  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints.at<Vec2f>(2,0)(1), -L, std::numeric_limits<float>::epsilon());
    }
    {
        vector<Point3f> objectPoints;
        objectPoints.push_back(Point3f(-L,  L, 0));
        objectPoints.push_back(Point3f( L,  L, 0));
        objectPoints.push_back(Point3f( L, -L, 0));

        //Nx1 2-channel
        Mat imagePoints(3,1,CV_32FC2);
        projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
        EXPECT_EQ(static_cast<int>(objectPoints.size()), imagePoints.rows);
        EXPECT_NEAR(imagePoints.at<Vec2f>(0,0)(0), -L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints.at<Vec2f>(0,0)(1),  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints.at<Vec2f>(1,0)(0),  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints.at<Vec2f>(1,0)(1),  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints.at<Vec2f>(2,0)(0),  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints.at<Vec2f>(2,0)(1), -L, std::numeric_limits<float>::epsilon());
    }
    {
        vector<Point3f> objectPoints;
        objectPoints.push_back(Point3f(-L,  L, 0));
        objectPoints.push_back(Point3f( L,  L, 0));
        objectPoints.push_back(Point3f( L, -L, 0));

        //1xN 2-channel
        Mat imagePoints(1,3,CV_32FC2);
        projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
        EXPECT_EQ(static_cast<int>(objectPoints.size()), imagePoints.cols);
        EXPECT_NEAR(imagePoints.at<Vec2f>(0,0)(0), -L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints.at<Vec2f>(0,0)(1),  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints.at<Vec2f>(0,1)(0),  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints.at<Vec2f>(0,1)(1),  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints.at<Vec2f>(0,2)(0),  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints.at<Vec2f>(0,2)(1), -L, std::numeric_limits<float>::epsilon());
    }
    {
        vector<Point3f> objectPoints;
        objectPoints.push_back(Point3f(-L, L, 0));
        objectPoints.push_back(Point3f(L, L, 0));

        //vector<Point2f>
        vector<Point2f> imagePoints;
        projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
        EXPECT_EQ(objectPoints.size(), imagePoints.size());
        EXPECT_NEAR(imagePoints[0].x, -L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints[0].y,  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints[1].x,  L, std::numeric_limits<float>::epsilon());
        EXPECT_NEAR(imagePoints[1].y,  L, std::numeric_limits<float>::epsilon());
    }
    {
        vector<Point3d> objectPoints;
        objectPoints.push_back(Point3d(-L, L, 0));
        objectPoints.push_back(Point3d(L, L, 0));

        //vector<Point2d>
        vector<Point2d> imagePoints;
        projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
        EXPECT_EQ(objectPoints.size(), imagePoints.size());
        EXPECT_NEAR(imagePoints[0].x, -L, std::numeric_limits<double>::epsilon());
        EXPECT_NEAR(imagePoints[0].y,  L, std::numeric_limits<double>::epsilon());
        EXPECT_NEAR(imagePoints[1].x,  L, std::numeric_limits<double>::epsilon());
        EXPECT_NEAR(imagePoints[1].y,  L, std::numeric_limits<double>::epsilon());
    }
}

TEST(Calib3d_StereoCalibrate_CPP, regression) { CV_StereoCalibrationTest_CPP test; test.safe_run(); }


TEST(Calib3d_StereoCalibrate_CPP, extended)
{
    cvtest::TS* ts = cvtest::TS::ptr();
    String filepath = cv::format("%scv/stereo/case%d/", ts->get_data_path().c_str(), 1 );

    Mat left = imread(filepath+"left01.png");
    Mat right = imread(filepath+"right01.png");
    if(left.empty() || right.empty())
    {
        ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
        return;
    }

    vector<vector<Point2f> > imgpt1(1), imgpt2(1);
    vector<vector<Point3f> > objpt(1);
    Size patternSize(9, 6), imageSize(640, 480);
    bool found1 = findChessboardCorners(left, patternSize, imgpt1[0]);
    bool found2 = findChessboardCorners(right, patternSize, imgpt2[0]);

    if(!found1 || !found2)
    {
        ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
        return;
    }

    for( int j = 0; j < patternSize.width*patternSize.height; j++ )
        objpt[0].push_back(Point3f((float)(j%patternSize.width), (float)(j/patternSize.width), 0.f));

    Mat K1, K2, c1, c2, R, T, E, F, err;
    int flags = 0;
    double res0 = stereoCalibrate( objpt, imgpt1, imgpt2,
                    K1, c1, K2, c2,
                    imageSize, R, T, E, F, err, flags);

    flags = CALIB_USE_EXTRINSIC_GUESS;
    double res1 = stereoCalibrate( objpt, imgpt1, imgpt2,
                    K1, c1, K2, c2,
                    imageSize, R, T, E, F, err, flags);
    EXPECT_LE(res1, res0);
    EXPECT_TRUE(err.total() == 2);
}

TEST(Calib3d_StereoCalibrate, regression_10791)
{
    const Matx33d M1(
        853.1387981631528, 0, 704.154907802121,
        0, 853.6445089162528, 520.3600712930319,
        0, 0, 1
    );
    const Matx33d M2(
        848.6090216909176, 0, 701.6162856852185,
        0, 849.7040162357157, 509.1864036137,
        0, 0, 1
    );
    const Matx<double, 14, 1> D1(-6.463598629567206, 79.00104930508179, -0.0001006144444464403, -0.0005437499822299972,
        12.56900616588467, -6.056719942752855, 76.3842481414836, 45.57460250612659,
        0, 0, 0, 0, 0, 0);
    const Matx<double, 14, 1> D2(0.6123436439798265, -0.4671756923224087, -0.0001261947899033442, -0.000597334584036978,
        -0.05660119809538371, 1.037075740629769, -0.3076042835831711, -0.2502169324283623,
        0, 0, 0, 0, 0, 0);

    const Matx33d R(
        0.9999926627018476, -0.0001095586963765905, 0.003829169539302921,
        0.0001021735876758584, 0.9999981346680941, 0.0019287874145156,
        -0.003829373712065528, -0.001928382022437616, 0.9999908085776333
    );
    const Matx31d T(-58.9161771697128, -0.01581306249996402, -0.8492960216760961);

    const Size imageSize(1280, 960);

    Mat R1, R2, P1, P2, Q;
    Rect roi1, roi2;
    stereoRectify(M1, D1, M2, D2, imageSize, R, T,
                  R1, R2, P1, P2, Q,
                  CALIB_ZERO_DISPARITY, 1, imageSize, &roi1, &roi2);

    EXPECT_GE(roi1.area(), 400*300) << roi1;
    EXPECT_GE(roi2.area(), 400*300) << roi2;
}

TEST(Calib3d_StereoCalibrate, regression_11131)
{
    const Matx33d M1(
        1457.572438721727, 0, 1212.945694211622,
        0, 1457.522226502963, 1007.32058848921,
        0, 0, 1
    );
    const Matx33d M2(
        1460.868570835972, 0, 1215.024068023046,
        0, 1460.791367088, 1011.107202932225,
        0, 0, 1
    );
    const Matx<double, 5, 1> D1(0, 0, 0, 0, 0);
    const Matx<double, 5, 1> D2(0, 0, 0, 0, 0);

    const Matx33d R(
        0.9985404059825475, 0.02963547172078553, -0.04515303352041626,
        -0.03103795276460111, 0.9990471552537432, -0.03068268351343364,
        0.04420071389006859, 0.03203935697372317, 0.9985087763742083
    );
    const Matx31d T(0.9995500167379527, 0.0116311595111068, 0.02764923448462666);

    const Size imageSize(2456, 2058);

    Mat R1, R2, P1, P2, Q;
    Rect roi1, roi2;
    stereoRectify(M1, D1, M2, D2, imageSize, R, T,
                  R1, R2, P1, P2, Q,
                  CALIB_ZERO_DISPARITY, 1, imageSize, &roi1, &roi2);

    EXPECT_GT(P1.at<double>(0, 0), 0);
    EXPECT_GT(P2.at<double>(0, 0), 0);
    EXPECT_GT(R1.at<double>(0, 0), 0);
    EXPECT_GT(R2.at<double>(0, 0), 0);
    EXPECT_GE(roi1.area(), 400*300) << roi1;
    EXPECT_GE(roi2.area(), 400*300) << roi2;
}

TEST(Calib3d_Triangulate, accuracy)
{
    // the testcase from http://code.opencv.org/issues/4334
    {
    double P1data[] = { 250, 0, 200, 0, 0, 250, 150, 0, 0, 0, 1, 0 };
    double P2data[] = { 250, 0, 200, -250, 0, 250, 150, 0, 0, 0, 1, 0 };
    Mat P1(3, 4, CV_64F, P1data), P2(3, 4, CV_64F, P2data);

    float x1data[] = { 200.f, 0.f };
    float x2data[] = { 170.f, 1.f };
    float Xdata[] = { 0.f, -5.f, 25/3.f };
    Mat x1(2, 1, CV_32F, x1data);
    Mat x2(2, 1, CV_32F, x2data);
    Mat res0(1, 3, CV_32F, Xdata);
    Mat res_, res;

    triangulatePoints(P1, P2, x1, x2, res_);
    cv::transpose(res_, res_); // TODO cvtest (transpose doesn't support inplace)
    convertPointsFromHomogeneous(res_, res);
    res = res.reshape(1, 1);

    cout << "[1]:" << endl;
    cout << "\tres0: " << res0 << endl;
    cout << "\tres: " << res << endl;

    ASSERT_LE(cvtest::norm(res, res0, NORM_INF), 1e-1);
    }

    // another testcase http://code.opencv.org/issues/3461
    {
    Matx33d K1(6137.147949, 0.000000, 644.974609,
               0.000000, 6137.147949, 573.442749,
               0.000000, 0.000000,  1.000000);
    Matx33d K2(6137.147949,  0.000000, 644.674438,
               0.000000, 6137.147949, 573.079834,
               0.000000,  0.000000, 1.000000);

    Matx34d RT1(1, 0, 0, 0,
                0, 1, 0, 0,
                0, 0, 1, 0);

    Matx34d RT2(0.998297, 0.0064108, -0.0579766,     143.614334,
               -0.0065818, 0.999975, -0.00275888,   -5.160085,
               0.0579574, 0.00313577, 0.998314,     96.066109);

    Matx34d P1 = K1*RT1;
    Matx34d P2 = K2*RT2;

    float x1data[] = { 438.f, 19.f };
    float x2data[] = { 452.363600f, 16.452225f };
    float Xdata[] = { -81.049530f, -215.702804f, 2401.645449f };
    Mat x1(2, 1, CV_32F, x1data);
    Mat x2(2, 1, CV_32F, x2data);
    Mat res0(1, 3, CV_32F, Xdata);
    Mat res_, res;

    triangulatePoints(P1, P2, x1, x2, res_);
    cv::transpose(res_, res_); // TODO cvtest (transpose doesn't support inplace)
    convertPointsFromHomogeneous(res_, res);
    res = res.reshape(1, 1);

    cout << "[2]:" << endl;
    cout << "\tres0: " << res0 << endl;
    cout << "\tres: " << res << endl;

    ASSERT_LE(cvtest::norm(res, res0, NORM_INF), 2);
    }
}

///////////////////////////////////////////////////////////////////////////////////////////////////

TEST(CV_RecoverPoseTest, regression_15341)
{
    // initialize test data
    const int invalid_point_count = 2;
    const float _points1_[] = {
        1537.7f, 166.8f,
        1599.1f, 179.6f,
        1288.0f, 207.5f,
        1507.1f, 193.2f,
        1742.7f, 210.0f,
        1041.6f, 271.7f,
        1591.8f, 247.2f,
        1524.0f, 261.3f,
        1330.3f, 285.0f,
        1403.1f, 284.0f,
        1506.6f, 342.9f,
        1502.8f, 347.3f,
        1344.9f, 364.9f,
        0.0f, 0.0f  // last point is initial invalid
    };

    const float _points2_[] = {
        1533.4f, 532.9f,
        1596.6f, 552.4f,
        1277.0f, 556.4f,
        1502.1f, 557.6f,
        1744.4f, 601.3f,
        1023.0f, 612.6f,
        1589.2f, 621.6f,
        1519.4f, 629.0f,
        1320.3f, 637.3f,
        1395.2f, 642.2f,
        1501.5f, 710.3f,
        1497.6f, 714.2f,
        1335.1f, 719.61f,
        1000.0f, 1000.0f  // last point is initial invalid
    };

    vector<Point2f> _points1; Mat(14, 1, CV_32FC2, (void*)_points1_).copyTo(_points1);
    vector<Point2f> _points2; Mat(14, 1, CV_32FC2, (void*)_points2_).copyTo(_points2);

    const int point_count = (int) _points1.size();
    CV_Assert(point_count == (int) _points2.size());

    // camera matrix with both focal lengths = 1, and principal point = (0, 0)
    const Mat cameraMatrix = Mat::eye(3, 3, CV_64F);

    // camera matrix with focal lengths 0.5 and 0.6 respectively and principal point = (100, 200)
    double cameraMatrix2Data[] = { 0.5, 0, 100,
                                   0, 0.6, 200,
                                   0, 0, 1 };
    const Mat cameraMatrix2( 3, 3, CV_64F, cameraMatrix2Data );

    // zero and nonzero distortion coefficients
    double nonZeroDistCoeffsData[] = { 0.01, 0.0001, 0, 0, 1e-04, 0.2, 0.02, 0.0002 }; // k1, k2, p1, p2, k3, k4, k5, k6
    vector<Mat> distCoeffsList = {Mat::zeros(1, 5, CV_64F), Mat{1, 8, CV_64F, nonZeroDistCoeffsData}};
    const auto &zeroDistCoeffs = distCoeffsList[0];

    int Inliers = 0;

    const int ntests = 3;
    for (int testcase = 1; testcase <= ntests; ++testcase)
    {
        if (testcase == 1) // testcase with vector input data
        {
            // init temporary test data
            vector<unsigned char> mask(point_count);
            vector<Point2f> points1(_points1);
            vector<Point2f> points2(_points2);

            // Estimation of fundamental matrix using the RANSAC algorithm
            Mat E, E2, R, t;

            // Check pose when camera matrices are different.
            for (const auto &distCoeffs: distCoeffsList)
            {
                E = findEssentialMat(points1, points2, cameraMatrix, distCoeffs, cameraMatrix2, distCoeffs, RANSAC, 0.999, 1.0, mask);
                recoverPose(points1, points2, cameraMatrix, distCoeffs, cameraMatrix2, distCoeffs, E2, R, t, RANSAC, 0.999, 1.0, mask);
                EXPECT_LT(cv::norm(E, E2, NORM_INF), 1e-4) <<
                    "Two big difference between the same essential matrices computed using different functions with different cameras, testcase " << testcase;
                EXPECT_EQ(0, (int)mask[13]) << "Detecting outliers in function failed with different cameras, testcase " << testcase;
            }

            // Check pose when camera matrices are the same.
            E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
            E2 = findEssentialMat(points1, points2, cameraMatrix, zeroDistCoeffs, cameraMatrix, zeroDistCoeffs, RANSAC, 0.999, 1.0, mask);
            EXPECT_LT(cv::norm(E, E2, NORM_INF), 1e-4) <<
                "Two big difference between the same essential matrices computed using different functions with same cameras, testcase " << testcase;
            EXPECT_EQ(0, (int)mask[13]) << "Detecting outliers in function findEssentialMat failed with same cameras, testcase " << testcase;
            points2[12] = Point2f(0.0f, 0.0f); // provoke another outlier detection for recover Pose
            Inliers = recoverPose(E, points1, points2, cameraMatrix, R, t, mask);
            EXPECT_EQ(0, (int)mask[12]) << "Detecting outliers in function failed with same cameras, testcase " << testcase;
        }
        else // testcase with mat input data
        {
            Mat points1(_points1, true);
            Mat points2(_points2, true);
            Mat mask;

            if (testcase == 2)
            {
                // init temporary testdata
                mask = Mat::zeros(point_count, 1, CV_8UC1);
            }
            else // testcase == 3 - with transposed mask
            {
                mask = Mat::zeros(1, point_count, CV_8UC1);
            }

            // Estimation of fundamental matrix using the RANSAC algorithm
            Mat E, E2, R, t;

            // Check pose when camera matrices are different.
            for (const auto &distCoeffs: distCoeffsList)
            {
                E = findEssentialMat(points1, points2, cameraMatrix, distCoeffs, cameraMatrix2, distCoeffs, RANSAC, 0.999, 1.0, mask);
                recoverPose(points1, points2, cameraMatrix, distCoeffs, cameraMatrix2, distCoeffs, E2, R, t, RANSAC, 0.999, 1.0, mask);
                EXPECT_LT(cv::norm(E, E2, NORM_INF), 1e-4) <<
                    "Two big difference between the same essential matrices computed using different functions with different cameras, testcase " << testcase;
                EXPECT_EQ(0, (int)mask.at<unsigned char>(13)) << "Detecting outliers in function failed with different cameras, testcase " << testcase;
            }

            // Check pose when camera matrices are the same.
            E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
            E2 = findEssentialMat(points1, points2, cameraMatrix, zeroDistCoeffs, cameraMatrix, zeroDistCoeffs, RANSAC, 0.999, 1.0, mask);
            EXPECT_LT(cv::norm(E, E2, NORM_INF), 1e-4) <<
                "Two big difference between the same essential matrices computed using different functions with same cameras, testcase " << testcase;
            EXPECT_EQ(0, (int)mask.at<unsigned char>(13)) << "Detecting outliers in function findEssentialMat failed with same cameras, testcase " << testcase;
            points2.at<Point2f>(12) = Point2f(0.0f, 0.0f); // provoke an outlier detection
            Inliers = recoverPose(E, points1, points2, cameraMatrix, R, t, mask);
            EXPECT_EQ(0, (int)mask.at<unsigned char>(12)) << "Detecting outliers in function failed with same cameras, testcase " << testcase;
        }
        EXPECT_EQ(Inliers, point_count - invalid_point_count) <<
            "Number of inliers differs from expected number of inliers, testcase " << testcase;
    }
}

}} // namespace