#include "test_precomp.hpp"

using namespace cv;
using namespace std;


class Core_ReduceTest : public cvtest::BaseTest
{
public:
    Core_ReduceTest() {};
protected:
    void run( int);
    int checkOp( const Mat& src, int dstType, int opType, const Mat& opRes, int dim );
    int checkCase( int srcType, int dstType, int dim, Size sz );
    int checkDim( int dim, Size sz );
    int checkSize( Size sz );
};

template<class Type>
void testReduce( const Mat& src, Mat& sum, Mat& avg, Mat& max, Mat& min, int dim )
{
    assert( src.channels() == 1 );
    if( dim == 0 ) // row
    {
        sum.create( 1, src.cols, CV_64FC1 ); 
        max.create( 1, src.cols, CV_64FC1 );
        min.create( 1, src.cols, CV_64FC1 );
    }
    else
    {
        sum.create( src.rows, 1, CV_64FC1 ); 
        max.create( src.rows, 1, CV_64FC1 );
        min.create( src.rows, 1, CV_64FC1 );
    }
    sum.setTo(Scalar(0));
    max.setTo(Scalar(-DBL_MAX));
    min.setTo(Scalar(DBL_MAX));
    
    const Mat_<Type>& src_ = src;
    Mat_<double>& sum_ = (Mat_<double>&)sum;
    Mat_<double>& min_ = (Mat_<double>&)min;
    Mat_<double>& max_ = (Mat_<double>&)max;
    
    if( dim == 0 )
    {
        for( int ri = 0; ri < src.rows; ri++ )
        {
            for( int ci = 0; ci < src.cols; ci++ )
            {
                sum_(0, ci) += src_(ri, ci);
                max_(0, ci) = std::max( max_(0, ci), (double)src_(ri, ci) );
                min_(0, ci) = std::min( min_(0, ci), (double)src_(ri, ci) );
            }
        }
    }
    else
    {
        for( int ci = 0; ci < src.cols; ci++ )
        {
            for( int ri = 0; ri < src.rows; ri++ )
            {
                sum_(ri, 0) += src_(ri, ci);
                max_(ri, 0) = std::max( max_(ri, 0), (double)src_(ri, ci) );
                min_(ri, 0) = std::min( min_(ri, 0), (double)src_(ri, ci) );
            }
        }
    }
    sum.convertTo( avg, CV_64FC1 );
    avg = avg * (1.0 / (dim==0 ? (double)src.rows : (double)src.cols));
}

void getMatTypeStr( int type, string& str)
{
    str = type == CV_8UC1 ? "CV_8UC1" :
    type == CV_8SC1 ? "CV_8SC1" :
    type == CV_16UC1 ? "CV_16UC1" :
    type == CV_16SC1 ? "CV_16SC1" :
    type == CV_32SC1 ? "CV_32SC1" :
    type == CV_32FC1 ? "CV_32FC1" :
    type == CV_64FC1 ? "CV_64FC1" : "unsupported matrix type";
}

int Core_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat& opRes, int dim )
{
    int srcType = src.type();
    bool support = false;
    if( opType == CV_REDUCE_SUM || opType == CV_REDUCE_AVG )
    {
        if( srcType == CV_8U && (dstType == CV_32S || dstType == CV_32F || dstType == CV_64F) )
            support = true;
        if( srcType == CV_16U && (dstType == CV_32F || dstType == CV_64F) )
            support = true;
        if( srcType == CV_16S && (dstType == CV_32F || dstType == CV_64F) )
            support = true;
        if( srcType == CV_32F && (dstType == CV_32F || dstType == CV_64F) )
            support = true;
        if( srcType == CV_64F && dstType == CV_64F)
            support = true;
    }
    else if( opType == CV_REDUCE_MAX )
    {
        if( srcType == CV_8U && dstType == CV_8U )
            support = true;
        if( srcType == CV_32F && dstType == CV_32F )
            support = true;
        if( srcType == CV_64F && dstType == CV_64F )
            support = true;
    }
    else if( opType == CV_REDUCE_MIN )
    {
        if( srcType == CV_8U && dstType == CV_8U)
            support = true;
        if( srcType == CV_32F && dstType == CV_32F)
            support = true;
        if( srcType == CV_64F && dstType == CV_64F)
            support = true;
    }
    if( !support )
        return cvtest::TS::OK;

    double eps = 0.0;
    if ( opType == CV_REDUCE_SUM || opType == CV_REDUCE_AVG )
    {
        if ( dstType == CV_32F )
            eps = 1.e-5;
        else if( dstType == CV_64F )
            eps = 1.e-8;
        else if ( dstType == CV_32S )
            eps = 0.6;
    }
    
    assert( opRes.type() == CV_64FC1 );
    Mat _dst, dst, diff;
    reduce( src, _dst, dim, opType, dstType );
    _dst.convertTo( dst, CV_64FC1 );

    absdiff( opRes,dst,diff );
    bool check = false;
    if (dstType == CV_32F || dstType == CV_64F)
        check = countNonZero(diff>eps*dst) > 0;
    else
        check = countNonZero(diff>eps) > 0;
    if( check )
    {
        char msg[100];
        const char* opTypeStr = opType == CV_REDUCE_SUM ? "CV_REDUCE_SUM" :
        opType == CV_REDUCE_AVG ? "CV_REDUCE_AVG" :
        opType == CV_REDUCE_MAX ? "CV_REDUCE_MAX" :
        opType == CV_REDUCE_MIN ? "CV_REDUCE_MIN" : "unknown operation type";
        string srcTypeStr, dstTypeStr;
        getMatTypeStr( src.type(), srcTypeStr );
        getMatTypeStr( dstType, dstTypeStr );
        const char* dimStr = dim == 0 ? "ROWS" : "COLS";
        
        sprintf( msg, "bad accuracy with srcType = %s, dstType = %s, opType = %s, dim = %s",
                srcTypeStr.c_str(), dstTypeStr.c_str(), opTypeStr, dimStr );
        ts->printf( cvtest::TS::LOG, msg );
        return cvtest::TS::FAIL_BAD_ACCURACY;
    }
    return cvtest::TS::OK;
}

int Core_ReduceTest::checkCase( int srcType, int dstType, int dim, Size sz )
{
    int code = cvtest::TS::OK, tempCode;
    Mat src, sum, avg, max, min;
    
    src.create( sz, srcType );
    randu( src, Scalar(0), Scalar(100) );
    
    if( srcType == CV_8UC1 )
        testReduce<uchar>( src, sum, avg, max, min, dim );
    else if( srcType == CV_8SC1 )
        testReduce<char>( src, sum, avg, max, min, dim );
    else if( srcType == CV_16UC1 )
        testReduce<unsigned short int>( src, sum, avg, max, min, dim );
    else if( srcType == CV_16SC1 )
        testReduce<short int>( src, sum, avg, max, min, dim );
    else if( srcType == CV_32SC1 )
        testReduce<int>( src, sum, avg, max, min, dim );
    else if( srcType == CV_32FC1 )
        testReduce<float>( src, sum, avg, max, min, dim );
    else if( srcType == CV_64FC1 )
        testReduce<double>( src, sum, avg, max, min, dim );
    else 
        assert( 0 );
    
    // 1. sum
    tempCode = checkOp( src, dstType, CV_REDUCE_SUM, sum, dim );
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    // 2. avg
    tempCode = checkOp( src, dstType, CV_REDUCE_AVG, avg, dim );
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    // 3. max
    tempCode = checkOp( src, dstType, CV_REDUCE_MAX, max, dim );
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    // 4. min
    tempCode = checkOp( src, dstType, CV_REDUCE_MIN, min, dim );
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    return code;
}

int Core_ReduceTest::checkDim( int dim, Size sz )
{
    int code = cvtest::TS::OK, tempCode;
    
    // CV_8UC1
    tempCode = checkCase( CV_8UC1, CV_8UC1, dim, sz );
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    tempCode = checkCase( CV_8UC1, CV_32SC1, dim, sz );
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    tempCode = checkCase( CV_8UC1, CV_32FC1, dim, sz );
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    tempCode = checkCase( CV_8UC1, CV_64FC1, dim, sz );
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    // CV_16UC1
    tempCode = checkCase( CV_16UC1, CV_32FC1, dim, sz );
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    tempCode = checkCase( CV_16UC1, CV_64FC1, dim, sz );
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    // CV_16SC1
    tempCode = checkCase( CV_16SC1, CV_32FC1, dim, sz );
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    tempCode = checkCase( CV_16SC1, CV_64FC1, dim, sz );
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    // CV_32FC1
    tempCode = checkCase( CV_32FC1, CV_32FC1, dim, sz );
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    tempCode = checkCase( CV_32FC1, CV_64FC1, dim, sz );
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    // CV_64FC1
    tempCode = checkCase( CV_64FC1, CV_64FC1, dim, sz );
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    return code;
}

int Core_ReduceTest::checkSize( Size sz )
{
    int code = cvtest::TS::OK, tempCode;
    
    tempCode = checkDim( 0, sz ); // rows
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    tempCode = checkDim( 1, sz ); // cols 
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    return code;
}

void Core_ReduceTest::run( int )
{
    int code = cvtest::TS::OK, tempCode;
    
    tempCode = checkSize( Size(1,1) );
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    tempCode = checkSize( Size(1,100) );
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    tempCode = checkSize( Size(100,1) );
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    tempCode = checkSize( Size(1000,500) );
    code = tempCode != cvtest::TS::OK ? tempCode : code;
    
    ts->set_failed_test_info( code );
}


#define CHECK_C

Size sz(200, 500);

class Core_PCATest : public cvtest::BaseTest
{
public:
    Core_PCATest() {}
protected:
    void run(int)
    {
        double diffPrjEps, diffBackPrjEps,
        prjEps, backPrjEps,
        evalEps, evecEps;
        int maxComponents = 100;
        Mat rPoints(sz, CV_32FC1), rTestPoints(sz, CV_32FC1);
        RNG& rng = ts->get_rng(); 
        
        rng.fill( rPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) );
        rng.fill( rTestPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) );
        
        PCA rPCA( rPoints, Mat(), CV_PCA_DATA_AS_ROW, maxComponents ), cPCA;
        
        // 1. check C++ PCA & ROW
        Mat rPrjTestPoints = rPCA.project( rTestPoints );
        Mat rBackPrjTestPoints = rPCA.backProject( rPrjTestPoints );
        
        Mat avg(1, sz.width, CV_32FC1 );
        reduce( rPoints, avg, 0, CV_REDUCE_AVG );
        Mat Q = rPoints - repeat( avg, rPoints.rows, 1 ), Qt = Q.t(), eval, evec;
        Q = Qt * Q;
        Q = Q /(float)rPoints.rows;
        
        eigen( Q, eval, evec );
        /*SVD svd(Q);
         evec = svd.vt;
         eval = svd.w;*/
        
        Mat subEval( maxComponents, 1, eval.type(), eval.data ),
        subEvec( maxComponents, evec.cols, evec.type(), evec.data );
        
    #ifdef CHECK_C
        Mat prjTestPoints, backPrjTestPoints, cPoints = rPoints.t(), cTestPoints = rTestPoints.t();
        CvMat _points, _testPoints, _avg, _eval, _evec, _prjTestPoints, _backPrjTestPoints;
    #endif
        
        // check eigen()
        double eigenEps = 1e-6;
        double err;
        for(int i = 0; i < Q.rows; i++ )
        {
            Mat v = evec.row(i).t();
            Mat Qv = Q * v;
            
            Mat lv = eval.at<float>(i,0) * v;
            err = norm( Qv, lv );
            if( err > eigenEps )
            {
                ts->printf( cvtest::TS::LOG, "bad accuracy of eigen(); err = %f\n", err );
                ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
                return;
            }
        }
        // check pca eigenvalues
        evalEps = 1e-6, evecEps = 1e-3;
        err = norm( rPCA.eigenvalues, subEval );
        if( err > evalEps )
        {
            ts->printf( cvtest::TS::LOG, "pca.eigenvalues is incorrect (CV_PCA_DATA_AS_ROW); err = %f\n", err );
            ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
            return;
        }
        // check pca eigenvectors
        for(int i = 0; i < subEvec.rows; i++)
        {
            Mat r0 = rPCA.eigenvectors.row(i);
            Mat r1 = subEvec.row(i);
            err = norm( r0, r1, CV_L2 );
            if( err > evecEps )
            {
                r1 *= -1;
                double err2 = norm(r0, r1, CV_L2);
                if( err2 > evecEps )
                {
                    Mat tmp;
                    absdiff(rPCA.eigenvectors, subEvec, tmp);
                    double mval = 0; Point mloc;
                    minMaxLoc(tmp, 0, &mval, 0, &mloc);
                    
                    ts->printf( cvtest::TS::LOG, "pca.eigenvectors is incorrect (CV_PCA_DATA_AS_ROW); err = %f\n", err );
                    ts->printf( cvtest::TS::LOG, "max diff is %g at (i=%d, j=%d) (%g vs %g)\n",
                               mval, mloc.y, mloc.x, rPCA.eigenvectors.at<float>(mloc.y, mloc.x),
                               subEvec.at<float>(mloc.y, mloc.x));
                    ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
                    return;
                }
            }
        }
        
        prjEps = 1.265, backPrjEps = 1.265;
        for( int i = 0; i < rTestPoints.rows; i++ )
        {
            // check pca project
            Mat subEvec_t = subEvec.t();
            Mat prj = rTestPoints.row(i) - avg; prj *= subEvec_t;
            err = norm(rPrjTestPoints.row(i), prj, CV_RELATIVE_L2);
            if( err > prjEps )
            {
                ts->printf( cvtest::TS::LOG, "bad accuracy of project() (CV_PCA_DATA_AS_ROW); err = %f\n", err );
                ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
                return;
            }
            // check pca backProject
            Mat backPrj = rPrjTestPoints.row(i) * subEvec + avg;
            err = norm( rBackPrjTestPoints.row(i), backPrj, CV_RELATIVE_L2 );
            if( err > backPrjEps )
            {
                ts->printf( cvtest::TS::LOG, "bad accuracy of backProject() (CV_PCA_DATA_AS_ROW); err = %f\n", err );
                ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
                return;
            }
        }
        
        // 2. check C++ PCA & COL
        cPCA( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, maxComponents );
        diffPrjEps = 1, diffBackPrjEps = 1;
        Mat ocvPrjTestPoints = cPCA.project(rTestPoints.t());
        err = norm(cv::abs(ocvPrjTestPoints), cv::abs(rPrjTestPoints.t()), CV_RELATIVE_L2 );
        if( err > diffPrjEps )
        {
            ts->printf( cvtest::TS::LOG, "bad accuracy of project() (CV_PCA_DATA_AS_COL); err = %f\n", err );
            ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
            return;
        }
        err = norm(cPCA.backProject(ocvPrjTestPoints), rBackPrjTestPoints.t(), CV_RELATIVE_L2 );
        if( err > diffBackPrjEps )
        {
            ts->printf( cvtest::TS::LOG, "bad accuracy of backProject() (CV_PCA_DATA_AS_COL); err = %f\n", err );
            ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
            return;
        }
        
    #ifdef CHECK_C
        // 3. check C PCA & ROW
        _points = rPoints;
        _testPoints = rTestPoints;
        _avg = avg;
        _eval = eval;
        _evec = evec;
        prjTestPoints.create(rTestPoints.rows, maxComponents, rTestPoints.type() );
        backPrjTestPoints.create(rPoints.size(), rPoints.type() );
        _prjTestPoints = prjTestPoints;
        _backPrjTestPoints = backPrjTestPoints;
        
        cvCalcPCA( &_points, &_avg, &_eval, &_evec, CV_PCA_DATA_AS_ROW );
        cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints );
        cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints );
        
        err = norm(prjTestPoints, rPrjTestPoints, CV_RELATIVE_L2);
        if( err > diffPrjEps )
        {
            ts->printf( cvtest::TS::LOG, "bad accuracy of cvProjectPCA() (CV_PCA_DATA_AS_ROW); err = %f\n", err );
            ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
            return;
        }
        err = norm(backPrjTestPoints, rBackPrjTestPoints, CV_RELATIVE_L2);
        if( err > diffBackPrjEps )
        {
            ts->printf( cvtest::TS::LOG, "bad accuracy of cvBackProjectPCA() (CV_PCA_DATA_AS_ROW); err = %f\n", err );
            ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
            return;
        }
        
        // 3. check C PCA & COL
        _points = cPoints;
        _testPoints = cTestPoints;
        avg = avg.t(); _avg = avg;
        eval = eval.t(); _eval = eval;
        evec = evec.t(); _evec = evec;
        prjTestPoints = prjTestPoints.t(); _prjTestPoints = prjTestPoints;
        backPrjTestPoints = backPrjTestPoints.t(); _backPrjTestPoints = backPrjTestPoints;
        
        cvCalcPCA( &_points, &_avg, &_eval, &_evec, CV_PCA_DATA_AS_COL );
        cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints );
        cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints );
        
        err = norm(cv::abs(prjTestPoints), cv::abs(rPrjTestPoints.t()), CV_RELATIVE_L2 );
        if( err > diffPrjEps )
        {
            ts->printf( cvtest::TS::LOG, "bad accuracy of cvProjectPCA() (CV_PCA_DATA_AS_COL); err = %f\n", err );
            ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
            return;
        }
        err = norm(backPrjTestPoints, rBackPrjTestPoints.t(), CV_RELATIVE_L2);
        if( err > diffBackPrjEps )
        {
            ts->printf( cvtest::TS::LOG, "bad accuracy of cvBackProjectPCA() (CV_PCA_DATA_AS_COL); err = %f\n", err );
            ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
            return;
        }
    #endif
    }
};

class Core_ArrayOpTest : public cvtest::BaseTest
{
public:
    Core_ArrayOpTest();
    ~Core_ArrayOpTest();    
protected:
    void run(int);    
};


Core_ArrayOpTest::Core_ArrayOpTest()
{
}
Core_ArrayOpTest::~Core_ArrayOpTest() {}

static string idx2string(const int* idx, int dims)
{
    char buf[256];
    char* ptr = buf;
    for( int k = 0; k < dims; k++ )
    {
        sprintf(ptr, "%4d ", idx[k]);
        ptr += strlen(ptr);
    }
    ptr[-1] = '\0';
    return string(buf);
}

static const int* string2idx(const string& s, int* idx, int dims)
{
    const char* ptr = s.c_str();
    for( int k = 0; k < dims; k++ )
    {
        int n = 0;
        sscanf(ptr, "%d%n", idx + k, &n);
        ptr += n;
    }
    return idx;
}

static double getValue(SparseMat& M, const int* idx, RNG& rng)
{
    int d = M.dims();
    size_t hv = 0, *phv = 0;
    if( (unsigned)rng % 2 )
    {
        hv = d == 2 ? M.hash(idx[0], idx[1]) :
        d == 3 ? M.hash(idx[0], idx[1], idx[2]) : M.hash(idx);
        phv = &hv;
    }
    
    const uchar* ptr = d == 2 ? M.ptr(idx[0], idx[1], false, phv) :
    d == 3 ? M.ptr(idx[0], idx[1], idx[2], false, phv) :
    M.ptr(idx, false, phv);
    return !ptr ? 0 : M.type() == CV_32F ? *(float*)ptr : M.type() == CV_64F ? *(double*)ptr : 0;
}

static double getValue(const CvSparseMat* M, const int* idx)
{
    int type = 0;
    const uchar* ptr = cvPtrND(M, idx, &type, 0);
    return !ptr ? 0 : type == CV_32F ? *(float*)ptr : type == CV_64F ? *(double*)ptr : 0;
}

static void eraseValue(SparseMat& M, const int* idx, RNG& rng)
{
    int d = M.dims();
    size_t hv = 0, *phv = 0;
    if( (unsigned)rng % 2 )
    {
        hv = d == 2 ? M.hash(idx[0], idx[1]) :
        d == 3 ? M.hash(idx[0], idx[1], idx[2]) : M.hash(idx);
        phv = &hv;
    }
    
    if( d == 2 )
        M.erase(idx[0], idx[1], phv);
    else if( d == 3 )
        M.erase(idx[0], idx[1], idx[2], phv);
    else
        M.erase(idx, phv);
}

static void eraseValue(CvSparseMat* M, const int* idx)
{
    cvClearND(M, idx);
}

static void setValue(SparseMat& M, const int* idx, double value, RNG& rng)
{
    int d = M.dims();
    size_t hv = 0, *phv = 0;
    if( (unsigned)rng % 2 )
    {
        hv = d == 2 ? M.hash(idx[0], idx[1]) :
        d == 3 ? M.hash(idx[0], idx[1], idx[2]) : M.hash(idx);
        phv = &hv;
    }
    
    uchar* ptr = d == 2 ? M.ptr(idx[0], idx[1], true, phv) :
    d == 3 ? M.ptr(idx[0], idx[1], idx[2], true, phv) :
    M.ptr(idx, true, phv);
    if( M.type() == CV_32F )
        *(float*)ptr = (float)value;
    else if( M.type() == CV_64F )
        *(double*)ptr = value;
    else
        CV_Error(CV_StsUnsupportedFormat, "");
}

void Core_ArrayOpTest::run( int /* start_from */)
{
    int errcount = 0;
    
    // dense matrix operations
    {
        int sz3[] = {5, 10, 15};
        MatND A(3, sz3, CV_32F), B(3, sz3, CV_16SC4);
        CvMatND matA = A, matB = B;
        RNG rng;
        rng.fill(A, CV_RAND_UNI, Scalar::all(-10), Scalar::all(10));
        rng.fill(B, CV_RAND_UNI, Scalar::all(-10), Scalar::all(10));
        
        int idx0[] = {3,4,5}, idx1[] = {0, 9, 7};
        float val0 = 130;
        Scalar val1(-1000, 30, 3, 8);
        cvSetRealND(&matA, idx0, val0);
        cvSetReal3D(&matA, idx1[0], idx1[1], idx1[2], -val0);
        cvSetND(&matB, idx0, val1);
        cvSet3D(&matB, idx1[0], idx1[1], idx1[2], -val1);
        Ptr<CvMatND> matC = cvCloneMatND(&matB);
        
        if( A.at<float>(idx0[0], idx0[1], idx0[2]) != val0 ||
           A.at<float>(idx1[0], idx1[1], idx1[2]) != -val0 ||
           cvGetReal3D(&matA, idx0[0], idx0[1], idx0[2]) != val0 ||
           cvGetRealND(&matA, idx1) != -val0 ||
           
           Scalar(B.at<Vec4s>(idx0[0], idx0[1], idx0[2])) != val1 ||
           Scalar(B.at<Vec4s>(idx1[0], idx1[1], idx1[2])) != -val1 ||
           Scalar(cvGet3D(matC, idx0[0], idx0[1], idx0[2])) != val1 ||
           Scalar(cvGetND(matC, idx1)) != -val1 )
        {
            ts->printf(cvtest::TS::LOG, "one of cvSetReal3D, cvSetRealND, cvSet3D, cvSetND "
                       "or the corresponding *Get* functions is not correct\n");
            errcount++;
        }
    }
    
    RNG rng;
    const int MAX_DIM = 5, MAX_DIM_SZ = 10;
    // sparse matrix operations
    for( int si = 0; si < 10; si++ )
    {
        int depth = (unsigned)rng % 2 == 0 ? CV_32F : CV_64F;
        int dims = ((unsigned)rng % MAX_DIM) + 1;
        int i, k, size[MAX_DIM]={0}, idx[MAX_DIM]={0};
        vector<string> all_idxs;
        vector<double> all_vals;
        vector<double> all_vals2;
        string sidx, min_sidx, max_sidx;
        double min_val=0, max_val=0;
        
        int p = 1;
        for( k = 0; k < dims; k++ )
        {
            size[k] = ((unsigned)rng % MAX_DIM_SZ) + 1;
            p *= size[k];
        }
        SparseMat M( dims, size, depth );
        map<string, double> M0;
        
        int nz0 = (unsigned)rng % max(p/5,10);
        nz0 = min(max(nz0, 1), p);
        all_vals.resize(nz0);
        all_vals2.resize(nz0);
        Mat_<double> _all_vals(all_vals), _all_vals2(all_vals2);
        rng.fill(_all_vals, CV_RAND_UNI, Scalar(-1000), Scalar(1000));
        if( depth == CV_32F )
        {
            Mat _all_vals_f;
            _all_vals.convertTo(_all_vals_f, CV_32F);
            _all_vals_f.convertTo(_all_vals, CV_64F);
        }
        _all_vals.convertTo(_all_vals2, _all_vals2.type(), 2);
        if( depth == CV_32F )
        {
            Mat _all_vals2_f;
            _all_vals2.convertTo(_all_vals2_f, CV_32F);
            _all_vals2_f.convertTo(_all_vals2, CV_64F);
        }
        
        minMaxLoc(_all_vals, &min_val, &max_val);
        double _norm0 = norm(_all_vals, CV_C);
        double _norm1 = norm(_all_vals, CV_L1);
        double _norm2 = norm(_all_vals, CV_L2);
        
        for( i = 0; i < nz0; i++ )
        {
            for(;;)
            {
                for( k = 0; k < dims; k++ )
                    idx[k] = (unsigned)rng % size[k];
                sidx = idx2string(idx, dims);
                if( M0.count(sidx) == 0 )
                    break;
            }
            all_idxs.push_back(sidx);
            M0[sidx] = all_vals[i];
            if( all_vals[i] == min_val )
                min_sidx = sidx;
            if( all_vals[i] == max_val )
                max_sidx = sidx;
            setValue(M, idx, all_vals[i], rng);
            double v = getValue(M, idx, rng);
            if( v != all_vals[i] )
            {
                ts->printf(cvtest::TS::LOG, "%d. immediately after SparseMat[%s]=%.20g the current value is %.20g\n",
                           i, sidx.c_str(), all_vals[i], v);
                errcount++;
                break;
            }
        }
        
        Ptr<CvSparseMat> M2 = (CvSparseMat*)M;
        MatND Md;
        M.copyTo(Md);
        SparseMat M3; SparseMat(Md).convertTo(M3, Md.type(), 2);
        
        int nz1 = (int)M.nzcount(), nz2 = (int)M3.nzcount();
        double norm0 = norm(M, CV_C);
        double norm1 = norm(M, CV_L1);
        double norm2 = norm(M, CV_L2);
        double eps = depth == CV_32F ? FLT_EPSILON*100 : DBL_EPSILON*1000;
        
        if( nz1 != nz0 || nz2 != nz0)
        {
            errcount++;
            ts->printf(cvtest::TS::LOG, "%d: The number of non-zero elements before/after converting to/from dense matrix is not correct: %d/%d (while it should be %d)\n",
                       si, nz1, nz2, nz0 );
            break;
        }
        
        if( fabs(norm0 - _norm0) > fabs(_norm0)*eps ||
           fabs(norm1 - _norm1) > fabs(_norm1)*eps ||
           fabs(norm2 - _norm2) > fabs(_norm2)*eps )
        {
            errcount++;
            ts->printf(cvtest::TS::LOG, "%d: The norms are different: %.20g/%.20g/%.20g vs %.20g/%.20g/%.20g\n",
                       si, norm0, norm1, norm2, _norm0, _norm1, _norm2 );
            break;
        }
        
        int n = (unsigned)rng % max(p/5,10);
        n = min(max(n, 1), p) + nz0;
        
        for( i = 0; i < n; i++ )
        {
            double val1, val2, val3, val0;
            if(i < nz0)
            {
                sidx = all_idxs[i];
                string2idx(sidx, idx, dims);
                val0 = all_vals[i];
            }
            else
            {
                for( k = 0; k < dims; k++ )
                    idx[k] = (unsigned)rng % size[k];
                sidx = idx2string(idx, dims);
                val0 = M0[sidx];
            }
            val1 = getValue(M, idx, rng);
            val2 = getValue(M2, idx);
            val3 = getValue(M3, idx, rng);
            
            if( val1 != val0 || val2 != val0 || fabs(val3 - val0*2) > fabs(val0*2)*FLT_EPSILON )
            {
                errcount++;
                ts->printf(cvtest::TS::LOG, "SparseMat M[%s] = %g/%g/%g (while it should be %g)\n", sidx.c_str(), val1, val2, val3, val0 );
                break;
            }
        }
        
        for( i = 0; i < n; i++ )
        {
            double val1, val2;
            if(i < nz0)
            {
                sidx = all_idxs[i];
                string2idx(sidx, idx, dims);
            }
            else
            {
                for( k = 0; k < dims; k++ )
                    idx[k] = (unsigned)rng % size[k];
                sidx = idx2string(idx, dims);
            }
            eraseValue(M, idx, rng);
            eraseValue(M2, idx);
            val1 = getValue(M, idx, rng);
            val2 = getValue(M2, idx);
            if( val1 != 0 || val2 != 0 )
            {
                errcount++;
                ts->printf(cvtest::TS::LOG, "SparseMat: after deleting M[%s], it is =%g/%g (while it should be 0)\n", sidx.c_str(), val1, val2 );
                break;
            }    
        }
        
        int nz = (int)M.nzcount();
        if( nz != 0 )
        {
            errcount++;
            ts->printf(cvtest::TS::LOG, "The number of non-zero elements after removing all the elements = %d (while it should be 0)\n", nz );
            break;
        }
        
        int idx1[MAX_DIM], idx2[MAX_DIM];
        double val1 = 0, val2 = 0;
        M3 = SparseMat(Md);
        minMaxLoc(M3, &val1, &val2, idx1, idx2);
        string s1 = idx2string(idx1, dims), s2 = idx2string(idx2, dims);
        if( val1 != min_val || val2 != max_val || s1 != min_sidx || s2 != max_sidx )
        {
            errcount++;
            ts->printf(cvtest::TS::LOG, "%d. Sparse: The value and positions of minimum/maximum elements are different from the reference values and positions:\n\t"
                       "(%g, %g, %s, %s) vs (%g, %g, %s, %s)\n", si, val1, val2, s1.c_str(), s2.c_str(),
                       min_val, max_val, min_sidx.c_str(), max_sidx.c_str());
            break;
        }
        
        minMaxIdx(Md, &val1, &val2, idx1, idx2);
        s1 = idx2string(idx1, dims), s2 = idx2string(idx2, dims);
        if( (min_val < 0 && (val1 != min_val || s1 != min_sidx)) ||
           (max_val > 0 && (val2 != max_val || s2 != max_sidx)) )
        {
            errcount++;
            ts->printf(cvtest::TS::LOG, "%d. Dense: The value and positions of minimum/maximum elements are different from the reference values and positions:\n\t"
                       "(%g, %g, %s, %s) vs (%g, %g, %s, %s)\n", si, val1, val2, s1.c_str(), s2.c_str(),
                       min_val, max_val, min_sidx.c_str(), max_sidx.c_str());
            break;
        }
    }
    
    ts->set_failed_test_info(errcount == 0 ? cvtest::TS::OK : cvtest::TS::FAIL_INVALID_OUTPUT);
}

TEST(Core_PCA, accuracy) { Core_PCATest test; test.safe_run(); }
TEST(Core_Reduce, accuracy) { Core_ReduceTest test; test.safe_run(); }
TEST(Core_Array, basic_operations) { Core_ArrayOpTest test; test.safe_run(); }


TEST(Core_IOArray, submat_assignment)
{
    Mat1f A = Mat1f::zeros(2,2);
    Mat1f B = Mat1f::ones(1,3);

    EXPECT_THROW( B.colRange(0,3).copyTo(A.row(0)), cv::Exception );

    EXPECT_NO_THROW( B.colRange(0,2).copyTo(A.row(0)) );

    EXPECT_EQ( 1.0f, A(0,0) );
    EXPECT_EQ( 1.0f, A(0,1) );
}

void OutputArray_create1(OutputArray m) { m.create(1, 2, CV_32S); }
void OutputArray_create2(OutputArray m) { m.create(1, 3, CV_32F); }

TEST(Core_IOArray, submat_create)
{
    Mat1f A = Mat1f::zeros(2,2);

    EXPECT_THROW( OutputArray_create1(A.row(0)), cv::Exception );
    EXPECT_THROW( OutputArray_create2(A.row(0)), cv::Exception );
}

TEST(Core_Mat, reshape_1942)
{
    cv::Mat A = (cv::Mat_<float>(2,3) << 3.4884074, 1.4159607, 0.78737736,  2.3456569, -0.88010466, 0.3009364);
    int cn = 0;
    ASSERT_NO_THROW(
        cv::Mat_<float> M = A.reshape(3);
        cn = M.channels();
    );
    ASSERT_EQ(1, cn);
}