opencv/modules/core/test/test_arithm.cpp

// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
#include "ref_reduce_arg.impl.hpp"
#include <algorithm>

namespace opencv_test { namespace {

const int ARITHM_NTESTS = 1000;
const int ARITHM_RNG_SEED = -1;
const int ARITHM_MAX_CHANNELS = 4;
const int ARITHM_MAX_NDIMS = 4;
const int ARITHM_MAX_SIZE_LOG = 10;

// fp8 (E4M3) is excluded from the tolerance-checked element-wise pool: its 3-bit
// mantissa can't meet these tests' error bounds and out-of-range inputs overflow to
// NaN. fp8 conversion/arithmetic is covered directly in test_fp8.cpp.
static const _OutputArray::DepthMask DEPTH_MASK_ALL_NO_FP8 =
    _OutputArray::DepthMask(_OutputArray::DEPTH_MASK_ALL &
        ~((1 << CV_8F_E4M3FN) | (1 << CV_8F_E4M3FNUZ)));
static const _OutputArray::DepthMask DEPTH_MASK_ALL_BUT_8S_NO_FP8 =
    _OutputArray::DepthMask(DEPTH_MASK_ALL_NO_FP8 & ~_OutputArray::DEPTH_MASK_8S);

struct BaseElemWiseOp
{
    enum
    {
        FIX_ALPHA=1, FIX_BETA=2, FIX_GAMMA=4, REAL_GAMMA=8,
        SUPPORT_MASK=16, SCALAR_OUTPUT=32, SUPPORT_MULTICHANNELMASK=64,
        MIXED_TYPE=128
   };
    BaseElemWiseOp(int _ninputs, int _flags, double _alpha, double _beta,
                   Scalar _gamma=Scalar::all(0), int _context=1)
    : ninputs(_ninputs), flags(_flags), alpha(_alpha), beta(_beta), gamma(_gamma), context(_context) {}
    BaseElemWiseOp() { flags = 0; alpha = beta = 0; gamma = Scalar::all(0); ninputs = 0; context = 1; }
    virtual ~BaseElemWiseOp() {}
    virtual void op(const vector<Mat>&, Mat&, const Mat&) {}
    virtual void refop(const vector<Mat>&, Mat&, const Mat&) {}
    virtual void getValueRange(int depth, double& minval, double& maxval)
    {
        minval = depth < CV_32S ? cvtest::getMinVal(depth) : depth == CV_32S ? -1000000 : -1000.;
        maxval = depth < CV_32S ? cvtest::getMaxVal(depth) : depth == CV_32S ? 1000000 : 1000.;
    }

    virtual void getRandomSize(RNG& rng, vector<int>& size)
    {
        cvtest::randomSize(rng, 2, ARITHM_MAX_NDIMS, ARITHM_MAX_SIZE_LOG, size);
    }

    virtual int getRandomType(RNG& rng)
    {
        return cvtest::randomType(rng, DEPTH_MASK_ALL_BUT_8S_NO_FP8, 1,
                                  ninputs > 1 ? ARITHM_MAX_CHANNELS : 4);
    }

    virtual double getMaxErr(int depth)
    {
        return depth < CV_32F || depth == CV_32U || depth == CV_64U || depth == CV_64S ? 1 :
               depth == CV_16F || depth == CV_16BF ? 1e-2 : depth == CV_32F ? 1e-5 : 1e-12;
    }
    virtual void generateScalars(int depth, RNG& rng)
    {
        const double m = 3.;

        if( !(flags & FIX_ALPHA) )
        {
            alpha = exp(rng.uniform(-0.5, 0.1)*m*2*CV_LOG2);
            alpha *= rng.uniform(0, 2) ? 1 : -1;
        }
        if( !(flags & FIX_BETA) )
        {
            beta = exp(rng.uniform(-0.5, 0.1)*m*2*CV_LOG2);
            beta *= rng.uniform(0, 2) ? 1 : -1;
        }

        if( !(flags & FIX_GAMMA) )
        {
            for( int i = 0; i < 4; i++ )
            {
                gamma[i] = exp(rng.uniform(-1, 6)*m*CV_LOG2);
                gamma[i] *= rng.uniform(0, 2) ? 1 : -1;
            }
            if( flags & REAL_GAMMA )
                gamma = Scalar::all(gamma[0]);
        }

        if( depth == CV_32F )
        {
            Mat fl, db;

            db = Mat(1, 1, CV_64F, &alpha);
            db.convertTo(fl, CV_32F);
            fl.convertTo(db, CV_64F);

            db = Mat(1, 1, CV_64F, &beta);
            db.convertTo(fl, CV_32F);
            fl.convertTo(db, CV_64F);

            db = Mat(1, 4, CV_64F, &gamma[0]);
            db.convertTo(fl, CV_32F);
            fl.convertTo(db, CV_64F);
        }
    }

    int ninputs;
    int flags;
    double alpha;
    double beta;
    Scalar gamma;
    int context;
};

static const _OutputArray::DepthMask baseArithmTypeMask =
    _OutputArray::DepthMask(
        _OutputArray::DEPTH_MASK_8U |
        _OutputArray::DEPTH_MASK_16U |
        _OutputArray::DEPTH_MASK_16S |
        _OutputArray::DEPTH_MASK_32S |
        _OutputArray::DEPTH_MASK_32F |
        _OutputArray::DEPTH_MASK_64F |
        _OutputArray::DEPTH_MASK_16F |
        _OutputArray::DEPTH_MASK_16BF |
        _OutputArray::DEPTH_MASK_32U |
        _OutputArray::DEPTH_MASK_64U |
        _OutputArray::DEPTH_MASK_64S );

struct BaseArithmOp : public BaseElemWiseOp
{
    BaseArithmOp(int _ninputs, int _flags, double _alpha, double _beta, Scalar _gamma=Scalar::all(0))
    : BaseElemWiseOp(_ninputs, _flags, _alpha, _beta, _gamma) {}

    int getRandomType(RNG& rng)
    {
        return cvtest::randomType(rng, baseArithmTypeMask, 1,
                                  ninputs > 1 ? ARITHM_MAX_CHANNELS : 4);
    }
};

struct BaseAddOp : public BaseArithmOp
{
    BaseAddOp(int _ninputs, int _flags, double _alpha, double _beta, Scalar _gamma=Scalar::all(0))
    : BaseArithmOp(_ninputs, _flags, _alpha, _beta, _gamma) {}

    void refop(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        int dstType = (flags & MIXED_TYPE) ? dst.type() : src[0].type();
        if( !mask.empty() )
        {
            Mat temp;
            cvtest::add(src[0], alpha, src.size() > 1 ? src[1] : Mat(), beta, gamma, temp, dstType);
            cvtest::copy(temp, dst, mask);
        }
        else
            cvtest::add(src[0], alpha, src.size() > 1 ? src[1] : Mat(), beta, gamma, dst, dstType);
    }

    double getMaxErr(int depth)
    {
        return depth == CV_16BF ? 1e-2 : depth == CV_16F ? 1e-3 : depth == CV_32F ? 1e-4 : depth == CV_64F ? 1e-12 : 2;
    }
};


struct AddOp : public BaseAddOp
{
    AddOp() : BaseAddOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK, 1, 1, Scalar::all(0)) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        int dtype = (flags & MIXED_TYPE) ? dst.type() : -1;
        cv::add(src[0], src[1], dst, mask, dtype);
    }
};


struct SubOp : public BaseAddOp
{
    SubOp() : BaseAddOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK, 1, -1, Scalar::all(0)) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        int dtype = (flags & MIXED_TYPE) ? dst.type() : -1;
        cv::subtract(src[0], src[1], dst, mask, dtype);
    }
};


struct AddSOp : public BaseAddOp
{
    AddSOp() : BaseAddOp(1, FIX_ALPHA+FIX_BETA+SUPPORT_MASK, 1, 0, Scalar::all(0)) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        int dtype = (flags & MIXED_TYPE) ? dst.type() : -1;
        cv::add(src[0], gamma, dst, mask, dtype);
    }
};


struct SubRSOp : public BaseAddOp
{
    SubRSOp() : BaseAddOp(1, FIX_ALPHA+FIX_BETA+SUPPORT_MASK, -1, 0, Scalar::all(0)) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        int dtype = (flags & MIXED_TYPE) ? dst.type() : -1;
        cv::subtract(gamma, src[0], dst, mask, dtype);
    }
};


struct ScaleAddOp : public BaseAddOp
{
    ScaleAddOp() : BaseAddOp(2, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cv::scaleAdd(src[0], alpha, src[1], dst);
    }
    double getMaxErr(int depth)
    {
        return depth == CV_16BF ? 1e-2 : depth == CV_16F ? 1e-3 : depth == CV_32F ? 3e-5 : depth == CV_64F ? 1e-12 : 2;
    }
};


struct AddWeightedOp : public BaseAddOp
{
    AddWeightedOp() : BaseAddOp(2, REAL_GAMMA, 1, 1, Scalar::all(0)) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        int dtype = (flags & MIXED_TYPE) ? dst.type() : -1;
        cv::addWeighted(src[0], alpha, src[1], beta, gamma[0], dst, dtype);
    }
};

struct MulOp : public BaseArithmOp
{
    MulOp() : BaseArithmOp(2, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
    void getValueRange(int depth, double& minval, double& maxval)
    {
        minval = depth < CV_32S ? cvtest::getMinVal(depth) : depth == CV_32S ? -1000000 : -1000.;
        maxval = depth < CV_32S ? cvtest::getMaxVal(depth) : depth == CV_32S ? 1000000 : 1000.;
        minval = std::max(minval, -30000.);
        maxval = std::min(maxval, 30000.);
    }
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        int dtype = (flags & MIXED_TYPE) ? dst.type() : -1;
        cv::multiply(src[0], src[1], dst, alpha, dtype);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        int dtype = (flags & MIXED_TYPE) ? dst.type() : -1;
        cvtest::multiply(src[0], src[1], dst, alpha, dtype);
    }
};

struct MulSOp : public BaseArithmOp
{
    MulSOp() : BaseArithmOp(1, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
    void getValueRange(int depth, double& minval, double& maxval)
    {
        minval = depth < CV_32S ? cvtest::getMinVal(depth) : depth == CV_32S ? -1000000 : -1000.;
        maxval = depth < CV_32S ? cvtest::getMaxVal(depth) : depth == CV_32S ? 1000000 : 1000.;
        minval = std::max(minval, -30000.);
        maxval = std::min(maxval, 30000.);
    }
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        int dtype = (flags & MIXED_TYPE) ? dst.type() : -1;
        cv::multiply(src[0], alpha, dst, /* scale */ 1.0, dtype);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        int dtype = (flags & MIXED_TYPE) ? dst.type() : -1;
        cvtest::multiply(Mat(), src[0], dst, alpha, dtype);
    }
};

struct DivOp : public BaseArithmOp
{
    DivOp() : BaseArithmOp(2, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        int dtype = (flags & MIXED_TYPE) ? dst.type() : -1;
        cv::divide(src[0], src[1], dst, alpha, dtype);
        if (flags & MIXED_TYPE)
        {
            // div by zero result is implementation-defined
            // since it may involve conversions to/from intermediate format
            Mat zeroMask = src[1] == 0;
            dst.setTo(0, zeroMask);
        }
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        int dtype = (flags & MIXED_TYPE) ? dst.type() : -1;
        cvtest::divide(src[0], src[1], dst, alpha, dtype);
    }
};

struct RecipOp : public BaseArithmOp
{
    RecipOp() : BaseArithmOp(1, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        int dtype = (flags & MIXED_TYPE) ? dst.type() : -1;
        cv::divide(alpha, src[0], dst, dtype);
        if (flags & MIXED_TYPE)
        {
            // div by zero result is implementation-defined
            // since it may involve conversions to/from intermediate format
            Mat zeroMask = src[0] == 0;
            dst.setTo(0, zeroMask);
        }
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        int dtype = (flags & MIXED_TYPE) ? dst.type() : -1;
        cvtest::divide(Mat(), src[0], dst, alpha, dtype);
    }
};

struct AbsDiffOp : public BaseAddOp
{
    AbsDiffOp() : BaseAddOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, -1, Scalar::all(0)) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        absdiff(src[0], src[1], dst);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cvtest::add(src[0], 1, src[1], -1, Scalar::all(0), dst, src[0].type(), true);
    }
};

struct AbsDiffSOp : public BaseAddOp
{
    AbsDiffSOp() : BaseAddOp(1, FIX_ALPHA+FIX_BETA, 1, 0, Scalar::all(0)) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        absdiff(src[0], gamma, dst);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cvtest::add(src[0], 1, Mat(), 0, -gamma, dst, src[0].type(), true);
    }
};

struct LogicOp : public BaseElemWiseOp
{
    LogicOp(char _opcode) : BaseElemWiseOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK, 1, 1, Scalar::all(0)), opcode(_opcode) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        if( opcode == '&' )
            cv::bitwise_and(src[0], src[1], dst, mask);
        else if( opcode == '|' )
            cv::bitwise_or(src[0], src[1], dst, mask);
        else
            cv::bitwise_xor(src[0], src[1], dst, mask);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        Mat temp;
        if( !mask.empty() )
        {
            cvtest::logicOp(src[0], src[1], temp, opcode);
            cvtest::copy(temp, dst, mask);
        }
        else
            cvtest::logicOp(src[0], src[1], dst, opcode);
    }
    double getMaxErr(int)
    {
        return 0;
    }
    char opcode;
};

struct LogicSOp : public BaseElemWiseOp
{
    LogicSOp(char _opcode)
    : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+(_opcode != '~' ? SUPPORT_MASK : 0), 1, 1, Scalar::all(0)), opcode(_opcode) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        if( opcode == '&' )
            cv::bitwise_and(src[0], gamma, dst, mask);
        else if( opcode == '|' )
            cv::bitwise_or(src[0], gamma, dst, mask);
        else if( opcode == '^' )
            cv::bitwise_xor(src[0], gamma, dst, mask);
        else
            cv::bitwise_not(src[0], dst);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        Mat temp;
        if( !mask.empty() )
        {
            cvtest::logicOp(src[0], gamma, temp, opcode);
            cvtest::copy(temp, dst, mask);
        }
        else
            cvtest::logicOp(src[0], gamma, dst, opcode);
    }
    double getMaxErr(int)
    {
        return 0;
    }
    char opcode;
};

struct MinOp : public BaseArithmOp
{
    MinOp() : BaseArithmOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cv::min(src[0], src[1], dst);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cvtest::min(src[0], src[1], dst);
    }
    double getMaxErr(int)
    {
        return 0;
    }
};

struct MaxOp : public BaseArithmOp
{
    MaxOp() : BaseArithmOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cv::max(src[0], src[1], dst);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cvtest::max(src[0], src[1], dst);
    }
    double getMaxErr(int)
    {
        return 0;
    }
};

struct MinSOp : public BaseArithmOp
{
    MinSOp() : BaseArithmOp(1, FIX_ALPHA+FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cv::min(src[0], gamma[0], dst);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cvtest::min(src[0], gamma[0], dst);
    }
    double getMaxErr(int)
    {
        return 0;
    }
};

struct MaxSOp : public BaseArithmOp
{
    MaxSOp() : BaseArithmOp(1, FIX_ALPHA+FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cv::max(src[0], gamma[0], dst);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cvtest::max(src[0], gamma[0], dst);
    }
    double getMaxErr(int)
    {
        return 0;
    }
};

struct CmpOp : public BaseArithmOp
{
    CmpOp() : BaseArithmOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) { cmpop = 0; }
    void generateScalars(int depth, RNG& rng)
    {
        BaseElemWiseOp::generateScalars(depth, rng);
        cmpop = rng.uniform(0, 6);
    }
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cv::compare(src[0], src[1], dst, cmpop);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cvtest::compare(src[0], src[1], dst, cmpop);
    }
    int getRandomType(RNG& rng)
    {
        return cvtest::randomType(rng, baseArithmTypeMask, 1, 1);
    }

    double getMaxErr(int)
    {
        return 0;
    }
    int cmpop;
};

struct CmpSOp : public BaseArithmOp
{
    CmpSOp() : BaseArithmOp(1, FIX_ALPHA+FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) { cmpop = 0; }
    void generateScalars(int depth, RNG& rng)
    {
        BaseElemWiseOp::generateScalars(depth, rng);
        cmpop = rng.uniform(0, 6);
        if( depth != CV_16F && depth != CV_16BF && depth != CV_32F && depth != CV_64F )
            gamma[0] = cvRound(gamma[0]);
    }
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cv::compare(src[0], gamma[0], dst, cmpop);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cvtest::compare(src[0], gamma[0], dst, cmpop);
    }
    int getRandomType(RNG& rng)
    {
        return cvtest::randomType(rng, baseArithmTypeMask, 1, 1);
    }
    double getMaxErr(int)
    {
        return 0;
    }
    int cmpop;
};


struct CopyOp : public BaseElemWiseOp
{
    CopyOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK+SUPPORT_MULTICHANNELMASK, 1, 1, Scalar::all(0)) {  }
    void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        src[0].copyTo(dst, mask);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        cvtest::copy(src[0], dst, mask);
    }
    int getRandomType(RNG& rng)
    {
        return cvtest::randomType(rng, _OutputArray::DEPTH_MASK_ALL, 1, ARITHM_MAX_CHANNELS);
    }
    double getMaxErr(int)
    {
        return 0;
    }
};


struct SetOp : public BaseElemWiseOp
{
    SetOp() : BaseElemWiseOp(0, FIX_ALPHA+FIX_BETA+SUPPORT_MASK+SUPPORT_MULTICHANNELMASK, 1, 1, Scalar::all(0)) {}
    void op(const vector<Mat>&, Mat& dst, const Mat& mask)
    {
        dst.setTo(gamma, mask);
    }
    void refop(const vector<Mat>&, Mat& dst, const Mat& mask)
    {
        cvtest::set(dst, gamma, mask);
    }
    int getRandomType(RNG& rng)
    {
        return cvtest::randomType(rng, _OutputArray::DEPTH_MASK_ALL, 1, ARITHM_MAX_CHANNELS);
    }
    double getMaxErr(int)
    {
        return 0;
    }
};

template<typename _Tp, typename _WTp=_Tp> static void
inRangeS_(const _Tp* src, const _WTp* a, const _WTp* b, uchar* dst, size_t total, int cn)
{
    size_t i;
    int c;
    for( i = 0; i < total; i++ )
    {
        _WTp val = (_WTp)src[i*cn];
        dst[i] = (a[0] <= val && val <= b[0]) ? uchar(255) : 0;
    }
    for( c = 1; c < cn; c++ )
    {
        for( i = 0; i < total; i++ )
        {
            _WTp val = (_WTp)src[i*cn + c];
            dst[i] = a[c] <= val && val <= b[c] ? dst[i] : 0;
        }
    }
}

template<typename _Tp, typename _WTp=_Tp> static void
inRange_(const _Tp* src, const _Tp* a, const _Tp* b,
         uchar* dst, size_t total, int cn)
{
    size_t i;
    int c;
    for( i = 0; i < total; i++ )
    {
        _Tp val = src[i*cn];
        dst[i] = a[i*cn] <= val && val <= b[i*cn] ? 255 : 0;
    }
    for( c = 1; c < cn; c++ )
    {
        for( i = 0; i < total; i++ )
        {
            _Tp val = src[i*cn + c];
            dst[i] = a[i*cn + c] <= val && val <= b[i*cn + c] ? dst[i] : 0;
        }
    }
}

namespace reference {

static void inRange(const Mat& src, const Mat& lb, const Mat& rb, Mat& dst)
{
    CV_Assert( src.type() == lb.type() && src.type() == rb.type() &&
              src.size == lb.size && src.size == rb.size );
    dst.create( src.size, CV_8U );
    const Mat *arrays[]={&src, &lb, &rb, &dst, 0};
    Mat planes[4];

    NAryMatIterator it(arrays, planes);
    size_t total = planes[0].total();
    size_t i, nplanes = it.nplanes;
    int depth = src.depth(), cn = src.channels();

    for( i = 0; i < nplanes; i++, ++it )
    {
        const uchar* sptr = planes[0].ptr();
        const uchar* aptr = planes[1].ptr();
        const uchar* bptr = planes[2].ptr();
        uchar* dptr = planes[3].ptr();

        switch( depth )
        {
        case CV_8U:
            inRange_((const uchar*)sptr, (const uchar*)aptr, (const uchar*)bptr, dptr, total, cn);
            break;
        case CV_8S:
            inRange_((const schar*)sptr, (const schar*)aptr, (const schar*)bptr, dptr, total, cn);
            break;
        case CV_16U:
            inRange_((const ushort*)sptr, (const ushort*)aptr, (const ushort*)bptr, dptr, total, cn);
            break;
        case CV_16S:
            inRange_((const short*)sptr, (const short*)aptr, (const short*)bptr, dptr, total, cn);
            break;
        case CV_32U:
            inRange_((const unsigned*)sptr, (const unsigned*)aptr, (const unsigned*)bptr, dptr, total, cn);
            break;
        case CV_32S:
            inRange_((const int*)sptr, (const int*)aptr, (const int*)bptr, dptr, total, cn);
            break;
        case CV_64U:
            inRange_((const uint64*)sptr, (const uint64*)aptr, (const uint64*)bptr, dptr, total, cn);
            break;
        case CV_64S:
            inRange_((const int64*)sptr, (const int64*)aptr, (const int64*)bptr, dptr, total, cn);
            break;
        case CV_32F:
            inRange_((const float*)sptr, (const float*)aptr, (const float*)bptr, dptr, total, cn);
            break;
        case CV_64F:
            inRange_((const double*)sptr, (const double*)aptr, (const double*)bptr, dptr, total, cn);
            break;
        case CV_16F:
            inRange_<cv::hfloat, float>((const cv::hfloat*)sptr, (const cv::hfloat*)aptr,
                                           (const cv::hfloat*)bptr, dptr, total, cn);
            break;
        case CV_16BF:
            inRange_<cv::bfloat, float>((const cv::bfloat*)sptr, (const cv::bfloat*)aptr,
                                            (const cv::bfloat*)bptr, dptr, total, cn);
            break;
        default:
            CV_Error(cv::Error::StsUnsupportedFormat, "");
        }
    }
}

static void inRangeS(const Mat& src, const Scalar& lb, const Scalar& rb, Mat& dst)
{
    dst.create( src.size, CV_8U );
    const Mat *arrays[]={&src, &dst, 0};
    Mat planes[2];

    NAryMatIterator it(arrays, planes);
    size_t total = planes[0].total();
    size_t i, nplanes = it.nplanes;
    int depth = src.depth(), cn = src.channels();
    union { double d[4]; float f[4]; int i[4]; unsigned u[4]; int64 L[4]; uint64 UL[4]; } lbuf, rbuf;
    int wtype = CV_MAKETYPE((depth <= CV_32S ? CV_32S :
        depth == CV_16F || depth == CV_16BF || depth == CV_32F ? CV_32F : depth), cn);
    scalarToRawData(lb, lbuf.d, wtype, cn);
    scalarToRawData(rb, rbuf.d, wtype, cn);

    for( i = 0; i < nplanes; i++, ++it )
    {
        const uchar* sptr = planes[0].ptr();
        uchar* dptr = planes[1].ptr();

        switch( depth )
        {
        case CV_8U:
            inRangeS_((const uchar*)sptr, lbuf.i, rbuf.i, dptr, total, cn);
            break;
        case CV_8S:
            inRangeS_((const schar*)sptr, lbuf.i, rbuf.i, dptr, total, cn);
            break;
        case CV_16U:
            inRangeS_((const ushort*)sptr, lbuf.i, rbuf.i, dptr, total, cn);
            break;
        case CV_16S:
            inRangeS_((const short*)sptr, lbuf.i, rbuf.i, dptr, total, cn);
            break;
        case CV_32U:
            inRangeS_((const unsigned*)sptr, lbuf.u, rbuf.u, dptr, total, cn);
            break;
        case CV_32S:
            inRangeS_((const int*)sptr, lbuf.i, rbuf.i, dptr, total, cn);
            break;
        case CV_64U:
            inRangeS_((const uint64*)sptr, lbuf.UL, rbuf.UL, dptr, total, cn);
            break;
        case CV_64S:
            inRangeS_((const int64*)sptr, lbuf.L, rbuf.L, dptr, total, cn);
            break;
        case CV_32F:
            inRangeS_((const float*)sptr, lbuf.f, rbuf.f, dptr, total, cn);
            break;
        case CV_64F:
            inRangeS_((const double*)sptr, lbuf.d, rbuf.d, dptr, total, cn);
            break;
        case CV_16F:
            inRangeS_((const cv::hfloat*)sptr, lbuf.f, rbuf.f, dptr, total, cn);
            break;
        case CV_16BF:
            inRangeS_((const cv::bfloat*)sptr, lbuf.f, rbuf.f, dptr, total, cn);
            break;
        default:
            CV_Error(cv::Error::StsUnsupportedFormat, "");
        }
    }
}

} // namespace
CVTEST_GUARD_SYMBOL(inRange)

struct InRangeSOp : public BaseArithmOp
{
    InRangeSOp() : BaseArithmOp(1, FIX_ALPHA+FIX_BETA, 1, 1, Scalar::all(0)) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cv::inRange(src[0], gamma, gamma1, dst);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        reference::inRangeS(src[0], gamma, gamma1, dst);
    }
    double getMaxErr(int)
    {
        return 0;
    }
    void generateScalars(int depth, RNG& rng)
    {
        BaseElemWiseOp::generateScalars(depth, rng);
        Scalar temp = gamma;
        BaseElemWiseOp::generateScalars(depth, rng);
        for( int i = 0; i < 4; i++ )
        {
            gamma1[i] = std::max(gamma[i], temp[i]);
            gamma[i] = std::min(gamma[i], temp[i]);
        }
    }
    Scalar gamma1;
};


struct InRangeOp : public BaseArithmOp
{
    InRangeOp() : BaseArithmOp(3, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        Mat lb, rb;
        cvtest::min(src[1], src[2], lb);
        cvtest::max(src[1], src[2], rb);

        cv::inRange(src[0], lb, rb, dst);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        Mat lb, rb;
        cvtest::min(src[1], src[2], lb);
        cvtest::max(src[1], src[2], rb);

        reference::inRange(src[0], lb, rb, dst);
    }
    double getMaxErr(int)
    {
        return 0;
    }
};

namespace reference {

template<typename _Tp>
struct SoftType;

template<>
struct SoftType<float>
{
    typedef softfloat type;
};

template<>
struct SoftType<double>
{
    typedef softdouble type;
};


template <typename _Tp>
static void finiteMask_(const _Tp *src, uchar *dst, size_t total, int cn)
{
    for(size_t i = 0; i < total; i++ )
    {
        bool good = true;
        for (int c = 0; c < cn; c++)
        {
            _Tp val = src[i * cn + c];
            typename SoftType<_Tp>::type sval(val);

            good = good && !sval.isNaN() && !sval.isInf();
        }
        dst[i] = good ? 255 : 0;
    }
}

static void finiteMask(const Mat& src, Mat& dst)
{
    dst.create(src.size, CV_8UC1);

    const Mat *arrays[]={&src, &dst, 0};
    Mat planes[2];
    NAryMatIterator it(arrays, planes);
    size_t total = planes[0].total();
    size_t i, nplanes = it.nplanes;
    int depth = src.depth(), cn = src.channels();

    for( i = 0; i < nplanes; i++, ++it )
    {
        const uchar* sptr = planes[0].ptr();
        uchar* dptr = planes[1].ptr();

        switch( depth )
        {
        case CV_32F: finiteMask_<float >((const  float*)sptr, dptr, total, cn); break;
        case CV_64F: finiteMask_<double>((const double*)sptr, dptr, total, cn); break;
        }
    }
}
}


struct FiniteMaskOp : public BaseElemWiseOp
{
    FiniteMaskOp() : BaseElemWiseOp(1, 0, 1, 1, Scalar::all(0)) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cv::finiteMask(src[0], dst);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        reference::finiteMask(src[0], dst);
    }
    int getRandomType(RNG& rng)
    {
        return cvtest::randomType(rng, _OutputArray::DEPTH_MASK_FLT, 1, 4);
    }
    double getMaxErr(int)
    {
        return 0;
    }
};


struct ConvertScaleOp : public BaseElemWiseOp
{
    ConvertScaleOp() : BaseElemWiseOp(1, FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)), ddepth(0) { }
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        src[0].convertTo(dst, ddepth, alpha, gamma[0]);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cvtest::convert(src[0], dst, CV_MAKETYPE(ddepth, src[0].channels()), alpha, gamma[0]);
    }
    int getRandomType(RNG& rng)
    {
        int srctype = cvtest::randomType(rng, DEPTH_MASK_ALL_NO_FP8, 1, ARITHM_MAX_CHANNELS);
        ddepth = cvtest::randomType(rng, DEPTH_MASK_ALL_NO_FP8, 1, 1);
        return srctype;
    }
    double getMaxErr(int)
    {
        return ddepth <= CV_32S || ddepth == CV_32U || ddepth == CV_64U || ddepth == CV_64S ? 2 : ddepth == CV_64F ? 1e-12 : ddepth == CV_Bool ? 0 : ddepth == CV_16BF ? 1e-2 : 2e-3;
    }
    void generateScalars(int depth, RNG& rng)
    {
        if( rng.uniform(0, 2) )
            BaseElemWiseOp::generateScalars(depth, rng);
        else
        {
            alpha = 1;
            gamma = Scalar::all(0);
        }
    }
    int ddepth;
};

struct ConvertScaleFp16Op : public BaseElemWiseOp
{
    ConvertScaleFp16Op() : BaseElemWiseOp(1, FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)), nextRange(0) { }
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        Mat m;
        if (src[0].depth() == CV_32F)
        {
            src[0].convertTo(m, CV_16F);
            m.convertTo(dst, CV_32F);
        }
        else
        {
            src[0].convertTo(m, CV_32F);
            m.convertTo(dst, CV_16F);
        }
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cvtest::copy(src[0], dst);
    }
    int getRandomType(RNG&)
    {
        // 0: FP32 -> FP16 -> FP32
        // 1: FP16 -> FP32 -> FP16
        int srctype = (nextRange & 1) == 0 ? CV_32F : CV_16F;
        return srctype;
    }
    void getValueRange(int, double& minval, double& maxval)
    {
        // 0: FP32 -> FP16 -> FP32
        // 1: FP16 -> FP32 -> FP16
        if( (nextRange & 1) == 0 )
        {
            // largest integer number that fp16 can express exactly
            maxval = 2048.f;
            minval = -maxval;
        }
        else
        {
            // 0: positive number range
            // 1: negative number range
            if( (nextRange & 2) == 0 )
            {
                minval = 0;      // 0x0000 +0
                maxval = 31744;  // 0x7C00 +Inf
            }
            else
            {
                minval = -32768; // 0x8000 -0
                maxval = -1024;  // 0xFC00 -Inf
            }
        }
    }
    double getMaxErr(int)
    {
        return 0.5f;
    }
    void generateScalars(int, RNG& rng)
    {
        nextRange = rng.next();
    }
    int nextRange;
};

struct ConvertScaleAbsOp : public BaseElemWiseOp
{
    ConvertScaleAbsOp() : BaseElemWiseOp(1, FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) {}
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cv::convertScaleAbs(src[0], dst, alpha, gamma[0]);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cvtest::add(src[0], alpha, Mat(), 0, Scalar::all(gamma[0]), dst, CV_8UC(src[0].channels()), true);
    }
    int getRandomType(RNG& rng)
    {
        return cvtest::randomType(rng, DEPTH_MASK_ALL_NO_FP8, 1,
            ninputs > 1 ? ARITHM_MAX_CHANNELS : 4);
    }
    double getMaxErr(int)
    {
        return 1;
    }
    void generateScalars(int depth, RNG& rng)
    {
        if( rng.uniform(0, 2) )
            BaseElemWiseOp::generateScalars(depth, rng);
        else
        {
            alpha = 1;
            gamma = Scalar::all(0);
        }
    }
};

namespace reference {

// does not support inplace operation
static void flip(const Mat& src, Mat& dst, int flipcode)
{
    CV_Assert(src.dims <= 2);
    dst.create(src.size, src.type());
    int i, j, k, esz = (int)src.elemSize(), width = src.cols*esz;

    for( i = 0; i < dst.rows; i++ )
    {
        const uchar* sptr = src.ptr(flipcode == 1 ? i : dst.rows - i - 1);
        uchar* dptr = dst.ptr(i);
        if( flipcode == 0 )
            memcpy(dptr, sptr, width);
        else
        {
            for( j = 0; j < width; j += esz )
                for( k = 0; k < esz; k++ )
                    dptr[j + k] = sptr[width - j - esz + k];
        }
    }
}

static void flip_inplace(Mat& dst, int flipcode)
{
    Mat m;
    m.create(dst.size(), dst.type());
    reference::flip(dst, m, flipcode);
    memcpy(dst.ptr<uchar>(), m.ptr<uchar>(), dst.total() * dst.elemSize());
}

static void rotate(const Mat& src, Mat& dst, int rotateMode)
{
    Mat tmp;
    switch (rotateMode)
    {
    case ROTATE_90_CLOCKWISE:
        cvtest::transpose(src, tmp);
        reference::flip(tmp, dst, 1);
        break;
    case ROTATE_180:
        reference::flip(src, dst, -1);
        break;
    case ROTATE_90_COUNTERCLOCKWISE:
        cvtest::transpose(src, tmp);
        reference::flip(tmp, dst, 0);
        break;
    default:
        break;
    }
}

static void setIdentity(Mat& dst, const Scalar& s)
{
    CV_Assert( dst.dims == 2 && dst.channels() <= 4 );
    double buf[4];
    scalarToRawData(s, buf, dst.type(), 0);
    int i, k, esz = (int)dst.elemSize(), width = dst.cols*esz;

    for( i = 0; i < dst.rows; i++ )
    {
        uchar* dptr = dst.ptr(i);
        memset( dptr, 0, width );
        if( i < dst.cols )
            for( k = 0; k < esz; k++ )
                dptr[i*esz + k] = ((uchar*)buf)[k];
    }
}

} // namespace

struct FlipOp : public BaseElemWiseOp
{
    FlipOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) { flipcode = 0; }
    void getRandomSize(RNG& rng, vector<int>& size)
    {
        cvtest::randomSize(rng, 2, 2, ARITHM_MAX_SIZE_LOG, size);
    }
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cv::flip(src[0], dst, flipcode);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        reference::flip(src[0], dst, flipcode);
    }
    void generateScalars(int, RNG& rng)
    {
        flipcode = rng.uniform(0, 3) - 1;
    }
    double getMaxErr(int)
    {
        return 0;
    }
    int flipcode;
};

struct FlipInplaceOp : public BaseElemWiseOp
{
    FlipInplaceOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) { flipcode = 0; }
    void getRandomSize(RNG& rng, vector<int>& size)
    {
        cvtest::randomSize(rng, 2, 2, ARITHM_MAX_SIZE_LOG, size);
    }
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        dst.create(src[0].size(), src[0].type());
        memcpy(dst.ptr<uchar>(), src[0].ptr<uchar>(), src[0].total() * src[0].elemSize());
        cv::flip(dst, dst, flipcode);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        dst.create(src[0].size(), src[0].type());
        memcpy(dst.ptr<uchar>(), src[0].ptr<uchar>(), src[0].total() * src[0].elemSize());
        reference::flip_inplace(dst, flipcode);
    }
    void generateScalars(int, RNG& rng)
    {
        flipcode = rng.uniform(0, 3) - 1;
    }
    double getMaxErr(int)
    {
        return 0;
    }
    int flipcode;
};

struct RotateOp : public BaseElemWiseOp
{
    RotateOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) { rotatecode = 0; }
    void getRandomSize(RNG& rng, vector<int>& size)
    {
        cvtest::randomSize(rng, 2, 2, ARITHM_MAX_SIZE_LOG, size);
    }
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cv::rotate(src[0], dst, rotatecode);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        reference::rotate(src[0], dst, rotatecode);
    }
    void generateScalars(int, RNG& rng)
    {
        rotatecode = rng.uniform(0, 3);
    }
    double getMaxErr(int)
    {
        return 0;
    }
    int rotatecode;
};

struct TransposeOp : public BaseElemWiseOp
{
    TransposeOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
    void getRandomSize(RNG& rng, vector<int>& size)
    {
        cvtest::randomSize(rng, 2, 2, ARITHM_MAX_SIZE_LOG, size);
    }
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cv::transpose(src[0], dst);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cvtest::transpose(src[0], dst);
    }
    double getMaxErr(int)
    {
        return 0;
    }
};

struct SetIdentityOp : public BaseElemWiseOp
{
    SetIdentityOp() : BaseElemWiseOp(0, FIX_ALPHA+FIX_BETA, 1, 1, Scalar::all(0)) {}
    void getRandomSize(RNG& rng, vector<int>& size)
    {
        cvtest::randomSize(rng, 2, 2, ARITHM_MAX_SIZE_LOG, size);
    }
    void op(const vector<Mat>&, Mat& dst, const Mat&)
    {
        cv::setIdentity(dst, gamma);
    }
    void refop(const vector<Mat>&, Mat& dst, const Mat&)
    {
        reference::setIdentity(dst, gamma);
    }
    double getMaxErr(int)
    {
        return 0;
    }
};

struct SetZeroOp : public BaseElemWiseOp
{
    SetZeroOp() : BaseElemWiseOp(0, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
    void op(const vector<Mat>&, Mat& dst, const Mat&)
    {
        dst = Scalar::all(0);
    }
    void refop(const vector<Mat>&, Mat& dst, const Mat&)
    {
        cvtest::set(dst, Scalar::all(0));
    }
    double getMaxErr(int)
    {
        return 0;
    }
};

namespace reference {
static void exp(const Mat& src, Mat& dst)
{
    dst.create( src.size, src.type() );
    const Mat *arrays[]={&src, &dst, 0};
    Mat planes[2];

    NAryMatIterator it(arrays, planes);
    size_t j, total = planes[0].total()*src.channels();
    size_t i, nplanes = it.nplanes;
    int depth = src.depth();

    for( i = 0; i < nplanes; i++, ++it )
    {
        const uchar* sptr = planes[0].ptr();
        uchar* dptr = planes[1].ptr();

        if( depth == CV_32F )
        {
            for( j = 0; j < total; j++ )
                ((float*)dptr)[j] = std::exp(((const float*)sptr)[j]);
        }
        else if( depth == CV_64F )
        {
            for( j = 0; j < total; j++ )
                ((double*)dptr)[j] = std::exp(((const double*)sptr)[j]);
        }
    }
}

static void log(const Mat& src, Mat& dst)
{
    dst.create( src.size, src.type() );
    const Mat *arrays[]={&src, &dst, 0};
    Mat planes[2];

    NAryMatIterator it(arrays, planes);
    size_t j, total = planes[0].total()*src.channels();
    size_t i, nplanes = it.nplanes;
    int depth = src.depth();

    for( i = 0; i < nplanes; i++, ++it )
    {
        const uchar* sptr = planes[0].ptr();
        uchar* dptr = planes[1].ptr();

        if( depth == CV_32F )
        {
            for( j = 0; j < total; j++ )
                ((float*)dptr)[j] = (float)std::log(fabs(((const float*)sptr)[j]));
        }
        else if( depth == CV_64F )
        {
            for( j = 0; j < total; j++ )
                ((double*)dptr)[j] = std::log(fabs(((const double*)sptr)[j]));
        }
    }
}

} // namespace

struct ExpOp : public BaseArithmOp
{
    ExpOp() : BaseArithmOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
    int getRandomType(RNG& rng)
    {
        return cvtest::randomType(rng, _OutputArray::DEPTH_MASK_FLT, 1, ARITHM_MAX_CHANNELS);
    }
    void getValueRange(int depth, double& minval, double& maxval)
    {
        maxval = depth == CV_32F ? 80 : 700;
        minval = -maxval;
    }
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        cv::exp(src[0], dst);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        reference::exp(src[0], dst);
    }
    double getMaxErr(int depth)
    {
        return depth == CV_32F ? 1e-5 : 1e-12;
    }
};


struct LogOp : public BaseArithmOp
{
    LogOp() : BaseArithmOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
    int getRandomType(RNG& rng)
    {
        return cvtest::randomType(rng, _OutputArray::DEPTH_MASK_FLT, 1, ARITHM_MAX_CHANNELS);
    }
    void getValueRange(int depth, double& minval, double& maxval)
    {
        maxval = depth == CV_32F ? 50 : 100;
        minval = -maxval;
    }
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        Mat temp;
        reference::exp(src[0], temp);
        cv::log(temp, dst);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        Mat temp;
        reference::exp(src[0], temp);
        reference::log(temp, dst);
    }
    double getMaxErr(int depth)
    {
        return depth == CV_32F ? 1e-5 : 1e-12;
    }
};


namespace reference {
static void cartToPolar(const Mat& mx, const Mat& my, Mat& mmag, Mat& mangle, bool angleInDegrees)
{
    CV_Assert( (mx.type() == CV_32F || mx.type() == CV_64F) &&
              mx.type() == my.type() && mx.size == my.size );
    mmag.create( mx.size, mx.type() );
    mangle.create( mx.size, mx.type() );
    const Mat *arrays[]={&mx, &my, &mmag, &mangle, 0};
    Mat planes[4];

    NAryMatIterator it(arrays, planes);
    size_t j, total = planes[0].total();
    size_t i, nplanes = it.nplanes;
    int depth = mx.depth();
    double scale = angleInDegrees ? 180/CV_PI : 1;

    for( i = 0; i < nplanes; i++, ++it )
    {
        if( depth == CV_32F )
        {
            const float* xptr = planes[0].ptr<float>();
            const float* yptr = planes[1].ptr<float>();
            float* mptr = planes[2].ptr<float>();
            float* aptr = planes[3].ptr<float>();

            for( j = 0; j < total; j++ )
            {
                mptr[j] = std::sqrt(xptr[j]*xptr[j] + yptr[j]*yptr[j]);
                double a = atan2((double)yptr[j], (double)xptr[j]);
                if( a < 0 ) a += CV_PI*2;
                aptr[j] = (float)(a*scale);
            }
        }
        else
        {
            const double* xptr = planes[0].ptr<double>();
            const double* yptr = planes[1].ptr<double>();
            double* mptr = planes[2].ptr<double>();
            double* aptr = planes[3].ptr<double>();

            for( j = 0; j < total; j++ )
            {
                mptr[j] = std::sqrt(xptr[j]*xptr[j] + yptr[j]*yptr[j]);
                double a = atan2(yptr[j], xptr[j]);
                if( a < 0 ) a += CV_PI*2;
                aptr[j] = a*scale;
            }
        }
    }
}

} // namespace

struct CartToPolarToCartOp : public BaseArithmOp
{
    CartToPolarToCartOp() : BaseArithmOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0))
    {
        context = 3;
        angleInDegrees = true;
    }
    int getRandomType(RNG& rng)
    {
        return cvtest::randomType(rng, _OutputArray::DEPTH_MASK_FLT, 1, 1);
    }
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        Mat mag, angle, x, y;

        cv::cartToPolar(src[0], src[1], mag, angle, angleInDegrees);
        cv::polarToCart(mag, angle, x, y, angleInDegrees);

        Mat msrc[] = {mag, angle, x, y};
        int pairs[] = {0, 0, 1, 1, 2, 2, 3, 3};
        dst.create(src[0].size, CV_MAKETYPE(src[0].depth(), 4));
        cv::mixChannels(msrc, 4, &dst, 1, pairs, 4);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        Mat mag, angle;
        reference::cartToPolar(src[0], src[1], mag, angle, angleInDegrees);
        Mat msrc[] = {mag, angle, src[0], src[1]};
        int pairs[] = {0, 0, 1, 1, 2, 2, 3, 3};
        dst.create(src[0].size, CV_MAKETYPE(src[0].depth(), 4));
        cv::mixChannels(msrc, 4, &dst, 1, pairs, 4);
    }
    void generateScalars(int, RNG& rng)
    {
        angleInDegrees = rng.uniform(0, 2) != 0;
    }
    double getMaxErr(int)
    {
        return 1e-3;
    }
    bool angleInDegrees;
};


struct MeanOp : public BaseArithmOp
{
    MeanOp() : BaseArithmOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK+SCALAR_OUTPUT, 1, 1, Scalar::all(0))
    {
        context = 3;
    }
    void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        dst.create(1, 1, CV_64FC4);
        dst.at<Scalar>(0,0) = cv::mean(src[0], mask);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        dst.create(1, 1, CV_64FC4);
        dst.at<Scalar>(0,0) = cvtest::mean(src[0], mask);
    }
    double getMaxErr(int)
    {
        return 1e-5;
    }
};


struct SumOp : public BaseArithmOp
{
    SumOp() : BaseArithmOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SCALAR_OUTPUT, 1, 1, Scalar::all(0))
    {
        context = 3;
    }
    void op(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        dst.create(1, 1, CV_64FC4);
        dst.at<Scalar>(0,0) = cv::sum(src[0]);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&)
    {
        dst.create(1, 1, CV_64FC4);
        dst.at<Scalar>(0,0) = cvtest::mean(src[0])*(double)src[0].total();
    }
    double getMaxErr(int depth)
    {
        return depth == CV_16F || depth == CV_16BF ? 1e-3 : 1e-5;
    }
};


struct CountNonZeroOp : public BaseArithmOp
{
    CountNonZeroOp() : BaseArithmOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SCALAR_OUTPUT+SUPPORT_MASK, 1, 1, Scalar::all(0))
    {}
    int getRandomType(RNG& rng)
    {
        return cvtest::randomType(rng, baseArithmTypeMask, 1, 1);
    }
    void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        Mat temp;
        src[0].copyTo(temp);
        if( !mask.empty() )
            temp.setTo(Scalar::all(0), mask);
        dst.create(1, 1, CV_32S);
        dst.at<int>(0,0) = cv::countNonZero(temp);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        Mat temp;
        cvtest::compare(src[0], 0, temp, CMP_NE);
        if( !mask.empty() )
            cvtest::set(temp, Scalar::all(0), mask);
        dst.create(1, 1, CV_32S);
        dst.at<int>(0,0) = saturate_cast<int>(cvtest::mean(temp)[0]/255*temp.total());
    }
    double getMaxErr(int)
    {
        return 0;
    }
};


struct MeanStdDevOp : public BaseArithmOp
{
    Scalar sqmeanRef;
    int cn;

    MeanStdDevOp() : BaseArithmOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK+SCALAR_OUTPUT, 1, 1, Scalar::all(0))
    {
        cn = 0;
        context = 7;
    }
    void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        dst.create(1, 2, CV_64FC4);
        cv::meanStdDev(src[0], dst.at<Scalar>(0,0), dst.at<Scalar>(0,1), mask);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        Mat temp;
        cvtest::convert(src[0], temp, CV_64F);
        cvtest::multiply(temp, temp, temp);
        Scalar mean = cvtest::mean(src[0], mask);
        Scalar sqmean = cvtest::mean(temp, mask);

        sqmeanRef = sqmean;
        cn = temp.channels();

        for( int c = 0; c < 4; c++ )
            sqmean[c] = std::sqrt(std::max(sqmean[c] - mean[c]*mean[c], 0.));

        dst.create(1, 2, CV_64FC4);
        dst.at<Scalar>(0,0) = mean;
        dst.at<Scalar>(0,1) = sqmean;
    }
    double getMaxErr(int)
    {
        CV_Assert(cn > 0);
        double err = sqmeanRef[0];
        for(int i = 1; i < cn; ++i)
            err = std::max(err, sqmeanRef[i]);
        return 3e-7 * err;
    }
};


struct NormOp : public BaseArithmOp
{
    NormOp() : BaseArithmOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK+SCALAR_OUTPUT, 1, 1, Scalar::all(0))
    {
        context = 1;
        normType = 0;
    }
    int getRandomType(RNG& rng)
    {
        int type = cvtest::randomType(rng, baseArithmTypeMask, 1, 4);
        for(;;)
        {
            normType = rng.uniform(1, 8);
            if( normType == NORM_INF || normType == NORM_L1 ||
                normType == NORM_L2 || normType == NORM_L2SQR ||
                normType == NORM_HAMMING || normType == NORM_HAMMING2 )
                break;
        }
        if( normType == NORM_HAMMING || normType == NORM_HAMMING2 )
        {
            type = CV_8U;
        }
        return type;
    }
    void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        dst.create(1, 2, CV_64FC1);
        dst.at<double>(0,0) = cv::norm(src[0], normType, mask);
        dst.at<double>(0,1) = cv::norm(src[0], src[1], normType, mask);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        dst.create(1, 2, CV_64FC1);
        dst.at<double>(0,0) = cvtest::norm(src[0], normType, mask);
        dst.at<double>(0,1) = cvtest::norm(src[0], src[1], normType, mask);
    }
    void generateScalars(int, RNG& /*rng*/)
    {
    }
    double getMaxErr(int depth)
    {
        return normType == NORM_INF && depth <= CV_32S ? 0 :
            depth == CV_16F || depth == CV_16BF ? 1e-5 : 1e-6;
    }
    int normType;
};


struct MinMaxLocOp : public BaseArithmOp
{
    MinMaxLocOp() : BaseArithmOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK+SCALAR_OUTPUT, 1, 1, Scalar::all(0))
    {
        context = ARITHM_MAX_NDIMS*2 + 2;
    }
    int getRandomType(RNG& rng)
    {
        return cvtest::randomType(rng, baseArithmTypeMask, 1, 1);
    }
    void saveOutput(const vector<int>& minidx, const vector<int>& maxidx,
                    double minval, double maxval, Mat& dst)
    {
        int i, ndims = (int)minidx.size();
        dst.create(1, ndims*2 + 2, CV_64FC1);

        for( i = 0; i < ndims; i++ )
        {
            dst.at<double>(0,i) = minidx[i];
            dst.at<double>(0,i+ndims) = maxidx[i];
        }
        dst.at<double>(0,ndims*2) = minval;
        dst.at<double>(0,ndims*2+1) = maxval;
    }
    void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        int ndims = src[0].dims;
        vector<int> minidx(ndims), maxidx(ndims);
        double minval=0, maxval=0;
        cv::minMaxIdx(src[0], &minval, &maxval, &minidx[0], &maxidx[0], mask);
        saveOutput(minidx, maxidx, minval, maxval, dst);
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat& mask)
    {
        int ndims=src[0].dims;
        vector<int> minidx(ndims), maxidx(ndims);
        double minval=0, maxval=0;
        cvtest::minMaxLoc(src[0], &minval, &maxval, &minidx, &maxidx, mask);
        saveOutput(minidx, maxidx, minval, maxval, dst);
    }
    double getMaxErr(int)
    {
        return 0;
    }
};

struct reduceArgMinMaxOp : public BaseArithmOp
{
    reduceArgMinMaxOp() : BaseArithmOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)),
                          isLast(false), isMax(false), axis(0)
    {
        context = ARITHM_MAX_NDIMS*2 + 2;
    }
    int getRandomType(RNG& rng) override
    {
        return cvtest::randomType(rng, baseArithmTypeMask, 1, 1);
    }
    void getRandomSize(RNG& rng, vector<int>& size) override
    {
        cvtest::randomSize(rng, 2, ARITHM_MAX_NDIMS, 6, size);
    }
    void generateScalars(int depth, RNG& rng) override
    {
        BaseElemWiseOp::generateScalars(depth, rng);
        isLast = (randInt(rng) % 2 == 0);
        isMax = (randInt(rng) % 2 == 0);
        axis = randInt(rng);
    }
    int getAxis(const Mat& src) const
    {
        int dims = src.dims;
        return static_cast<int>(axis % (2 * dims)) - dims; // [-dims; dims - 1]
    }
    void op(const vector<Mat>& src, Mat& dst, const Mat&) override
    {
        const Mat& inp = src[0];
        const int axis_ = getAxis(inp);
        if (isMax)
        {
            cv::reduceArgMax(inp, dst, axis_, isLast);
        }
        else
        {
            cv::reduceArgMin(inp, dst, axis_, isLast);
        }
    }
    void refop(const vector<Mat>& src, Mat& dst, const Mat&) override
    {
        const Mat& inp = src[0];
        const int axis_ = getAxis(inp);

        if (!isLast && !isMax)
        {
            cvtest::MinMaxReducer<std::less>::reduce(inp, dst, axis_);
        }
        else if (!isLast && isMax)
        {
            cvtest::MinMaxReducer<std::greater>::reduce(inp, dst, axis_);
        }
        else if (isLast && !isMax)
        {
            cvtest::MinMaxReducer<std::less_equal>::reduce(inp, dst, axis_);
        }
        else
        {
            cvtest::MinMaxReducer<std::greater_equal>::reduce(inp, dst, axis_);
        }
    }

    bool isLast;
    bool isMax;
    uint32_t axis;
};


typedef Ptr<BaseElemWiseOp> ElemWiseOpPtr;
class ElemWiseTest : public ::testing::TestWithParam<ElemWiseOpPtr> {};

TEST_P(ElemWiseTest, accuracy)
{
    ElemWiseOpPtr op = GetParam();

    int testIdx = 0;
    RNG rng((uint64)ARITHM_RNG_SEED);
    for( testIdx = 0; testIdx < ARITHM_NTESTS; testIdx++ )
    {
        vector<int> size;
        op->getRandomSize(rng, size);
        int type = op->getRandomType(rng);
        int depth = CV_MAT_DEPTH(type);
        bool haveMask = ((op->flags & BaseElemWiseOp::SUPPORT_MASK) != 0
                || (op->flags & BaseElemWiseOp::SUPPORT_MULTICHANNELMASK) != 0) && rng.uniform(0, 4) == 0;

        double minval=0, maxval=0;
        op->getValueRange(depth, minval, maxval);
        int i, ninputs = op->ninputs;
        vector<Mat> src(ninputs);
        for( i = 0; i < ninputs; i++ )
            src[i] = cvtest::randomMat(rng, size, type, minval, maxval, true);
        Mat dst0, dst, mask;
        if( haveMask ) {
            bool multiChannelMask = (op->flags & BaseElemWiseOp::SUPPORT_MULTICHANNELMASK) != 0
                    && rng.uniform(0, 2) == 0;
            int masktype = CV_8UC(multiChannelMask ? CV_MAT_CN(type) : 1);
            mask = cvtest::randomMat(rng, size, masktype, 0, 2, true);
        }

        if( (haveMask || ninputs == 0) && !(op->flags & BaseElemWiseOp::SCALAR_OUTPUT))
        {
            dst0 = cvtest::randomMat(rng, size, type, minval, maxval, false);
            dst = cvtest::randomMat(rng, size, type, minval, maxval, true);
            cvtest::copy(dst, dst0);
        }
        op->generateScalars(depth, rng);

        /*printf("testIdx=%d, depth=%d, channels=%d, have_mask=%d\n", testIdx, depth, src[0].channels(), (int)haveMask);
        if (testIdx == 22)
            printf(">>>\n");*/

        op->refop(src, dst0, mask);
        op->op(src, dst, mask);

        double maxErr = op->getMaxErr(depth);

        ASSERT_PRED_FORMAT2(cvtest::MatComparator(maxErr, op->context), dst0, dst) << "\nsrc[0] ~ " <<
            cvtest::MatInfo(!src.empty() ? src[0] : Mat()) << "\ntestCase #" << testIdx << "\n";
    }
}


INSTANTIATE_TEST_CASE_P(Core_Copy, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new CopyOp)));
INSTANTIATE_TEST_CASE_P(Core_Set, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new SetOp)));
INSTANTIATE_TEST_CASE_P(Core_SetZero, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new SetZeroOp)));
INSTANTIATE_TEST_CASE_P(Core_ConvertScale, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new ConvertScaleOp)));
INSTANTIATE_TEST_CASE_P(Core_ConvertScaleFp16, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new ConvertScaleFp16Op)));
INSTANTIATE_TEST_CASE_P(Core_ConvertScaleAbs, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new ConvertScaleAbsOp)));

INSTANTIATE_TEST_CASE_P(Core_Add, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new AddOp)));
INSTANTIATE_TEST_CASE_P(Core_Sub, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new SubOp)));
INSTANTIATE_TEST_CASE_P(Core_AddS, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new AddSOp)));
INSTANTIATE_TEST_CASE_P(Core_SubRS, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new SubRSOp)));
INSTANTIATE_TEST_CASE_P(Core_ScaleAdd, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new ScaleAddOp)));
INSTANTIATE_TEST_CASE_P(Core_AddWeighted, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new AddWeightedOp)));
INSTANTIATE_TEST_CASE_P(Core_AbsDiff, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new AbsDiffOp)));


INSTANTIATE_TEST_CASE_P(Core_AbsDiffS, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new AbsDiffSOp)));

INSTANTIATE_TEST_CASE_P(Core_And, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new LogicOp('&'))));
INSTANTIATE_TEST_CASE_P(Core_AndS, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new LogicSOp('&'))));
INSTANTIATE_TEST_CASE_P(Core_Or, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new LogicOp('|'))));
INSTANTIATE_TEST_CASE_P(Core_OrS, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new LogicSOp('|'))));
INSTANTIATE_TEST_CASE_P(Core_Xor, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new LogicOp('^'))));
INSTANTIATE_TEST_CASE_P(Core_XorS, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new LogicSOp('^'))));
INSTANTIATE_TEST_CASE_P(Core_Not, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new LogicSOp('~'))));

INSTANTIATE_TEST_CASE_P(Core_Max, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new MaxOp)));
INSTANTIATE_TEST_CASE_P(Core_MaxS, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new MaxSOp)));
INSTANTIATE_TEST_CASE_P(Core_Min, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new MinOp)));
INSTANTIATE_TEST_CASE_P(Core_MinS, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new MinSOp)));

INSTANTIATE_TEST_CASE_P(Core_Mul, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new MulOp)));
INSTANTIATE_TEST_CASE_P(Core_Div, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new DivOp)));
INSTANTIATE_TEST_CASE_P(Core_Recip, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new RecipOp)));

INSTANTIATE_TEST_CASE_P(Core_Cmp, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new CmpOp)));
INSTANTIATE_TEST_CASE_P(Core_CmpS, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new CmpSOp)));

INSTANTIATE_TEST_CASE_P(Core_InRangeS, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new InRangeSOp)));
INSTANTIATE_TEST_CASE_P(Core_InRange, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new InRangeOp)));

INSTANTIATE_TEST_CASE_P(Core_FiniteMask, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new FiniteMaskOp)));

INSTANTIATE_TEST_CASE_P(Core_Flip, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new FlipOp)));
INSTANTIATE_TEST_CASE_P(Core_FlipInplace, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new FlipInplaceOp)));
INSTANTIATE_TEST_CASE_P(Core_Rotate, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new RotateOp)));
INSTANTIATE_TEST_CASE_P(Core_Transpose, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new TransposeOp)));
INSTANTIATE_TEST_CASE_P(Core_SetIdentity, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new SetIdentityOp)));

INSTANTIATE_TEST_CASE_P(Core_Exp, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new ExpOp)));
INSTANTIATE_TEST_CASE_P(Core_Log, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new LogOp)));

INSTANTIATE_TEST_CASE_P(Core_CountNonZero, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new CountNonZeroOp)));
INSTANTIATE_TEST_CASE_P(Core_Mean, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new MeanOp)));
INSTANTIATE_TEST_CASE_P(Core_MeanStdDev, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new MeanStdDevOp)));
INSTANTIATE_TEST_CASE_P(Core_Sum, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new SumOp)));
INSTANTIATE_TEST_CASE_P(Core_Norm, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new NormOp)));
INSTANTIATE_TEST_CASE_P(Core_MinMaxLoc, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new MinMaxLocOp)));
INSTANTIATE_TEST_CASE_P(Core_reduceArgMinMax, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new reduceArgMinMaxOp)));
INSTANTIATE_TEST_CASE_P(Core_CartToPolarToCart, ElemWiseTest, ::testing::Values(ElemWiseOpPtr(new CartToPolarToCartOp)));

// Mixed Type Arithmetic Operations

typedef std::tuple<ElemWiseOpPtr, std::tuple<cvtest::MatDepth, cvtest::MatDepth>, int> SomeType;
class ArithmMixedTest : public ::testing::TestWithParam<SomeType> {};

TEST_P(ArithmMixedTest, accuracy)
{
    auto p = GetParam();
    ElemWiseOpPtr op = std::get<0>(p);
    int srcDepth = std::get<0>(std::get<1>(p));
    int dstDepth = std::get<1>(std::get<1>(p));
    int channels = std::get<2>(p);

    int srcType = CV_MAKETYPE(srcDepth, channels);
    int dstType = CV_MAKETYPE(dstDepth, channels);
    op->flags |= BaseElemWiseOp::MIXED_TYPE;
    int testIdx = 0;
    RNG rng((uint64)ARITHM_RNG_SEED);
    for( testIdx = 0; testIdx < ARITHM_NTESTS; testIdx++ )
    {
        vector<int> size;
        op->getRandomSize(rng, size);
        bool haveMask = ((op->flags & BaseElemWiseOp::SUPPORT_MASK) != 0) && rng.uniform(0, 4) == 0;

        double minval=0, maxval=0;
        op->getValueRange(srcDepth, minval, maxval);
        int ninputs = op->ninputs;
        vector<Mat> src(ninputs);
        for(int i = 0; i < ninputs; i++ )
            src[i] = cvtest::randomMat(rng, size, srcType, minval, maxval, true);
        Mat dst0, dst, mask;
        if( haveMask )
        {
            mask = cvtest::randomMat(rng, size, CV_8UC1, 0, 2, true);
        }

        dst0 = cvtest::randomMat(rng, size, dstType, minval, maxval, false);
        dst = cvtest::randomMat(rng, size, dstType, minval, maxval, true);
        cvtest::copy(dst, dst0);

        op->generateScalars(dstDepth, rng);

        op->refop(src, dst0, mask);
        op->op(src, dst, mask);

        double maxErr = op->getMaxErr(dstDepth);
        ASSERT_PRED_FORMAT2(cvtest::MatComparator(maxErr, op->context), dst0, dst) << "\nsrc[0] ~ " <<
            cvtest::MatInfo(!src.empty() ? src[0] : Mat()) << "\ntestCase #" << testIdx << "\n";
    }
}


INSTANTIATE_TEST_CASE_P(Core_AddMixed, ArithmMixedTest,
                        ::testing::Combine(::testing::Values(ElemWiseOpPtr(new AddOp)),
                                           ::testing::Values(std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8U, CV_16U},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8S, CV_16S},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8U, CV_32F},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8S, CV_32F}),
                                           ::testing::Values(1, 3, 4)));
INSTANTIATE_TEST_CASE_P(Core_AddScalarMixed, ArithmMixedTest,
                        ::testing::Combine(::testing::Values(ElemWiseOpPtr(new AddSOp)),
                                           ::testing::Values(std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8U, CV_16U},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8S, CV_16S},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8U, CV_32F},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8S, CV_32F}),
                                           ::testing::Values(1, 3, 4)));
INSTANTIATE_TEST_CASE_P(Core_AddWeightedMixed, ArithmMixedTest,
                        ::testing::Combine(::testing::Values(ElemWiseOpPtr(new AddWeightedOp)),
                                           ::testing::Values(std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8U, CV_16U},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8S, CV_16S},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8U, CV_32F},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8S, CV_32F}),
                                           ::testing::Values(1, 3, 4)));
INSTANTIATE_TEST_CASE_P(Core_SubMixed, ArithmMixedTest,
                        ::testing::Combine(::testing::Values(ElemWiseOpPtr(new SubOp)),
                                           ::testing::Values(std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8U, CV_16U},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8S, CV_16S},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8U, CV_32F},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8S, CV_32F}),
                                           ::testing::Values(1, 3, 4)));
INSTANTIATE_TEST_CASE_P(Core_SubScalarMinusArgMixed, ArithmMixedTest,
                        ::testing::Combine(::testing::Values(ElemWiseOpPtr(new SubRSOp)),
                                           ::testing::Values(std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8U, CV_16U},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8S, CV_16S},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8U, CV_32F},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8S, CV_32F}),
                                           ::testing::Values(1, 3, 4)));
INSTANTIATE_TEST_CASE_P(Core_MulMixed, ArithmMixedTest,
                        ::testing::Combine(::testing::Values(ElemWiseOpPtr(new MulOp)),
                                           ::testing::Values(std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8U, CV_16U},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8S, CV_16S},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8U, CV_32F},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8S, CV_32F}),
                                           ::testing::Values(1, 3, 4)));
INSTANTIATE_TEST_CASE_P(Core_MulScalarMixed, ArithmMixedTest,
                        ::testing::Combine(::testing::Values(ElemWiseOpPtr(new MulSOp)),
                                           ::testing::Values(std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8U, CV_16U},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8S, CV_16S},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8U, CV_32F},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8S, CV_32F}),
                                           ::testing::Values(1, 3, 4)));
INSTANTIATE_TEST_CASE_P(Core_DivMixed, ArithmMixedTest,
                        ::testing::Combine(::testing::Values(ElemWiseOpPtr(new DivOp)),
                                           ::testing::Values(std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8U, CV_16U},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8S, CV_16S},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8U, CV_32F},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8S, CV_32F}),
                                           ::testing::Values(1, 3, 4)));
INSTANTIATE_TEST_CASE_P(Core_RecipMixed, ArithmMixedTest,
                        ::testing::Combine(::testing::Values(ElemWiseOpPtr(new RecipOp)),
                                           ::testing::Values(std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8U, CV_16U},
                                                             std::tuple<cvtest::MatDepth, cvtest::MatDepth>{CV_8S, CV_32F}),
                                           ::testing::Values(1, 3, 4)));

TEST(Core_ArithmMask, uninitialized)
{
    RNG& rng = theRNG();
    const int MAX_DIM=3;
    int sizes[MAX_DIM];
    for( int iter = 0; iter < 100; iter++ )
    {
        int dims = rng.uniform(1, MAX_DIM+1);
        int depth = rng.uniform(CV_8U, CV_64F+1);
        int cn = rng.uniform(1, 6);
        int type = CV_MAKETYPE(depth, cn);
        int op = rng.uniform(0, depth < CV_32F ? 5 : 2); // don't run binary operations between floating-point values
        int depth1 = op <= 1 ? CV_64F : depth;
        for (int k = 0; k < MAX_DIM; k++)
        {
            sizes[k] = k < dims ? rng.uniform(1, 30) : 0;
        }
        SCOPED_TRACE(cv::format("iter=%d dims=%d depth=%d cn=%d type=%d op=%d depth1=%d dims=[%d; %d; %d]",
                                 iter,   dims,   depth,   cn,   type,   op,   depth1, sizes[0], sizes[1], sizes[2]));

        Mat a(dims, sizes, type), a1;
        Mat b(dims, sizes, type), b1;
        Mat mask(dims, sizes, CV_8U);
        Mat mask1;
        Mat c, d;

        rng.fill(a, RNG::UNIFORM, 0, 100);
        rng.fill(b, RNG::UNIFORM, 0, 100);

        // [-2,2) range means that the each generated random number
        // will be one of -2, -1, 0, 1. Saturated to [0,255], it will become
        // 0, 0, 0, 1 => the mask will be filled by ~25%.
        rng.fill(mask, RNG::UNIFORM, -2, 2);

        a.convertTo(a1, depth1);
        b.convertTo(b1, depth1);
        // invert the mask
        cv::compare(mask, 0, mask1, CMP_EQ);
        a1.setTo(0, mask1);
        b1.setTo(0, mask1);

        if( op == 0 )
        {
            cv::add(a, b, c, mask);
            cv::add(a1, b1, d);
        }
        else if( op == 1 )
        {
            cv::subtract(a, b, c, mask);
            cv::subtract(a1, b1, d);
        }
        else if( op == 2 )
        {
            cv::bitwise_and(a, b, c, mask);
            cv::bitwise_and(a1, b1, d);
        }
        else if( op == 3 )
        {
            cv::bitwise_or(a, b, c, mask);
            cv::bitwise_or(a1, b1, d);
        }
        else if( op == 4 )
        {
            cv::bitwise_xor(a, b, c, mask);
            cv::bitwise_xor(a1, b1, d);
        }
        Mat d1;
        d.convertTo(d1, depth);
        EXPECT_LE(cvtest::norm(c, d1, NORM_INF), DBL_EPSILON);
    }

    Mat_<uchar> tmpSrc(100,100);
    tmpSrc = 124;
    Mat_<uchar> tmpMask(100,100);
    tmpMask = 255;
    Mat_<uchar> tmpDst(100,100);
    tmpDst = 2;
    tmpSrc.copyTo(tmpDst,tmpMask);
}

TEST(Multiply, FloatingPointRounding)
{
    cv::Mat src(1, 1, CV_8UC1, cv::Scalar::all(110)), dst;
    cv::Scalar s(147.286359696927, 1, 1 ,1);

    cv::multiply(src, s, dst, 1, CV_16U);
    // with CV_32F this produce result 16202
    ASSERT_EQ(dst.at<ushort>(0,0), 16201);
}

TEST(Core_Add, AddToColumnWhen3Rows)
{
    cv::Mat m1 = (cv::Mat_<double>(3, 2) << 1, 2, 3, 4, 5, 6);
    m1.col(1) += 10;

    cv::Mat m2 = (cv::Mat_<double>(3, 2) << 1, 12, 3, 14, 5, 16);
    cv::MatExpr diff = m1 - m2;
    int nz = countNonZero(diff);

    ASSERT_EQ(0, nz);
}

TEST(Core_Add, AddToColumnWhen4Rows)
{
    cv::Mat m1 = (cv::Mat_<double>(4, 2) << 1, 2, 3, 4, 5, 6, 7, 8);
    m1.col(1) += 10;

    cv::Mat m2 = (cv::Mat_<double>(4, 2) << 1, 12, 3, 14, 5, 16, 7, 18);

    ASSERT_EQ(0, countNonZero(m1 - m2));
}

TEST(Core_round, CvRound)
{
    ASSERT_EQ(2, cvRound(2.0));
    ASSERT_EQ(2, cvRound(2.1));
    ASSERT_EQ(-2, cvRound(-2.1));
    ASSERT_EQ(3, cvRound(2.8));
    ASSERT_EQ(-3, cvRound(-2.8));
    ASSERT_EQ(2, cvRound(2.5));
    ASSERT_EQ(4, cvRound(3.5));
    ASSERT_EQ(-2, cvRound(-2.5));
    ASSERT_EQ(-4, cvRound(-3.5));
}


typedef testing::TestWithParam<Size> Mul1;

TEST_P(Mul1, One)
{
    Size size = GetParam();
    cv::Mat src(size, CV_32FC1, cv::Scalar::all(2)), dst,
            ref_dst(size, CV_32FC1, cv::Scalar::all(6));

    cv::multiply(3, src, dst);

    ASSERT_EQ(0, cvtest::norm(dst, ref_dst, cv::NORM_INF));
}

INSTANTIATE_TEST_CASE_P(Arithm, Mul1, testing::Values(Size(2, 2), Size(1, 1)));

class SubtractOutputMatNotEmpty : public testing::TestWithParam< tuple<cv::Size, perf::MatType, perf::MatDepth, bool> >
{
public:
    cv::Size size;
    int src_type;
    int dst_depth;
    bool fixed;

    void SetUp()
    {
        size = get<0>(GetParam());
        src_type = get<1>(GetParam());
        dst_depth = get<2>(GetParam());
        fixed = get<3>(GetParam());
    }
};

TEST_P(SubtractOutputMatNotEmpty, Mat_Mat)
{
    cv::Mat src1(size, src_type, cv::Scalar::all(16));
    cv::Mat src2(size, src_type, cv::Scalar::all(16));

    cv::Mat dst;

    if (!fixed)
    {
        cv::subtract(src1, src2, dst, cv::noArray(), dst_depth);
    }
    else
    {
        const cv::Mat fixed_dst(size, CV_MAKE_TYPE((dst_depth > 0 ? dst_depth : CV_16S), src1.channels()));
        cv::subtract(src1, src2, fixed_dst, cv::noArray(), dst_depth);
        dst = fixed_dst;
        dst_depth = fixed_dst.depth();
    }

    ASSERT_FALSE(dst.empty());
    ASSERT_EQ(src1.size(), dst.size());
    ASSERT_EQ(dst_depth > 0 ? dst_depth : src1.depth(), dst.depth());
    ASSERT_EQ(0, cv::countNonZero(dst.reshape(1)));
}

TEST_P(SubtractOutputMatNotEmpty, Mat_Mat_WithMask)
{
    cv::Mat src1(size, src_type, cv::Scalar::all(16));
    cv::Mat src2(size, src_type, cv::Scalar::all(16));
    cv::Mat mask(size, CV_8UC1, cv::Scalar::all(255));

    cv::Mat dst;

    if (!fixed)
    {
        cv::subtract(src1, src2, dst, mask, dst_depth);
    }
    else
    {
        const cv::Mat fixed_dst(size, CV_MAKE_TYPE((dst_depth > 0 ? dst_depth : CV_16S), src1.channels()));
        cv::subtract(src1, src2, fixed_dst, mask, dst_depth);
        dst = fixed_dst;
        dst_depth = fixed_dst.depth();
    }

    ASSERT_FALSE(dst.empty());
    ASSERT_EQ(src1.size(), dst.size());
    ASSERT_EQ(dst_depth > 0 ? dst_depth : src1.depth(), dst.depth());
    ASSERT_EQ(0, cv::countNonZero(dst.reshape(1)));
}

TEST_P(SubtractOutputMatNotEmpty, Mat_Mat_Expr)
{
    cv::Mat src1(size, src_type, cv::Scalar::all(16));
    cv::Mat src2(size, src_type, cv::Scalar::all(16));

    cv::Mat dst = src1 - src2;

    ASSERT_FALSE(dst.empty());
    ASSERT_EQ(src1.size(), dst.size());
    ASSERT_EQ(src1.depth(), dst.depth());
    ASSERT_EQ(0, cv::countNonZero(dst.reshape(1)));
}

TEST_P(SubtractOutputMatNotEmpty, Mat_Scalar)
{
    cv::Mat src(size, src_type, cv::Scalar::all(16));

    cv::Mat dst;

    if (!fixed)
    {
        cv::subtract(src, cv::Scalar::all(16), dst, cv::noArray(), dst_depth);
    }
    else
    {
        const cv::Mat fixed_dst(size, CV_MAKE_TYPE((dst_depth > 0 ? dst_depth : CV_16S), src.channels()));
        cv::subtract(src, cv::Scalar::all(16), fixed_dst, cv::noArray(), dst_depth);
        dst = fixed_dst;
        dst_depth = fixed_dst.depth();
    }

    ASSERT_FALSE(dst.empty());
    ASSERT_EQ(src.size(), dst.size());
    ASSERT_EQ(dst_depth > 0 ? dst_depth : src.depth(), dst.depth());
    ASSERT_EQ(0, cv::countNonZero(dst.reshape(1)));
}

TEST_P(SubtractOutputMatNotEmpty, Mat_Scalar_WithMask)
{
    cv::Mat src(size, src_type, cv::Scalar::all(16));
    cv::Mat mask(size, CV_8UC1, cv::Scalar::all(255));

    cv::Mat dst;

    if (!fixed)
    {
        cv::subtract(src, cv::Scalar::all(16), dst, mask, dst_depth);
    }
    else
    {
        const cv::Mat fixed_dst(size, CV_MAKE_TYPE((dst_depth > 0 ? dst_depth : CV_16S), src.channels()));
        cv::subtract(src, cv::Scalar::all(16), fixed_dst, mask, dst_depth);
        dst = fixed_dst;
        dst_depth = fixed_dst.depth();
    }

    ASSERT_FALSE(dst.empty());
    ASSERT_EQ(src.size(), dst.size());
    ASSERT_EQ(dst_depth > 0 ? dst_depth : src.depth(), dst.depth());
    ASSERT_EQ(0, cv::countNonZero(dst.reshape(1)));
}

TEST_P(SubtractOutputMatNotEmpty, Scalar_Mat)
{
    cv::Mat src(size, src_type, cv::Scalar::all(16));

    cv::Mat dst;

    if (!fixed)
    {
        cv::subtract(cv::Scalar::all(16), src, dst, cv::noArray(), dst_depth);
    }
    else
    {
        const cv::Mat fixed_dst(size, CV_MAKE_TYPE((dst_depth > 0 ? dst_depth : CV_16S), src.channels()));
        cv::subtract(cv::Scalar::all(16), src, fixed_dst, cv::noArray(), dst_depth);
        dst = fixed_dst;
        dst_depth = fixed_dst.depth();
    }

    ASSERT_FALSE(dst.empty());
    ASSERT_EQ(src.size(), dst.size());
    ASSERT_EQ(dst_depth > 0 ? dst_depth : src.depth(), dst.depth());
    ASSERT_EQ(0, cv::countNonZero(dst.reshape(1)));
}

TEST_P(SubtractOutputMatNotEmpty, Scalar_Mat_WithMask)
{
    cv::Mat src(size, src_type, cv::Scalar::all(16));
    cv::Mat mask(size, CV_8UC1, cv::Scalar::all(255));

    cv::Mat dst;

    if (!fixed)
    {
        cv::subtract(cv::Scalar::all(16), src, dst, mask, dst_depth);
    }
    else
    {
        const cv::Mat fixed_dst(size, CV_MAKE_TYPE((dst_depth > 0 ? dst_depth : CV_16S), src.channels()));
        cv::subtract(cv::Scalar::all(16), src, fixed_dst, mask, dst_depth);
        dst = fixed_dst;
        dst_depth = fixed_dst.depth();
    }

    ASSERT_FALSE(dst.empty());
    ASSERT_EQ(src.size(), dst.size());
    ASSERT_EQ(dst_depth > 0 ? dst_depth : src.depth(), dst.depth());
    ASSERT_EQ(0, cv::countNonZero(dst.reshape(1)));
}

TEST_P(SubtractOutputMatNotEmpty, Mat_Mat_3d)
{
    int dims[] = {5, size.height, size.width};

    cv::Mat src1(3, dims, src_type, cv::Scalar::all(16));
    cv::Mat src2(3, dims, src_type, cv::Scalar::all(16));

    cv::Mat dst;

    if (!fixed)
    {
        cv::subtract(src1, src2, dst, cv::noArray(), dst_depth);
    }
    else
    {
        const cv::Mat fixed_dst(3, dims, CV_MAKE_TYPE((dst_depth > 0 ? dst_depth : CV_16S), src1.channels()));
        cv::subtract(src1, src2, fixed_dst, cv::noArray(), dst_depth);
        dst = fixed_dst;
        dst_depth = fixed_dst.depth();
    }

    ASSERT_FALSE(dst.empty());
    ASSERT_EQ(src1.dims, dst.dims);
    ASSERT_EQ(src1.size, dst.size);
    ASSERT_EQ(dst_depth > 0 ? dst_depth : src1.depth(), dst.depth());
    ASSERT_EQ(0, cv::countNonZero(dst.reshape(1)));
}

INSTANTIATE_TEST_CASE_P(Arithm, SubtractOutputMatNotEmpty, testing::Combine(
    testing::Values(cv::Size(16, 16), cv::Size(13, 13), cv::Size(16, 13), cv::Size(13, 16)),
    testing::Values(perf::MatType(CV_8UC1), CV_8UC3, CV_8UC4, CV_16SC1, CV_16SC3),
    testing::Values(-1, CV_16S, CV_32S, CV_32F),
    testing::Bool()));

TEST(Core_FindNonZero, regression)
{
    Mat img(10, 10, CV_8U, Scalar::all(0));
    vector<Point> pts, pts2(5);
    findNonZero(img, pts);
    findNonZero(img, pts2);
    ASSERT_TRUE(pts.empty() && pts2.empty());

    RNG rng((uint64)-1);
    size_t nz = 0;
    for( int i = 0; i < 10; i++ )
    {
        int idx = rng.uniform(0, img.rows*img.cols);
        if( !img.data[idx] ) nz++;
        img.data[idx] = (uchar)rng.uniform(1, 256);
    }
    findNonZero(img, pts);
    ASSERT_TRUE(pts.size() == nz);

    img.convertTo( img, CV_8S );
    pts.clear();
    findNonZero(img, pts);
    ASSERT_TRUE(pts.size() == nz);

    img.convertTo( img, CV_16U );
    pts.resize(pts.size()*2);
    findNonZero(img, pts);
    ASSERT_TRUE(pts.size() == nz);

    img.convertTo( img, CV_16S );
    pts.resize(pts.size()*3);
    findNonZero(img, pts);
    ASSERT_TRUE(pts.size() == nz);

    img.convertTo( img, CV_32S );
    pts.resize(pts.size()*4);
    findNonZero(img, pts);
    ASSERT_TRUE(pts.size() == nz);

    img.convertTo( img, CV_32U );
    pts.resize(pts.size()*3);
    findNonZero(img, pts);
    ASSERT_TRUE(pts.size() == nz);

    img.convertTo( img, CV_64U );
    pts.resize(pts.size()*2);
    findNonZero(img, pts);
    ASSERT_TRUE(pts.size() == nz);

    img.convertTo( img, CV_64S );
    pts.resize(pts.size()*5);
    findNonZero(img, pts);
    ASSERT_TRUE(pts.size() == nz);

    img.convertTo( img, CV_16F );
    pts.resize(pts.size()*3);
    findNonZero(img, pts);
    ASSERT_TRUE(pts.size() == nz);

    img.convertTo( img, CV_16BF );
    pts.resize(pts.size()*4);
    findNonZero(img, pts);
    ASSERT_TRUE(pts.size() == nz);

    img.convertTo( img, CV_32F );
    pts.resize(pts.size()*5);
    findNonZero(img, pts);
    ASSERT_TRUE(pts.size() == nz);

    img.convertTo( img, CV_64F );
    pts.clear();
    findNonZero(img, pts);
    ASSERT_TRUE(pts.size() == nz);
}

TEST(Core_BoolVector, support)
{
    std::vector<bool> test;
    int i, n = 205;
    int nz = 0;
    test.resize(n);
    for( i = 0; i < n; i++ )
    {
        test[i] = theRNG().uniform(0, 2) != 0;
        nz += (int)test[i];
    }
    ASSERT_EQ( nz, countNonZero(test) );
    ASSERT_FLOAT_EQ((float)nz/n, (float)(cv::mean(test)[0]));
}

TEST(MinMaxLoc, Mat_UcharMax_Without_Loc)
{
    Mat_<uchar> mat(50, 50);
    uchar iMaxVal = std::numeric_limits<uchar>::max();
    mat.setTo(iMaxVal);

    double min, max;
    Point minLoc, maxLoc;

    minMaxLoc(mat, &min, &max, &minLoc, &maxLoc, Mat());

    ASSERT_EQ(iMaxVal, min);
    ASSERT_EQ(iMaxVal, max);

    ASSERT_EQ(Point(0, 0), minLoc);
    ASSERT_EQ(Point(0, 0), maxLoc);
}

TEST(MinMaxLoc, Mat_IntMax_Without_Mask)
{
    Mat_<int> mat(50, 50);
    int iMaxVal = std::numeric_limits<int>::max();
    mat.setTo(iMaxVal);

    double min, max;
    Point minLoc, maxLoc;

    minMaxLoc(mat, &min, &max, &minLoc, &maxLoc, Mat());

    ASSERT_EQ(iMaxVal, min);
    ASSERT_EQ(iMaxVal, max);

    ASSERT_EQ(Point(0, 0), minLoc);
    ASSERT_EQ(Point(0, 0), maxLoc);
}

TEST(Normalize, regression_5876_inplace_change_type)
{
    double initial_values[] = {1, 2, 5, 4, 3};
    float result_values[] = {0, 0.25, 1, 0.75, 0.5};
    Mat m(Size(5, 1), CV_64FC1, initial_values);
    Mat result(Size(5, 1), CV_32FC1, result_values);

    normalize(m, m, 1, 0, NORM_MINMAX, CV_32F);
    EXPECT_EQ(0, cvtest::norm(m, result, NORM_INF));
}

TEST(Normalize, regression_6125)
{
    float initial_values[] = {
        1888, 1692, 369, 263, 199,
        280, 326, 129, 143, 126,
        233, 221, 130, 126, 150,
        249, 575, 574, 63, 12
    };

    Mat src(Size(20, 1), CV_32F, initial_values);
    float min = 0., max = 400.;
    normalize(src, src, 0, 400, NORM_MINMAX, CV_32F);
    for(int i = 0; i < 20; i++)
    {
        EXPECT_GE(src.at<float>(i), min) << "Value should be >= 0";
        EXPECT_LE(src.at<float>(i), max) << "Value should be <= 400";
    }
}

TEST(MinMaxLoc, regression_4955_nans)
{
    cv::Mat one_mat(2, 2, CV_32F, cv::Scalar(1));
    cv::minMaxLoc(one_mat, NULL, NULL, NULL, NULL);

    cv::Mat nan_mat(2, 2, CV_32F, cv::Scalar(std::numeric_limits<float>::quiet_NaN()));
    cv::minMaxLoc(nan_mat, NULL, NULL, NULL, NULL);
}

TEST(Subtract, scalarc1_matc3)
{
    int scalar = 255;
    cv::Mat srcImage(5, 5, CV_8UC3, cv::Scalar::all(5)), destImage;
    cv::subtract(scalar, srcImage, destImage);

    ASSERT_EQ(0, cv::norm(cv::Mat(5, 5, CV_8UC3, cv::Scalar::all(250)), destImage, cv::NORM_INF));
}

TEST(Subtract, scalarc4_matc4)
{
    cv::Scalar sc(255, 255, 255, 255);
    cv::Mat srcImage(5, 5, CV_8UC4, cv::Scalar::all(5)), destImage;
    cv::subtract(sc, srcImage, destImage);

    ASSERT_EQ(0, cv::norm(cv::Mat(5, 5, CV_8UC4, cv::Scalar::all(250)), destImage, cv::NORM_INF));
}

TEST(Compare, empty)
{
    cv::Mat temp, dst1, dst2;
    EXPECT_NO_THROW(cv::compare(temp, temp, dst1, cv::CMP_EQ));
    EXPECT_TRUE(dst1.empty());
    EXPECT_THROW(dst2 = temp > 5, cv::Exception);
}

TEST(Compare, regression_8999)
{
    // Issue #8999 predates broadcasting element-wise ops: comparing a 4x1 array against a 1x1 operand
    // used to throw (both look like a Scalar). It now broadcasts the 1x1 operand across the 4x1 array.
    Mat_<double> A(4,1); A << 1, 3, 2, 4;
    Mat_<double> B(1,1); B << 2;
    Mat C;
    cv::compare(A, B, C, CMP_LT);
    Mat expected = (Mat_<uchar>(4,1) << 255, 0, 0, 0);   // A < 2
    EXPECT_EQ(0, cvtest::norm(C, expected, NORM_INF));
}

TEST(Compare, regression_16F_do_not_crash)
{
    cv::Mat mat1(2, 2, CV_16F, cv::Scalar(1));
    cv::Mat mat2(2, 2, CV_16F, cv::Scalar(2));
    cv::Mat dst;
    EXPECT_NO_THROW(cv::compare(mat1, mat2, dst, cv::CMP_EQ));
}

TEST(Core_minMaxIdx, regression_9207_1)
{
    const int rows = 4;
    const int cols = 3;
    uchar mask_[rows*cols] = {
        255, 255, 255,
        255,   0, 255,
        0, 255, 255,
        0,   0, 255
    };
    uchar src_[rows*cols] = {
        1,   1,   1,
        1,   1,   1,
        2,   1,   1,
        2,   2,   1
    };
    Mat mask(Size(cols, rows), CV_8UC1, mask_);
    Mat src(Size(cols, rows), CV_8UC1, src_);
    double minVal = -0.0, maxVal = -0.0;
    int minIdx[2] = { -2, -2 }, maxIdx[2] = { -2, -2 };
    cv::minMaxIdx(src, &minVal, &maxVal, minIdx, maxIdx, mask);
    EXPECT_EQ(0, minIdx[0]);
    EXPECT_EQ(0, minIdx[1]);
    EXPECT_EQ(0, maxIdx[0]);
    EXPECT_EQ(0, maxIdx[1]);
}

class TransposeND : public testing::TestWithParam< tuple<std::vector<int>, perf::MatType> >
{
public:
    std::vector<int> m_shape;
    int m_type;

    void SetUp()
    {
        std::tie(m_shape, m_type) = GetParam();
    }
};


TEST_P(TransposeND, basic)
{
    Mat inp(m_shape, m_type);
    randu(inp, 0, 255);

    std::vector<int> order(m_shape.size());
    std::iota(order.begin(), order.end(), 0);
    auto transposer = [&order] (const std::vector<int>& id)
    {
        std::vector<int> ret(id.size());
        for (size_t i = 0; i < id.size(); ++i)
        {
            ret[i] = id[order[i]];
        }
        return ret;
    };
    auto advancer = [&inp] (std::vector<int>& id)
    {
        for (int j = static_cast<int>(id.size() - 1); j >= 0; --j)
        {
            ++id[j];
            if (id[j] != inp.size[j])
            {
                break;
            }
            id[j] = 0;
        }
    };

    do
    {
        Mat out;
        cv::transposeND(inp, order, out);
        std::vector<int> id(order.size());
        for (size_t i = 0; i < inp.total(); ++i)
        {
            auto new_id = transposer(id);
            switch (inp.type())
            {
            case CV_8UC1:
                ASSERT_EQ(inp.at<uint8_t>(id.data()), out.at<uint8_t>(new_id.data()));
                break;
            case CV_32FC1:
                ASSERT_EQ(inp.at<float>(id.data()), out.at<float>(new_id.data()));
                break;
            default:
                FAIL() << "Unsupported type: " << inp.type();
            }
            advancer(id);
        }
    } while (std::next_permutation(order.begin(), order.end()));
}


INSTANTIATE_TEST_CASE_P(Arithm, TransposeND, testing::Combine(
    testing::Values(std::vector<int>{2, 3, 4}, std::vector<int>{5, 10}),
    testing::Values(perf::MatType(CV_8UC1), CV_32FC1)
));

class FlipND : public testing::TestWithParam< tuple<std::vector<int>, perf::MatType> >
{
public:
    std::vector<int> m_shape;
    int m_type;

    void SetUp()
    {
        std::tie(m_shape, m_type) = GetParam();
    }
};

TEST_P(FlipND, basic)
{
    Mat inp(m_shape, m_type);
    randu(inp, 0, 255);

    int ndim = static_cast<int>(m_shape.size());
    std::vector<int> axes(ndim*2); // [-shape, shape)
    std::iota(axes.begin(), axes.end(), -ndim);
    auto get_flipped_indices = [&inp, ndim] (size_t total, std::vector<int>& indices, int axis)
    {
        const int* shape = inp.size.p;
        size_t t = total, idx;
        for (int i = ndim - 1; i >= 0; --i)
        {
            idx = t / shape[i];
            indices[i] = int(t - idx * shape[i]);
            t = idx;
        }

        int _axis = (axis + ndim) % ndim;
        std::vector<int> flipped_indices = indices;
        flipped_indices[_axis] = shape[_axis] - 1 - indices[_axis];
        return flipped_indices;
    };

    for (size_t i = 0; i < axes.size(); ++i)
    {
        int axis = axes[i];
        Mat out;
        cv::flipND(inp, out, axis);
        // check values
        std::vector<int> indices(ndim, 0);
        for (size_t j = 0; j < inp.total(); ++j)
        {
            auto flipped_indices = get_flipped_indices(j, indices, axis);
            switch (inp.type())
            {
            case CV_8UC1:
                ASSERT_EQ(inp.at<uint8_t>(indices.data()), out.at<uint8_t>(flipped_indices.data()));
                break;
            case CV_32FC1:
                ASSERT_EQ(inp.at<float>(indices.data()), out.at<float>(flipped_indices.data()));
                break;
            default:
                FAIL() << "Unsupported type: " << inp.type();
            }
        }
    }
}

INSTANTIATE_TEST_CASE_P(Arithm, FlipND, testing::Combine(
    testing::Values(std::vector<int>{5, 10}, std::vector<int>{2, 3, 4}),
    testing::Values(perf::MatType(CV_8UC1), CV_32FC1)
));

TEST(BroadcastTo, basic) {
    std::vector<int> shape_src{2, 1};
    std::vector<int> data_src{1, 2};
    Mat src(static_cast<int>(shape_src.size()), shape_src.data(), CV_32SC1, data_src.data());

    auto get_index = [](const std::vector<int>& shape, size_t cnt) {
        std::vector<int> index(shape.size());
        size_t t = cnt;
        for (int i = static_cast<int>(shape.size() - 1); i >= 0; --i) {
            size_t idx = t / shape[i];
            index[i] = static_cast<int>(t - idx * shape[i]);
            t = idx;
        }
        return index;
    };

    auto fn_verify = [&get_index](const Mat& ref, const Mat& res) {
        // check type
        EXPECT_EQ(ref.type(), res.type());
        // check shape
        EXPECT_EQ(ref.dims, res.dims);
        for (int i = 0; i < ref.dims; ++i) {
            EXPECT_EQ(ref.size[i], res.size[i]);
        }
        // check value
        std::vector<int> shape{ref.size.p, ref.size.p + ref.dims};
        for (size_t i = 0; i < ref.total(); ++i) {
            auto index = get_index(shape, i);
            switch (ref.type()) {
                case CV_32SC1: {
                    ASSERT_EQ(ref.at<int>(index.data()), res.at<int>(index.data()));
                } break;
                case CV_8UC1: {
                    ASSERT_EQ(ref.at<uint8_t>(index.data()), res.at<uint8_t>(index.data()));
                } break;
                case CV_32FC1: {
                    ASSERT_EQ(ref.at<float>(index.data()), res.at<float>(index.data()));
                } break;
                default: FAIL() << "Unsupported type: " << ref.type();
            }
        }
    };

    {
        std::vector<int> shape{4, 2, 3};
        std::vector<int> data_ref{
            1, 1, 1, // [0, 0, :]
            2, 2, 2, // [0, 1, :]
            1, 1, 1, // [1, 0, :]
            2, 2, 2, // [1, 1, :]
            1, 1, 1, // [2, 0, :]
            2, 2, 2, // [2, 1, :]
            1, 1, 1, // [3, 0, :]
            2, 2, 2  // [3, 1, :]
        };
        Mat ref(static_cast<int>(shape.size()), shape.data(), src.type(), data_ref.data());
        Mat dst;
        broadcast(src, shape, dst);
        fn_verify(ref, dst);
    }

    {
        Mat _src;
        src.convertTo(_src, CV_8U);
        std::vector<int> shape{4, 2, 3};
        std::vector<uint8_t> data_ref{
            1, 1, 1, // [0, 0, :]
            2, 2, 2, // [0, 1, :]
            1, 1, 1, // [1, 0, :]
            2, 2, 2, // [1, 1, :]
            1, 1, 1, // [2, 0, :]
            2, 2, 2, // [2, 1, :]
            1, 1, 1, // [3, 0, :]
            2, 2, 2  // [3, 1, :]
        };
        Mat ref(static_cast<int>(shape.size()), shape.data(), _src.type(), data_ref.data());
        Mat dst;
        broadcast(_src, shape, dst);
        fn_verify(ref, dst);
    }

    {
        Mat _src;
        src.convertTo(_src, CV_32F);
        std::vector<int> shape{1, 1, 2, 1}; // {2, 1}
        std::vector<float> data_ref{
            1.f, // [0, 0, 0, 0]
            2.f, // [0, 0, 1, 0]
        };
        Mat ref(static_cast<int>(shape.size()), shape.data(), _src.type(), data_ref.data());
        Mat dst;
        broadcast(_src, shape, dst);
        fn_verify(ref, dst);
    }

    {
        std::vector<int> _shape_src{2, 3, 4};
        std::vector<float> _data_src{
            1.f, 2.f, 3.f, 4.f, // [0, 0, :]
            2.f, 3.f, 4.f, 5.f, // [0, 1, :]
            3.f, 4.f, 5.f, 6.f, // [0, 2, :]

            4.f, 5.f, 6.f, 7.f, // [1, 0, :]
            5.f, 6.f, 7.f, 8.f, // [1, 1, :]
            6.f, 7.f, 8.f, 9.f, // [1, 2, :]
        };
        Mat _src(static_cast<int>(_shape_src.size()), _shape_src.data(), CV_32FC1, _data_src.data());

        std::vector<int> shape{2, 1, 2, 3, 4};
        std::vector<float> data_ref{
            1.f, 2.f, 3.f, 4.f, // [0, 0, 0, 0, :]
            2.f, 3.f, 4.f, 5.f, // [0, 0, 0, 1, :]
            3.f, 4.f, 5.f, 6.f, // [0, 0, 0, 2, :]

            4.f, 5.f, 6.f, 7.f, // [0, 0, 1, 0, :]
            5.f, 6.f, 7.f, 8.f, // [0, 0, 1, 1, :]
            6.f, 7.f, 8.f, 9.f, // [0, 0, 1, 2, :]

            1.f, 2.f, 3.f, 4.f, // [1, 0, 0, 0, :]
            2.f, 3.f, 4.f, 5.f, // [1, 0, 0, 1, :]
            3.f, 4.f, 5.f, 6.f, // [1, 0, 0, 2, :]

            4.f, 5.f, 6.f, 7.f, // [1, 0, 1, 0, :]
            5.f, 6.f, 7.f, 8.f, // [1, 0, 1, 1, :]
            6.f, 7.f, 8.f, 9.f, // [1, 0, 1, 2, :]
        };
        Mat ref(static_cast<int>(shape.size()), shape.data(), _src.type(), data_ref.data());
        Mat dst;
        broadcast(_src, shape, dst);
        fn_verify(ref, dst);
    }
}

TEST(BroadcastTo, regression_dst_dp_zero_when_last_dim_is_one)
{
    std::vector<int> shape_src{10, 1, 1};
    std::vector<float> data_src(10);
    for (int i = 0; i < 10; ++i)
    {
        data_src[i] = static_cast<float>(i + 1);
    }
    Mat src(static_cast<int>(shape_src.size()), shape_src.data(), CV_32FC1, data_src.data());

    std::vector<int> shape_dst{10, 5, 1};
    Mat dst;

    // Regression for broadcast() path where the innermost destination dimension is 1
    // and flattened destination step can legitimately be 0.
    ASSERT_NO_THROW(broadcast(src, shape_dst, dst));

    EXPECT_EQ(dst.dims, 3);
    EXPECT_EQ(dst.size[0], 10);
    EXPECT_EQ(dst.size[1], 5);
    EXPECT_EQ(dst.size[2], 1);
    EXPECT_EQ(dst.type(), CV_32FC1);

    for (int i = 0; i < shape_dst[0]; ++i)
    {
        for (int j = 0; j < shape_dst[1]; ++j)
        {
            int idx[] = {i, j, 0};
            EXPECT_FLOAT_EQ(dst.at<float>(idx), static_cast<float>(i + 1));
        }
    }
}

TEST(Core_minMaxIdx, regression_9207_2)
{
    const int rows = 13;
    const int cols = 15;
    uchar mask_[rows*cols] = {
       0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 255,
       0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 255,
       0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 255,
       0, 255, 255, 255, 255,   0,   0,   0,   0,   0,   0,   0,   0,   0, 255,
     255,   0,   0,   0,   0, 255,   0,   0,   0,   0,   0,   0,   0,   0, 255,
     255,   0,   0,   0,   0,   0, 255,   0,   0,   0,   0,   0,   0, 255, 255,
     255,   0,   0,   0,   0,   0,   0, 255, 255,   0,   0, 255, 255, 255,   0,
     255,   0,   0,   0,   0,   0,   0,   0,   0, 255, 255, 255,   0, 255,   0,
     255,   0,   0,   0,   0,   0,   0, 255, 255,   0,   0,   0, 255, 255,   0,
     255,   0,   0,   0,   0,   0, 255,   0,   0,   0,   0,   0,   0, 255,   0,
     255,   0,   0,   0,   0, 255,   0,   0,   0,   0,   0,   0,   0,   0,   0,
       0, 255,   0,   0,   0, 255,   0,   0,   0,   0,   0,   0,   0,   0,   0,
       0, 255, 255, 255, 255,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0
    };
    uchar src_[15*13] = {
       5,   5,   5,   5,   5,   6,   5,   2,   0,   4,   6,   6,   4,   1,   0,
       6,   5,   4,   4,   5,   6,   6,   5,   2,   0,   4,   6,   5,   2,   0,
       3,   2,   1,   1,   2,   4,   6,   6,   4,   2,   3,   4,   4,   2,   0,
       1,   0,   0,   0,   0,   1,   4,   5,   4,   4,   4,   4,   3,   2,   0,
       0,   0,   0,   0,   0,   0,   2,   3,   4,   4,   4,   3,   2,   1,   0,
       0,   0,   0,   0,   0,   0,   0,   2,   3,   4,   3,   2,   1,   0,   0,
       0,   0,   0,   0,   0,   0,   0,   0,   0,   1,   1,   0,   0,   0,   1,
       0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   1,
       0,   0,   0,   0,   0,   0,   0,   0,   0,   1,   1,   1,   0,   0,   1,
       0,   0,   0,   0,   0,   0,   0,   1,   2,   4,   3,   3,   1,   0,   1,
       0,   0,   0,   0,   0,   0,   1,   4,   5,   6,   5,   4,   3,   2,   0,
       1,   0,   0,   0,   0,   0,   3,   5,   5,   4,   3,   4,   4,   3,   0,
       2,   0,   0,   0,   0,   2,   5,   6,   5,   2,   2,   5,   4,   3,   0
    };
    Mat mask(Size(cols, rows), CV_8UC1, mask_);
    Mat src(Size(cols, rows), CV_8UC1, src_);
    double minVal = -0.0, maxVal = -0.0;
    int minIdx[2] = { -2, -2 }, maxIdx[2] = { -2, -2 };
    cv::minMaxIdx(src, &minVal, &maxVal, minIdx, maxIdx, mask);
    EXPECT_EQ(0, minIdx[0]);
    EXPECT_EQ(14, minIdx[1]);
    EXPECT_EQ(0, maxIdx[0]);
    EXPECT_EQ(14, maxIdx[1]);
}

TEST(Core_MinMaxIdx, MatND)
{
    const int shape[3] = {5,5,3};
    cv::Mat src = cv::Mat(3, shape, CV_8UC1);
    src.setTo(1);
    src.data[1] = 0;
    src.data[5*5*3-2] = 2;

    int minIdx[3];
    int maxIdx[3];
    double minVal, maxVal;

    cv::minMaxIdx(src, &minVal, &maxVal, minIdx, maxIdx);

    EXPECT_EQ(0, minVal);
    EXPECT_EQ(2, maxVal);

    EXPECT_EQ(0, minIdx[0]);
    EXPECT_EQ(0, minIdx[1]);
    EXPECT_EQ(1, minIdx[2]);

    EXPECT_EQ(4, maxIdx[0]);
    EXPECT_EQ(4, maxIdx[1]);
    EXPECT_EQ(1, maxIdx[2]);
}

TEST(Core_Set, regression_11044)
{
    Mat testFloat(Size(3, 3), CV_32FC1);
    Mat testDouble(Size(3, 3), CV_64FC1);

    testFloat.setTo(1);
    EXPECT_EQ(1, testFloat.at<float>(0,0));
    testFloat.setTo(std::numeric_limits<float>::infinity());
    EXPECT_EQ(std::numeric_limits<float>::infinity(), testFloat.at<float>(0, 0));
    testFloat.setTo(1);
    EXPECT_EQ(1, testFloat.at<float>(0, 0));
    testFloat.setTo(std::numeric_limits<double>::infinity());
    EXPECT_EQ(std::numeric_limits<float>::infinity(), testFloat.at<float>(0, 0));

    testDouble.setTo(1);
    EXPECT_EQ(1, testDouble.at<double>(0, 0));
    testDouble.setTo(std::numeric_limits<float>::infinity());
    EXPECT_EQ(std::numeric_limits<double>::infinity(), testDouble.at<double>(0, 0));
    testDouble.setTo(1);
    EXPECT_EQ(1, testDouble.at<double>(0, 0));
    testDouble.setTo(std::numeric_limits<double>::infinity());
    EXPECT_EQ(std::numeric_limits<double>::infinity(), testDouble.at<double>(0, 0));

    Mat testMask(Size(3, 3), CV_8UC1, Scalar(1));

    testFloat.setTo(1);
    EXPECT_EQ(1, testFloat.at<float>(0, 0));
    testFloat.setTo(std::numeric_limits<float>::infinity(), testMask);
    EXPECT_EQ(std::numeric_limits<float>::infinity(), testFloat.at<float>(0, 0));
    testFloat.setTo(1);
    EXPECT_EQ(1, testFloat.at<float>(0, 0));
    testFloat.setTo(std::numeric_limits<double>::infinity(), testMask);
    EXPECT_EQ(std::numeric_limits<float>::infinity(), testFloat.at<float>(0, 0));


    testDouble.setTo(1);
    EXPECT_EQ(1, testDouble.at<double>(0, 0));
    testDouble.setTo(std::numeric_limits<float>::infinity(), testMask);
    EXPECT_EQ(std::numeric_limits<double>::infinity(), testDouble.at<double>(0, 0));
    testDouble.setTo(1);
    EXPECT_EQ(1, testDouble.at<double>(0, 0));
    testDouble.setTo(std::numeric_limits<double>::infinity(), testMask);
    EXPECT_EQ(std::numeric_limits<double>::infinity(), testDouble.at<double>(0, 0));
}

TEST(Core_Norm, IPP_regression_NORM_L1_16UC3_small)
{
    int cn = 3;
    Size sz(9, 4);  // width < 16
    Mat a(sz, CV_MAKE_TYPE(CV_16U, cn), Scalar::all(1));
    Mat b(sz, CV_MAKE_TYPE(CV_16U, cn), Scalar::all(2));
    uchar mask_[9*4] = {
        255, 255, 255,   0, 255, 255,   0, 255,   0,
        0, 255,   0,   0, 255, 255, 255, 255,   0,
        0,   0,   0, 255,   0, 255,   0, 255, 255,
        0,   0, 255,   0, 255, 255, 255,   0, 255
    };
    Mat mask(sz, CV_8UC1, mask_);

    EXPECT_EQ((double)9*4*cn, cv::norm(a, b, NORM_L1)); // without mask, IPP works well
    EXPECT_EQ((double)20*cn, cv::norm(a, b, NORM_L1, mask));
}

TEST(Core_Norm, NORM_L2_8UC4)
{
    // Tests there is no integer overflow in norm computation for multiple channels.
    const int kSide = 100;
    cv::Mat4b a(kSide, kSide, cv::Scalar(255, 255, 255, 255));
    cv::Mat4b b = cv::Mat4b::zeros(kSide, kSide);
    const double kNorm = 2.*kSide*255.;
    EXPECT_EQ(kNorm, cv::norm(a, b, NORM_L2));
}

TEST(Core_Norm, NORM_L2SQR_16SC4_large)
{
    const int sizes[] = {1, 116, 40};
    Mat src(3, sizes, CV_16SC4, Scalar::all(16384));
    const double expected = static_cast<double>(src.total()) * src.channels() * 16384.0 * 16384.0;
    EXPECT_EQ(expected, cv::norm(src, NORM_L2SQR));
}

TEST(Core_ConvertTo, regression_12121)
{
    {
        Mat src(4, 64, CV_32SC1, Scalar(-1));
        Mat dst;
        src.convertTo(dst, CV_8U);
        EXPECT_EQ(0, dst.at<uchar>(0, 0)) << "src=" << src.at<int>(0, 0);
    }

    {
        Mat src(4, 64, CV_32SC1, Scalar(INT_MIN));
        Mat dst;
        src.convertTo(dst, CV_8U);
        EXPECT_EQ(0, dst.at<uchar>(0, 0)) << "src=" << src.at<int>(0, 0);
    }

    {
        Mat src(4, 64, CV_32SC1, Scalar(INT_MIN + 32767));
        Mat dst;
        src.convertTo(dst, CV_8U);
        EXPECT_EQ(0, dst.at<uchar>(0, 0)) << "src=" << src.at<int>(0, 0);
    }

    {
        Mat src(4, 64, CV_32SC1, Scalar(INT_MIN + 32768));
        Mat dst;
        src.convertTo(dst, CV_8U);
        EXPECT_EQ(0, dst.at<uchar>(0, 0)) << "src=" << src.at<int>(0, 0);
    }

    {
        Mat src(4, 64, CV_32SC1, Scalar(32768));
        Mat dst;
        src.convertTo(dst, CV_8U);
        EXPECT_EQ(255, dst.at<uchar>(0, 0)) << "src=" << src.at<int>(0, 0);
    }

    {
        Mat src(4, 64, CV_32SC1, Scalar(INT_MIN));
        Mat dst;
        src.convertTo(dst, CV_16U);
        EXPECT_EQ(0, dst.at<ushort>(0, 0)) << "src=" << src.at<int>(0, 0);
    }

    {
        Mat src(4, 64, CV_32SC1, Scalar(INT_MIN + 32767));
        Mat dst;
        src.convertTo(dst, CV_16U);
        EXPECT_EQ(0, dst.at<ushort>(0, 0)) << "src=" << src.at<int>(0, 0);
    }

    {
        Mat src(4, 64, CV_32SC1, Scalar(INT_MIN + 32768));
        Mat dst;
        src.convertTo(dst, CV_16U);
        EXPECT_EQ(0, dst.at<ushort>(0, 0)) << "src=" << src.at<int>(0, 0);
    }

    {
        Mat src(4, 64, CV_32SC1, Scalar(65536));
        Mat dst;
        src.convertTo(dst, CV_16U);
        EXPECT_EQ(65535, dst.at<ushort>(0, 0)) << "src=" << src.at<int>(0, 0);
    }
}

TEST(Core_MeanStdDev, regression_multichannel)
{
    {
        uchar buf[] = { 1, 2, 3, 4, 5, 6, 7, 8,
                        3, 4, 5, 6, 7, 8, 9, 10 };
        double ref_buf[] = { 2., 3., 4., 5., 6., 7., 8., 9.,
                             1., 1., 1., 1., 1., 1., 1., 1. };
        Mat src(1, 2, CV_MAKETYPE(CV_8U, 8), buf);
        Mat ref_m(8, 1, CV_64FC1, ref_buf);
        Mat ref_sd(8, 1, CV_64FC1, ref_buf + 8);
        Mat dst_m, dst_sd;
        meanStdDev(src, dst_m, dst_sd);
        EXPECT_EQ(0, cv::norm(dst_m, ref_m, NORM_L1));
        EXPECT_EQ(0, cv::norm(dst_sd, ref_sd, NORM_L1));
    }
}

// Related issue : https://github.com/opencv/opencv/issues/26861
TEST(Core_MeanStdDevTest, LargeImage)
{
    applyTestTag(CV_TEST_TAG_VERYLONG);
    applyTestTag(CV_TEST_TAG_MEMORY_14GB);
    // (1<<16) * ((1<<15)+10) = ~2.147 billion
    cv::Mat largeImage = cv::Mat::ones((1 << 16), ((1 << 15) + 10), CV_8U);
    cv::Scalar mean, stddev;
    cv::meanStdDev(largeImage, mean, stddev);
    EXPECT_NEAR(mean[0], 1.0, 1e-5);
    EXPECT_NEAR(stddev[0], 0.0, 1e-5);
}

template <typename T> static inline
void testDivideInitData(Mat& src1, Mat& src2)
{
    CV_StaticAssert(std::numeric_limits<T>::is_integer, "");
    const static T src1_[] = {
         0,  0,  0,  0,
         8,  8,  8,  8,
        -8, -8, -8, -8
    };
    Mat(3, 4, traits::Type<T>::value, (void*)src1_).copyTo(src1);
    const static T src2_[] = {
        1, 2, 0, std::numeric_limits<T>::max(),
        1, 2, 0, std::numeric_limits<T>::max(),
        1, 2, 0, std::numeric_limits<T>::max(),
    };
    Mat(3, 4, traits::Type<T>::value, (void*)src2_).copyTo(src2);
}

template <typename T> static inline
void testDivideInitDataFloat(Mat& src1, Mat& src2)
{
    CV_StaticAssert(!std::numeric_limits<T>::is_integer, "");
    const static T src1_[] = {
         0,  0,  0,  0,
         8,  8,  8,  8,
        -8, -8, -8, -8
    };
    Mat(3, 4, traits::Type<T>::value, (void*)src1_).copyTo(src1);
    const static T src2_[] = {
        1, 2, 0, std::numeric_limits<T>::infinity(),
        1, 2, 0, std::numeric_limits<T>::infinity(),
        1, 2, 0, std::numeric_limits<T>::infinity(),
    };
    Mat(3, 4, traits::Type<T>::value, (void*)src2_).copyTo(src2);
}

template <> inline void testDivideInitData<float>(Mat& src1, Mat& src2) { testDivideInitDataFloat<float>(src1, src2); }
template <> inline void testDivideInitData<double>(Mat& src1, Mat& src2) { testDivideInitDataFloat<double>(src1, src2); }


template <typename T> static inline
void testDivideChecks(const Mat& dst)
{
    ASSERT_FALSE(dst.empty());
    CV_StaticAssert(std::numeric_limits<T>::is_integer, "");
    for (int y = 0; y < dst.rows; y++)
    {
        for (int x = 0; x < dst.cols; x++)
        {
            if ((x % 4) == 2)
            {
                EXPECT_EQ(0, dst.at<T>(y, x)) << "dst(" << y << ", " << x << ") = " << dst.at<T>(y, x);
            }
            else
            {
                EXPECT_TRUE(0 == cvIsNaN((double)dst.at<T>(y, x))) << "dst(" << y << ", " << x << ") = " << dst.at<T>(y, x);
                EXPECT_TRUE(0 == cvIsInf((double)dst.at<T>(y, x))) << "dst(" << y << ", " << x << ") = " << dst.at<T>(y, x);
            }
        }
    }
}

template <typename T> static inline
void testDivideChecksFP(const Mat& dst)
{
    ASSERT_FALSE(dst.empty());
    CV_StaticAssert(!std::numeric_limits<T>::is_integer, "");
    for (int y = 0; y < dst.rows; y++)
    {
        for (int x = 0; x < dst.cols; x++)
        {
            if ((y % 3) == 0 && (x % 4) == 2)
            {
                EXPECT_TRUE(cvIsNaN(dst.at<T>(y, x))) << "dst(" << y << ", " << x << ") = " << dst.at<T>(y, x);
            }
            else if ((x % 4) == 2)
            {
                EXPECT_TRUE(cvIsInf(dst.at<T>(y, x))) << "dst(" << y << ", " << x << ") = " << dst.at<T>(y, x);
            }
            else
            {
                EXPECT_FALSE(cvIsNaN(dst.at<T>(y, x))) << "dst(" << y << ", " << x << ") = " << dst.at<T>(y, x);
                EXPECT_FALSE(cvIsInf(dst.at<T>(y, x))) << "dst(" << y << ", " << x << ") = " << dst.at<T>(y, x);
            }
        }
    }
}

template <> inline void testDivideChecks<float>(const Mat& dst) { testDivideChecksFP<float>(dst); }
template <> inline void testDivideChecks<double>(const Mat& dst) { testDivideChecksFP<double>(dst); }


template <typename T> static inline
void testDivide(bool isUMat, double scale, bool largeSize, bool tailProcessing, bool roi)
{
    Mat src1, src2;
    testDivideInitData<T>(src1, src2);
    ASSERT_FALSE(src1.empty()); ASSERT_FALSE(src2.empty());

    if (largeSize)
    {
        repeat(src1.clone(), 1, 8, src1);
        repeat(src2.clone(), 1, 8, src2);
    }
    if (tailProcessing)
    {
        src1 = src1(Rect(0, 0, src1.cols - 1, src1.rows));
        src2 = src2(Rect(0, 0, src2.cols - 1, src2.rows));
    }
    if (!roi && tailProcessing)
    {
        src1 = src1.clone();
        src2 = src2.clone();
    }

    Mat dst;
    if (!isUMat)
    {
        cv::divide(src1, src2, dst, scale);
    }
    else
    {
        UMat usrc1, usrc2, udst;
        src1.copyTo(usrc1);
        src2.copyTo(usrc2);
        cv::divide(usrc1, usrc2, udst, scale);
        udst.copyTo(dst);
    }

    testDivideChecks<T>(dst);

    if (::testing::Test::HasFailure())
    {
        std::cout << "src1 = " << std::endl << src1 << std::endl;
        std::cout << "src2 = " << std::endl << src2 << std::endl;
        std::cout << "dst = " << std::endl << dst << std::endl;
    }
}

typedef tuple<bool, double, bool, bool, bool> DivideRulesParam;
typedef testing::TestWithParam<DivideRulesParam> Core_DivideRules;

TEST_P(Core_DivideRules, type_32s)
{
    DivideRulesParam param = GetParam();
    testDivide<int>(get<0>(param), get<1>(param), get<2>(param), get<3>(param), get<4>(param));
}
TEST_P(Core_DivideRules, type_16s)
{
    DivideRulesParam param = GetParam();
    testDivide<short>(get<0>(param), get<1>(param), get<2>(param), get<3>(param), get<4>(param));
}
TEST_P(Core_DivideRules, type_32f)
{
    DivideRulesParam param = GetParam();
    testDivide<float>(get<0>(param), get<1>(param), get<2>(param), get<3>(param), get<4>(param));
}
TEST_P(Core_DivideRules, type_64f)
{
    DivideRulesParam param = GetParam();
    testDivide<double>(get<0>(param), get<1>(param), get<2>(param), get<3>(param), get<4>(param));
}


INSTANTIATE_TEST_CASE_P(/* */, Core_DivideRules, testing::Combine(
/* isMat */     testing::Values(false),
/* scale */     testing::Values(1.0, 5.0),
/* largeSize */ testing::Bool(),
/* tail */      testing::Bool(),
/* roi */       testing::Bool()
));

INSTANTIATE_TEST_CASE_P(UMat, Core_DivideRules, testing::Combine(
/* isMat */     testing::Values(true),
/* scale */     testing::Values(1.0, 5.0),
/* largeSize */ testing::Bool(),
/* tail */      testing::Bool(),
/* roi */       testing::Bool()
));


TEST(Core_MinMaxIdx, rows_overflow)
{
    const int N = 65536 + 1;
    const int M = 1;
    {
        setRNGSeed(123);
        Mat m(N, M, CV_32FC1);
        randu(m, -100, 100);
        double minVal = 0, maxVal = 0;
        int minIdx[CV_MAX_DIM] = { 0 }, maxIdx[CV_MAX_DIM] = { 0 };
        cv::minMaxIdx(m, &minVal, &maxVal, minIdx, maxIdx);

        double minVal0 = 0, maxVal0 = 0;
        int minIdx0[CV_MAX_DIM] = { 0 }, maxIdx0[CV_MAX_DIM] = { 0 };
        cv::ipp::setUseIPP(false);
        cv::minMaxIdx(m, &minVal0, &maxVal0, minIdx0, maxIdx0);
        cv::ipp::setUseIPP(true);

        EXPECT_FALSE(fabs(minVal0 - minVal) > 1e-6 || fabs(maxVal0 - maxVal) > 1e-6) << "NxM=" << N << "x" << M <<
            "    min=" << minVal0 << " vs " <<  minVal <<
            "    max=" << maxVal0 << " vs " << maxVal;
    }
}

TEST(Core_Magnitude, regression_19506)
{
    for (int N = 1; N <= 64; ++N)
    {
        Mat a(1, N, CV_32FC1, Scalar::all(1e-20));
        Mat res;
        magnitude(a, a, res);
        EXPECT_LE(cvtest::norm(res, NORM_L1), 1e-15) << N;
    }
}

PARAM_TEST_CASE(Core_CartPolar_reverse, int, bool)
{
    int  depth;
    bool angleInDegrees;

    virtual void SetUp()
    {
        depth = GET_PARAM(0);
        angleInDegrees = GET_PARAM(1);
    }
};

TEST_P(Core_CartPolar_reverse, reverse)
{
    const int type = CV_MAKETYPE(depth, 1);
    cv::Mat A[2] = {cv::Mat(10, 10, type), cv::Mat(10, 10, type)};
    cv::Mat B[2], C[2];
    cv::UMat uA[2];
    cv::UMat uB[2];
    cv::UMat uC[2];

    for(int i = 0; i < 2; ++i)
    {
        cvtest::randUni(rng, A[i], Scalar::all(-1000), Scalar::all(1000));
        A[i].copyTo(uA[i]);
    }

    // Reverse
    cv::cartToPolar(A[0], A[1], B[0], B[1], angleInDegrees);
    cv::polarToCart(B[0], B[1], C[0], C[1], angleInDegrees);
    EXPECT_MAT_NEAR(A[0], C[0], 2);
    EXPECT_MAT_NEAR(A[1], C[1], 2);
}

INSTANTIATE_TEST_CASE_P(Core_CartPolar, Core_CartPolar_reverse,
    testing::Combine(
        testing::Values(CV_32F, CV_64F),
        testing::Values(false, true)
    )
);

PARAM_TEST_CASE(Core_CartToPolar_inplace, int, bool)
{
    int  depth;
    bool angleInDegrees;

    virtual void SetUp()
    {
        depth = GET_PARAM(0);
        angleInDegrees = GET_PARAM(1);
    }
};

TEST_P(Core_CartToPolar_inplace, inplace)
{
    const int type = CV_MAKETYPE(depth, 1);
    cv::Mat A[2] = {cv::Mat(10, 10, type), cv::Mat(10, 10, type)};
    cv::Mat B[2], C[2];
    cv::UMat uA[2];
    cv::UMat uB[2];
    cv::UMat uC[2];

    for(int i = 0; i < 2; ++i)
    {
        cvtest::randUni(rng, A[i], Scalar::all(-1000), Scalar::all(1000));
        A[i].copyTo(uA[i]);
    }

    // Inplace x<->mag y<->angle
    for(int i = 0; i < 2; ++i)
        A[i].copyTo(B[i]);
    cv::cartToPolar(A[0], A[1], C[0], C[1], angleInDegrees);
    cv::cartToPolar(B[0], B[1], B[0], B[1], angleInDegrees);
    EXPECT_MAT_NEAR(C[0], B[0], 2);
    EXPECT_MAT_NEAR(C[1], B[1], 2);

    // Inplace x<->angle y<->mag
    for(int i = 0; i < 2; ++i)
        A[i].copyTo(B[i]);
    cv::cartToPolar(A[0], A[1], C[0], C[1], angleInDegrees);
    cv::cartToPolar(B[0], B[1], B[1], B[0], angleInDegrees);
    EXPECT_MAT_NEAR(C[0], B[1], 2);
    EXPECT_MAT_NEAR(C[1], B[0], 2);

    // Inplace OCL x<->mag y<->angle
    for(int i = 0; i < 2; ++i)
        uA[i].copyTo(uB[i]);
    cv::cartToPolar(uA[0], uA[1], uC[0], uC[1], angleInDegrees);
    cv::cartToPolar(uB[0], uB[1], uB[0], uB[1], angleInDegrees);
    EXPECT_MAT_NEAR(uC[0], uB[0], 2);
    EXPECT_MAT_NEAR(uC[1], uB[1], 2);

    // Inplace OCL x<->angle y<->mag
    for(int i = 0; i < 2; ++i)
        uA[i].copyTo(uB[i]);
    cv::cartToPolar(uA[0], uA[1], uC[0], uC[1], angleInDegrees);
    cv::cartToPolar(uB[0], uB[1], uB[1], uB[0], angleInDegrees);
    EXPECT_MAT_NEAR(uC[0], uB[1], 2);
    EXPECT_MAT_NEAR(uC[1], uB[0], 2);
}

INSTANTIATE_TEST_CASE_P(Core_CartPolar, Core_CartToPolar_inplace,
    testing::Combine(
        testing::Values(CV_32F, CV_64F),
        testing::Values(false, true)
    )
);

PARAM_TEST_CASE(Core_PolarToCart_inplace, int, bool, bool)
{
    int  depth;
    bool angleInDegrees;
    bool implicitMagnitude;

    virtual void SetUp()
    {
        depth = GET_PARAM(0);
        angleInDegrees = GET_PARAM(1);
        implicitMagnitude = GET_PARAM(2);
    }
};

TEST_P(Core_PolarToCart_inplace, inplace)
{
    const int type = CV_MAKETYPE(depth, 1);
    cv::Mat A[2] = {cv::Mat(10, 10, type), cv::Mat(10, 10, type)};
    cv::Mat B[2], C[2];
    cv::UMat uA[2];
    cv::UMat uB[2];
    cv::UMat uC[2];

    for(int i = 0; i < 2; ++i)
    {
        cvtest::randUni(rng, A[i], Scalar::all(-1000), Scalar::all(1000));
        A[i].copyTo(uA[i]);
    }

    // Inplace OCL x<->mag y<->angle
    for(int i = 0; i < 2; ++i)
        A[i].copyTo(B[i]);
    cv::polarToCart(implicitMagnitude ? cv::noArray() : A[0], A[1], C[0], C[1], angleInDegrees);
    cv::polarToCart(implicitMagnitude ? cv::noArray() : B[0], B[1], B[0], B[1], angleInDegrees);
    EXPECT_MAT_NEAR(C[0], B[0], 2);
    EXPECT_MAT_NEAR(C[1], B[1], 2);

    // Inplace OCL x<->angle y<->mag
    for(int i = 0; i < 2; ++i)
        A[i].copyTo(B[i]);
    cv::polarToCart(implicitMagnitude ? cv::noArray() : A[0], A[1], C[0], C[1], angleInDegrees);
    cv::polarToCart(implicitMagnitude ? cv::noArray() : B[0], B[1], B[1], B[0], angleInDegrees);
    EXPECT_MAT_NEAR(C[0], B[1], 2);
    EXPECT_MAT_NEAR(C[1], B[0], 2);

    // Inplace OCL x<->mag y<->angle
    for(int i = 0; i < 2; ++i)
        uA[i].copyTo(uB[i]);
    cv::polarToCart(implicitMagnitude ? cv::noArray() : uA[0], uA[1], uC[0], uC[1], angleInDegrees);
    cv::polarToCart(implicitMagnitude ? cv::noArray() : uB[0], uB[1], uB[0], uB[1], angleInDegrees);
    EXPECT_MAT_NEAR(uC[0], uB[0], 2);
    EXPECT_MAT_NEAR(uC[1], uB[1], 2);

    // Inplace OCL x<->angle y<->mag
    for(int i = 0; i < 2; ++i)
        uA[i].copyTo(uB[i]);
    cv::polarToCart(implicitMagnitude ? cv::noArray() : uA[0], uA[1], uC[0], uC[1], angleInDegrees);
    cv::polarToCart(implicitMagnitude ? cv::noArray() : uB[0], uB[1], uB[1], uB[0], angleInDegrees);
    EXPECT_MAT_NEAR(uC[0], uB[1], 2);
    EXPECT_MAT_NEAR(uC[1], uB[0], 2);
}

INSTANTIATE_TEST_CASE_P(Core_CartPolar, Core_PolarToCart_inplace,
    testing::Combine(
        testing::Values(CV_32F, CV_64F),
        testing::Values(false, true),
        testing::Values(true, false)
    )
);

// Check different values for finiteMask()

template<typename _Tp>
_Tp randomNan(RNG& rng);

template<>
float randomNan(RNG& rng)
{
    uint32_t r = rng.next();
    Cv32suf v;
    v.u = r;
    // exp & set a bit to avoid zero mantissa
    v.u = v.u | 0x7f800001;
    return v.f;
}

template<>
double randomNan(RNG& rng)
{
    uint32_t r0 = rng.next();
    uint32_t r1 = rng.next();
    Cv64suf v;
    v.u = (uint64_t(r0) << 32) | uint64_t(r1);
    // exp &set a bit to avoid zero mantissa
    v.u = v.u | 0x7ff0000000000001;
    return v.f;
}

template<typename T>
Mat generateFiniteMaskData(int cn, RNG& rng)
{
    typedef typename reference::SoftType<T>::type SFT;

    SFT pinf = SFT::inf();
    SFT ninf = SFT::inf().setSign(true);

    const int len = 100;
    Mat_<T> plainData(1, cn*len);
    for(int i = 0; i < cn*len; i++)
    {
        int r = rng.uniform(0, 3);
        plainData(i) = r == 0 ? T(rng.uniform(0, 2) ? pinf : ninf) :
                       r == 1 ? randomNan<T>(rng) : T(0);
    }

    return Mat(plainData).reshape(cn);
}

typedef std::tuple<int, int> FiniteMaskFixtureParams;
class FiniteMaskFixture : public ::testing::TestWithParam<FiniteMaskFixtureParams> {};

TEST_P(FiniteMaskFixture, flags)
{
    auto p = GetParam();
    int depth = get<0>(p);
    int channels = get<1>(p);

    RNG rng((uint64)ARITHM_RNG_SEED);
    Mat data = (depth == CV_32F) ? generateFiniteMaskData<float >(channels, rng)
                  /* CV_64F */   : generateFiniteMaskData<double>(channels, rng);

    Mat nans, gtNans;
    cv::finiteMask(data, nans);
    reference::finiteMask(data, gtNans);

    EXPECT_MAT_NEAR(nans, gtNans, 0);
}

// Params are: depth, channels 1 to 4
INSTANTIATE_TEST_CASE_P(Core_FiniteMask, FiniteMaskFixture, ::testing::Combine(::testing::Values(CV_32F, CV_64F), ::testing::Range(1, 5)));


///////////////////////////////////////////////////////////////////////////////////
typedef testing::TestWithParam<perf::MatDepth> NonZeroSupportedMatDepth;

TEST_P(NonZeroSupportedMatDepth, findNonZero)
{
    cv::Mat src = cv::Mat::zeros(16,16, CV_MAKETYPE(GetParam(), 1));
    vector<Point> pts;
    EXPECT_NO_THROW(findNonZero(src, pts));
}

TEST_P(NonZeroSupportedMatDepth, countNonZero)
{
    cv::Mat src = cv::Mat::zeros(16,16, CV_MAKETYPE(GetParam(), 1));
    EXPECT_NO_THROW(countNonZero(src));
}

TEST_P(NonZeroSupportedMatDepth, hasNonZero)
{
    cv::Mat src = cv::Mat::zeros(16,16, CV_MAKETYPE(GetParam(), 1));
    EXPECT_NO_THROW(hasNonZero(src));
}

INSTANTIATE_TEST_CASE_P(
    NonZero,
    NonZeroSupportedMatDepth,
    testing::Values(CV_16BF, CV_Bool, CV_64U, CV_64S, CV_32U)
);

typedef testing::TestWithParam<perf::MatDepth> NonZeroAccuracyNewTypes;

TEST_P(NonZeroAccuracyNewTypes, accuracy)
{
    const int depth = GetParam();
    const Size sz(123, 71);
    cv::Mat src = cv::Mat::zeros(sz, CV_MAKETYPE(depth, 1));

    std::vector<Point> expected_pts;
    const int total = sz.area();
    const int approx_nz = std::max(1, total / 17);
    std::vector<uchar> is_nz(total, 0);
    for (int n = 0; n < approx_nz; )
    {
        int idx = theRNG().uniform(0, total);
        if (is_nz[idx])
            continue;
        is_nz[idx] = 1;
        ++n;
    }

    auto setNonZero = [&](int y, int x)
    {
        switch(depth)
        {
            case CV_Bool: src.at<uchar>(y, x) = 1; break;
            case CV_16BF: src.at<uint16_t>(y, x) = 0x3F80; /* bf16(1.0f) */ break;
            case CV_32U:  src.at<uint32_t>(y, x) = 7u; break;
            case CV_64U:  src.at<uint64_t>(y, x) = 7ULL; break;
            case CV_64S:  src.at<int64_t>(y, x)  = -7LL; break;
            default: FAIL() << "Unexpected depth " << depth;
        }
    };

    int nz_ref = 0;
    for (int y = 0; y < sz.height; ++y)
        for (int x = 0; x < sz.width; ++x)
            if (is_nz[y*sz.width + x])
            {
                setNonZero(y, x);
                expected_pts.emplace_back(x, y);
                ++nz_ref;
            }

    EXPECT_EQ(nz_ref, cv::countNonZero(src));
    EXPECT_EQ(nz_ref > 0, cv::hasNonZero(src));
    cv::Mat zeros = cv::Mat::zeros(sz, src.type());
    EXPECT_FALSE(cv::hasNonZero(zeros));

    std::vector<Point> pts;
    cv::findNonZero(src, pts);
    ASSERT_EQ(expected_pts.size(), pts.size());
    for (size_t i = 0; i < pts.size(); ++i)
    {
        EXPECT_EQ(expected_pts[i].x, pts[i].x) << "i=" << i;
        EXPECT_EQ(expected_pts[i].y, pts[i].y) << "i=" << i;
    }
}

TEST(NonZeroAccuracyNewTypes_BF16, negative_zero_is_zero)
{
    const Size sz(64, 32);
    cv::Mat src(sz, CV_16BFC1);
    src.setTo(cv::Scalar::all(-0.0));
    EXPECT_EQ(0, cv::countNonZero(src));
    EXPECT_FALSE(cv::hasNonZero(src));
    std::vector<Point> pts;
    cv::findNonZero(src, pts);
    EXPECT_TRUE(pts.empty());
}

INSTANTIATE_TEST_CASE_P(
    NonZeroAcc,
    NonZeroAccuracyNewTypes,
    testing::Values(CV_16BF, CV_Bool, CV_64U, CV_64S, CV_32U)
);

///////////////////////////////////////////////////////////////////////////////////
typedef testing::TestWithParam<perf::MatDepth> MinMaxSupportedMatDepth;

TEST_P(MinMaxSupportedMatDepth, minMaxLoc)
{
    cv::Mat src = cv::Mat::zeros(16,16, CV_MAKETYPE(GetParam(), 1));
    double minV=0.0, maxV=0.0;
    Point minLoc, maxLoc;
    EXPECT_NO_THROW(cv::minMaxLoc(src, &minV, &maxV, &minLoc, &maxLoc));
}

TEST_P(MinMaxSupportedMatDepth, minMaxIdx)
{
    cv::Mat src = cv::Mat::zeros(16,16, CV_MAKETYPE(GetParam(), 1));
    double minV=0.0, maxV=0.0;
    int minIdx[2] = {0, 0};
    int maxIdx[2] = {0, 0};
    EXPECT_NO_THROW(cv::minMaxIdx(src, &minV, &maxV, minIdx, maxIdx));
}

INSTANTIATE_TEST_CASE_P(
    MinMaxLoc,
    MinMaxSupportedMatDepth,
    testing::Values(perf::MatDepth(CV_16F), CV_16BF, CV_Bool, CV_64U, CV_64S, CV_32U)
);

typedef testing::TestWithParam<perf::MatDepth> MinMaxAccuracyNewTypes;

TEST_P(MinMaxAccuracyNewTypes, accuracy)
{
    const int depth = GetParam();
    const Size sz(173, 91);

    double fill_val = 0, min_val = 0, max_val = 0;
    Point min_pos(11, 7), max_pos(150, 80);
    ASSERT_TRUE(min_pos != max_pos);

    switch (depth)
    {
        case CV_Bool: fill_val = 0; min_val = 0; max_val = 1; break;
        case CV_16BF: fill_val = 1.5; min_val = -123.5; max_val = 4096.0; break;
        case CV_32U:  fill_val = 1000.0; min_val = 7.0;
                      max_val = (double)std::numeric_limits<uint32_t>::max() - 1.0; break;
        case CV_64U:  fill_val = 1e15; min_val = 0.0; max_val = 1e18; break;
        case CV_64S:  fill_val = 0.0; min_val = -1e17; max_val = 1e17; break;
        default: FAIL() << "Unexpected depth " << depth;
    }

    cv::Mat src(sz, CV_MAKETYPE(depth, 1));
    if (depth == CV_Bool) src.setTo(cv::Scalar::all(0));
    else src.setTo(cv::Scalar::all(fill_val));

    auto put = [&](Point p, double v)
    {
        switch(depth)
        {
            case CV_Bool: src.at<uchar>(p.y, p.x)    = (uchar)v; break;
            case CV_16BF: src.at<cv::bfloat>(p.y, p.x) = cv::bfloat((float)v); break;
            case CV_32U:  src.at<uint32_t>(p.y, p.x) = (uint32_t)v; break;
            case CV_64U:  src.at<uint64_t>(p.y, p.x) = (uint64_t)v; break;
            case CV_64S:  src.at<int64_t>(p.y, p.x)  = (int64_t)v; break;
            default: FAIL() << "Unexpected depth " << depth;
        }
    };
    put(min_pos, min_val);
    put(max_pos, max_val);

    // minMaxLoc: 2D-only, returns Points.
    {
        double minV = 0, maxV = 0;
        Point minLoc(-1, -1), maxLoc(-1, -1);
        cv::minMaxLoc(src, &minV, &maxV, &minLoc, &maxLoc);
        EXPECT_NEAR(min_val, minV, 1e-5 * std::max(1.0, std::fabs(min_val)));
        EXPECT_NEAR(max_val, maxV, 1e-5 * std::max(1.0, std::fabs(max_val)));
        if (depth != CV_Bool)
        {
            EXPECT_EQ(min_pos, minLoc);
        }
        EXPECT_EQ(max_pos, maxLoc);
    }

    // minMaxIdx: same data, idx[] form (row, col).
    {
        double minV = 0, maxV = 0;
        int minIdx[2] = {-1, -1}, maxIdx[2] = {-1, -1};
        cv::minMaxIdx(src, &minV, &maxV, minIdx, maxIdx);
        EXPECT_NEAR(min_val, minV, 1e-5 * std::max(1.0, std::fabs(min_val)));
        EXPECT_NEAR(max_val, maxV, 1e-5 * std::max(1.0, std::fabs(max_val)));
        if (depth != CV_Bool)
        {
            EXPECT_EQ(min_pos.y, minIdx[0]);
            EXPECT_EQ(min_pos.x, minIdx[1]);
        }
        EXPECT_EQ(max_pos.y, maxIdx[0]);
        EXPECT_EQ(max_pos.x, maxIdx[1]);
    }
}

// Mask-aware accuracy: extreme values outside the mask must be ignored.
TEST_P(MinMaxAccuracyNewTypes, accuracy_with_mask)
{
    const int depth = GetParam();
    const Size sz(64, 48);
    cv::Mat src(sz, CV_MAKETYPE(depth, 1));
    cv::Mat mask = cv::Mat::zeros(sz, CV_8UC1);

    double fill_val = 0, masked_min = 0, masked_max = 0, outlier_min = 0, outlier_max = 0;
    Point min_pos(5, 5), max_pos(40, 30), outlier_min_pos(1, 1), outlier_max_pos(60, 45);

    switch (depth)
    {
        case CV_Bool:
            fill_val = 0; masked_min = 0; masked_max = 1;
            outlier_min = 0; outlier_max = 1; break;
        case CV_16BF:
            fill_val = 1.0; masked_min = -10.0; masked_max = 10.0;
            outlier_min = -1000.0; outlier_max = 1000.0; break;
        case CV_32U:
            fill_val = 100; masked_min = 1; masked_max = 1000;
            outlier_min = 0; outlier_max = (double)std::numeric_limits<uint32_t>::max(); break;
        case CV_64U:
            fill_val = 1e10; masked_min = 1.0; masked_max = 1e12;
            outlier_min = 0.0; outlier_max = 1e18; break;
        case CV_64S:
            fill_val = 0.0; masked_min = -1e10; masked_max = 1e10;
            outlier_min = -1e17; outlier_max = 1e17; break;
        default: FAIL() << "Unexpected depth " << depth;
    }

    src.setTo(cv::Scalar::all(fill_val));

    auto put = [&](Point p, double v)
    {
        switch(depth)
        {
            case CV_Bool: src.at<uchar>(p.y, p.x)    = (uchar)v; break;
            case CV_16BF: src.at<cv::bfloat>(p.y, p.x) = cv::bfloat((float)v); break;
            case CV_32U:  src.at<uint32_t>(p.y, p.x) = (uint32_t)v; break;
            case CV_64U:  src.at<uint64_t>(p.y, p.x) = (uint64_t)v; break;
            case CV_64S:  src.at<int64_t>(p.y, p.x)  = (int64_t)v; break;
            default: FAIL() << "Unexpected depth " << depth;
        }
    };
    put(min_pos, masked_min);
    put(max_pos, masked_max);
    put(outlier_min_pos, outlier_min);
    put(outlier_max_pos, outlier_max);

    mask.setTo(255);
    mask.at<uchar>(outlier_min_pos.y, outlier_min_pos.x) = 0;
    mask.at<uchar>(outlier_max_pos.y, outlier_max_pos.x) = 0;

    double minV = 0, maxV = 0;
    int minIdx[2] = {-1, -1}, maxIdx[2] = {-1, -1};
    cv::minMaxIdx(src, &minV, &maxV, minIdx, maxIdx, mask);

    EXPECT_NEAR(masked_min, minV, 1e-5 * std::max(1.0, std::fabs(masked_min)));
    EXPECT_NEAR(masked_max, maxV, 1e-5 * std::max(1.0, std::fabs(masked_max)));

    if (depth != CV_Bool)
    {
        EXPECT_EQ(min_pos.y, minIdx[0]);
        EXPECT_EQ(min_pos.x, minIdx[1]);
        EXPECT_EQ(max_pos.y, maxIdx[0]);
        EXPECT_EQ(max_pos.x, maxIdx[1]);
    }
}

INSTANTIATE_TEST_CASE_P(
    MinMaxAcc,
    MinMaxAccuracyNewTypes,
    testing::Values(CV_16BF, CV_Bool, CV_64U, CV_64S, CV_32U)
);

typedef testing::TestWithParam<perf::MatDepth> LUTAccuracyNewDstTypes;

TEST_P(LUTAccuracyNewDstTypes, accuracy)
{
    const int dst_depth = GetParam();
    cv::Mat lut(1, 256, CV_MAKETYPE(dst_depth, 1));
    for (int i = 0; i < 256; ++i)
    {
        switch (dst_depth)
        {
            case CV_Bool: lut.at<uchar>(0, i)    = (uchar)(i & 1); break;
            case CV_16BF: lut.at<cv::bfloat>(0, i) = cv::bfloat((float)(i - 128) * 0.5f); break;
            case CV_32U:  lut.at<uint32_t>(0, i) = (uint32_t)i * 16777619u; break;
            case CV_64U:  lut.at<uint64_t>(0, i) = ((uint64_t)i << 56) | (uint64_t)i; break;
            case CV_64S:  lut.at<int64_t>(0, i)  = ((int64_t)i - 128) * 1000000000LL; break;
            default: FAIL() << "Unexpected depth " << dst_depth;
        }
    }

    cv::Mat src(16, 16, CV_8UC1);
    for (int y = 0; y < 16; ++y)
        for (int x = 0; x < 16; ++x)
            src.at<uchar>(y, x) = (uchar)(y * 16 + x);

    cv::Mat dst;
    cv::LUT(src, lut, dst);
    ASSERT_EQ(dst.size(), src.size());
    ASSERT_EQ(dst.type(), CV_MAKETYPE(dst_depth, 1));

    for (int y = 0; y < 16; ++y)
    {
        for (int x = 0; x < 16; ++x)
        {
            int idx = src.at<uchar>(y, x);
            switch (dst_depth)
            {
                case CV_Bool:
                    EXPECT_EQ(lut.at<uchar>(0, idx), dst.at<uchar>(y, x)); break;
                case CV_16BF:
                    EXPECT_EQ(lut.at<uint16_t>(0, idx), dst.at<uint16_t>(y, x)); break;
                case CV_32U:
                    EXPECT_EQ(lut.at<uint32_t>(0, idx), dst.at<uint32_t>(y, x)); break;
                case CV_64U:
                    EXPECT_EQ(lut.at<uint64_t>(0, idx), dst.at<uint64_t>(y, x)); break;
                case CV_64S:
                    EXPECT_EQ(lut.at<int64_t>(0, idx),  dst.at<int64_t>(y, x)); break;
                default: FAIL() << "Unexpected depth " << dst_depth;
            }
        }
    }
}

INSTANTIATE_TEST_CASE_P(
    LUTAcc,
    LUTAccuracyNewDstTypes,
    testing::Values(CV_16BF, CV_Bool, CV_64U, CV_64S, CV_32U)
);

CV_ENUM(LutIdxType, CV_8U, CV_8S, CV_16U, CV_16S)
CV_ENUM(LutMatType, CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F, CV_16F, CV_16BF, CV_Bool, CV_64U, CV_64S, CV_32U)

struct Core_LUT: public testing::TestWithParam< std::tuple<LutIdxType, LutMatType> >
{
    template<typename Ti, typename T, int ch, bool same_cn>
    cv::Mat referenceWithType(cv::Mat input, cv::Mat table)
    {
        cv::Mat ref(input.size(), CV_MAKE_TYPE(table.depth(), ch));
        for (int i = 0; i < input.rows; i++)
        {
            for (int j = 0; j < input.cols; j++)
            {
                if(ch == 1)
                {
                    ref.at<T>(i, j) = table.at<T>(input.at<Ti>(i, j));
                }
                else
                {
                    Vec<T, ch> val;
                    for (int k = 0; k < ch; k++)
                    {
                        if (same_cn)
                        {
                            val[k] = table.at<Vec<T, ch>>(input.at<Vec<Ti, ch>>(i, j)[k])[k];
                        }
                        else
                        {
                            val[k] = table.at<T>(input.at<Vec<Ti, ch>>(i, j)[k]);
                        }
                    }
                    ref.at<Vec<T, ch>>(i, j) = val;
                }
            }
        }
        return ref;
    }

    template<int ch = 1, bool same_cn = false>
    cv::Mat reference(cv::Mat input, cv::Mat table)
    {
        cv::Mat ret = cv::Mat();
        if ((input.depth() == CV_8U) || (input.depth() == CV_8S)) // Index type for LUT operation
        {
            switch(table.depth()) // Value type for LUT operation
            {
                case CV_8U:   ret = referenceWithType<uint8_t, uint8_t,  ch, same_cn>(input, table); break;
                case CV_8S:   ret = referenceWithType<uint8_t, int8_t,   ch, same_cn>(input, table); break;
                case CV_16U:  ret = referenceWithType<uint8_t, uint16_t, ch, same_cn>(input, table); break;
                case CV_16S:  ret = referenceWithType<uint8_t, int16_t,  ch, same_cn>(input, table); break;
                case CV_32S:  ret = referenceWithType<uint8_t, int32_t,  ch, same_cn>(input, table); break;
                case CV_32F:  ret = referenceWithType<uint8_t, float,    ch, same_cn>(input, table); break;
                case CV_64F:  ret = referenceWithType<uint8_t, double,   ch, same_cn>(input, table); break;
                case CV_16F:  ret = referenceWithType<uint8_t, uint16_t, ch, same_cn>(input, table); break;
                case CV_16BF: ret = referenceWithType<uint8_t, uint16_t, ch, same_cn>(input, table); break;
                case CV_Bool: ret = referenceWithType<uint8_t, uint8_t,  ch, same_cn>(input, table); break;
                case CV_64U:  ret = referenceWithType<uint8_t, uint64_t, ch, same_cn>(input, table); break;
                case CV_64S:  ret = referenceWithType<uint8_t, int64_t,  ch, same_cn>(input, table); break;
                case CV_32U:  ret = referenceWithType<uint8_t, uint32_t, ch, same_cn>(input, table); break;
                default:      ret = cv::Mat();                                                       break;
            }
        }
        else if ((input.depth() == CV_16U) || (input.depth() == CV_16S))
        {
            switch(table.depth()) // Value type for LUT operation
            {
                case CV_8U:   ret = referenceWithType<uint16_t, uint8_t,  ch, same_cn>(input, table); break;
                case CV_8S:   ret = referenceWithType<uint16_t, int8_t,   ch, same_cn>(input, table); break;
                case CV_16U:  ret = referenceWithType<uint16_t, uint16_t, ch, same_cn>(input, table); break;
                case CV_16S:  ret = referenceWithType<uint16_t, int16_t,  ch, same_cn>(input, table); break;
                case CV_32S:  ret = referenceWithType<uint16_t, int32_t,  ch, same_cn>(input, table); break;
                case CV_32F:  ret = referenceWithType<uint16_t, float,    ch, same_cn>(input, table); break;
                case CV_64F:  ret = referenceWithType<uint16_t, double,   ch, same_cn>(input, table); break;
                case CV_16F:  ret = referenceWithType<uint16_t, uint16_t, ch, same_cn>(input, table); break;
                case CV_16BF: ret = referenceWithType<uint16_t, uint16_t, ch, same_cn>(input, table); break;
                case CV_Bool: ret = referenceWithType<uint16_t, uint8_t,  ch, same_cn>(input, table); break;
                case CV_64U:  ret = referenceWithType<uint16_t, uint64_t, ch, same_cn>(input, table); break;
                case CV_64S:  ret = referenceWithType<uint16_t, int64_t,  ch, same_cn>(input, table); break;
                case CV_32U:  ret = referenceWithType<uint16_t, uint32_t, ch, same_cn>(input, table); break;
                default:      ret = cv::Mat();                                                        break;
            }
        }

        return ret;
    }
};

TEST_P(Core_LUT, accuracy)
{
    int idx_type = get<0>(GetParam());
    int value_type = get<1>(GetParam());

    ASSERT_TRUE((idx_type == CV_8U) || (idx_type == CV_8S) || (idx_type == CV_16U ) || (idx_type == CV_16S));
    const int tableSize = ((idx_type == CV_8U) || (idx_type == CV_8S)) ? 256: 65536;

    cv::Mat input(117, 113, CV_MAKE_TYPE(idx_type, 1));
    randu(input, getMinVal(idx_type), getMaxVal(idx_type));

    cv::Mat table(1, tableSize, CV_MAKE_TYPE(value_type, 1));
    randu(table, getMinVal(value_type), getMaxVal(value_type));

    cv::Mat output;
    ASSERT_NO_THROW(cv::LUT(input, table, output));
    ASSERT_FALSE(output.empty());

    cv::Mat gt = reference(input, table);
    ASSERT_FALSE(gt.empty());

    // Force convert to 8U as CV_Bool is not supported in cv::norm for now
    // TODO: Remove conversion after cv::norm fix
    if (value_type == CV_Bool)
    {
        output.convertTo(output, CV_8U);
        gt.convertTo(gt, CV_8U);
    }

    ASSERT_EQ(0, cv::norm(output, gt, cv::NORM_INF));
}

TEST_P(Core_LUT, accuracy_multi)
{
    int idx_type = get<0>(GetParam());
    int value_type = get<1>(GetParam());

    ASSERT_TRUE((idx_type == CV_8U) || (idx_type == CV_8S) || (idx_type == CV_16U) || (idx_type == CV_16S));
    const int tableSize = ((idx_type == CV_8U) || (idx_type == CV_8S) ) ? 256: 65536;

    cv::Mat input(117, 113, CV_MAKE_TYPE(idx_type, 3));
    randu(input, getMinVal(idx_type), getMaxVal(idx_type));

    cv::Mat table(1, tableSize, CV_MAKE_TYPE(value_type, 1));
    randu(table, getMinVal(value_type), getMaxVal(value_type));

    cv::Mat output;
    ASSERT_NO_THROW(cv::LUT(input, table, output));
    ASSERT_FALSE(output.empty());

    cv::Mat gt = reference<3>(input, table);
    ASSERT_FALSE(gt.empty());

    // Force convert to 8U as CV_Bool is not supported in cv::norm for now
    // TODO: Remove conversion after cv::norm fix
    if (value_type == CV_Bool)
    {
        output.convertTo(output, CV_8U);
        gt.convertTo(gt, CV_8U);
    }

    ASSERT_EQ(0, cv::norm(output, gt, cv::NORM_INF));
}

TEST_P(Core_LUT, accuracy_multi2)
{
    int idx_type = get<0>(GetParam());
    int value_type = get<1>(GetParam());

    ASSERT_TRUE((idx_type == CV_8U) || (idx_type == CV_8S) || (idx_type == CV_16U) || (idx_type == CV_16S));
    const int tableSize = ((idx_type == CV_8U) || (idx_type == CV_8S)) ? 256: 65536;

    cv::Mat input(117, 113, CV_MAKE_TYPE(idx_type, 3));
    randu(input, getMinVal(idx_type), getMaxVal(idx_type));

    cv::Mat table(1, tableSize, CV_MAKE_TYPE(value_type, 3));
    randu(table, getMinVal(value_type), getMaxVal(value_type));

    cv::Mat output;
    ASSERT_NO_THROW(cv::LUT(input, table, output));
    ASSERT_FALSE(output.empty());

    cv::Mat gt = reference<3, true>(input, table);
    ASSERT_FALSE(gt.empty());

    // Force convert to 8U as CV_Bool is not supported in cv::norm for now
    // TODO: Remove conversion after cv::norm fix
    if (value_type == CV_Bool)
    {
        output.convertTo(output, CV_8U);
        gt.convertTo(gt, CV_8U);
    }

    ASSERT_EQ(0, cv::norm(output, gt, cv::NORM_INF));
}

INSTANTIATE_TEST_CASE_P(/**/, Core_LUT, testing::Combine( LutIdxType::all(), LutMatType::all()));

CV_ENUM(MaskType, CV_8U, CV_8S, CV_Bool)
typedef testing::TestWithParam<MaskType> Core_MaskTypeTest;

TEST_P(Core_MaskTypeTest, BasicArithm)
{
    int mask_type = GetParam();
    RNG& rng = theRNG();
    const int MAX_DIM=3;
    int sizes[MAX_DIM];
    for( int iter = 0; iter < 100; iter++ )
    {
        int dims = rng.uniform(1, MAX_DIM+1);
        int depth = rng.uniform(CV_8U, CV_64F+1);
        int cn = rng.uniform(1, 6);
        int type = CV_MAKETYPE(depth, cn);
        int op = rng.uniform(0, depth < CV_32F ? 5 : 2); // don't run binary operations between floating-point values
        int depth1 = op <= 1 ? CV_64F : depth;
        for (int k = 0; k < MAX_DIM; k++)
        {
            sizes[k] = k < dims ? rng.uniform(1, 30) : 0;
        }

        Mat a(dims, sizes, type), a1;
        Mat b(dims, sizes, type), b1;
        Mat mask(dims, sizes, mask_type);
        Mat mask1;
        Mat c, d;

        rng.fill(a, RNG::UNIFORM, 0, 100);
        rng.fill(b, RNG::UNIFORM, 0, 100);

        // [-2,2) range means that the each generated random number
        // will be one of -2, -1, 0, 1. Saturated to [0,255], it will become
        // 0, 0, 0, 1 => the mask will be filled by ~25%.
        rng.fill(mask, RNG::UNIFORM, -2, 2);

        a.convertTo(a1, depth1);
        b.convertTo(b1, depth1);
        // invert the mask
        cv::compare(mask, 0, mask1, CMP_EQ);
        a1.setTo(0, mask1);
        b1.setTo(0, mask1);

        if( op == 0 )
        {
            cv::add(a, b, c, mask);
            cv::add(a1, b1, d);
        }
        else if( op == 1 )
        {
            cv::subtract(a, b, c, mask);
            cv::subtract(a1, b1, d);
        }
        else if( op == 2 )
        {
            cv::bitwise_and(a, b, c, mask);
            cv::bitwise_and(a1, b1, d);
        }
        else if( op == 3 )
        {
            cv::bitwise_or(a, b, c, mask);
            cv::bitwise_or(a1, b1, d);
        }
        else if( op == 4 )
        {
            cv::bitwise_xor(a, b, c, mask);
            cv::bitwise_xor(a1, b1, d);
        }
        Mat d1;
        d.convertTo(d1, depth);
        EXPECT_LE(cvtest::norm(c, d1, NORM_INF), DBL_EPSILON);
    }
}

TEST_P(Core_MaskTypeTest, MinMaxIdx)
{
    int mask_type = GetParam();
    const int rows = 4;
    const int cols = 3;
    uchar mask_[rows*cols] = {
        255, 255, 1,
        255,   0, 255,
        0, 1, 255,
        0,   0, 255
    };
    uchar src_[rows*cols] = {
        1,   1,   1,
        1,   1,   1,
        2,   1,   1,
        2,   2,   1
    };
    Mat mask(Size(cols, rows), mask_type, mask_);
    Mat src(Size(cols, rows), CV_8UC1, src_);
    double minVal = -0.0, maxVal = -0.0;
    int minIdx[2] = { -2, -2 }, maxIdx[2] = { -2, -2 };
    cv::minMaxIdx(src, &minVal, &maxVal, minIdx, maxIdx, mask);
    EXPECT_EQ(0, minIdx[0]);
    EXPECT_EQ(0, minIdx[1]);
    EXPECT_EQ(0, maxIdx[0]);
    EXPECT_EQ(0, maxIdx[1]);
}

TEST_P(Core_MaskTypeTest, Norm)
{
    int mask_type = GetParam();
    int cn = 3;
    Size sz(9, 4);  // width < 16
    Mat a(sz, CV_MAKE_TYPE(CV_16U, cn), Scalar::all(1));
    Mat b(sz, CV_MAKE_TYPE(CV_16U, cn), Scalar::all(2));
    uchar mask_[9*4] = {
        255, 255, 255,   0, 1, 255,   0, 255,   0,
        0, 255,   0,   0, 255, 255, 255, 255,   0,
        0,   0,   0, 255,   0, 1,   0, 255, 255,
        0,   0, 255,   0, 255, 255, 1,   0, 255
    };
    Mat mask(sz, mask_type, mask_);

    EXPECT_EQ((double)9*4*cn, cv::norm(a, b, NORM_L1)); // without mask, IPP works well
    EXPECT_EQ((double)20*cn, cv::norm(a, b, NORM_L1, mask));
}

TEST_P(Core_MaskTypeTest, Mean)
{
    int mask_type = GetParam();
    Size sz(9, 4);
    Mat a(sz, CV_16UC1, Scalar::all(1));
    uchar mask_[9*4] = {
        255, 255, 255,   0, 1, 255,   0, 255,   0,
        0, 255,   0,   0, 255, 255, 255, 255,   0,
        0,   0,   0, 1,   0, 255,   0, 1, 255,
        0,   0, 255,   0, 255, 255, 255,   0, 255
    };
    Mat mask(sz, mask_type, mask_);
    a.setTo(2, mask);

    Scalar result = cv::mean(a, mask);
    EXPECT_NEAR(result[0], 2, 1e-6);
}

TEST_P(Core_MaskTypeTest, MeanStdDev)
{
    int mask_type = GetParam();
    Size sz(9, 4);
    Mat a(sz, CV_16UC1, Scalar::all(1));
    uchar mask_[9*4] = {
        255, 255, 255,   0, 1, 255,   0, 255,   0,
        0, 255,   0,   0, 255, 255, 255, 255,   0,
        0,   0,   0, 1,   0, 255,   0, 1, 255,
        0,   0, 255,   0, 255, 255, 255,   0, 255
    };
    Mat mask(sz, mask_type, mask_);
    a.setTo(2, mask);

    Scalar m, stddev;
    cv::meanStdDev(a, m, stddev, mask);

    EXPECT_NEAR(m[0], 2, 1e-6);
    EXPECT_NEAR(stddev[0], 0, 1e-6);
}

INSTANTIATE_TEST_CASE_P(/**/, Core_MaskTypeTest, MaskType::all());

// Still fails in 5.x: https://github.com/opencv/opencv/issues/28557
TEST(Core_Arithm, DISABLED_mul_overflow_28557)
{
    uint16_t data[] = {5000, 60000, 5000, 60000, 5000, 60000};
    cv::Mat m(1, 6, CV_16U, data);
    cv::Mat res = m.mul(m);

    for (int i = 0; i < 6; i++)
    {
        EXPECT_EQ(65535, res.at<uint16_t>(0, i));
    }
}

}} // namespace