Split matrix.cpp into smaller pieces

modules/core/src/array.cpp

@@ -1448,7 +1448,6 @@ cvGetDiag( const CvArr* arr, CvMat* submat, int diag )
     return res;
 }
 
-
 /****************************************************************************************\
 *                      Operations on CvScalar and accessing array elements               *
 \****************************************************************************************/
@@ -3215,6 +3214,51 @@ template<> void DefaultDeleter<CvMemStorage>::operator ()(CvMemStorage* obj) con
 template<> void DefaultDeleter<CvFileStorage>::operator ()(CvFileStorage* obj) const
 { cvReleaseFileStorage(&obj); }
 
+template <typename T> static inline
+void scalarToRawData_(const Scalar& s, T * const buf, const int cn, const int unroll_to)
+{
+    int i = 0;
+    for(; i < cn; i++)
+        buf[i] = saturate_cast<T>(s.val[i]);
+    for(; i < unroll_to; i++)
+        buf[i] = buf[i-cn];
 }
 
+void scalarToRawData(const Scalar& s, void* _buf, int type, int unroll_to)
+{
+    CV_INSTRUMENT_REGION()
+
+    const int depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
+    CV_Assert(cn <= 4);
+    switch(depth)
+    {
+    case CV_8U:
+        scalarToRawData_<uchar>(s, (uchar*)_buf, cn, unroll_to);
+        break;
+    case CV_8S:
+        scalarToRawData_<schar>(s, (schar*)_buf, cn, unroll_to);
+        break;
+    case CV_16U:
+        scalarToRawData_<ushort>(s, (ushort*)_buf, cn, unroll_to);
+        break;
+    case CV_16S:
+        scalarToRawData_<short>(s, (short*)_buf, cn, unroll_to);
+        break;
+    case CV_32S:
+        scalarToRawData_<int>(s, (int*)_buf, cn, unroll_to);
+        break;
+    case CV_32F:
+        scalarToRawData_<float>(s, (float*)_buf, cn, unroll_to);
+        break;
+    case CV_64F:
+        scalarToRawData_<double>(s, (double*)_buf, cn, unroll_to);
+        break;
+    default:
+        CV_Error(CV_StsUnsupportedFormat,"");
+    }
+}
+
+} // cv::
+
+
 /* End of file. */

modules/core/src/matrix_c.cpp

+#include "opencv2/core/mat.hpp"
+#include "opencv2/core/types_c.h"
+#include "precomp.hpp"
+
+// glue
+
+CvMatND::CvMatND(const cv::Mat& m)
+{
+    cvInitMatNDHeader(this, m.dims, m.size, m.type(), m.data );
+    int i, d = m.dims;
+    for( i = 0; i < d; i++ )
+        dim[i].step = (int)m.step[i];
+    type |= m.flags & cv::Mat::CONTINUOUS_FLAG;
+}
+
+_IplImage::_IplImage(const cv::Mat& m)
+{
+    CV_Assert( m.dims <= 2 );
+    cvInitImageHeader(this, m.size(), cvIplDepth(m.flags), m.channels());
+    cvSetData(this, m.data, (int)m.step[0]);
+}
+
+namespace cv {
+
+static Mat cvMatToMat(const CvMat* m, bool copyData)
+{
+    Mat thiz;
+
+    if( !m )
+        return thiz;
+
+    if( !copyData )
+    {
+        thiz.flags = Mat::MAGIC_VAL + (m->type & (CV_MAT_TYPE_MASK|CV_MAT_CONT_FLAG));
+        thiz.dims = 2;
+        thiz.rows = m->rows;
+        thiz.cols = m->cols;
+        thiz.datastart = thiz.data = m->data.ptr;
+        size_t esz = CV_ELEM_SIZE(m->type), minstep = thiz.cols*esz, _step = m->step;
+        if( _step == 0 )
+            _step = minstep;
+        thiz.datalimit = thiz.datastart + _step*thiz.rows;
+        thiz.dataend = thiz.datalimit - _step + minstep;
+        thiz.step[0] = _step; thiz.step[1] = esz;
+    }
+    else
+    {
+        thiz.datastart = thiz.dataend = thiz.data = 0;
+        Mat(m->rows, m->cols, m->type, m->data.ptr, m->step).copyTo(thiz);
+    }
+
+    return thiz;
+}
+
+static Mat cvMatNDToMat(const CvMatND* m, bool copyData)
+{
+    Mat thiz;
+
+    if( !m )
+        return thiz;
+    thiz.datastart = thiz.data = m->data.ptr;
+    thiz.flags |= CV_MAT_TYPE(m->type);
+    int _sizes[CV_MAX_DIM];
+    size_t _steps[CV_MAX_DIM];
+
+    int d = m->dims;
+    for( int i = 0; i < d; i++ )
+    {
+        _sizes[i] = m->dim[i].size;
+        _steps[i] = m->dim[i].step;
+    }
+
+    setSize(thiz, d, _sizes, _steps);
+    finalizeHdr(thiz);
+
+    if( copyData )
+    {
+        Mat temp(thiz);
+        thiz.release();
+        temp.copyTo(thiz);
+    }
+
+    return thiz;
+}
+
+static Mat iplImageToMat(const IplImage* img, bool copyData)
+{
+    Mat m;
+
+    if( !img )
+        return m;
+
+    m.dims = 2;
+    CV_DbgAssert(CV_IS_IMAGE(img) && img->imageData != 0);
+
+    int imgdepth = IPL2CV_DEPTH(img->depth);
+    size_t esz;
+    m.step[0] = img->widthStep;
+
+    if(!img->roi)
+    {
+        CV_Assert(img->dataOrder == IPL_DATA_ORDER_PIXEL);
+        m.flags = Mat::MAGIC_VAL + CV_MAKETYPE(imgdepth, img->nChannels);
+        m.rows = img->height;
+        m.cols = img->width;
+        m.datastart = m.data = (uchar*)img->imageData;
+        esz = CV_ELEM_SIZE(m.flags);
+    }
+    else
+    {
+        CV_Assert(img->dataOrder == IPL_DATA_ORDER_PIXEL || img->roi->coi != 0);
+        bool selectedPlane = img->roi->coi && img->dataOrder == IPL_DATA_ORDER_PLANE;
+        m.flags = Mat::MAGIC_VAL + CV_MAKETYPE(imgdepth, selectedPlane ? 1 : img->nChannels);
+        m.rows = img->roi->height;
+        m.cols = img->roi->width;
+        esz = CV_ELEM_SIZE(m.flags);
+        m.datastart = m.data = (uchar*)img->imageData +
+            (selectedPlane ? (img->roi->coi - 1)*m.step*img->height : 0) +
+            img->roi->yOffset*m.step[0] + img->roi->xOffset*esz;
+    }
+    m.datalimit = m.datastart + m.step.p[0]*m.rows;
+    m.dataend = m.datastart + m.step.p[0]*(m.rows-1) + esz*m.cols;
+    m.flags |= (m.cols*esz == m.step.p[0] || m.rows == 1 ? Mat::CONTINUOUS_FLAG : 0);
+    m.step[1] = esz;
+
+    if( copyData )
+    {
+        Mat m2 = m;
+        m.release();
+        if( !img->roi || !img->roi->coi ||
+            img->dataOrder == IPL_DATA_ORDER_PLANE)
+            m2.copyTo(m);
+        else
+        {
+            int ch[] = {img->roi->coi - 1, 0};
+            m.create(m2.rows, m2.cols, m2.type());
+            mixChannels(&m2, 1, &m, 1, ch, 1);
+        }
+    }
+
+    return m;
+}
+
+Mat cvarrToMat(const CvArr* arr, bool copyData,
+               bool /*allowND*/, int coiMode, AutoBuffer<double>* abuf )
+{
+    if( !arr )
+        return Mat();
+    if( CV_IS_MAT_HDR_Z(arr) )
+        return cvMatToMat((const CvMat*)arr, copyData);
+    if( CV_IS_MATND(arr) )
+        return cvMatNDToMat((const CvMatND*)arr, copyData );
+    if( CV_IS_IMAGE(arr) )
+    {
+        const IplImage* iplimg = (const IplImage*)arr;
+        if( coiMode == 0 && iplimg->roi && iplimg->roi->coi > 0 )
+            CV_Error(CV_BadCOI, "COI is not supported by the function");
+        return iplImageToMat(iplimg, copyData);
+    }
+    if( CV_IS_SEQ(arr) )
+    {
+        CvSeq* seq = (CvSeq*)arr;
+        int total = seq->total, type = CV_MAT_TYPE(seq->flags), esz = seq->elem_size;
+        if( total == 0 )
+            return Mat();
+        CV_Assert(total > 0 && CV_ELEM_SIZE(seq->flags) == esz);
+        if(!copyData && seq->first->next == seq->first)
+            return Mat(total, 1, type, seq->first->data);
+        if( abuf )
+        {
+            abuf->allocate(((size_t)total*esz + sizeof(double)-1)/sizeof(double));
+            double* bufdata = *abuf;
+            cvCvtSeqToArray(seq, bufdata, CV_WHOLE_SEQ);
+            return Mat(total, 1, type, bufdata);
+        }
+
+        Mat buf(total, 1, type);
+        cvCvtSeqToArray(seq, buf.ptr(), CV_WHOLE_SEQ);
+        return buf;
+    }
+    CV_Error(CV_StsBadArg, "Unknown array type");
+    return Mat();
+}
+
+void extractImageCOI(const CvArr* arr, OutputArray _ch, int coi)
+{
+    Mat mat = cvarrToMat(arr, false, true, 1);
+    _ch.create(mat.dims, mat.size, mat.depth());
+    Mat ch = _ch.getMat();
+    if(coi < 0)
+    {
+        CV_Assert( CV_IS_IMAGE(arr) );
+        coi = cvGetImageCOI((const IplImage*)arr)-1;
+    }
+    CV_Assert(0 <= coi && coi < mat.channels());
+    int _pairs[] = { coi, 0 };
+    mixChannels( &mat, 1, &ch, 1, _pairs, 1 );
+}
+
+void insertImageCOI(InputArray _ch, CvArr* arr, int coi)
+{
+    Mat ch = _ch.getMat(), mat = cvarrToMat(arr, false, true, 1);
+    if(coi < 0)
+    {
+        CV_Assert( CV_IS_IMAGE(arr) );
+        coi = cvGetImageCOI((const IplImage*)arr)-1;
+    }
+    CV_Assert(ch.size == mat.size && ch.depth() == mat.depth() && 0 <= coi && coi < mat.channels());
+    int _pairs[] = { 0, coi };
+    mixChannels( &ch, 1, &mat, 1, _pairs, 1 );
+}
+
+} // cv::
+
+// operations
+
+CV_IMPL void cvSetIdentity( CvArr* arr, CvScalar value )
+{
+    cv::Mat m = cv::cvarrToMat(arr);
+    cv::setIdentity(m, value);
+}
+
+
+CV_IMPL CvScalar cvTrace( const CvArr* arr )
+{
+    return cv::trace(cv::cvarrToMat(arr));
+}
+
+
+CV_IMPL void cvTranspose( const CvArr* srcarr, CvArr* dstarr )
+{
+    cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
+
+    CV_Assert( src.rows == dst.cols && src.cols == dst.rows && src.type() == dst.type() );
+    transpose( src, dst );
+}
+
+
+CV_IMPL void cvCompleteSymm( CvMat* matrix, int LtoR )
+{
+    cv::Mat m = cv::cvarrToMat(matrix);
+    cv::completeSymm( m, LtoR != 0 );
+}
+
+
+CV_IMPL void cvCrossProduct( const CvArr* srcAarr, const CvArr* srcBarr, CvArr* dstarr )
+{
+    cv::Mat srcA = cv::cvarrToMat(srcAarr), dst = cv::cvarrToMat(dstarr);
+
+    CV_Assert( srcA.size() == dst.size() && srcA.type() == dst.type() );
+    srcA.cross(cv::cvarrToMat(srcBarr)).copyTo(dst);
+}
+
+
+CV_IMPL void
+cvReduce( const CvArr* srcarr, CvArr* dstarr, int dim, int op )
+{
+    cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
+
+    if( dim < 0 )
+        dim = src.rows > dst.rows ? 0 : src.cols > dst.cols ? 1 : dst.cols == 1;
+
+    if( dim > 1 )
+        CV_Error( CV_StsOutOfRange, "The reduced dimensionality index is out of range" );
+
+    if( (dim == 0 && (dst.cols != src.cols || dst.rows != 1)) ||
+        (dim == 1 && (dst.rows != src.rows || dst.cols != 1)) )
+        CV_Error( CV_StsBadSize, "The output array size is incorrect" );
+
+    if( src.channels() != dst.channels() )
+        CV_Error( CV_StsUnmatchedFormats, "Input and output arrays must have the same number of channels" );
+
+    cv::reduce(src, dst, dim, op, dst.type());
+}
+
+
+CV_IMPL CvArr*
+cvRange( CvArr* arr, double start, double end )
+{
+    CvMat stub, *mat = (CvMat*)arr;
+    int step;
+    double val = start;
+
+    if( !CV_IS_MAT(mat) )
+        mat = cvGetMat( mat, &stub);
+
+    int rows = mat->rows;
+    int cols = mat->cols;
+    int type = CV_MAT_TYPE(mat->type);
+    double delta = (end-start)/(rows*cols);
+
+    if( CV_IS_MAT_CONT(mat->type) )
+    {
+        cols *= rows;
+        rows = 1;
+        step = 1;
+    }
+    else
+        step = mat->step / CV_ELEM_SIZE(type);
+
+    if( type == CV_32SC1 )
+    {
+        int* idata = mat->data.i;
+        int ival = cvRound(val), idelta = cvRound(delta);
+
+        if( fabs(val - ival) < DBL_EPSILON &&
+            fabs(delta - idelta) < DBL_EPSILON )
+        {
+            for( int i = 0; i < rows; i++, idata += step )
+                for( int j = 0; j < cols; j++, ival += idelta )
+                    idata[j] = ival;
+        }
+        else
+        {
+            for( int i = 0; i < rows; i++, idata += step )
+                for( int j = 0; j < cols; j++, val += delta )
+                    idata[j] = cvRound(val);
+        }
+    }
+    else if( type == CV_32FC1 )
+    {
+        float* fdata = mat->data.fl;
+        for( int i = 0; i < rows; i++, fdata += step )
+            for( int j = 0; j < cols; j++, val += delta )
+                fdata[j] = (float)val;
+    }
+    else
+        CV_Error( CV_StsUnsupportedFormat, "The function only supports 32sC1 and 32fC1 datatypes" );
+
+    return arr;
+}
+
+
+CV_IMPL void
+cvSort( const CvArr* _src, CvArr* _dst, CvArr* _idx, int flags )
+{
+    cv::Mat src = cv::cvarrToMat(_src);
+
+    if( _idx )
+    {
+        cv::Mat idx0 = cv::cvarrToMat(_idx), idx = idx0;
+        CV_Assert( src.size() == idx.size() && idx.type() == CV_32S && src.data != idx.data );
+        cv::sortIdx( src, idx, flags );
+        CV_Assert( idx0.data == idx.data );
+    }
+
+    if( _dst )
+    {
+        cv::Mat dst0 = cv::cvarrToMat(_dst), dst = dst0;
+        CV_Assert( src.size() == dst.size() && src.type() == dst.type() );
+        cv::sort( src, dst, flags );
+        CV_Assert( dst0.data == dst.data );
+    }
+}
+
+
+CV_IMPL int
+cvKMeans2( const CvArr* _samples, int cluster_count, CvArr* _labels,
+           CvTermCriteria termcrit, int attempts, CvRNG*,
+           int flags, CvArr* _centers, double* _compactness )
+{
+    cv::Mat data = cv::cvarrToMat(_samples), labels = cv::cvarrToMat(_labels), centers;
+    if( _centers )
+    {
+        centers = cv::cvarrToMat(_centers);
+
+        centers = centers.reshape(1);
+        data = data.reshape(1);
+
+        CV_Assert( !centers.empty() );
+        CV_Assert( centers.rows == cluster_count );
+        CV_Assert( centers.cols == data.cols );
+        CV_Assert( centers.depth() == data.depth() );
+    }
+    CV_Assert( labels.isContinuous() && labels.type() == CV_32S &&
+        (labels.cols == 1 || labels.rows == 1) &&
+        labels.cols + labels.rows - 1 == data.rows );
+
+    double compactness = cv::kmeans(data, cluster_count, labels, termcrit, attempts,
+                                    flags, _centers ? cv::_OutputArray(centers) : cv::_OutputArray() );
+    if( _compactness )
+        *_compactness = compactness;
+    return 1;
+}

modules/core/src/matrix_decomp.cpp

-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
+// 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 "precomp.hpp"
 
@@ -371,5 +334,4 @@ bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n)
     return CholImpl(A, astep, m, b, bstep, n);
 }
 
-
-}}
+}} // cv::hal::

modules/core/src/matop.cpp → modules/core/src/matrix_expressions.cpp

-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2010, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
+// 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
+
 
 /* ////////////////////////////////////////////////////////////////////
 //
@@ -1769,6 +1732,4 @@ MatExpr Mat::eye(Size size, int type)
     return e;
 }
 
-}
-
-/* End of file. */
+} // cv::

modules/core/src/matrix_iterator.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 "opencv2/core/mat.hpp"
+#include "precomp.hpp"
+
+namespace cv {
+
+NAryMatIterator::NAryMatIterator()
+    : arrays(0), planes(0), ptrs(0), narrays(0), nplanes(0), size(0), iterdepth(0), idx(0)
+{
+}
+
+NAryMatIterator::NAryMatIterator(const Mat** _arrays, Mat* _planes, int _narrays)
+: arrays(0), planes(0), ptrs(0), narrays(0), nplanes(0), size(0), iterdepth(0), idx(0)
+{
+    init(_arrays, _planes, 0, _narrays);
+}
+
+NAryMatIterator::NAryMatIterator(const Mat** _arrays, uchar** _ptrs, int _narrays)
+    : arrays(0), planes(0), ptrs(0), narrays(0), nplanes(0), size(0), iterdepth(0), idx(0)
+{
+    init(_arrays, 0, _ptrs, _narrays);
+}
+
+void NAryMatIterator::init(const Mat** _arrays, Mat* _planes, uchar** _ptrs, int _narrays)
+{
+    CV_Assert( _arrays && (_ptrs || _planes) );
+    int i, j, d1=0, i0 = -1, d = -1;
+
+    arrays = _arrays;
+    ptrs = _ptrs;
+    planes = _planes;
+    narrays = _narrays;
+    nplanes = 0;
+    size = 0;
+
+    if( narrays < 0 )
+    {
+        for( i = 0; _arrays[i] != 0; i++ )
+            ;
+        narrays = i;
+        CV_Assert(narrays <= 1000);
+    }
+
+    iterdepth = 0;
+
+    for( i = 0; i < narrays; i++ )
+    {
+        CV_Assert(arrays[i] != 0);
+        const Mat& A = *arrays[i];
+        if( ptrs )
+            ptrs[i] = A.data;
+
+        if( !A.data )
+            continue;
+
+        if( i0 < 0 )
+        {
+            i0 = i;
+            d = A.dims;
+
+            // find the first dimensionality which is different from 1;
+            // in any of the arrays the first "d1" step do not affect the continuity
+            for( d1 = 0; d1 < d; d1++ )
+                if( A.size[d1] > 1 )
+                    break;
+        }
+        else
+            CV_Assert( A.size == arrays[i0]->size );
+
+        if( !A.isContinuous() )
+        {
+            CV_Assert( A.step[d-1] == A.elemSize() );
+            for( j = d-1; j > d1; j-- )
+                if( A.step[j]*A.size[j] < A.step[j-1] )
+                    break;
+            iterdepth = std::max(iterdepth, j);
+        }
+    }
+
+    if( i0 >= 0 )
+    {
+        size = arrays[i0]->size[d-1];
+        for( j = d-1; j > iterdepth; j-- )
+        {
+            int64 total1 = (int64)size*arrays[i0]->size[j-1];
+            if( total1 != (int)total1 )
+                break;
+            size = (int)total1;
+        }
+
+        iterdepth = j;
+        if( iterdepth == d1 )
+            iterdepth = 0;
+
+        nplanes = 1;
+        for( j = iterdepth-1; j >= 0; j-- )
+            nplanes *= arrays[i0]->size[j];
+    }
+    else
+        iterdepth = 0;
+
+    idx = 0;
+
+    if( !planes )
+        return;
+
+    for( i = 0; i < narrays; i++ )
+    {
+        CV_Assert(arrays[i] != 0);
+        const Mat& A = *arrays[i];
+
+        if( !A.data )
+        {
+            planes[i] = Mat();
+            continue;
+        }
+
+        planes[i] = Mat(1, (int)size, A.type(), A.data);
+    }
+}
+
+
+NAryMatIterator& NAryMatIterator::operator ++()
+{
+    if( idx >= nplanes-1 )
+        return *this;
+    ++idx;
+
+    if( iterdepth == 1 )
+    {
+        if( ptrs )
+        {
+            for( int i = 0; i < narrays; i++ )
+            {
+                if( !ptrs[i] )
+                    continue;
+                ptrs[i] = arrays[i]->data + arrays[i]->step[0]*idx;
+            }
+        }
+        if( planes )
+        {
+            for( int i = 0; i < narrays; i++ )
+            {
+                if( !planes[i].data )
+                    continue;
+                planes[i].data = arrays[i]->data + arrays[i]->step[0]*idx;
+            }
+        }
+    }
+    else
+    {
+        for( int i = 0; i < narrays; i++ )
+        {
+            const Mat& A = *arrays[i];
+            if( !A.data )
+                continue;
+            int _idx = (int)idx;
+            uchar* data = A.data;
+            for( int j = iterdepth-1; j >= 0 && _idx > 0; j-- )
+            {
+                int szi = A.size[j], t = _idx/szi;
+                data += (_idx - t * szi)*A.step[j];
+                _idx = t;
+            }
+            if( ptrs )
+                ptrs[i] = data;
+            if( planes )
+                planes[i].data = data;
+        }
+    }
+
+    return *this;
+}
+
+NAryMatIterator NAryMatIterator::operator ++(int)
+{
+    NAryMatIterator it = *this;
+    ++*this;
+    return it;
+}
+
+//==================================================================================================
+
+Point MatConstIterator::pos() const
+{
+    if( !m )
+        return Point();
+    CV_DbgAssert(m->dims <= 2);
+
+    ptrdiff_t ofs = ptr - m->ptr();
+    int y = (int)(ofs/m->step[0]);
+    return Point((int)((ofs - y*m->step[0])/elemSize), y);
+}
+
+void MatConstIterator::pos(int* _idx) const
+{
+    CV_Assert(m != 0 && _idx);
+    ptrdiff_t ofs = ptr - m->ptr();
+    for( int i = 0; i < m->dims; i++ )
+    {
+        size_t s = m->step[i], v = ofs/s;
+        ofs -= v*s;
+        _idx[i] = (int)v;
+    }
+}
+
+ptrdiff_t MatConstIterator::lpos() const
+{
+    if(!m)
+        return 0;
+    if( m->isContinuous() )
+        return (ptr - sliceStart)/elemSize;
+    ptrdiff_t ofs = ptr - m->ptr();
+    int i, d = m->dims;
+    if( d == 2 )
+    {
+        ptrdiff_t y = ofs/m->step[0];
+        return y*m->cols + (ofs - y*m->step[0])/elemSize;
+    }
+    ptrdiff_t result = 0;
+    for( i = 0; i < d; i++ )
+    {
+        size_t s = m->step[i], v = ofs/s;
+        ofs -= v*s;
+        result = result*m->size[i] + v;
+    }
+    return result;
+}
+
+void MatConstIterator::seek(ptrdiff_t ofs, bool relative)
+{
+    if( m->isContinuous() )
+    {
+        ptr = (relative ? ptr : sliceStart) + ofs*elemSize;
+        if( ptr < sliceStart )
+            ptr = sliceStart;
+        else if( ptr > sliceEnd )
+            ptr = sliceEnd;
+        return;
+    }
+
+    int d = m->dims;
+    if( d == 2 )
+    {
+        ptrdiff_t ofs0, y;
+        if( relative )
+        {
+            ofs0 = ptr - m->ptr();
+            y = ofs0/m->step[0];
+            ofs += y*m->cols + (ofs0 - y*m->step[0])/elemSize;
+        }
+        y = ofs/m->cols;
+        int y1 = std::min(std::max((int)y, 0), m->rows-1);
+        sliceStart = m->ptr(y1);
+        sliceEnd = sliceStart + m->cols*elemSize;
+        ptr = y < 0 ? sliceStart : y >= m->rows ? sliceEnd :
+            sliceStart + (ofs - y*m->cols)*elemSize;
+        return;
+    }
+
+    if( relative )
+        ofs += lpos();
+
+    if( ofs < 0 )
+        ofs = 0;
+
+    int szi = m->size[d-1];
+    ptrdiff_t t = ofs/szi;
+    int v = (int)(ofs - t*szi);
+    ofs = t;
+    ptr = m->ptr() + v*elemSize;
+    sliceStart = m->ptr();
+
+    for( int i = d-2; i >= 0; i-- )
+    {
+        szi = m->size[i];
+        t = ofs/szi;
+        v = (int)(ofs - t*szi);
+        ofs = t;
+        sliceStart += v*m->step[i];
+    }
+
+    sliceEnd = sliceStart + m->size[d-1]*elemSize;
+    if( ofs > 0 )
+        ptr = sliceEnd;
+    else
+        ptr = sliceStart + (ptr - m->ptr());
+}
+
+void MatConstIterator::seek(const int* _idx, bool relative)
+{
+    int d = m->dims;
+    ptrdiff_t ofs = 0;
+    if( !_idx )
+        ;
+    else if( d == 2 )
+        ofs = _idx[0]*m->size[1] + _idx[1];
+    else
+    {
+        for( int i = 0; i < d; i++ )
+            ofs = ofs*m->size[i] + _idx[i];
+    }
+    seek(ofs, relative);
+}
+
+//==================================================================================================
+
+SparseMatConstIterator::SparseMatConstIterator(const SparseMat* _m) : m((SparseMat*)_m), hashidx(0), ptr(0)
+{
+    if(!_m || !_m->hdr)
+        return;
+    SparseMat::Hdr& hdr = *m->hdr;
+    const std::vector<size_t>& htab = hdr.hashtab;
+    size_t i, hsize = htab.size();
+    for( i = 0; i < hsize; i++ )
+    {
+        size_t nidx = htab[i];
+        if( nidx )
+        {
+            hashidx = i;
+            ptr = &hdr.pool[nidx] + hdr.valueOffset;
+            return;
+        }
+    }
+}
+
+SparseMatConstIterator& SparseMatConstIterator::operator ++()
+{
+    if( !ptr || !m || !m->hdr )
+        return *this;
+    SparseMat::Hdr& hdr = *m->hdr;
+    size_t next = ((const SparseMat::Node*)(ptr - hdr.valueOffset))->next;
+    if( next )
+    {
+        ptr = &hdr.pool[next] + hdr.valueOffset;
+        return *this;
+    }
+    size_t i = hashidx + 1, sz = hdr.hashtab.size();
+    for( ; i < sz; i++ )
+    {
+        size_t nidx = hdr.hashtab[i];
+        if( nidx )
+        {
+            hashidx = i;
+            ptr = &hdr.pool[nidx] + hdr.valueOffset;
+            return *this;
+        }
+    }
+    hashidx = sz;
+    ptr = 0;
+    return *this;
+}
+
+} // cv::

modules/core/src/matrix_operations.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 "opencv2/core/mat.hpp"
+#include "opencv2/core/types_c.h"
+#include "opencl_kernels_core.hpp"
+#include "precomp.hpp"
+
+
+/*************************************************************************************************\
+                                        Matrix Operations
+\*************************************************************************************************/
+
+void cv::swap( Mat& a, Mat& b )
+{
+    std::swap(a.flags, b.flags);
+    std::swap(a.dims, b.dims);
+    std::swap(a.rows, b.rows);
+    std::swap(a.cols, b.cols);
+    std::swap(a.data, b.data);
+    std::swap(a.datastart, b.datastart);
+    std::swap(a.dataend, b.dataend);
+    std::swap(a.datalimit, b.datalimit);
+    std::swap(a.allocator, b.allocator);
+    std::swap(a.u, b.u);
+
+    std::swap(a.size.p, b.size.p);
+    std::swap(a.step.p, b.step.p);
+    std::swap(a.step.buf[0], b.step.buf[0]);
+    std::swap(a.step.buf[1], b.step.buf[1]);
+
+    if( a.step.p == b.step.buf )
+    {
+        a.step.p = a.step.buf;
+        a.size.p = &a.rows;
+    }
+
+    if( b.step.p == a.step.buf )
+    {
+        b.step.p = b.step.buf;
+        b.size.p = &b.rows;
+    }
+}
+
+
+void cv::hconcat(const Mat* src, size_t nsrc, OutputArray _dst)
+{
+    CV_INSTRUMENT_REGION()
+
+    if( nsrc == 0 || !src )
+    {
+        _dst.release();
+        return;
+    }
+
+    int totalCols = 0, cols = 0;
+    for( size_t i = 0; i < nsrc; i++ )
+    {
+        CV_Assert( src[i].dims <= 2 &&
+                   src[i].rows == src[0].rows &&
+                   src[i].type() == src[0].type());
+        totalCols += src[i].cols;
+    }
+    _dst.create( src[0].rows, totalCols, src[0].type());
+    Mat dst = _dst.getMat();
+    for( size_t i = 0; i < nsrc; i++ )
+    {
+        Mat dpart = dst(Rect(cols, 0, src[i].cols, src[i].rows));
+        src[i].copyTo(dpart);
+        cols += src[i].cols;
+    }
+}
+
+void cv::hconcat(InputArray src1, InputArray src2, OutputArray dst)
+{
+    CV_INSTRUMENT_REGION()
+
+    Mat src[] = {src1.getMat(), src2.getMat()};
+    hconcat(src, 2, dst);
+}
+
+void cv::hconcat(InputArray _src, OutputArray dst)
+{
+    CV_INSTRUMENT_REGION()
+
+    std::vector<Mat> src;
+    _src.getMatVector(src);
+    hconcat(!src.empty() ? &src[0] : 0, src.size(), dst);
+}
+
+void cv::vconcat(const Mat* src, size_t nsrc, OutputArray _dst)
+{
+    CV_TRACE_FUNCTION_SKIP_NESTED()
+
+    if( nsrc == 0 || !src )
+    {
+        _dst.release();
+        return;
+    }
+
+    int totalRows = 0, rows = 0;
+    for( size_t i = 0; i < nsrc; i++ )
+    {
+        CV_Assert(src[i].dims <= 2 &&
+                  src[i].cols == src[0].cols &&
+                  src[i].type() == src[0].type());
+        totalRows += src[i].rows;
+    }
+    _dst.create( totalRows, src[0].cols, src[0].type());
+    Mat dst = _dst.getMat();
+    for( size_t i = 0; i < nsrc; i++ )
+    {
+        Mat dpart(dst, Rect(0, rows, src[i].cols, src[i].rows));
+        src[i].copyTo(dpart);
+        rows += src[i].rows;
+    }
+}
+
+void cv::vconcat(InputArray src1, InputArray src2, OutputArray dst)
+{
+    CV_INSTRUMENT_REGION()
+
+    Mat src[] = {src1.getMat(), src2.getMat()};
+    vconcat(src, 2, dst);
+}
+
+void cv::vconcat(InputArray _src, OutputArray dst)
+{
+    CV_INSTRUMENT_REGION()
+
+    std::vector<Mat> src;
+    _src.getMatVector(src);
+    vconcat(!src.empty() ? &src[0] : 0, src.size(), dst);
+}
+
+//////////////////////////////////////// set identity ////////////////////////////////////////////
+
+#ifdef HAVE_OPENCL
+
+namespace cv {
+
+static bool ocl_setIdentity( InputOutputArray _m, const Scalar& s )
+{
+    int type = _m.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type), kercn = cn, rowsPerWI = 1;
+    int sctype = CV_MAKE_TYPE(depth, cn == 3 ? 4 : cn);
+    if (ocl::Device::getDefault().isIntel())
+    {
+        rowsPerWI = 4;
+        if (cn == 1)
+        {
+            kercn = std::min(ocl::predictOptimalVectorWidth(_m), 4);
+            if (kercn != 4)
+                kercn = 1;
+        }
+    }
+
+    ocl::Kernel k("setIdentity", ocl::core::set_identity_oclsrc,
+                  format("-D T=%s -D T1=%s -D cn=%d -D ST=%s -D kercn=%d -D rowsPerWI=%d",
+                         ocl::memopTypeToStr(CV_MAKE_TYPE(depth, kercn)),
+                         ocl::memopTypeToStr(depth), cn,
+                         ocl::memopTypeToStr(sctype),
+                         kercn, rowsPerWI));
+    if (k.empty())
+        return false;
+
+    UMat m = _m.getUMat();
+    k.args(ocl::KernelArg::WriteOnly(m, cn, kercn),
+           ocl::KernelArg::Constant(Mat(1, 1, sctype, s)));
+
+    size_t globalsize[2] = { (size_t)m.cols * cn / kercn, ((size_t)m.rows + rowsPerWI - 1) / rowsPerWI };
+    return k.run(2, globalsize, NULL, false);
+}
+
+}
+
+#endif
+
+void cv::setIdentity( InputOutputArray _m, const Scalar& s )
+{
+    CV_INSTRUMENT_REGION()
+
+    CV_Assert( _m.dims() <= 2 );
+
+    CV_OCL_RUN(_m.isUMat(),
+               ocl_setIdentity(_m, s))
+
+    Mat m = _m.getMat();
+    int rows = m.rows, cols = m.cols, type = m.type();
+
+    if( type == CV_32FC1 )
+    {
+        float* data = m.ptr<float>();
+        float val = (float)s[0];
+        size_t step = m.step/sizeof(data[0]);
+
+        for( int i = 0; i < rows; i++, data += step )
+        {
+            for( int j = 0; j < cols; j++ )
+                data[j] = 0;
+            if( i < cols )
+                data[i] = val;
+        }
+    }
+    else if( type == CV_64FC1 )
+    {
+        double* data = m.ptr<double>();
+        double val = s[0];
+        size_t step = m.step/sizeof(data[0]);
+
+        for( int i = 0; i < rows; i++, data += step )
+        {
+            for( int j = 0; j < cols; j++ )
+                data[j] = j == i ? val : 0;
+        }
+    }
+    else
+    {
+        m = Scalar(0);
+        m.diag() = s;
+    }
+}
+
+//////////////////////////////////////////// trace ///////////////////////////////////////////
+
+cv::Scalar cv::trace( InputArray _m )
+{
+    CV_INSTRUMENT_REGION()
+
+    Mat m = _m.getMat();
+    CV_Assert( m.dims <= 2 );
+    int type = m.type();
+    int nm = std::min(m.rows, m.cols);
+
+    if( type == CV_32FC1 )
+    {
+        const float* ptr = m.ptr<float>();
+        size_t step = m.step/sizeof(ptr[0]) + 1;
+        double _s = 0;
+        for( int i = 0; i < nm; i++ )
+            _s += ptr[i*step];
+        return _s;
+    }
+
+    if( type == CV_64FC1 )
+    {
+        const double* ptr = m.ptr<double>();
+        size_t step = m.step/sizeof(ptr[0]) + 1;
+        double _s = 0;
+        for( int i = 0; i < nm; i++ )
+            _s += ptr[i*step];
+        return _s;
+    }
+
+    return cv::sum(m.diag());
+}
+
+////////////////////////////////////// transpose /////////////////////////////////////////
+
+namespace cv
+{
+
+template<typename T> static void
+transpose_( const uchar* src, size_t sstep, uchar* dst, size_t dstep, Size sz )
+{
+    int i=0, j, m = sz.width, n = sz.height;
+
+    #if CV_ENABLE_UNROLLED
+    for(; i <= m - 4; i += 4 )
+    {
+        T* d0 = (T*)(dst + dstep*i);
+        T* d1 = (T*)(dst + dstep*(i+1));
+        T* d2 = (T*)(dst + dstep*(i+2));
+        T* d3 = (T*)(dst + dstep*(i+3));
+
+        for( j = 0; j <= n - 4; j += 4 )
+        {
+            const T* s0 = (const T*)(src + i*sizeof(T) + sstep*j);
+            const T* s1 = (const T*)(src + i*sizeof(T) + sstep*(j+1));
+            const T* s2 = (const T*)(src + i*sizeof(T) + sstep*(j+2));
+            const T* s3 = (const T*)(src + i*sizeof(T) + sstep*(j+3));
+
+            d0[j] = s0[0]; d0[j+1] = s1[0]; d0[j+2] = s2[0]; d0[j+3] = s3[0];
+            d1[j] = s0[1]; d1[j+1] = s1[1]; d1[j+2] = s2[1]; d1[j+3] = s3[1];
+            d2[j] = s0[2]; d2[j+1] = s1[2]; d2[j+2] = s2[2]; d2[j+3] = s3[2];
+            d3[j] = s0[3]; d3[j+1] = s1[3]; d3[j+2] = s2[3]; d3[j+3] = s3[3];
+        }
+
+        for( ; j < n; j++ )
+        {
+            const T* s0 = (const T*)(src + i*sizeof(T) + j*sstep);
+            d0[j] = s0[0]; d1[j] = s0[1]; d2[j] = s0[2]; d3[j] = s0[3];
+        }
+    }
+    #endif
+    for( ; i < m; i++ )
+    {
+        T* d0 = (T*)(dst + dstep*i);
+        j = 0;
+        #if CV_ENABLE_UNROLLED
+        for(; j <= n - 4; j += 4 )
+        {
+            const T* s0 = (const T*)(src + i*sizeof(T) + sstep*j);
+            const T* s1 = (const T*)(src + i*sizeof(T) + sstep*(j+1));
+            const T* s2 = (const T*)(src + i*sizeof(T) + sstep*(j+2));
+            const T* s3 = (const T*)(src + i*sizeof(T) + sstep*(j+3));
+
+            d0[j] = s0[0]; d0[j+1] = s1[0]; d0[j+2] = s2[0]; d0[j+3] = s3[0];
+        }
+        #endif
+        for( ; j < n; j++ )
+        {
+            const T* s0 = (const T*)(src + i*sizeof(T) + j*sstep);
+            d0[j] = s0[0];
+        }
+    }
+}
+
+template<typename T> static void
+transposeI_( uchar* data, size_t step, int n )
+{
+    for( int i = 0; i < n; i++ )
+    {
+        T* row = (T*)(data + step*i);
+        uchar* data1 = data + i*sizeof(T);
+        for( int j = i+1; j < n; j++ )
+            std::swap( row[j], *(T*)(data1 + step*j) );
+    }
+}
+
+typedef void (*TransposeFunc)( const uchar* src, size_t sstep, uchar* dst, size_t dstep, Size sz );
+typedef void (*TransposeInplaceFunc)( uchar* data, size_t step, int n );
+
+#define DEF_TRANSPOSE_FUNC(suffix, type) \
+static void transpose_##suffix( const uchar* src, size_t sstep, uchar* dst, size_t dstep, Size sz ) \
+{ transpose_<type>(src, sstep, dst, dstep, sz); } \
+\
+static void transposeI_##suffix( uchar* data, size_t step, int n ) \
+{ transposeI_<type>(data, step, n); }
+
+DEF_TRANSPOSE_FUNC(8u, uchar)
+DEF_TRANSPOSE_FUNC(16u, ushort)
+DEF_TRANSPOSE_FUNC(8uC3, Vec3b)
+DEF_TRANSPOSE_FUNC(32s, int)
+DEF_TRANSPOSE_FUNC(16uC3, Vec3s)
+DEF_TRANSPOSE_FUNC(32sC2, Vec2i)
+DEF_TRANSPOSE_FUNC(32sC3, Vec3i)
+DEF_TRANSPOSE_FUNC(32sC4, Vec4i)
+DEF_TRANSPOSE_FUNC(32sC6, Vec6i)
+DEF_TRANSPOSE_FUNC(32sC8, Vec8i)
+
+static TransposeFunc transposeTab[] =
+{
+    0, transpose_8u, transpose_16u, transpose_8uC3, transpose_32s, 0, transpose_16uC3, 0,
+    transpose_32sC2, 0, 0, 0, transpose_32sC3, 0, 0, 0, transpose_32sC4,
+    0, 0, 0, 0, 0, 0, 0, transpose_32sC6, 0, 0, 0, 0, 0, 0, 0, transpose_32sC8
+};
+
+static TransposeInplaceFunc transposeInplaceTab[] =
+{
+    0, transposeI_8u, transposeI_16u, transposeI_8uC3, transposeI_32s, 0, transposeI_16uC3, 0,
+    transposeI_32sC2, 0, 0, 0, transposeI_32sC3, 0, 0, 0, transposeI_32sC4,
+    0, 0, 0, 0, 0, 0, 0, transposeI_32sC6, 0, 0, 0, 0, 0, 0, 0, transposeI_32sC8
+};
+
+#ifdef HAVE_OPENCL
+
+static bool ocl_transpose( InputArray _src, OutputArray _dst )
+{
+    const ocl::Device & dev = ocl::Device::getDefault();
+    const int TILE_DIM = 32, BLOCK_ROWS = 8;
+    int type = _src.type(), cn = CV_MAT_CN(type), depth = CV_MAT_DEPTH(type),
+        rowsPerWI = dev.isIntel() ? 4 : 1;
+
+    UMat src = _src.getUMat();
+    _dst.create(src.cols, src.rows, type);
+    UMat dst = _dst.getUMat();
+
+    String kernelName("transpose");
+    bool inplace = dst.u == src.u;
+
+    if (inplace)
+    {
+        CV_Assert(dst.cols == dst.rows);
+        kernelName += "_inplace";
+    }
+    else
+    {
+        // check required local memory size
+        size_t required_local_memory = (size_t) TILE_DIM*(TILE_DIM+1)*CV_ELEM_SIZE(type);
+        if (required_local_memory > ocl::Device::getDefault().localMemSize())
+            return false;
+    }
+
+    ocl::Kernel k(kernelName.c_str(), ocl::core::transpose_oclsrc,
+                  format("-D T=%s -D T1=%s -D cn=%d -D TILE_DIM=%d -D BLOCK_ROWS=%d -D rowsPerWI=%d%s",
+                         ocl::memopTypeToStr(type), ocl::memopTypeToStr(depth),
+                         cn, TILE_DIM, BLOCK_ROWS, rowsPerWI, inplace ? " -D INPLACE" : ""));
+    if (k.empty())
+        return false;
+
+    if (inplace)
+        k.args(ocl::KernelArg::ReadWriteNoSize(dst), dst.rows);
+    else
+        k.args(ocl::KernelArg::ReadOnly(src),
+               ocl::KernelArg::WriteOnlyNoSize(dst));
+
+    size_t localsize[2]  = { TILE_DIM, BLOCK_ROWS };
+    size_t globalsize[2] = { (size_t)src.cols, inplace ? ((size_t)src.rows + rowsPerWI - 1) / rowsPerWI : (divUp((size_t)src.rows, TILE_DIM) * BLOCK_ROWS) };
+
+    if (inplace && dev.isIntel())
+    {
+        localsize[0] = 16;
+        localsize[1] = dev.maxWorkGroupSize() / localsize[0];
+    }
+
+    return k.run(2, globalsize, localsize, false);
+}
+
+#endif
+
+#ifdef HAVE_IPP
+static bool ipp_transpose( Mat &src, Mat &dst )
+{
+    CV_INSTRUMENT_REGION_IPP()
+
+    int type = src.type();
+    typedef IppStatus (CV_STDCALL * IppiTranspose)(const void * pSrc, int srcStep, void * pDst, int dstStep, IppiSize roiSize);
+    typedef IppStatus (CV_STDCALL * IppiTransposeI)(const void * pSrcDst, int srcDstStep, IppiSize roiSize);
+    IppiTranspose ippiTranspose = 0;
+    IppiTransposeI ippiTranspose_I = 0;
+
+    if (dst.data == src.data && dst.cols == dst.rows)
+    {
+        CV_SUPPRESS_DEPRECATED_START
+        ippiTranspose_I =
+            type == CV_8UC1 ? (IppiTransposeI)ippiTranspose_8u_C1IR :
+            type == CV_8UC3 ? (IppiTransposeI)ippiTranspose_8u_C3IR :
+            type == CV_8UC4 ? (IppiTransposeI)ippiTranspose_8u_C4IR :
+            type == CV_16UC1 ? (IppiTransposeI)ippiTranspose_16u_C1IR :
+            type == CV_16UC3 ? (IppiTransposeI)ippiTranspose_16u_C3IR :
+            type == CV_16UC4 ? (IppiTransposeI)ippiTranspose_16u_C4IR :
+            type == CV_16SC1 ? (IppiTransposeI)ippiTranspose_16s_C1IR :
+            type == CV_16SC3 ? (IppiTransposeI)ippiTranspose_16s_C3IR :
+            type == CV_16SC4 ? (IppiTransposeI)ippiTranspose_16s_C4IR :
+            type == CV_32SC1 ? (IppiTransposeI)ippiTranspose_32s_C1IR :
+            type == CV_32SC3 ? (IppiTransposeI)ippiTranspose_32s_C3IR :
+            type == CV_32SC4 ? (IppiTransposeI)ippiTranspose_32s_C4IR :
+            type == CV_32FC1 ? (IppiTransposeI)ippiTranspose_32f_C1IR :
+            type == CV_32FC3 ? (IppiTransposeI)ippiTranspose_32f_C3IR :
+            type == CV_32FC4 ? (IppiTransposeI)ippiTranspose_32f_C4IR : 0;
+        CV_SUPPRESS_DEPRECATED_END
+    }
+    else
+    {
+        ippiTranspose =
+            type == CV_8UC1 ? (IppiTranspose)ippiTranspose_8u_C1R :
+            type == CV_8UC3 ? (IppiTranspose)ippiTranspose_8u_C3R :
+            type == CV_8UC4 ? (IppiTranspose)ippiTranspose_8u_C4R :
+            type == CV_16UC1 ? (IppiTranspose)ippiTranspose_16u_C1R :
+            type == CV_16UC3 ? (IppiTranspose)ippiTranspose_16u_C3R :
+            type == CV_16UC4 ? (IppiTranspose)ippiTranspose_16u_C4R :
+            type == CV_16SC1 ? (IppiTranspose)ippiTranspose_16s_C1R :
+            type == CV_16SC3 ? (IppiTranspose)ippiTranspose_16s_C3R :
+            type == CV_16SC4 ? (IppiTranspose)ippiTranspose_16s_C4R :
+            type == CV_32SC1 ? (IppiTranspose)ippiTranspose_32s_C1R :
+            type == CV_32SC3 ? (IppiTranspose)ippiTranspose_32s_C3R :
+            type == CV_32SC4 ? (IppiTranspose)ippiTranspose_32s_C4R :
+            type == CV_32FC1 ? (IppiTranspose)ippiTranspose_32f_C1R :
+            type == CV_32FC3 ? (IppiTranspose)ippiTranspose_32f_C3R :
+            type == CV_32FC4 ? (IppiTranspose)ippiTranspose_32f_C4R : 0;
+    }
+
+    IppiSize roiSize = { src.cols, src.rows };
+    if (ippiTranspose != 0)
+    {
+        if (CV_INSTRUMENT_FUN_IPP(ippiTranspose, src.ptr(), (int)src.step, dst.ptr(), (int)dst.step, roiSize) >= 0)
+            return true;
+    }
+    else if (ippiTranspose_I != 0)
+    {
+        if (CV_INSTRUMENT_FUN_IPP(ippiTranspose_I, dst.ptr(), (int)dst.step, roiSize) >= 0)
+            return true;
+    }
+    return false;
+}
+#endif
+
+}
+
+
+void cv::transpose( InputArray _src, OutputArray _dst )
+{
+    CV_INSTRUMENT_REGION()
+
+    int type = _src.type(), esz = CV_ELEM_SIZE(type);
+    CV_Assert( _src.dims() <= 2 && esz <= 32 );
+
+    CV_OCL_RUN(_dst.isUMat(),
+               ocl_transpose(_src, _dst))
+
+    Mat src = _src.getMat();
+    if( src.empty() )
+    {
+        _dst.release();
+        return;
+    }
+
+    _dst.create(src.cols, src.rows, src.type());
+    Mat dst = _dst.getMat();
+
+    // handle the case of single-column/single-row matrices, stored in STL vectors.
+    if( src.rows != dst.cols || src.cols != dst.rows )
+    {
+        CV_Assert( src.size() == dst.size() && (src.cols == 1 || src.rows == 1) );
+        src.copyTo(dst);
+        return;
+    }
+
+    CV_IPP_RUN_FAST(ipp_transpose(src, dst))
+
+    if( dst.data == src.data )
+    {
+        TransposeInplaceFunc func = transposeInplaceTab[esz];
+        CV_Assert( func != 0 );
+        CV_Assert( dst.cols == dst.rows );
+        func( dst.ptr(), dst.step, dst.rows );
+    }
+    else
+    {
+        TransposeFunc func = transposeTab[esz];
+        CV_Assert( func != 0 );
+        func( src.ptr(), src.step, dst.ptr(), dst.step, src.size() );
+    }
+}
+
+
+////////////////////////////////////// completeSymm /////////////////////////////////////////
+
+void cv::completeSymm( InputOutputArray _m, bool LtoR )
+{
+    CV_INSTRUMENT_REGION()
+
+    Mat m = _m.getMat();
+    size_t step = m.step, esz = m.elemSize();
+    CV_Assert( m.dims <= 2 && m.rows == m.cols );
+
+    int rows = m.rows;
+    int j0 = 0, j1 = rows;
+
+    uchar* data = m.ptr();
+    for( int i = 0; i < rows; i++ )
+    {
+        if( !LtoR ) j1 = i; else j0 = i+1;
+        for( int j = j0; j < j1; j++ )
+            memcpy(data + (i*step + j*esz), data + (j*step + i*esz), esz);
+    }
+}
+
+
+cv::Mat cv::Mat::cross(InputArray _m) const
+{
+    Mat m = _m.getMat();
+    int tp = type(), d = CV_MAT_DEPTH(tp);
+    CV_Assert( dims <= 2 && m.dims <= 2 && size() == m.size() && tp == m.type() &&
+        ((rows == 3 && cols == 1) || (cols*channels() == 3 && rows == 1)));
+    Mat result(rows, cols, tp);
+
+    if( d == CV_32F )
+    {
+        const float *a = (const float*)data, *b = (const float*)m.data;
+        float* c = (float*)result.data;
+        size_t lda = rows > 1 ? step/sizeof(a[0]) : 1;
+        size_t ldb = rows > 1 ? m.step/sizeof(b[0]) : 1;
+
+        c[0] = a[lda] * b[ldb*2] - a[lda*2] * b[ldb];
+        c[1] = a[lda*2] * b[0] - a[0] * b[ldb*2];
+        c[2] = a[0] * b[ldb] - a[lda] * b[0];
+    }
+    else if( d == CV_64F )
+    {
+        const double *a = (const double*)data, *b = (const double*)m.data;
+        double* c = (double*)result.data;
+        size_t lda = rows > 1 ? step/sizeof(a[0]) : 1;
+        size_t ldb = rows > 1 ? m.step/sizeof(b[0]) : 1;
+
+        c[0] = a[lda] * b[ldb*2] - a[lda*2] * b[ldb];
+        c[1] = a[lda*2] * b[0] - a[0] * b[ldb*2];
+        c[2] = a[0] * b[ldb] - a[lda] * b[0];
+    }
+
+    return result;
+}
+
+
+////////////////////////////////////////// reduce ////////////////////////////////////////////
+
+namespace cv
+{
+
+template<typename T, typename ST, class Op> static void
+reduceR_( const Mat& srcmat, Mat& dstmat )
+{
+    typedef typename Op::rtype WT;
+    Size size = srcmat.size();
+    size.width *= srcmat.channels();
+    AutoBuffer<WT> buffer(size.width);
+    WT* buf = buffer;
+    ST* dst = dstmat.ptr<ST>();
+    const T* src = srcmat.ptr<T>();
+    size_t srcstep = srcmat.step/sizeof(src[0]);
+    int i;
+    Op op;
+
+    for( i = 0; i < size.width; i++ )
+        buf[i] = src[i];
+
+    for( ; --size.height; )
+    {
+        src += srcstep;
+        i = 0;
+        #if CV_ENABLE_UNROLLED
+        for(; i <= size.width - 4; i += 4 )
+        {
+            WT s0, s1;
+            s0 = op(buf[i], (WT)src[i]);
+            s1 = op(buf[i+1], (WT)src[i+1]);
+            buf[i] = s0; buf[i+1] = s1;
+
+            s0 = op(buf[i+2], (WT)src[i+2]);
+            s1 = op(buf[i+3], (WT)src[i+3]);
+            buf[i+2] = s0; buf[i+3] = s1;
+        }
+        #endif
+        for( ; i < size.width; i++ )
+            buf[i] = op(buf[i], (WT)src[i]);
+    }
+
+    for( i = 0; i < size.width; i++ )
+        dst[i] = (ST)buf[i];
+}
+
+
+template<typename T, typename ST, class Op> static void
+reduceC_( const Mat& srcmat, Mat& dstmat )
+{
+    typedef typename Op::rtype WT;
+    Size size = srcmat.size();
+    int cn = srcmat.channels();
+    size.width *= cn;
+    Op op;
+
+    for( int y = 0; y < size.height; y++ )
+    {
+        const T* src = srcmat.ptr<T>(y);
+        ST* dst = dstmat.ptr<ST>(y);
+        if( size.width == cn )
+            for( int k = 0; k < cn; k++ )
+                dst[k] = src[k];
+        else
+        {
+            for( int k = 0; k < cn; k++ )
+            {
+                WT a0 = src[k], a1 = src[k+cn];
+                int i;
+                for( i = 2*cn; i <= size.width - 4*cn; i += 4*cn )
+                {
+                    a0 = op(a0, (WT)src[i+k]);
+                    a1 = op(a1, (WT)src[i+k+cn]);
+                    a0 = op(a0, (WT)src[i+k+cn*2]);
+                    a1 = op(a1, (WT)src[i+k+cn*3]);
+                }
+
+                for( ; i < size.width; i += cn )
+                {
+                    a0 = op(a0, (WT)src[i+k]);
+                }
+                a0 = op(a0, a1);
+                dst[k] = (ST)a0;
+            }
+        }
+    }
+}
+
+typedef void (*ReduceFunc)( const Mat& src, Mat& dst );
+
+}
+
+#define reduceSumR8u32s  reduceR_<uchar, int,   OpAdd<int> >
+#define reduceSumR8u32f  reduceR_<uchar, float, OpAdd<int> >
+#define reduceSumR8u64f  reduceR_<uchar, double,OpAdd<int> >
+#define reduceSumR16u32f reduceR_<ushort,float, OpAdd<float> >
+#define reduceSumR16u64f reduceR_<ushort,double,OpAdd<double> >
+#define reduceSumR16s32f reduceR_<short, float, OpAdd<float> >
+#define reduceSumR16s64f reduceR_<short, double,OpAdd<double> >
+#define reduceSumR32f32f reduceR_<float, float, OpAdd<float> >
+#define reduceSumR32f64f reduceR_<float, double,OpAdd<double> >
+#define reduceSumR64f64f reduceR_<double,double,OpAdd<double> >
+
+#define reduceMaxR8u  reduceR_<uchar, uchar, OpMax<uchar> >
+#define reduceMaxR16u reduceR_<ushort,ushort,OpMax<ushort> >
+#define reduceMaxR16s reduceR_<short, short, OpMax<short> >
+#define reduceMaxR32f reduceR_<float, float, OpMax<float> >
+#define reduceMaxR64f reduceR_<double,double,OpMax<double> >
+
+#define reduceMinR8u  reduceR_<uchar, uchar, OpMin<uchar> >
+#define reduceMinR16u reduceR_<ushort,ushort,OpMin<ushort> >
+#define reduceMinR16s reduceR_<short, short, OpMin<short> >
+#define reduceMinR32f reduceR_<float, float, OpMin<float> >
+#define reduceMinR64f reduceR_<double,double,OpMin<double> >
+
+#ifdef HAVE_IPP
+static inline bool ipp_reduceSumC_8u16u16s32f_64f(const cv::Mat& srcmat, cv::Mat& dstmat)
+{
+    int sstep = (int)srcmat.step, stype = srcmat.type(),
+            ddepth = dstmat.depth();
+
+    IppiSize roisize = { srcmat.size().width, 1 };
+
+    typedef IppStatus (CV_STDCALL * IppiSum)(const void * pSrc, int srcStep, IppiSize roiSize, Ipp64f* pSum);
+    typedef IppStatus (CV_STDCALL * IppiSumHint)(const void * pSrc, int srcStep, IppiSize roiSize, Ipp64f* pSum, IppHintAlgorithm hint);
+    IppiSum ippiSum = 0;
+    IppiSumHint ippiSumHint = 0;
+
+    if(ddepth == CV_64F)
+    {
+        ippiSum =
+            stype == CV_8UC1 ? (IppiSum)ippiSum_8u_C1R :
+            stype == CV_8UC3 ? (IppiSum)ippiSum_8u_C3R :
+            stype == CV_8UC4 ? (IppiSum)ippiSum_8u_C4R :
+            stype == CV_16UC1 ? (IppiSum)ippiSum_16u_C1R :
+            stype == CV_16UC3 ? (IppiSum)ippiSum_16u_C3R :
+            stype == CV_16UC4 ? (IppiSum)ippiSum_16u_C4R :
+            stype == CV_16SC1 ? (IppiSum)ippiSum_16s_C1R :
+            stype == CV_16SC3 ? (IppiSum)ippiSum_16s_C3R :
+            stype == CV_16SC4 ? (IppiSum)ippiSum_16s_C4R : 0;
+        ippiSumHint =
+            stype == CV_32FC1 ? (IppiSumHint)ippiSum_32f_C1R :
+            stype == CV_32FC3 ? (IppiSumHint)ippiSum_32f_C3R :
+            stype == CV_32FC4 ? (IppiSumHint)ippiSum_32f_C4R : 0;
+    }
+
+    if(ippiSum)
+    {
+        for(int y = 0; y < srcmat.size().height; y++)
+        {
+            if(CV_INSTRUMENT_FUN_IPP(ippiSum, srcmat.ptr(y), sstep, roisize, dstmat.ptr<Ipp64f>(y)) < 0)
+                return false;
+        }
+        return true;
+    }
+    else if(ippiSumHint)
+    {
+        for(int y = 0; y < srcmat.size().height; y++)
+        {
+            if(CV_INSTRUMENT_FUN_IPP(ippiSumHint, srcmat.ptr(y), sstep, roisize, dstmat.ptr<Ipp64f>(y), ippAlgHintAccurate) < 0)
+                return false;
+        }
+        return true;
+    }
+
+    return false;
+}
+
+static inline void reduceSumC_8u16u16s32f_64f(const cv::Mat& srcmat, cv::Mat& dstmat)
+{
+    CV_IPP_RUN_FAST(ipp_reduceSumC_8u16u16s32f_64f(srcmat, dstmat));
+
+    cv::ReduceFunc func = 0;
+
+    if(dstmat.depth() == CV_64F)
+    {
+        int sdepth = CV_MAT_DEPTH(srcmat.type());
+        func =
+            sdepth == CV_8U ? (cv::ReduceFunc)cv::reduceC_<uchar, double,   cv::OpAdd<double> > :
+            sdepth == CV_16U ? (cv::ReduceFunc)cv::reduceC_<ushort, double,   cv::OpAdd<double> > :
+            sdepth == CV_16S ? (cv::ReduceFunc)cv::reduceC_<short, double,   cv::OpAdd<double> > :
+            sdepth == CV_32F ? (cv::ReduceFunc)cv::reduceC_<float, double,   cv::OpAdd<double> > : 0;
+    }
+    CV_Assert(func);
+
+    func(srcmat, dstmat);
+}
+
+#endif
+
+#define reduceSumC8u32s  reduceC_<uchar, int,   OpAdd<int> >
+#define reduceSumC8u32f  reduceC_<uchar, float, OpAdd<int> >
+#define reduceSumC16u32f reduceC_<ushort,float, OpAdd<float> >
+#define reduceSumC16s32f reduceC_<short, float, OpAdd<float> >
+#define reduceSumC32f32f reduceC_<float, float, OpAdd<float> >
+#define reduceSumC64f64f reduceC_<double,double,OpAdd<double> >
+
+#ifdef HAVE_IPP
+#define reduceSumC8u64f  reduceSumC_8u16u16s32f_64f
+#define reduceSumC16u64f reduceSumC_8u16u16s32f_64f
+#define reduceSumC16s64f reduceSumC_8u16u16s32f_64f
+#define reduceSumC32f64f reduceSumC_8u16u16s32f_64f
+#else
+#define reduceSumC8u64f  reduceC_<uchar, double,OpAdd<int> >
+#define reduceSumC16u64f reduceC_<ushort,double,OpAdd<double> >
+#define reduceSumC16s64f reduceC_<short, double,OpAdd<double> >
+#define reduceSumC32f64f reduceC_<float, double,OpAdd<double> >
+#endif
+
+#ifdef HAVE_IPP
+#define REDUCE_OP(favor, optype, type1, type2) \
+static inline bool ipp_reduce##optype##C##favor(const cv::Mat& srcmat, cv::Mat& dstmat) \
+{ \
+    if((srcmat.channels() == 1)) \
+    { \
+        int sstep = (int)srcmat.step; \
+        typedef Ipp##favor IppType; \
+        IppiSize roisize = ippiSize(srcmat.size().width, 1);\
+        for(int y = 0; y < srcmat.size().height; y++)\
+        {\
+            if(CV_INSTRUMENT_FUN_IPP(ippi##optype##_##favor##_C1R, srcmat.ptr<IppType>(y), sstep, roisize, dstmat.ptr<IppType>(y)) < 0)\
+                return false;\
+        }\
+        return true;\
+    }\
+    return false; \
+} \
+static inline void reduce##optype##C##favor(const cv::Mat& srcmat, cv::Mat& dstmat) \
+{ \
+    CV_IPP_RUN_FAST(ipp_reduce##optype##C##favor(srcmat, dstmat)); \
+    cv::reduceC_ < type1, type2, cv::Op##optype < type2 > >(srcmat, dstmat); \
+}
+#endif
+
+#ifdef HAVE_IPP
+REDUCE_OP(8u, Max, uchar, uchar)
+REDUCE_OP(16u, Max, ushort, ushort)
+REDUCE_OP(16s, Max, short, short)
+REDUCE_OP(32f, Max, float, float)
+#else
+#define reduceMaxC8u  reduceC_<uchar, uchar, OpMax<uchar> >
+#define reduceMaxC16u reduceC_<ushort,ushort,OpMax<ushort> >
+#define reduceMaxC16s reduceC_<short, short, OpMax<short> >
+#define reduceMaxC32f reduceC_<float, float, OpMax<float> >
+#endif
+#define reduceMaxC64f reduceC_<double,double,OpMax<double> >
+
+#ifdef HAVE_IPP
+REDUCE_OP(8u, Min, uchar, uchar)
+REDUCE_OP(16u, Min, ushort, ushort)
+REDUCE_OP(16s, Min, short, short)
+REDUCE_OP(32f, Min, float, float)
+#else
+#define reduceMinC8u  reduceC_<uchar, uchar, OpMin<uchar> >
+#define reduceMinC16u reduceC_<ushort,ushort,OpMin<ushort> >
+#define reduceMinC16s reduceC_<short, short, OpMin<short> >
+#define reduceMinC32f reduceC_<float, float, OpMin<float> >
+#endif
+#define reduceMinC64f reduceC_<double,double,OpMin<double> >
+
+#ifdef HAVE_OPENCL
+
+namespace cv {
+
+static bool ocl_reduce(InputArray _src, OutputArray _dst,
+                       int dim, int op, int op0, int stype, int dtype)
+{
+    const int min_opt_cols = 128, buf_cols = 32;
+    int sdepth = CV_MAT_DEPTH(stype), cn = CV_MAT_CN(stype),
+            ddepth = CV_MAT_DEPTH(dtype), ddepth0 = ddepth;
+    const ocl::Device &defDev = ocl::Device::getDefault();
+    bool doubleSupport = defDev.doubleFPConfig() > 0;
+
+    size_t wgs = defDev.maxWorkGroupSize();
+    bool useOptimized = 1 == dim && _src.cols() > min_opt_cols && (wgs >= buf_cols);
+
+    if (!doubleSupport && (sdepth == CV_64F || ddepth == CV_64F))
+        return false;
+
+    if (op == CV_REDUCE_AVG)
+    {
+        if (sdepth < CV_32S && ddepth < CV_32S)
+            ddepth = CV_32S;
+    }
+
+    const char * const ops[4] = { "OCL_CV_REDUCE_SUM", "OCL_CV_REDUCE_AVG",
+                                  "OCL_CV_REDUCE_MAX", "OCL_CV_REDUCE_MIN" };
+    int wdepth = std::max(ddepth, CV_32F);
+    if (useOptimized)
+    {
+        size_t tileHeight = (size_t)(wgs / buf_cols);
+        if (defDev.isIntel())
+        {
+            static const size_t maxItemInGroupCount = 16;
+            tileHeight = min(tileHeight, defDev.localMemSize() / buf_cols / CV_ELEM_SIZE(CV_MAKETYPE(wdepth, cn)) / maxItemInGroupCount);
+        }
+        char cvt[3][40];
+        cv::String build_opt = format("-D OP_REDUCE_PRE -D BUF_COLS=%d -D TILE_HEIGHT=%d -D %s -D dim=1"
+                                            " -D cn=%d -D ddepth=%d"
+                                            " -D srcT=%s -D bufT=%s -D dstT=%s"
+                                            " -D convertToWT=%s -D convertToBufT=%s -D convertToDT=%s%s",
+                                            buf_cols, tileHeight, ops[op], cn, ddepth,
+                                            ocl::typeToStr(sdepth),
+                                            ocl::typeToStr(ddepth),
+                                            ocl::typeToStr(ddepth0),
+                                            ocl::convertTypeStr(ddepth, wdepth, 1, cvt[0]),
+                                            ocl::convertTypeStr(sdepth, ddepth, 1, cvt[1]),
+                                            ocl::convertTypeStr(wdepth, ddepth0, 1, cvt[2]),
+                                            doubleSupport ? " -D DOUBLE_SUPPORT" : "");
+        ocl::Kernel k("reduce_horz_opt", ocl::core::reduce2_oclsrc, build_opt);
+        if (k.empty())
+            return false;
+        UMat src = _src.getUMat();
+        Size dsize(1, src.rows);
+        _dst.create(dsize, dtype);
+        UMat dst = _dst.getUMat();
+
+        if (op0 == CV_REDUCE_AVG)
+            k.args(ocl::KernelArg::ReadOnly(src),
+                      ocl::KernelArg::WriteOnlyNoSize(dst), 1.0f / src.cols);
+        else
+            k.args(ocl::KernelArg::ReadOnly(src),
+                      ocl::KernelArg::WriteOnlyNoSize(dst));
+
+        size_t localSize[2] = { (size_t)buf_cols, (size_t)tileHeight};
+        size_t globalSize[2] = { (size_t)buf_cols, (size_t)src.rows };
+        return k.run(2, globalSize, localSize, false);
+    }
+    else
+    {
+        char cvt[2][40];
+        cv::String build_opt = format("-D %s -D dim=%d -D cn=%d -D ddepth=%d"
+                                      " -D srcT=%s -D dstT=%s -D dstT0=%s -D convertToWT=%s"
+                                      " -D convertToDT=%s -D convertToDT0=%s%s",
+                                      ops[op], dim, cn, ddepth, ocl::typeToStr(useOptimized ? ddepth : sdepth),
+                                      ocl::typeToStr(ddepth), ocl::typeToStr(ddepth0),
+                                      ocl::convertTypeStr(ddepth, wdepth, 1, cvt[0]),
+                                      ocl::convertTypeStr(sdepth, ddepth, 1, cvt[0]),
+                                      ocl::convertTypeStr(wdepth, ddepth0, 1, cvt[1]),
+                                      doubleSupport ? " -D DOUBLE_SUPPORT" : "");
+
+        ocl::Kernel k("reduce", ocl::core::reduce2_oclsrc, build_opt);
+        if (k.empty())
+            return false;
+
+        UMat src = _src.getUMat();
+        Size dsize(dim == 0 ? src.cols : 1, dim == 0 ? 1 : src.rows);
+        _dst.create(dsize, dtype);
+        UMat dst = _dst.getUMat();
+
+        ocl::KernelArg srcarg = ocl::KernelArg::ReadOnly(src),
+                temparg = ocl::KernelArg::WriteOnlyNoSize(dst);
+
+        if (op0 == CV_REDUCE_AVG)
+            k.args(srcarg, temparg, 1.0f / (dim == 0 ? src.rows : src.cols));
+        else
+            k.args(srcarg, temparg);
+
+        size_t globalsize = std::max(dsize.width, dsize.height);
+        return k.run(1, &globalsize, NULL, false);
+    }
+}
+
+}
+
+#endif
+
+void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
+{
+    CV_INSTRUMENT_REGION()
+
+    CV_Assert( _src.dims() <= 2 );
+    int op0 = op;
+    int stype = _src.type(), sdepth = CV_MAT_DEPTH(stype), cn = CV_MAT_CN(stype);
+    if( dtype < 0 )
+        dtype = _dst.fixedType() ? _dst.type() : stype;
+    dtype = CV_MAKETYPE(dtype >= 0 ? dtype : stype, cn);
+    int ddepth = CV_MAT_DEPTH(dtype);
+
+    CV_Assert( cn == CV_MAT_CN(dtype) );
+    CV_Assert( op == CV_REDUCE_SUM || op == CV_REDUCE_MAX ||
+               op == CV_REDUCE_MIN || op == CV_REDUCE_AVG );
+
+    CV_OCL_RUN(_dst.isUMat(),
+               ocl_reduce(_src, _dst, dim, op, op0, stype, dtype))
+
+    // Fake reference to source. Resolves issue 8693 in case of src == dst.
+    UMat srcUMat;
+    if (_src.isUMat())
+        srcUMat = _src.getUMat();
+
+    Mat src = _src.getMat();
+    _dst.create(dim == 0 ? 1 : src.rows, dim == 0 ? src.cols : 1, dtype);
+    Mat dst = _dst.getMat(), temp = dst;
+
+    if( op == CV_REDUCE_AVG )
+    {
+        op = CV_REDUCE_SUM;
+        if( sdepth < CV_32S && ddepth < CV_32S )
+        {
+            temp.create(dst.rows, dst.cols, CV_32SC(cn));
+            ddepth = CV_32S;
+        }
+    }
+
+    ReduceFunc func = 0;
+    if( dim == 0 )
+    {
+        if( op == CV_REDUCE_SUM )
+        {
+            if(sdepth == CV_8U && ddepth == CV_32S)
+                func = GET_OPTIMIZED(reduceSumR8u32s);
+            else if(sdepth == CV_8U && ddepth == CV_32F)
+                func = reduceSumR8u32f;
+            else if(sdepth == CV_8U && ddepth == CV_64F)
+                func = reduceSumR8u64f;
+            else if(sdepth == CV_16U && ddepth == CV_32F)
+                func = reduceSumR16u32f;
+            else if(sdepth == CV_16U && ddepth == CV_64F)
+                func = reduceSumR16u64f;
+            else if(sdepth == CV_16S && ddepth == CV_32F)
+                func = reduceSumR16s32f;
+            else if(sdepth == CV_16S && ddepth == CV_64F)
+                func = reduceSumR16s64f;
+            else if(sdepth == CV_32F && ddepth == CV_32F)
+                func = GET_OPTIMIZED(reduceSumR32f32f);
+            else if(sdepth == CV_32F && ddepth == CV_64F)
+                func = reduceSumR32f64f;
+            else if(sdepth == CV_64F && ddepth == CV_64F)
+                func = reduceSumR64f64f;
+        }
+        else if(op == CV_REDUCE_MAX)
+        {
+            if(sdepth == CV_8U && ddepth == CV_8U)
+                func = GET_OPTIMIZED(reduceMaxR8u);
+            else if(sdepth == CV_16U && ddepth == CV_16U)
+                func = reduceMaxR16u;
+            else if(sdepth == CV_16S && ddepth == CV_16S)
+                func = reduceMaxR16s;
+            else if(sdepth == CV_32F && ddepth == CV_32F)
+                func = GET_OPTIMIZED(reduceMaxR32f);
+            else if(sdepth == CV_64F && ddepth == CV_64F)
+                func = reduceMaxR64f;
+        }
+        else if(op == CV_REDUCE_MIN)
+        {
+            if(sdepth == CV_8U && ddepth == CV_8U)
+                func = GET_OPTIMIZED(reduceMinR8u);
+            else if(sdepth == CV_16U && ddepth == CV_16U)
+                func = reduceMinR16u;
+            else if(sdepth == CV_16S && ddepth == CV_16S)
+                func = reduceMinR16s;
+            else if(sdepth == CV_32F && ddepth == CV_32F)
+                func = GET_OPTIMIZED(reduceMinR32f);
+            else if(sdepth == CV_64F && ddepth == CV_64F)
+                func = reduceMinR64f;
+        }
+    }
+    else
+    {
+        if(op == CV_REDUCE_SUM)
+        {
+            if(sdepth == CV_8U && ddepth == CV_32S)
+                func = GET_OPTIMIZED(reduceSumC8u32s);
+            else if(sdepth == CV_8U && ddepth == CV_32F)
+                func = reduceSumC8u32f;
+            else if(sdepth == CV_8U && ddepth == CV_64F)
+                func = reduceSumC8u64f;
+            else if(sdepth == CV_16U && ddepth == CV_32F)
+                func = reduceSumC16u32f;
+            else if(sdepth == CV_16U && ddepth == CV_64F)
+                func = reduceSumC16u64f;
+            else if(sdepth == CV_16S && ddepth == CV_32F)
+                func = reduceSumC16s32f;
+            else if(sdepth == CV_16S && ddepth == CV_64F)
+                func = reduceSumC16s64f;
+            else if(sdepth == CV_32F && ddepth == CV_32F)
+                func = GET_OPTIMIZED(reduceSumC32f32f);
+            else if(sdepth == CV_32F && ddepth == CV_64F)
+                func = reduceSumC32f64f;
+            else if(sdepth == CV_64F && ddepth == CV_64F)
+                func = reduceSumC64f64f;
+        }
+        else if(op == CV_REDUCE_MAX)
+        {
+            if(sdepth == CV_8U && ddepth == CV_8U)
+                func = GET_OPTIMIZED(reduceMaxC8u);
+            else if(sdepth == CV_16U && ddepth == CV_16U)
+                func = reduceMaxC16u;
+            else if(sdepth == CV_16S && ddepth == CV_16S)
+                func = reduceMaxC16s;
+            else if(sdepth == CV_32F && ddepth == CV_32F)
+                func = GET_OPTIMIZED(reduceMaxC32f);
+            else if(sdepth == CV_64F && ddepth == CV_64F)
+                func = reduceMaxC64f;
+        }
+        else if(op == CV_REDUCE_MIN)
+        {
+            if(sdepth == CV_8U && ddepth == CV_8U)
+                func = GET_OPTIMIZED(reduceMinC8u);
+            else if(sdepth == CV_16U && ddepth == CV_16U)
+                func = reduceMinC16u;
+            else if(sdepth == CV_16S && ddepth == CV_16S)
+                func = reduceMinC16s;
+            else if(sdepth == CV_32F && ddepth == CV_32F)
+                func = GET_OPTIMIZED(reduceMinC32f);
+            else if(sdepth == CV_64F && ddepth == CV_64F)
+                func = reduceMinC64f;
+        }
+    }
+
+    if( !func )
+        CV_Error( CV_StsUnsupportedFormat,
+                  "Unsupported combination of input and output array formats" );
+
+    func( src, temp );
+
+    if( op0 == CV_REDUCE_AVG )
+        temp.convertTo(dst, dst.type(), 1./(dim == 0 ? src.rows : src.cols));
+}
+
+
+//////////////////////////////////////// sort ///////////////////////////////////////////
+
+namespace cv
+{
+
+template<typename T> static void sort_( const Mat& src, Mat& dst, int flags )
+{
+    AutoBuffer<T> buf;
+    T* bptr;
+    int n, len;
+    bool sortRows = (flags & 1) == CV_SORT_EVERY_ROW;
+    bool inplace = src.data == dst.data;
+    bool sortDescending = (flags & CV_SORT_DESCENDING) != 0;
+
+    if( sortRows )
+        n = src.rows, len = src.cols;
+    else
+    {
+        n = src.cols, len = src.rows;
+        buf.allocate(len);
+    }
+    bptr = (T*)buf;
+
+    for( int i = 0; i < n; i++ )
+    {
+        T* ptr = bptr;
+        if( sortRows )
+        {
+            T* dptr = dst.ptr<T>(i);
+            if( !inplace )
+            {
+                const T* sptr = src.ptr<T>(i);
+                memcpy(dptr, sptr, sizeof(T) * len);
+            }
+            ptr = dptr;
+        }
+        else
+        {
+            for( int j = 0; j < len; j++ )
+                ptr[j] = src.ptr<T>(j)[i];
+        }
+
+        std::sort( ptr, ptr + len );
+        if( sortDescending )
+        {
+            for( int j = 0; j < len/2; j++ )
+                std::swap(ptr[j], ptr[len-1-j]);
+        }
+
+        if( !sortRows )
+            for( int j = 0; j < len; j++ )
+                dst.ptr<T>(j)[i] = ptr[j];
+    }
+}
+
+#ifdef HAVE_IPP
+typedef IppStatus (CV_STDCALL *IppSortFunc)(void  *pSrcDst, int    len, Ipp8u *pBuffer);
+
+static IppSortFunc getSortFunc(int depth, bool sortDescending)
+{
+    if (!sortDescending)
+        return depth == CV_8U ? (IppSortFunc)ippsSortRadixAscend_8u_I :
+            depth == CV_16U ? (IppSortFunc)ippsSortRadixAscend_16u_I :
+            depth == CV_16S ? (IppSortFunc)ippsSortRadixAscend_16s_I :
+            depth == CV_32S ? (IppSortFunc)ippsSortRadixAscend_32s_I :
+            depth == CV_32F ? (IppSortFunc)ippsSortRadixAscend_32f_I :
+            depth == CV_64F ? (IppSortFunc)ippsSortRadixAscend_64f_I :
+            0;
+    else
+        return depth == CV_8U ? (IppSortFunc)ippsSortRadixDescend_8u_I :
+            depth == CV_16U ? (IppSortFunc)ippsSortRadixDescend_16u_I :
+            depth == CV_16S ? (IppSortFunc)ippsSortRadixDescend_16s_I :
+            depth == CV_32S ? (IppSortFunc)ippsSortRadixDescend_32s_I :
+            depth == CV_32F ? (IppSortFunc)ippsSortRadixDescend_32f_I :
+            depth == CV_64F ? (IppSortFunc)ippsSortRadixDescend_64f_I :
+            0;
+}
+
+static bool ipp_sort(const Mat& src, Mat& dst, int flags)
+{
+    CV_INSTRUMENT_REGION_IPP()
+
+    bool        sortRows        = (flags & 1) == CV_SORT_EVERY_ROW;
+    bool        sortDescending  = (flags & CV_SORT_DESCENDING) != 0;
+    bool        inplace         = (src.data == dst.data);
+    int         depth           = src.depth();
+    IppDataType type            = ippiGetDataType(depth);
+
+    IppSortFunc ippsSortRadix_I = getSortFunc(depth, sortDescending);
+    if(!ippsSortRadix_I)
+        return false;
+
+    if(sortRows)
+    {
+        AutoBuffer<Ipp8u> buffer;
+        int               bufferSize;
+        if(ippsSortRadixGetBufferSize(src.cols, type, &bufferSize) < 0)
+            return false;
+
+        buffer.allocate(bufferSize);
+
+        if(!inplace)
+            src.copyTo(dst);
+
+        for(int i = 0; i < dst.rows; i++)
+        {
+            if(CV_INSTRUMENT_FUN_IPP(ippsSortRadix_I, (void*)dst.ptr(i), dst.cols, buffer) < 0)
+                return false;
+        }
+    }
+    else
+    {
+        AutoBuffer<Ipp8u> buffer;
+        int               bufferSize;
+        if(ippsSortRadixGetBufferSize(src.rows, type, &bufferSize) < 0)
+            return false;
+
+        buffer.allocate(bufferSize);
+
+        Mat  row(1, src.rows, src.type());
+        Mat  srcSub;
+        Mat  dstSub;
+        Rect subRect(0,0,1,src.rows);
+
+        for(int i = 0; i < src.cols; i++)
+        {
+            subRect.x = i;
+            srcSub = Mat(src, subRect);
+            dstSub = Mat(dst, subRect);
+            srcSub.copyTo(row);
+
+            if(CV_INSTRUMENT_FUN_IPP(ippsSortRadix_I, (void*)row.ptr(), dst.rows, buffer) < 0)
+                return false;
+
+            row = row.reshape(1, dstSub.rows);
+            row.copyTo(dstSub);
+        }
+    }
+
+    return true;
+}
+#endif
+
+template<typename _Tp> class LessThanIdx
+{
+public:
+    LessThanIdx( const _Tp* _arr ) : arr(_arr) {}
+    bool operator()(int a, int b) const { return arr[a] < arr[b]; }
+    const _Tp* arr;
+};
+
+template<typename T> static void sortIdx_( const Mat& src, Mat& dst, int flags )
+{
+    AutoBuffer<T> buf;
+    AutoBuffer<int> ibuf;
+    bool sortRows = (flags & 1) == CV_SORT_EVERY_ROW;
+    bool sortDescending = (flags & CV_SORT_DESCENDING) != 0;
+
+    CV_Assert( src.data != dst.data );
+
+    int n, len;
+    if( sortRows )
+        n = src.rows, len = src.cols;
+    else
+    {
+        n = src.cols, len = src.rows;
+        buf.allocate(len);
+        ibuf.allocate(len);
+    }
+    T* bptr = (T*)buf;
+    int* _iptr = (int*)ibuf;
+
+    for( int i = 0; i < n; i++ )
+    {
+        T* ptr = bptr;
+        int* iptr = _iptr;
+
+        if( sortRows )
+        {
+            ptr = (T*)(src.data + src.step*i);
+            iptr = dst.ptr<int>(i);
+        }
+        else
+        {
+            for( int j = 0; j < len; j++ )
+                ptr[j] = src.ptr<T>(j)[i];
+        }
+        for( int j = 0; j < len; j++ )
+            iptr[j] = j;
+
+        std::sort( iptr, iptr + len, LessThanIdx<T>(ptr) );
+        if( sortDescending )
+        {
+            for( int j = 0; j < len/2; j++ )
+                std::swap(iptr[j], iptr[len-1-j]);
+        }
+
+        if( !sortRows )
+            for( int j = 0; j < len; j++ )
+                dst.ptr<int>(j)[i] = iptr[j];
+    }
+}
+
+#ifdef HAVE_IPP
+typedef IppStatus (CV_STDCALL *IppSortIndexFunc)(const void*  pSrc, Ipp32s srcStrideBytes, Ipp32s *pDstIndx, int len, Ipp8u *pBuffer);
+
+static IppSortIndexFunc getSortIndexFunc(int depth, bool sortDescending)
+{
+    if (!sortDescending)
+        return depth == CV_8U ? (IppSortIndexFunc)ippsSortRadixIndexAscend_8u :
+            depth == CV_16U ? (IppSortIndexFunc)ippsSortRadixIndexAscend_16u :
+            depth == CV_16S ? (IppSortIndexFunc)ippsSortRadixIndexAscend_16s :
+            depth == CV_32S ? (IppSortIndexFunc)ippsSortRadixIndexAscend_32s :
+            depth == CV_32F ? (IppSortIndexFunc)ippsSortRadixIndexAscend_32f :
+            0;
+    else
+        return depth == CV_8U ? (IppSortIndexFunc)ippsSortRadixIndexDescend_8u :
+            depth == CV_16U ? (IppSortIndexFunc)ippsSortRadixIndexDescend_16u :
+            depth == CV_16S ? (IppSortIndexFunc)ippsSortRadixIndexDescend_16s :
+            depth == CV_32S ? (IppSortIndexFunc)ippsSortRadixIndexDescend_32s :
+            depth == CV_32F ? (IppSortIndexFunc)ippsSortRadixIndexDescend_32f :
+            0;
+}
+
+static bool ipp_sortIdx( const Mat& src, Mat& dst, int flags )
+{
+    CV_INSTRUMENT_REGION_IPP()
+
+    bool        sortRows        = (flags & 1) == SORT_EVERY_ROW;
+    bool        sortDescending  = (flags & SORT_DESCENDING) != 0;
+    int         depth           = src.depth();
+    IppDataType type            = ippiGetDataType(depth);
+
+    IppSortIndexFunc ippsSortRadixIndex = getSortIndexFunc(depth, sortDescending);
+    if(!ippsSortRadixIndex)
+        return false;
+
+    if(sortRows)
+    {
+        AutoBuffer<Ipp8u> buffer;
+        int               bufferSize;
+        if(ippsSortRadixIndexGetBufferSize(src.cols, type, &bufferSize) < 0)
+            return false;
+
+        buffer.allocate(bufferSize);
+
+        for(int i = 0; i < src.rows; i++)
+        {
+            if(CV_INSTRUMENT_FUN_IPP(ippsSortRadixIndex, (const void*)src.ptr(i), (Ipp32s)src.step[1], (Ipp32s*)dst.ptr(i), src.cols, buffer) < 0)
+                return false;
+        }
+    }
+    else
+    {
+        Mat  dstRow(1, dst.rows, dst.type());
+        Mat  dstSub;
+        Rect subRect(0,0,1,src.rows);
+
+        AutoBuffer<Ipp8u> buffer;
+        int               bufferSize;
+        if(ippsSortRadixIndexGetBufferSize(src.rows, type, &bufferSize) < 0)
+            return false;
+
+        buffer.allocate(bufferSize);
+
+        Ipp32s srcStep = (Ipp32s)src.step[0];
+        for(int i = 0; i < src.cols; i++)
+        {
+            subRect.x = i;
+            dstSub = Mat(dst, subRect);
+
+            if(CV_INSTRUMENT_FUN_IPP(ippsSortRadixIndex, (const void*)src.ptr(0, i), srcStep, (Ipp32s*)dstRow.ptr(), src.rows, buffer) < 0)
+                return false;
+
+            dstRow = dstRow.reshape(1, dstSub.rows);
+            dstRow.copyTo(dstSub);
+        }
+    }
+
+    return true;
+}
+#endif
+
+typedef void (*SortFunc)(const Mat& src, Mat& dst, int flags);
+}
+
+void cv::sort( InputArray _src, OutputArray _dst, int flags )
+{
+    CV_INSTRUMENT_REGION()
+
+    Mat src = _src.getMat();
+    CV_Assert( src.dims <= 2 && src.channels() == 1 );
+    _dst.create( src.size(), src.type() );
+    Mat dst = _dst.getMat();
+    CV_IPP_RUN_FAST(ipp_sort(src, dst, flags));
+
+    static SortFunc tab[] =
+    {
+        sort_<uchar>, sort_<schar>, sort_<ushort>, sort_<short>,
+        sort_<int>, sort_<float>, sort_<double>, 0
+    };
+    SortFunc func = tab[src.depth()];
+    CV_Assert( func != 0 );
+
+    func( src, dst, flags );
+}
+
+void cv::sortIdx( InputArray _src, OutputArray _dst, int flags )
+{
+    CV_INSTRUMENT_REGION()
+
+    Mat src = _src.getMat();
+    CV_Assert( src.dims <= 2 && src.channels() == 1 );
+    Mat dst = _dst.getMat();
+    if( dst.data == src.data )
+        _dst.release();
+    _dst.create( src.size(), CV_32S );
+    dst = _dst.getMat();
+
+    CV_IPP_RUN_FAST(ipp_sortIdx(src, dst, flags));
+
+    static SortFunc tab[] =
+    {
+        sortIdx_<uchar>, sortIdx_<schar>, sortIdx_<ushort>, sortIdx_<short>,
+        sortIdx_<int>, sortIdx_<float>, sortIdx_<double>, 0
+    };
+    SortFunc func = tab[src.depth()];
+    CV_Assert( func != 0 );
+    func( src, dst, flags );
+}

modules/core/src/matrix_sparse.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 "opencv2/core/mat.hpp"
+#include "opencv2/core/types_c.h"
+#include "precomp.hpp"
+
+namespace cv {
+
+template<typename T1, typename T2> void
+convertData_(const void* _from, void* _to, int cn)
+{
+    const T1* from = (const T1*)_from;
+    T2* to = (T2*)_to;
+    if( cn == 1 )
+        *to = saturate_cast<T2>(*from);
+    else
+        for( int i = 0; i < cn; i++ )
+            to[i] = saturate_cast<T2>(from[i]);
+}
+
+template<typename T1, typename T2> void
+convertScaleData_(const void* _from, void* _to, int cn, double alpha, double beta)
+{
+    const T1* from = (const T1*)_from;
+    T2* to = (T2*)_to;
+    if( cn == 1 )
+        *to = saturate_cast<T2>(*from*alpha + beta);
+    else
+        for( int i = 0; i < cn; i++ )
+            to[i] = saturate_cast<T2>(from[i]*alpha + beta);
+}
+
+typedef void (*ConvertData)(const void* from, void* to, int cn);
+typedef void (*ConvertScaleData)(const void* from, void* to, int cn, double alpha, double beta);
+
+static ConvertData getConvertElem(int fromType, int toType)
+{
+    static ConvertData tab[][8] =
+    {{ convertData_<uchar, uchar>, convertData_<uchar, schar>,
+      convertData_<uchar, ushort>, convertData_<uchar, short>,
+      convertData_<uchar, int>, convertData_<uchar, float>,
+      convertData_<uchar, double>, 0 },
+
+    { convertData_<schar, uchar>, convertData_<schar, schar>,
+      convertData_<schar, ushort>, convertData_<schar, short>,
+      convertData_<schar, int>, convertData_<schar, float>,
+      convertData_<schar, double>, 0 },
+
+    { convertData_<ushort, uchar>, convertData_<ushort, schar>,
+      convertData_<ushort, ushort>, convertData_<ushort, short>,
+      convertData_<ushort, int>, convertData_<ushort, float>,
+      convertData_<ushort, double>, 0 },
+
+    { convertData_<short, uchar>, convertData_<short, schar>,
+      convertData_<short, ushort>, convertData_<short, short>,
+      convertData_<short, int>, convertData_<short, float>,
+      convertData_<short, double>, 0 },
+
+    { convertData_<int, uchar>, convertData_<int, schar>,
+      convertData_<int, ushort>, convertData_<int, short>,
+      convertData_<int, int>, convertData_<int, float>,
+      convertData_<int, double>, 0 },
+
+    { convertData_<float, uchar>, convertData_<float, schar>,
+      convertData_<float, ushort>, convertData_<float, short>,
+      convertData_<float, int>, convertData_<float, float>,
+      convertData_<float, double>, 0 },
+
+    { convertData_<double, uchar>, convertData_<double, schar>,
+      convertData_<double, ushort>, convertData_<double, short>,
+      convertData_<double, int>, convertData_<double, float>,
+      convertData_<double, double>, 0 },
+
+    { 0, 0, 0, 0, 0, 0, 0, 0 }};
+
+    ConvertData func = tab[CV_MAT_DEPTH(fromType)][CV_MAT_DEPTH(toType)];
+    CV_Assert( func != 0 );
+    return func;
+}
+
+static ConvertScaleData getConvertScaleElem(int fromType, int toType)
+{
+    static ConvertScaleData tab[][8] =
+    {{ convertScaleData_<uchar, uchar>, convertScaleData_<uchar, schar>,
+      convertScaleData_<uchar, ushort>, convertScaleData_<uchar, short>,
+      convertScaleData_<uchar, int>, convertScaleData_<uchar, float>,
+      convertScaleData_<uchar, double>, 0 },
+
+    { convertScaleData_<schar, uchar>, convertScaleData_<schar, schar>,
+      convertScaleData_<schar, ushort>, convertScaleData_<schar, short>,
+      convertScaleData_<schar, int>, convertScaleData_<schar, float>,
+      convertScaleData_<schar, double>, 0 },
+
+    { convertScaleData_<ushort, uchar>, convertScaleData_<ushort, schar>,
+      convertScaleData_<ushort, ushort>, convertScaleData_<ushort, short>,
+      convertScaleData_<ushort, int>, convertScaleData_<ushort, float>,
+      convertScaleData_<ushort, double>, 0 },
+
+    { convertScaleData_<short, uchar>, convertScaleData_<short, schar>,
+      convertScaleData_<short, ushort>, convertScaleData_<short, short>,
+      convertScaleData_<short, int>, convertScaleData_<short, float>,
+      convertScaleData_<short, double>, 0 },
+
+    { convertScaleData_<int, uchar>, convertScaleData_<int, schar>,
+      convertScaleData_<int, ushort>, convertScaleData_<int, short>,
+      convertScaleData_<int, int>, convertScaleData_<int, float>,
+      convertScaleData_<int, double>, 0 },
+
+    { convertScaleData_<float, uchar>, convertScaleData_<float, schar>,
+      convertScaleData_<float, ushort>, convertScaleData_<float, short>,
+      convertScaleData_<float, int>, convertScaleData_<float, float>,
+      convertScaleData_<float, double>, 0 },
+
+    { convertScaleData_<double, uchar>, convertScaleData_<double, schar>,
+      convertScaleData_<double, ushort>, convertScaleData_<double, short>,
+      convertScaleData_<double, int>, convertScaleData_<double, float>,
+      convertScaleData_<double, double>, 0 },
+
+    { 0, 0, 0, 0, 0, 0, 0, 0 }};
+
+    ConvertScaleData func = tab[CV_MAT_DEPTH(fromType)][CV_MAT_DEPTH(toType)];
+    CV_Assert( func != 0 );
+    return func;
+}
+
+enum { HASH_SIZE0 = 8 };
+
+static inline void copyElem(const uchar* from, uchar* to, size_t elemSize)
+{
+    size_t i;
+    for( i = 0; i + sizeof(int) <= elemSize; i += sizeof(int) )
+        *(int*)(to + i) = *(const int*)(from + i);
+    for( ; i < elemSize; i++ )
+        to[i] = from[i];
+}
+
+static inline bool isZeroElem(const uchar* data, size_t elemSize)
+{
+    size_t i;
+    for( i = 0; i + sizeof(int) <= elemSize; i += sizeof(int) )
+        if( *(int*)(data + i) != 0 )
+            return false;
+    for( ; i < elemSize; i++ )
+        if( data[i] != 0 )
+            return false;
+    return true;
+}
+
+SparseMat::Hdr::Hdr( int _dims, const int* _sizes, int _type )
+{
+    refcount = 1;
+
+    dims = _dims;
+    valueOffset = (int)alignSize(sizeof(SparseMat::Node) - MAX_DIM*sizeof(int) +
+                                 dims*sizeof(int), CV_ELEM_SIZE1(_type));
+    nodeSize = alignSize(valueOffset +
+        CV_ELEM_SIZE(_type), (int)sizeof(size_t));
+
+    int i;
+    for( i = 0; i < dims; i++ )
+        size[i] = _sizes[i];
+    for( ; i < CV_MAX_DIM; i++ )
+        size[i] = 0;
+    clear();
+}
+
+void SparseMat::Hdr::clear()
+{
+    hashtab.clear();
+    hashtab.resize(HASH_SIZE0);
+    pool.clear();
+    pool.resize(nodeSize);
+    nodeCount = freeList = 0;
+}
+
+
+SparseMat::SparseMat(const Mat& m)
+: flags(MAGIC_VAL), hdr(0)
+{
+    create( m.dims, m.size, m.type() );
+
+    int i, idx[CV_MAX_DIM] = {0}, d = m.dims, lastSize = m.size[d - 1];
+    size_t esz = m.elemSize();
+    const uchar* dptr = m.ptr();
+
+    for(;;)
+    {
+        for( i = 0; i < lastSize; i++, dptr += esz )
+        {
+            if( isZeroElem(dptr, esz) )
+                continue;
+            idx[d-1] = i;
+            uchar* to = newNode(idx, hash(idx));
+            copyElem( dptr, to, esz );
+        }
+
+        for( i = d - 2; i >= 0; i-- )
+        {
+            dptr += m.step[i] - m.size[i+1]*m.step[i+1];
+            if( ++idx[i] < m.size[i] )
+                break;
+            idx[i] = 0;
+        }
+        if( i < 0 )
+            break;
+    }
+}
+
+void SparseMat::create(int d, const int* _sizes, int _type)
+{
+    CV_Assert( _sizes && 0 < d && d <= CV_MAX_DIM );
+    for( int i = 0; i < d; i++ )
+        CV_Assert( _sizes[i] > 0 );
+    _type = CV_MAT_TYPE(_type);
+    if( hdr && _type == type() && hdr->dims == d && hdr->refcount == 1 )
+    {
+        int i;
+        for( i = 0; i < d; i++ )
+            if( _sizes[i] != hdr->size[i] )
+                break;
+        if( i == d )
+        {
+            clear();
+            return;
+        }
+    }
+    int _sizes_backup[CV_MAX_DIM]; // #5991
+    if (_sizes == hdr->size)
+    {
+        for(int i = 0; i < d; i++ )
+            _sizes_backup[i] = _sizes[i];
+        _sizes = _sizes_backup;
+    }
+    release();
+    flags = MAGIC_VAL | _type;
+    hdr = new Hdr(d, _sizes, _type);
+}
+
+void SparseMat::copyTo( SparseMat& m ) const
+{
+    if( hdr == m.hdr )
+        return;
+    if( !hdr )
+    {
+        m.release();
+        return;
+    }
+    m.create( hdr->dims, hdr->size, type() );
+    SparseMatConstIterator from = begin();
+    size_t N = nzcount(), esz = elemSize();
+
+    for( size_t i = 0; i < N; i++, ++from )
+    {
+        const Node* n = from.node();
+        uchar* to = m.newNode(n->idx, n->hashval);
+        copyElem( from.ptr, to, esz );
+    }
+}
+
+void SparseMat::copyTo( Mat& m ) const
+{
+    CV_Assert( hdr );
+    int ndims = dims();
+    m.create( ndims, hdr->size, type() );
+    m = Scalar(0);
+
+    SparseMatConstIterator from = begin();
+    size_t N = nzcount(), esz = elemSize();
+
+    for( size_t i = 0; i < N; i++, ++from )
+    {
+        const Node* n = from.node();
+        copyElem( from.ptr, (ndims > 1 ? m.ptr(n->idx) : m.ptr(n->idx[0])), esz);
+    }
+}
+
+
+void SparseMat::convertTo( SparseMat& m, int rtype, double alpha ) const
+{
+    int cn = channels();
+    if( rtype < 0 )
+        rtype = type();
+    rtype = CV_MAKETYPE(rtype, cn);
+    if( hdr == m.hdr && rtype != type()  )
+    {
+        SparseMat temp;
+        convertTo(temp, rtype, alpha);
+        m = temp;
+        return;
+    }
+
+    CV_Assert(hdr != 0);
+    if( hdr != m.hdr )
+        m.create( hdr->dims, hdr->size, rtype );
+
+    SparseMatConstIterator from = begin();
+    size_t N = nzcount();
+
+    if( alpha == 1 )
+    {
+        ConvertData cvtfunc = getConvertElem(type(), rtype);
+        for( size_t i = 0; i < N; i++, ++from )
+        {
+            const Node* n = from.node();
+            uchar* to = hdr == m.hdr ? from.ptr : m.newNode(n->idx, n->hashval);
+            cvtfunc( from.ptr, to, cn );
+        }
+    }
+    else
+    {
+        ConvertScaleData cvtfunc = getConvertScaleElem(type(), rtype);
+        for( size_t i = 0; i < N; i++, ++from )
+        {
+            const Node* n = from.node();
+            uchar* to = hdr == m.hdr ? from.ptr : m.newNode(n->idx, n->hashval);
+            cvtfunc( from.ptr, to, cn, alpha, 0 );
+        }
+    }
+}
+
+
+void SparseMat::convertTo( Mat& m, int rtype, double alpha, double beta ) const
+{
+    int cn = channels();
+    if( rtype < 0 )
+        rtype = type();
+    rtype = CV_MAKETYPE(rtype, cn);
+
+    CV_Assert( hdr );
+    m.create( dims(), hdr->size, rtype );
+    m = Scalar(beta);
+
+    SparseMatConstIterator from = begin();
+    size_t N = nzcount();
+
+    if( alpha == 1 && beta == 0 )
+    {
+        ConvertData cvtfunc = getConvertElem(type(), rtype);
+        for( size_t i = 0; i < N; i++, ++from )
+        {
+            const Node* n = from.node();
+            uchar* to = m.ptr(n->idx);
+            cvtfunc( from.ptr, to, cn );
+        }
+    }
+    else
+    {
+        ConvertScaleData cvtfunc = getConvertScaleElem(type(), rtype);
+        for( size_t i = 0; i < N; i++, ++from )
+        {
+            const Node* n = from.node();
+            uchar* to = m.ptr(n->idx);
+            cvtfunc( from.ptr, to, cn, alpha, beta );
+        }
+    }
+}
+
+void SparseMat::clear()
+{
+    if( hdr )
+        hdr->clear();
+}
+
+uchar* SparseMat::ptr(int i0, bool createMissing, size_t* hashval)
+{
+    CV_Assert( hdr && hdr->dims == 1 );
+    size_t h = hashval ? *hashval : hash(i0);
+    size_t hidx = h & (hdr->hashtab.size() - 1), nidx = hdr->hashtab[hidx];
+    uchar* pool = &hdr->pool[0];
+    while( nidx != 0 )
+    {
+        Node* elem = (Node*)(pool + nidx);
+        if( elem->hashval == h && elem->idx[0] == i0 )
+            return &value<uchar>(elem);
+        nidx = elem->next;
+    }
+
+    if( createMissing )
+    {
+        int idx[] = { i0 };
+        return newNode( idx, h );
+    }
+    return NULL;
+}
+
+uchar* SparseMat::ptr(int i0, int i1, bool createMissing, size_t* hashval)
+{
+    CV_Assert( hdr && hdr->dims == 2 );
+    size_t h = hashval ? *hashval : hash(i0, i1);
+    size_t hidx = h & (hdr->hashtab.size() - 1), nidx = hdr->hashtab[hidx];
+    uchar* pool = &hdr->pool[0];
+    while( nidx != 0 )
+    {
+        Node* elem = (Node*)(pool + nidx);
+        if( elem->hashval == h && elem->idx[0] == i0 && elem->idx[1] == i1 )
+            return &value<uchar>(elem);
+        nidx = elem->next;
+    }
+
+    if( createMissing )
+    {
+        int idx[] = { i0, i1 };
+        return newNode( idx, h );
+    }
+    return NULL;
+}
+
+uchar* SparseMat::ptr(int i0, int i1, int i2, bool createMissing, size_t* hashval)
+{
+    CV_Assert( hdr && hdr->dims == 3 );
+    size_t h = hashval ? *hashval : hash(i0, i1, i2);
+    size_t hidx = h & (hdr->hashtab.size() - 1), nidx = hdr->hashtab[hidx];
+    uchar* pool = &hdr->pool[0];
+    while( nidx != 0 )
+    {
+        Node* elem = (Node*)(pool + nidx);
+        if( elem->hashval == h && elem->idx[0] == i0 &&
+            elem->idx[1] == i1 && elem->idx[2] == i2 )
+            return &value<uchar>(elem);
+        nidx = elem->next;
+    }
+
+    if( createMissing )
+    {
+        int idx[] = { i0, i1, i2 };
+        return newNode( idx, h );
+    }
+    return NULL;
+}
+
+uchar* SparseMat::ptr(const int* idx, bool createMissing, size_t* hashval)
+{
+    CV_Assert( hdr );
+    int i, d = hdr->dims;
+    size_t h = hashval ? *hashval : hash(idx);
+    size_t hidx = h & (hdr->hashtab.size() - 1), nidx = hdr->hashtab[hidx];
+    uchar* pool = &hdr->pool[0];
+    while( nidx != 0 )
+    {
+        Node* elem = (Node*)(pool + nidx);
+        if( elem->hashval == h )
+        {
+            for( i = 0; i < d; i++ )
+                if( elem->idx[i] != idx[i] )
+                    break;
+            if( i == d )
+                return &value<uchar>(elem);
+        }
+        nidx = elem->next;
+    }
+
+    return createMissing ? newNode(idx, h) : NULL;
+}
+
+void SparseMat::erase(int i0, int i1, size_t* hashval)
+{
+    CV_Assert( hdr && hdr->dims == 2 );
+    size_t h = hashval ? *hashval : hash(i0, i1);
+    size_t hidx = h & (hdr->hashtab.size() - 1), nidx = hdr->hashtab[hidx], previdx=0;
+    uchar* pool = &hdr->pool[0];
+    while( nidx != 0 )
+    {
+        Node* elem = (Node*)(pool + nidx);
+        if( elem->hashval == h && elem->idx[0] == i0 && elem->idx[1] == i1 )
+            break;
+        previdx = nidx;
+        nidx = elem->next;
+    }
+
+    if( nidx )
+        removeNode(hidx, nidx, previdx);
+}
+
+void SparseMat::erase(int i0, int i1, int i2, size_t* hashval)
+{
+    CV_Assert( hdr && hdr->dims == 3 );
+    size_t h = hashval ? *hashval : hash(i0, i1, i2);
+    size_t hidx = h & (hdr->hashtab.size() - 1), nidx = hdr->hashtab[hidx], previdx=0;
+    uchar* pool = &hdr->pool[0];
+    while( nidx != 0 )
+    {
+        Node* elem = (Node*)(pool + nidx);
+        if( elem->hashval == h && elem->idx[0] == i0 &&
+            elem->idx[1] == i1 && elem->idx[2] == i2 )
+            break;
+        previdx = nidx;
+        nidx = elem->next;
+    }
+
+    if( nidx )
+        removeNode(hidx, nidx, previdx);
+}
+
+void SparseMat::erase(const int* idx, size_t* hashval)
+{
+    CV_Assert( hdr );
+    int i, d = hdr->dims;
+    size_t h = hashval ? *hashval : hash(idx);
+    size_t hidx = h & (hdr->hashtab.size() - 1), nidx = hdr->hashtab[hidx], previdx=0;
+    uchar* pool = &hdr->pool[0];
+    while( nidx != 0 )
+    {
+        Node* elem = (Node*)(pool + nidx);
+        if( elem->hashval == h )
+        {
+            for( i = 0; i < d; i++ )
+                if( elem->idx[i] != idx[i] )
+                    break;
+            if( i == d )
+                break;
+        }
+        previdx = nidx;
+        nidx = elem->next;
+    }
+
+    if( nidx )
+        removeNode(hidx, nidx, previdx);
+}
+
+void SparseMat::resizeHashTab(size_t newsize)
+{
+    newsize = std::max(newsize, (size_t)8);
+    if((newsize & (newsize-1)) != 0)
+        newsize = (size_t)1 << cvCeil(std::log((double)newsize)/CV_LOG2);
+
+    size_t hsize = hdr->hashtab.size();
+    std::vector<size_t> _newh(newsize);
+    size_t* newh = &_newh[0];
+    for( size_t i = 0; i < newsize; i++ )
+        newh[i] = 0;
+    uchar* pool = &hdr->pool[0];
+    for( size_t i = 0; i < hsize; i++ )
+    {
+        size_t nidx = hdr->hashtab[i];
+        while( nidx )
+        {
+            Node* elem = (Node*)(pool + nidx);
+            size_t next = elem->next;
+            size_t newhidx = elem->hashval & (newsize - 1);
+            elem->next = newh[newhidx];
+            newh[newhidx] = nidx;
+            nidx = next;
+        }
+    }
+    hdr->hashtab = _newh;
+}
+
+uchar* SparseMat::newNode(const int* idx, size_t hashval)
+{
+    const int HASH_MAX_FILL_FACTOR=3;
+    assert(hdr);
+    size_t hsize = hdr->hashtab.size();
+    if( ++hdr->nodeCount > hsize*HASH_MAX_FILL_FACTOR )
+    {
+        resizeHashTab(std::max(hsize*2, (size_t)8));
+        hsize = hdr->hashtab.size();
+    }
+
+    if( !hdr->freeList )
+    {
+        size_t i, nsz = hdr->nodeSize, psize = hdr->pool.size(),
+            newpsize = std::max(psize*3/2, 8*nsz);
+        newpsize = (newpsize/nsz)*nsz;
+        hdr->pool.resize(newpsize);
+        uchar* pool = &hdr->pool[0];
+        hdr->freeList = std::max(psize, nsz);
+        for( i = hdr->freeList; i < newpsize - nsz; i += nsz )
+            ((Node*)(pool + i))->next = i + nsz;
+        ((Node*)(pool + i))->next = 0;
+    }
+    size_t nidx = hdr->freeList;
+    Node* elem = (Node*)&hdr->pool[nidx];
+    hdr->freeList = elem->next;
+    elem->hashval = hashval;
+    size_t hidx = hashval & (hsize - 1);
+    elem->next = hdr->hashtab[hidx];
+    hdr->hashtab[hidx] = nidx;
+
+    int i, d = hdr->dims;
+    for( i = 0; i < d; i++ )
+        elem->idx[i] = idx[i];
+    size_t esz = elemSize();
+    uchar* p = &value<uchar>(elem);
+    if( esz == sizeof(float) )
+        *((float*)p) = 0.f;
+    else if( esz == sizeof(double) )
+        *((double*)p) = 0.;
+    else
+        memset(p, 0, esz);
+
+    return p;
+}
+
+
+void SparseMat::removeNode(size_t hidx, size_t nidx, size_t previdx)
+{
+    Node* n = node(nidx);
+    if( previdx )
+    {
+        Node* prev = node(previdx);
+        prev->next = n->next;
+    }
+    else
+        hdr->hashtab[hidx] = n->next;
+    n->next = hdr->freeList;
+    hdr->freeList = nidx;
+    --hdr->nodeCount;
+}
+
+//
+// Operations
+//
+double norm( const SparseMat& src, int normType )
+{
+    CV_INSTRUMENT_REGION()
+
+    SparseMatConstIterator it = src.begin();
+
+    size_t i, N = src.nzcount();
+    normType &= NORM_TYPE_MASK;
+    int type = src.type();
+    double result = 0;
+
+    CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 );
+
+    if( type == CV_32F )
+    {
+        if( normType == NORM_INF )
+            for( i = 0; i < N; i++, ++it )
+            {
+                CV_Assert(it.ptr);
+                result = std::max(result, std::abs((double)it.value<float>()));
+            }
+        else if( normType == NORM_L1 )
+            for( i = 0; i < N; i++, ++it )
+            {
+                CV_Assert(it.ptr);
+                result += std::abs(it.value<float>());
+            }
+        else
+            for( i = 0; i < N; i++, ++it )
+            {
+                CV_Assert(it.ptr);
+                double v = it.value<float>();
+                result += v*v;
+            }
+    }
+    else if( type == CV_64F )
+    {
+        if( normType == NORM_INF )
+            for( i = 0; i < N; i++, ++it )
+            {
+                CV_Assert(it.ptr);
+                result = std::max(result, std::abs(it.value<double>()));
+            }
+        else if( normType == NORM_L1 )
+            for( i = 0; i < N; i++, ++it )
+            {
+                CV_Assert(it.ptr);
+                result += std::abs(it.value<double>());
+            }
+        else
+            for( i = 0; i < N; i++, ++it )
+            {
+                CV_Assert(it.ptr);
+                double v = it.value<double>();
+                result += v*v;
+            }
+    }
+    else
+        CV_Error( CV_StsUnsupportedFormat, "Only 32f and 64f are supported" );
+
+    if( normType == NORM_L2 )
+        result = std::sqrt(result);
+    return result;
+}
+
+void minMaxLoc( const SparseMat& src, double* _minval, double* _maxval, int* _minidx, int* _maxidx )
+{
+    CV_INSTRUMENT_REGION()
+
+    SparseMatConstIterator it = src.begin();
+    size_t i, N = src.nzcount(), d = src.hdr ? src.hdr->dims : 0;
+    int type = src.type();
+    const int *minidx = 0, *maxidx = 0;
+
+    if( type == CV_32F )
+    {
+        float minval = FLT_MAX, maxval = -FLT_MAX;
+        for( i = 0; i < N; i++, ++it )
+        {
+            CV_Assert(it.ptr);
+            float v = it.value<float>();
+            if( v < minval )
+            {
+                minval = v;
+                minidx = it.node()->idx;
+            }
+            if( v > maxval )
+            {
+                maxval = v;
+                maxidx = it.node()->idx;
+            }
+        }
+        if( _minval )
+            *_minval = minval;
+        if( _maxval )
+            *_maxval = maxval;
+    }
+    else if( type == CV_64F )
+    {
+        double minval = DBL_MAX, maxval = -DBL_MAX;
+        for( i = 0; i < N; i++, ++it )
+        {
+            CV_Assert(it.ptr);
+            double v = it.value<double>();
+            if( v < minval )
+            {
+                minval = v;
+                minidx = it.node()->idx;
+            }
+            if( v > maxval )
+            {
+                maxval = v;
+                maxidx = it.node()->idx;
+            }
+        }
+        if( _minval )
+            *_minval = minval;
+        if( _maxval )
+            *_maxval = maxval;
+    }
+    else
+        CV_Error( CV_StsUnsupportedFormat, "Only 32f and 64f are supported" );
+
+    if( _minidx && minidx )
+        for( i = 0; i < d; i++ )
+            _minidx[i] = minidx[i];
+    if( _maxidx && maxidx )
+        for( i = 0; i < d; i++ )
+            _maxidx[i] = maxidx[i];
+}
+
+
+void normalize( const SparseMat& src, SparseMat& dst, double a, int norm_type )
+{
+    CV_INSTRUMENT_REGION()
+
+    double scale = 1;
+    if( norm_type == CV_L2 || norm_type == CV_L1 || norm_type == CV_C )
+    {
+        scale = norm( src, norm_type );
+        scale = scale > DBL_EPSILON ? a/scale : 0.;
+    }
+    else
+        CV_Error( CV_StsBadArg, "Unknown/unsupported norm type" );
+
+    src.convertTo( dst, -1, scale );
+}
+
+} // cv::
+
+//
+// C-API glue
+//
+CvSparseMat* cvCreateSparseMat(const cv::SparseMat& sm)
+{
+    if( !sm.hdr || sm.hdr->dims > (int)cv::SparseMat::MAX_DIM)
+        return 0;
+
+    CvSparseMat* m = cvCreateSparseMat(sm.hdr->dims, sm.hdr->size, sm.type());
+
+    cv::SparseMatConstIterator from = sm.begin();
+    size_t i, N = sm.nzcount(), esz = sm.elemSize();
+
+    for( i = 0; i < N; i++, ++from )
+    {
+        const cv::SparseMat::Node* n = from.node();
+        uchar* to = cvPtrND(m, n->idx, 0, -2, 0);
+        cv::copyElem(from.ptr, to, esz);
+    }
+    return m;
+}
+
+void CvSparseMat::copyToSparseMat(cv::SparseMat& m) const
+{
+    m.create( dims, &size[0], type );
+
+    CvSparseMatIterator it;
+    CvSparseNode* n = cvInitSparseMatIterator(this, &it);
+    size_t esz = m.elemSize();
+
+    for( ; n != 0; n = cvGetNextSparseNode(&it) )
+    {
+        const int* idx = CV_NODE_IDX(this, n);
+        uchar* to = m.newNode(idx, m.hash(idx));
+        cv::copyElem((const uchar*)CV_NODE_VAL(this, n), to, esz);
+    }
+}

modules/core/src/matrix_wrap.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 "opencv2/core/mat.hpp"
+#include "precomp.hpp"
+
+namespace cv {
+
+/*************************************************************************************************\
+                                        Input/Output Array
+\*************************************************************************************************/
+
+Mat _InputArray::getMat_(int i) const
+{
+    int k = kind();
+    int accessFlags = flags & ACCESS_MASK;
+
+    if( k == MAT )
+    {
+        const Mat* m = (const Mat*)obj;
+        if( i < 0 )
+            return *m;
+        return m->row(i);
+    }
+
+    if( k == UMAT )
+    {
+        const UMat* m = (const UMat*)obj;
+        if( i < 0 )
+            return m->getMat(accessFlags);
+        return m->getMat(accessFlags).row(i);
+    }
+
+    if( k == EXPR )
+    {
+        CV_Assert( i < 0 );
+        return (Mat)*((const MatExpr*)obj);
+    }
+
+    if( k == MATX || k == STD_ARRAY )
+    {
+        CV_Assert( i < 0 );
+        return Mat(sz, flags, obj);
+    }
+
+    if( k == STD_VECTOR )
+    {
+        CV_Assert( i < 0 );
+        int t = CV_MAT_TYPE(flags);
+        const std::vector<uchar>& v = *(const std::vector<uchar>*)obj;
+
+        return !v.empty() ? Mat(size(), t, (void*)&v[0]) : Mat();
+    }
+
+    if( k == STD_BOOL_VECTOR )
+    {
+        CV_Assert( i < 0 );
+        int t = CV_8U;
+        const std::vector<bool>& v = *(const std::vector<bool>*)obj;
+        int j, n = (int)v.size();
+        if( n == 0 )
+            return Mat();
+        Mat m(1, n, t);
+        uchar* dst = m.data;
+        for( j = 0; j < n; j++ )
+            dst[j] = (uchar)v[j];
+        return m;
+    }
+
+    if( k == NONE )
+        return Mat();
+
+    if( k == STD_VECTOR_VECTOR )
+    {
+        int t = type(i);
+        const std::vector<std::vector<uchar> >& vv = *(const std::vector<std::vector<uchar> >*)obj;
+        CV_Assert( 0 <= i && i < (int)vv.size() );
+        const std::vector<uchar>& v = vv[i];
+
+        return !v.empty() ? Mat(size(i), t, (void*)&v[0]) : Mat();
+    }
+
+    if( k == STD_VECTOR_MAT )
+    {
+        const std::vector<Mat>& v = *(const std::vector<Mat>*)obj;
+        CV_Assert( 0 <= i && i < (int)v.size() );
+
+        return v[i];
+    }
+
+    if( k == STD_ARRAY_MAT )
+    {
+        const Mat* v = (const Mat*)obj;
+        CV_Assert( 0 <= i && i < sz.height );
+
+        return v[i];
+    }
+
+    if( k == STD_VECTOR_UMAT )
+    {
+        const std::vector<UMat>& v = *(const std::vector<UMat>*)obj;
+        CV_Assert( 0 <= i && i < (int)v.size() );
+
+        return v[i].getMat(accessFlags);
+    }
+
+    if( k == OPENGL_BUFFER )
+    {
+        CV_Assert( i < 0 );
+        CV_Error(cv::Error::StsNotImplemented, "You should explicitly call mapHost/unmapHost methods for ogl::Buffer object");
+        return Mat();
+    }
+
+    if( k == CUDA_GPU_MAT )
+    {
+        CV_Assert( i < 0 );
+        CV_Error(cv::Error::StsNotImplemented, "You should explicitly call download method for cuda::GpuMat object");
+        return Mat();
+    }
+
+    if( k == CUDA_HOST_MEM )
+    {
+        CV_Assert( i < 0 );
+
+        const cuda::HostMem* cuda_mem = (const cuda::HostMem*)obj;
+
+        return cuda_mem->createMatHeader();
+    }
+
+    CV_Error(Error::StsNotImplemented, "Unknown/unsupported array type");
+    return Mat();
+}
+
+UMat _InputArray::getUMat(int i) const
+{
+    int k = kind();
+    int accessFlags = flags & ACCESS_MASK;
+
+    if( k == UMAT )
+    {
+        const UMat* m = (const UMat*)obj;
+        if( i < 0 )
+            return *m;
+        return m->row(i);
+    }
+
+    if( k == STD_VECTOR_UMAT )
+    {
+        const std::vector<UMat>& v = *(const std::vector<UMat>*)obj;
+        CV_Assert( 0 <= i && i < (int)v.size() );
+
+        return v[i];
+    }
+
+    if( k == MAT )
+    {
+        const Mat* m = (const Mat*)obj;
+        if( i < 0 )
+            return m->getUMat(accessFlags);
+        return m->row(i).getUMat(accessFlags);
+    }
+
+    return getMat(i).getUMat(accessFlags);
+}
+
+void _InputArray::getMatVector(std::vector<Mat>& mv) const
+{
+    int k = kind();
+    int accessFlags = flags & ACCESS_MASK;
+
+    if( k == MAT )
+    {
+        const Mat& m = *(const Mat*)obj;
+        int n = (int)m.size[0];
+        mv.resize(n);
+
+        for( int i = 0; i < n; i++ )
+            mv[i] = m.dims == 2 ? Mat(1, m.cols, m.type(), (void*)m.ptr(i)) :
+                Mat(m.dims-1, &m.size[1], m.type(), (void*)m.ptr(i), &m.step[1]);
+        return;
+    }
+
+    if( k == EXPR )
+    {
+        Mat m = *(const MatExpr*)obj;
+        int n = m.size[0];
+        mv.resize(n);
+
+        for( int i = 0; i < n; i++ )
+            mv[i] = m.row(i);
+        return;
+    }
+
+    if( k == MATX || k == STD_ARRAY )
+    {
+        size_t n = sz.height, esz = CV_ELEM_SIZE(flags);
+        mv.resize(n);
+
+        for( size_t i = 0; i < n; i++ )
+            mv[i] = Mat(1, sz.width, CV_MAT_TYPE(flags), (uchar*)obj + esz*sz.width*i);
+        return;
+    }
+
+    if( k == STD_VECTOR )
+    {
+        const std::vector<uchar>& v = *(const std::vector<uchar>*)obj;
+
+        size_t n = size().width, esz = CV_ELEM_SIZE(flags);
+        int t = CV_MAT_DEPTH(flags), cn = CV_MAT_CN(flags);
+        mv.resize(n);
+
+        for( size_t i = 0; i < n; i++ )
+            mv[i] = Mat(1, cn, t, (void*)(&v[0] + esz*i));
+        return;
+    }
+
+    if( k == NONE )
+    {
+        mv.clear();
+        return;
+    }
+
+    if( k == STD_VECTOR_VECTOR )
+    {
+        const std::vector<std::vector<uchar> >& vv = *(const std::vector<std::vector<uchar> >*)obj;
+        int n = (int)vv.size();
+        int t = CV_MAT_TYPE(flags);
+        mv.resize(n);
+
+        for( int i = 0; i < n; i++ )
+        {
+            const std::vector<uchar>& v = vv[i];
+            mv[i] = Mat(size(i), t, (void*)&v[0]);
+        }
+        return;
+    }
+
+    if( k == STD_VECTOR_MAT )
+    {
+        const std::vector<Mat>& v = *(const std::vector<Mat>*)obj;
+        size_t n = v.size();
+        mv.resize(n);
+
+        for( size_t i = 0; i < n; i++ )
+            mv[i] = v[i];
+        return;
+    }
+
+    if( k == STD_ARRAY_MAT )
+    {
+        const Mat* v = (const Mat*)obj;
+        size_t n = sz.height;
+        mv.resize(n);
+
+        for( size_t i = 0; i < n; i++ )
+            mv[i] = v[i];
+        return;
+    }
+
+    if( k == STD_VECTOR_UMAT )
+    {
+        const std::vector<UMat>& v = *(const std::vector<UMat>*)obj;
+        size_t n = v.size();
+        mv.resize(n);
+
+        for( size_t i = 0; i < n; i++ )
+            mv[i] = v[i].getMat(accessFlags);
+        return;
+    }
+
+    CV_Error(Error::StsNotImplemented, "Unknown/unsupported array type");
+}
+
+void _InputArray::getUMatVector(std::vector<UMat>& umv) const
+{
+    int k = kind();
+    int accessFlags = flags & ACCESS_MASK;
+
+    if( k == NONE )
+    {
+        umv.clear();
+        return;
+    }
+
+    if( k == STD_VECTOR_MAT )
+    {
+        const std::vector<Mat>& v = *(const std::vector<Mat>*)obj;
+        size_t n = v.size();
+        umv.resize(n);
+
+        for( size_t i = 0; i < n; i++ )
+            umv[i] = v[i].getUMat(accessFlags);
+        return;
+    }
+
+    if( k == STD_ARRAY_MAT )
+    {
+        const Mat* v = (const Mat*)obj;
+        size_t n = sz.height;
+        umv.resize(n);
+
+        for( size_t i = 0; i < n; i++ )
+            umv[i] = v[i].getUMat(accessFlags);
+        return;
+    }
+
+    if( k == STD_VECTOR_UMAT )
+    {
+        const std::vector<UMat>& v = *(const std::vector<UMat>*)obj;
+        size_t n = v.size();
+        umv.resize(n);
+
+        for( size_t i = 0; i < n; i++ )
+            umv[i] = v[i];
+        return;
+    }
+
+    if( k == UMAT )
+    {
+        UMat& v = *(UMat*)obj;
+        umv.resize(1);
+        umv[0] = v;
+        return;
+    }
+    if( k == MAT )
+    {
+        Mat& v = *(Mat*)obj;
+        umv.resize(1);
+        umv[0] = v.getUMat(accessFlags);
+        return;
+    }
+
+    CV_Error(Error::StsNotImplemented, "Unknown/unsupported array type");
+}
+
+cuda::GpuMat _InputArray::getGpuMat() const
+{
+    int k = kind();
+
+    if (k == CUDA_GPU_MAT)
+    {
+        const cuda::GpuMat* d_mat = (const cuda::GpuMat*)obj;
+        return *d_mat;
+    }
+
+    if (k == CUDA_HOST_MEM)
+    {
+        const cuda::HostMem* cuda_mem = (const cuda::HostMem*)obj;
+        return cuda_mem->createGpuMatHeader();
+    }
+
+    if (k == OPENGL_BUFFER)
+    {
+        CV_Error(cv::Error::StsNotImplemented, "You should explicitly call mapDevice/unmapDevice methods for ogl::Buffer object");
+        return cuda::GpuMat();
+    }
+
+    if (k == NONE)
+        return cuda::GpuMat();
+
+    CV_Error(cv::Error::StsNotImplemented, "getGpuMat is available only for cuda::GpuMat and cuda::HostMem");
+    return cuda::GpuMat();
+}
+void _InputArray::getGpuMatVector(std::vector<cuda::GpuMat>& gpumv) const
+{
+    int k = kind();
+    if (k == STD_VECTOR_CUDA_GPU_MAT)
+    {
+        gpumv = *(std::vector<cuda::GpuMat>*)obj;
+    }
+}
+ogl::Buffer _InputArray::getOGlBuffer() const
+{
+    int k = kind();
+
+    CV_Assert(k == OPENGL_BUFFER);
+
+    const ogl::Buffer* gl_buf = (const ogl::Buffer*)obj;
+    return *gl_buf;
+}
+
+int _InputArray::kind() const
+{
+    return flags & KIND_MASK;
+}
+
+int _InputArray::rows(int i) const
+{
+    return size(i).height;
+}
+
+int _InputArray::cols(int i) const
+{
+    return size(i).width;
+}
+
+Size _InputArray::size(int i) const
+{
+    int k = kind();
+
+    if( k == MAT )
+    {
+        CV_Assert( i < 0 );
+        return ((const Mat*)obj)->size();
+    }
+
+    if( k == EXPR )
+    {
+        CV_Assert( i < 0 );
+        return ((const MatExpr*)obj)->size();
+    }
+
+    if( k == UMAT )
+    {
+        CV_Assert( i < 0 );
+        return ((const UMat*)obj)->size();
+    }
+
+    if( k == MATX || k == STD_ARRAY )
+    {
+        CV_Assert( i < 0 );
+        return sz;
+    }
+
+    if( k == STD_VECTOR )
+    {
+        CV_Assert( i < 0 );
+        const std::vector<uchar>& v = *(const std::vector<uchar>*)obj;
+        const std::vector<int>& iv = *(const std::vector<int>*)obj;
+        size_t szb = v.size(), szi = iv.size();
+        return szb == szi ? Size((int)szb, 1) : Size((int)(szb/CV_ELEM_SIZE(flags)), 1);
+    }
+
+    if( k == STD_BOOL_VECTOR )
+    {
+        CV_Assert( i < 0 );
+        const std::vector<bool>& v = *(const std::vector<bool>*)obj;
+        return Size((int)v.size(), 1);
+    }
+
+    if( k == NONE )
+        return Size();
+
+    if( k == STD_VECTOR_VECTOR )
+    {
+        const std::vector<std::vector<uchar> >& vv = *(const std::vector<std::vector<uchar> >*)obj;
+        if( i < 0 )
+            return vv.empty() ? Size() : Size((int)vv.size(), 1);
+        CV_Assert( i < (int)vv.size() );
+        const std::vector<std::vector<int> >& ivv = *(const std::vector<std::vector<int> >*)obj;
+
+        size_t szb = vv[i].size(), szi = ivv[i].size();
+        return szb == szi ? Size((int)szb, 1) : Size((int)(szb/CV_ELEM_SIZE(flags)), 1);
+    }
+
+    if( k == STD_VECTOR_MAT )
+    {
+        const std::vector<Mat>& vv = *(const std::vector<Mat>*)obj;
+        if( i < 0 )
+            return vv.empty() ? Size() : Size((int)vv.size(), 1);
+        CV_Assert( i < (int)vv.size() );
+
+        return vv[i].size();
+    }
+
+    if( k == STD_ARRAY_MAT )
+    {
+        const Mat* vv = (const Mat*)obj;
+        if( i < 0 )
+            return sz.height==0 ? Size() : Size(sz.height, 1);
+        CV_Assert( i < sz.height );
+
+        return vv[i].size();
+    }
+
+    if (k == STD_VECTOR_CUDA_GPU_MAT)
+    {
+        const std::vector<cuda::GpuMat>& vv = *(const std::vector<cuda::GpuMat>*)obj;
+        if (i < 0)
+            return vv.empty() ? Size() : Size((int)vv.size(), 1);
+        CV_Assert(i < (int)vv.size());
+        return vv[i].size();
+    }
+
+    if( k == STD_VECTOR_UMAT )
+    {
+        const std::vector<UMat>& vv = *(const std::vector<UMat>*)obj;
+        if( i < 0 )
+            return vv.empty() ? Size() : Size((int)vv.size(), 1);
+        CV_Assert( i < (int)vv.size() );
+
+        return vv[i].size();
+    }
+
+    if( k == OPENGL_BUFFER )
+    {
+        CV_Assert( i < 0 );
+        const ogl::Buffer* buf = (const ogl::Buffer*)obj;
+        return buf->size();
+    }
+
+    if( k == CUDA_GPU_MAT )
+    {
+        CV_Assert( i < 0 );
+        const cuda::GpuMat* d_mat = (const cuda::GpuMat*)obj;
+        return d_mat->size();
+    }
+
+    if( k == CUDA_HOST_MEM )
+    {
+        CV_Assert( i < 0 );
+        const cuda::HostMem* cuda_mem = (const cuda::HostMem*)obj;
+        return cuda_mem->size();
+    }
+
+    CV_Error(Error::StsNotImplemented, "Unknown/unsupported array type");
+    return Size();
+}
+
+int _InputArray::sizend(int* arrsz, int i) const
+{
+    int j, d=0, k = kind();
+
+    if( k == NONE )
+        ;
+    else if( k == MAT )
+    {
+        CV_Assert( i < 0 );
+        const Mat& m = *(const Mat*)obj;
+        d = m.dims;
+        if(arrsz)
+            for(j = 0; j < d; j++)
+                arrsz[j] = m.size.p[j];
+    }
+    else if( k == UMAT )
+    {
+        CV_Assert( i < 0 );
+        const UMat& m = *(const UMat*)obj;
+        d = m.dims;
+        if(arrsz)
+            for(j = 0; j < d; j++)
+                arrsz[j] = m.size.p[j];
+    }
+    else if( k == STD_VECTOR_MAT && i >= 0 )
+    {
+        const std::vector<Mat>& vv = *(const std::vector<Mat>*)obj;
+        CV_Assert( i < (int)vv.size() );
+        const Mat& m = vv[i];
+        d = m.dims;
+        if(arrsz)
+            for(j = 0; j < d; j++)
+                arrsz[j] = m.size.p[j];
+    }
+    else if( k == STD_ARRAY_MAT && i >= 0 )
+    {
+        const Mat* vv = (const Mat*)obj;
+        CV_Assert( i < sz.height );
+        const Mat& m = vv[i];
+        d = m.dims;
+        if(arrsz)
+            for(j = 0; j < d; j++)
+                arrsz[j] = m.size.p[j];
+    }
+    else if( k == STD_VECTOR_UMAT && i >= 0 )
+    {
+        const std::vector<UMat>& vv = *(const std::vector<UMat>*)obj;
+        CV_Assert( i < (int)vv.size() );
+        const UMat& m = vv[i];
+        d = m.dims;
+        if(arrsz)
+            for(j = 0; j < d; j++)
+                arrsz[j] = m.size.p[j];
+    }
+    else
+    {
+        Size sz2d = size(i);
+        d = 2;
+        if(arrsz)
+        {
+            arrsz[0] = sz2d.height;
+            arrsz[1] = sz2d.width;
+        }
+    }
+
+    return d;
+}
+
+bool _InputArray::sameSize(const _InputArray& arr) const
+{
+    int k1 = kind(), k2 = arr.kind();
+    Size sz1;
+
+    if( k1 == MAT )
+    {
+        const Mat* m = ((const Mat*)obj);
+        if( k2 == MAT )
+            return m->size == ((const Mat*)arr.obj)->size;
+        if( k2 == UMAT )
+            return m->size == ((const UMat*)arr.obj)->size;
+        if( m->dims > 2 )
+            return false;
+        sz1 = m->size();
+    }
+    else if( k1 == UMAT )
+    {
+        const UMat* m = ((const UMat*)obj);
+        if( k2 == MAT )
+            return m->size == ((const Mat*)arr.obj)->size;
+        if( k2 == UMAT )
+            return m->size == ((const UMat*)arr.obj)->size;
+        if( m->dims > 2 )
+            return false;
+        sz1 = m->size();
+    }
+    else
+        sz1 = size();
+    if( arr.dims() > 2 )
+        return false;
+    return sz1 == arr.size();
+}
+
+int _InputArray::dims(int i) const
+{
+    int k = kind();
+
+    if( k == MAT )
+    {
+        CV_Assert( i < 0 );
+        return ((const Mat*)obj)->dims;
+    }
+
+    if( k == EXPR )
+    {
+        CV_Assert( i < 0 );
+        return ((const MatExpr*)obj)->a.dims;
+    }
+
+    if( k == UMAT )
+    {
+        CV_Assert( i < 0 );
+        return ((const UMat*)obj)->dims;
+    }
+
+    if( k == MATX || k == STD_ARRAY )
+    {
+        CV_Assert( i < 0 );
+        return 2;
+    }
+
+    if( k == STD_VECTOR || k == STD_BOOL_VECTOR )
+    {
+        CV_Assert( i < 0 );
+        return 2;
+    }
+
+    if( k == NONE )
+        return 0;
+
+    if( k == STD_VECTOR_VECTOR )
+    {
+        const std::vector<std::vector<uchar> >& vv = *(const std::vector<std::vector<uchar> >*)obj;
+        if( i < 0 )
+            return 1;
+        CV_Assert( i < (int)vv.size() );
+        return 2;
+    }
+
+    if( k == STD_VECTOR_MAT )
+    {
+        const std::vector<Mat>& vv = *(const std::vector<Mat>*)obj;
+        if( i < 0 )
+            return 1;
+        CV_Assert( i < (int)vv.size() );
+
+        return vv[i].dims;
+    }
+
+    if( k == STD_ARRAY_MAT )
+    {
+        const Mat* vv = (const Mat*)obj;
+        if( i < 0 )
+            return 1;
+        CV_Assert( i < sz.height );
+
+        return vv[i].dims;
+    }
+
+    if( k == STD_VECTOR_UMAT )
+    {
+        const std::vector<UMat>& vv = *(const std::vector<UMat>*)obj;
+        if( i < 0 )
+            return 1;
+        CV_Assert( i < (int)vv.size() );
+
+        return vv[i].dims;
+    }
+
+    if( k == OPENGL_BUFFER )
+    {
+        CV_Assert( i < 0 );
+        return 2;
+    }
+
+    if( k == CUDA_GPU_MAT )
+    {
+        CV_Assert( i < 0 );
+        return 2;
+    }
+
+    if( k == CUDA_HOST_MEM )
+    {
+        CV_Assert( i < 0 );
+        return 2;
+    }
+
+    CV_Error(Error::StsNotImplemented, "Unknown/unsupported array type");
+    return 0;
+}
+
+size_t _InputArray::total(int i) const
+{
+    int k = kind();
+
+    if( k == MAT )
+    {
+        CV_Assert( i < 0 );
+        return ((const Mat*)obj)->total();
+    }
+
+    if( k == UMAT )
+    {
+        CV_Assert( i < 0 );
+        return ((const UMat*)obj)->total();
+    }
+
+    if( k == STD_VECTOR_MAT )
+    {
+        const std::vector<Mat>& vv = *(const std::vector<Mat>*)obj;
+        if( i < 0 )
+            return vv.size();
+
+        CV_Assert( i < (int)vv.size() );
+        return vv[i].total();
+    }
+
+    if( k == STD_ARRAY_MAT )
+    {
+        const Mat* vv = (const Mat*)obj;
+        if( i < 0 )
+            return sz.height;
+
+        CV_Assert( i < sz.height );
+        return vv[i].total();
+    }
+
+    if( k == STD_VECTOR_UMAT )
+    {
+        const std::vector<UMat>& vv = *(const std::vector<UMat>*)obj;
+        if( i < 0 )
+            return vv.size();
+
+        CV_Assert( i < (int)vv.size() );
+        return vv[i].total();
+    }
+
+    return size(i).area();
+}
+
+int _InputArray::type(int i) const
+{
+    int k = kind();
+
+    if( k == MAT )
+        return ((const Mat*)obj)->type();
+
+    if( k == UMAT )
+        return ((const UMat*)obj)->type();
+
+    if( k == EXPR )
+        return ((const MatExpr*)obj)->type();
+
+    if( k == MATX || k == STD_VECTOR || k == STD_ARRAY || k == STD_VECTOR_VECTOR || k == STD_BOOL_VECTOR )
+        return CV_MAT_TYPE(flags);
+
+    if( k == NONE )
+        return -1;
+
+    if( k == STD_VECTOR_UMAT )
+    {
+        const std::vector<UMat>& vv = *(const std::vector<UMat>*)obj;
+        if( vv.empty() )
+        {
+            CV_Assert((flags & FIXED_TYPE) != 0);
+            return CV_MAT_TYPE(flags);
+        }
+        CV_Assert( i < (int)vv.size() );
+        return vv[i >= 0 ? i : 0].type();
+    }
+
+    if( k == STD_VECTOR_MAT )
+    {
+        const std::vector<Mat>& vv = *(const std::vector<Mat>*)obj;
+        if( vv.empty() )
+        {
+            CV_Assert((flags & FIXED_TYPE) != 0);
+            return CV_MAT_TYPE(flags);
+        }
+        CV_Assert( i < (int)vv.size() );
+        return vv[i >= 0 ? i : 0].type();
+    }
+
+    if( k == STD_ARRAY_MAT )
+    {
+        const Mat* vv = (const Mat*)obj;
+        if( sz.height == 0 )
+        {
+            CV_Assert((flags & FIXED_TYPE) != 0);
+            return CV_MAT_TYPE(flags);
+        }
+        CV_Assert( i < sz.height );
+        return vv[i >= 0 ? i : 0].type();
+    }
+
+    if (k == STD_VECTOR_CUDA_GPU_MAT)
+    {
+        const std::vector<cuda::GpuMat>& vv = *(const std::vector<cuda::GpuMat>*)obj;
+        if (vv.empty())
+        {
+            CV_Assert((flags & FIXED_TYPE) != 0);
+            return CV_MAT_TYPE(flags);
+        }
+        CV_Assert(i < (int)vv.size());
+        return vv[i >= 0 ? i : 0].type();
+    }
+
+    if( k == OPENGL_BUFFER )
+        return ((const ogl::Buffer*)obj)->type();
+
+    if( k == CUDA_GPU_MAT )
+        return ((const cuda::GpuMat*)obj)->type();
+
+    if( k == CUDA_HOST_MEM )
+        return ((const cuda::HostMem*)obj)->type();
+
+    CV_Error(Error::StsNotImplemented, "Unknown/unsupported array type");
+    return 0;
+}
+
+int _InputArray::depth(int i) const
+{
+    return CV_MAT_DEPTH(type(i));
+}
+
+int _InputArray::channels(int i) const
+{
+    return CV_MAT_CN(type(i));
+}
+
+bool _InputArray::empty() const
+{
+    int k = kind();
+
+    if( k == MAT )
+        return ((const Mat*)obj)->empty();
+
+    if( k == UMAT )
+        return ((const UMat*)obj)->empty();
+
+    if( k == EXPR )
+        return false;
+
+    if( k == MATX || k == STD_ARRAY )
+        return false;
+
+    if( k == STD_VECTOR )
+    {
+        const std::vector<uchar>& v = *(const std::vector<uchar>*)obj;
+        return v.empty();
+    }
+
+    if( k == STD_BOOL_VECTOR )
+    {
+        const std::vector<bool>& v = *(const std::vector<bool>*)obj;
+        return v.empty();
+    }
+
+    if( k == NONE )
+        return true;
+
+    if( k == STD_VECTOR_VECTOR )
+    {
+        const std::vector<std::vector<uchar> >& vv = *(const std::vector<std::vector<uchar> >*)obj;
+        return vv.empty();
+    }
+
+    if( k == STD_VECTOR_MAT )
+    {
+        const std::vector<Mat>& vv = *(const std::vector<Mat>*)obj;
+        return vv.empty();
+    }
+
+    if( k == STD_ARRAY_MAT )
+    {
+        return sz.height == 0;
+    }
+
+    if( k == STD_VECTOR_UMAT )
+    {
+        const std::vector<UMat>& vv = *(const std::vector<UMat>*)obj;
+        return vv.empty();
+    }
+
+    if( k == OPENGL_BUFFER )
+        return ((const ogl::Buffer*)obj)->empty();
+
+    if( k == CUDA_GPU_MAT )
+        return ((const cuda::GpuMat*)obj)->empty();
+
+    if (k == STD_VECTOR_CUDA_GPU_MAT)
+    {
+        const std::vector<cuda::GpuMat>& vv = *(const std::vector<cuda::GpuMat>*)obj;
+        return vv.empty();
+    }
+
+    if( k == CUDA_HOST_MEM )
+        return ((const cuda::HostMem*)obj)->empty();
+
+    CV_Error(Error::StsNotImplemented, "Unknown/unsupported array type");
+    return true;
+}
+
+bool _InputArray::isContinuous(int i) const
+{
+    int k = kind();
+
+    if( k == MAT )
+        return i < 0 ? ((const Mat*)obj)->isContinuous() : true;
+
+    if( k == UMAT )
+        return i < 0 ? ((const UMat*)obj)->isContinuous() : true;
+
+    if( k == EXPR || k == MATX || k == STD_VECTOR || k == STD_ARRAY ||
+        k == NONE || k == STD_VECTOR_VECTOR || k == STD_BOOL_VECTOR )
+        return true;
+
+    if( k == STD_VECTOR_MAT )
+    {
+        const std::vector<Mat>& vv = *(const std::vector<Mat>*)obj;
+        CV_Assert((size_t)i < vv.size());
+        return vv[i].isContinuous();
+    }
+
+    if( k == STD_ARRAY_MAT )
+    {
+        const Mat* vv = (const Mat*)obj;
+        CV_Assert(i > 0 && i < sz.height);
+        return vv[i].isContinuous();
+    }
+
+    if( k == STD_VECTOR_UMAT )
+    {
+        const std::vector<UMat>& vv = *(const std::vector<UMat>*)obj;
+        CV_Assert((size_t)i < vv.size());
+        return vv[i].isContinuous();
+    }
+
+    if( k == CUDA_GPU_MAT )
+      return i < 0 ? ((const cuda::GpuMat*)obj)->isContinuous() : true;
+
+    CV_Error(CV_StsNotImplemented, "Unknown/unsupported array type");
+    return false;
+}
+
+bool _InputArray::isSubmatrix(int i) const
+{
+    int k = kind();
+
+    if( k == MAT )
+        return i < 0 ? ((const Mat*)obj)->isSubmatrix() : false;
+
+    if( k == UMAT )
+        return i < 0 ? ((const UMat*)obj)->isSubmatrix() : false;
+
+    if( k == EXPR || k == MATX || k == STD_VECTOR || k == STD_ARRAY ||
+        k == NONE || k == STD_VECTOR_VECTOR || k == STD_BOOL_VECTOR )
+        return false;
+
+    if( k == STD_VECTOR_MAT )
+    {
+        const std::vector<Mat>& vv = *(const std::vector<Mat>*)obj;
+        CV_Assert((size_t)i < vv.size());
+        return vv[i].isSubmatrix();
+    }
+
+    if( k == STD_ARRAY_MAT )
+    {
+        const Mat* vv = (const Mat*)obj;
+        CV_Assert(i < sz.height);
+        return vv[i].isSubmatrix();
+    }
+
+    if( k == STD_VECTOR_UMAT )
+    {
+        const std::vector<UMat>& vv = *(const std::vector<UMat>*)obj;
+        CV_Assert((size_t)i < vv.size());
+        return vv[i].isSubmatrix();
+    }
+
+    CV_Error(CV_StsNotImplemented, "");
+    return false;
+}
+
+size_t _InputArray::offset(int i) const
+{
+    int k = kind();
+
+    if( k == MAT )
+    {
+        CV_Assert( i < 0 );
+        const Mat * const m = ((const Mat*)obj);
+        return (size_t)(m->ptr() - m->datastart);
+    }
+
+    if( k == UMAT )
+    {
+        CV_Assert( i < 0 );
+        return ((const UMat*)obj)->offset;
+    }
+
+    if( k == EXPR || k == MATX || k == STD_VECTOR || k == STD_ARRAY ||
+        k == NONE || k == STD_VECTOR_VECTOR || k == STD_BOOL_VECTOR )
+        return 0;
+
+    if( k == STD_VECTOR_MAT )
+    {
+        const std::vector<Mat>& vv = *(const std::vector<Mat>*)obj;
+        if( i < 0 )
+            return 1;
+        CV_Assert( i < (int)vv.size() );
+
+        return (size_t)(vv[i].ptr() - vv[i].datastart);
+    }
+
+    if( k == STD_ARRAY_MAT )
+    {
+        const Mat* vv = (const Mat*)obj;
+        if( i < 0 )
+            return 1;
+        CV_Assert( i < sz.height );
+        return (size_t)(vv[i].ptr() - vv[i].datastart);
+    }
+
+    if( k == STD_VECTOR_UMAT )
+    {
+        const std::vector<UMat>& vv = *(const std::vector<UMat>*)obj;
+        CV_Assert((size_t)i < vv.size());
+        return vv[i].offset;
+    }
+
+    if( k == CUDA_GPU_MAT )
+    {
+        CV_Assert( i < 0 );
+        const cuda::GpuMat * const m = ((const cuda::GpuMat*)obj);
+        return (size_t)(m->data - m->datastart);
+    }
+
+    if (k == STD_VECTOR_CUDA_GPU_MAT)
+    {
+        const std::vector<cuda::GpuMat>& vv = *(const std::vector<cuda::GpuMat>*)obj;
+        CV_Assert((size_t)i < vv.size());
+        return (size_t)(vv[i].data - vv[i].datastart);
+    }
+
+    CV_Error(Error::StsNotImplemented, "");
+    return 0;
+}
+
+size_t _InputArray::step(int i) const
+{
+    int k = kind();
+
+    if( k == MAT )
+    {
+        CV_Assert( i < 0 );
+        return ((const Mat*)obj)->step;
+    }
+
+    if( k == UMAT )
+    {
+        CV_Assert( i < 0 );
+        return ((const UMat*)obj)->step;
+    }
+
+    if( k == EXPR || k == MATX || k == STD_VECTOR || k == STD_ARRAY ||
+        k == NONE || k == STD_VECTOR_VECTOR || k == STD_BOOL_VECTOR )
+        return 0;
+
+    if( k == STD_VECTOR_MAT )
+    {
+        const std::vector<Mat>& vv = *(const std::vector<Mat>*)obj;
+        if( i < 0 )
+            return 1;
+        CV_Assert( i < (int)vv.size() );
+        return vv[i].step;
+    }
+
+    if( k == STD_ARRAY_MAT )
+    {
+        const Mat* vv = (const Mat*)obj;
+        if( i < 0 )
+            return 1;
+        CV_Assert( i < sz.height );
+        return vv[i].step;
+    }
+
+    if( k == STD_VECTOR_UMAT )
+    {
+        const std::vector<UMat>& vv = *(const std::vector<UMat>*)obj;
+        CV_Assert((size_t)i < vv.size());
+        return vv[i].step;
+    }
+
+    if( k == CUDA_GPU_MAT )
+    {
+        CV_Assert( i < 0 );
+        return ((const cuda::GpuMat*)obj)->step;
+    }
+    if (k == STD_VECTOR_CUDA_GPU_MAT)
+    {
+        const std::vector<cuda::GpuMat>& vv = *(const std::vector<cuda::GpuMat>*)obj;
+        CV_Assert((size_t)i < vv.size());
+        return vv[i].step;
+    }
+
+    CV_Error(Error::StsNotImplemented, "");
+    return 0;
+}
+
+void _InputArray::copyTo(const _OutputArray& arr) const
+{
+    int k = kind();
+
+    if( k == NONE )
+        arr.release();
+    else if( k == MAT || k == MATX || k == STD_VECTOR || k == STD_ARRAY || k == STD_BOOL_VECTOR )
+    {
+        Mat m = getMat();
+        m.copyTo(arr);
+    }
+    else if( k == EXPR )
+    {
+        const MatExpr& e = *((MatExpr*)obj);
+        if( arr.kind() == MAT )
+            arr.getMatRef() = e;
+        else
+            Mat(e).copyTo(arr);
+    }
+    else if( k == UMAT )
+        ((UMat*)obj)->copyTo(arr);
+    else
+        CV_Error(Error::StsNotImplemented, "");
+}
+
+void _InputArray::copyTo(const _OutputArray& arr, const _InputArray & mask) const
+{
+    int k = kind();
+
+    if( k == NONE )
+        arr.release();
+    else if( k == MAT || k == MATX || k == STD_VECTOR || k == STD_ARRAY || k == STD_BOOL_VECTOR )
+    {
+        Mat m = getMat();
+        m.copyTo(arr, mask);
+    }
+    else if( k == UMAT )
+        ((UMat*)obj)->copyTo(arr, mask);
+    else
+        CV_Error(Error::StsNotImplemented, "");
+}
+
+bool _OutputArray::fixedSize() const
+{
+    return (flags & FIXED_SIZE) == FIXED_SIZE;
+}
+
+bool _OutputArray::fixedType() const
+{
+    return (flags & FIXED_TYPE) == FIXED_TYPE;
+}
+
+void _OutputArray::create(Size _sz, int mtype, int i, bool allowTransposed, int fixedDepthMask) const
+{
+    int k = kind();
+    if( k == MAT && i < 0 && !allowTransposed && fixedDepthMask == 0 )
+    {
+        CV_Assert(!fixedSize() || ((Mat*)obj)->size.operator()() == _sz);
+        CV_Assert(!fixedType() || ((Mat*)obj)->type() == mtype);
+        ((Mat*)obj)->create(_sz, mtype);
+        return;
+    }
+    if( k == UMAT && i < 0 && !allowTransposed && fixedDepthMask == 0 )
+    {
+        CV_Assert(!fixedSize() || ((UMat*)obj)->size.operator()() == _sz);
+        CV_Assert(!fixedType() || ((UMat*)obj)->type() == mtype);
+        ((UMat*)obj)->create(_sz, mtype);
+        return;
+    }
+    if( k == CUDA_GPU_MAT && i < 0 && !allowTransposed && fixedDepthMask == 0 )
+    {
+        CV_Assert(!fixedSize() || ((cuda::GpuMat*)obj)->size() == _sz);
+        CV_Assert(!fixedType() || ((cuda::GpuMat*)obj)->type() == mtype);
+        ((cuda::GpuMat*)obj)->create(_sz, mtype);
+        return;
+    }
+    if( k == OPENGL_BUFFER && i < 0 && !allowTransposed && fixedDepthMask == 0 )
+    {
+        CV_Assert(!fixedSize() || ((ogl::Buffer*)obj)->size() == _sz);
+        CV_Assert(!fixedType() || ((ogl::Buffer*)obj)->type() == mtype);
+        ((ogl::Buffer*)obj)->create(_sz, mtype);
+        return;
+    }
+    if( k == CUDA_HOST_MEM && i < 0 && !allowTransposed && fixedDepthMask == 0 )
+    {
+        CV_Assert(!fixedSize() || ((cuda::HostMem*)obj)->size() == _sz);
+        CV_Assert(!fixedType() || ((cuda::HostMem*)obj)->type() == mtype);
+        ((cuda::HostMem*)obj)->create(_sz, mtype);
+        return;
+    }
+    int sizes[] = {_sz.height, _sz.width};
+    create(2, sizes, mtype, i, allowTransposed, fixedDepthMask);
+}
+
+void _OutputArray::create(int _rows, int _cols, int mtype, int i, bool allowTransposed, int fixedDepthMask) const
+{
+    int k = kind();
+    if( k == MAT && i < 0 && !allowTransposed && fixedDepthMask == 0 )
+    {
+        CV_Assert(!fixedSize() || ((Mat*)obj)->size.operator()() == Size(_cols, _rows));
+        CV_Assert(!fixedType() || ((Mat*)obj)->type() == mtype);
+        ((Mat*)obj)->create(_rows, _cols, mtype);
+        return;
+    }
+    if( k == UMAT && i < 0 && !allowTransposed && fixedDepthMask == 0 )
+    {
+        CV_Assert(!fixedSize() || ((UMat*)obj)->size.operator()() == Size(_cols, _rows));
+        CV_Assert(!fixedType() || ((UMat*)obj)->type() == mtype);
+        ((UMat*)obj)->create(_rows, _cols, mtype);
+        return;
+    }
+    if( k == CUDA_GPU_MAT && i < 0 && !allowTransposed && fixedDepthMask == 0 )
+    {
+        CV_Assert(!fixedSize() || ((cuda::GpuMat*)obj)->size() == Size(_cols, _rows));
+        CV_Assert(!fixedType() || ((cuda::GpuMat*)obj)->type() == mtype);
+        ((cuda::GpuMat*)obj)->create(_rows, _cols, mtype);
+        return;
+    }
+    if( k == OPENGL_BUFFER && i < 0 && !allowTransposed && fixedDepthMask == 0 )
+    {
+        CV_Assert(!fixedSize() || ((ogl::Buffer*)obj)->size() == Size(_cols, _rows));
+        CV_Assert(!fixedType() || ((ogl::Buffer*)obj)->type() == mtype);
+        ((ogl::Buffer*)obj)->create(_rows, _cols, mtype);
+        return;
+    }
+    if( k == CUDA_HOST_MEM && i < 0 && !allowTransposed && fixedDepthMask == 0 )
+    {
+        CV_Assert(!fixedSize() || ((cuda::HostMem*)obj)->size() == Size(_cols, _rows));
+        CV_Assert(!fixedType() || ((cuda::HostMem*)obj)->type() == mtype);
+        ((cuda::HostMem*)obj)->create(_rows, _cols, mtype);
+        return;
+    }
+    int sizes[] = {_rows, _cols};
+    create(2, sizes, mtype, i, allowTransposed, fixedDepthMask);
+}
+
+void _OutputArray::create(int d, const int* sizes, int mtype, int i,
+                          bool allowTransposed, int fixedDepthMask) const
+{
+    int k = kind();
+    mtype = CV_MAT_TYPE(mtype);
+
+    if( k == MAT )
+    {
+        CV_Assert( i < 0 );
+        Mat& m = *(Mat*)obj;
+        if( allowTransposed )
+        {
+            if( !m.isContinuous() )
+            {
+                CV_Assert(!fixedType() && !fixedSize());
+                m.release();
+            }
+
+            if( d == 2 && m.dims == 2 && m.data &&
+                m.type() == mtype && m.rows == sizes[1] && m.cols == sizes[0] )
+                return;
+        }
+
+        if(fixedType())
+        {
+            if(CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 )
+                mtype = m.type();
+            else
+                CV_Assert(CV_MAT_TYPE(mtype) == m.type());
+        }
+        if(fixedSize())
+        {
+            CV_Assert(m.dims == d);
+            for(int j = 0; j < d; ++j)
+                CV_Assert(m.size[j] == sizes[j]);
+        }
+        m.create(d, sizes, mtype);
+        return;
+    }
+
+    if( k == UMAT )
+    {
+        CV_Assert( i < 0 );
+        UMat& m = *(UMat*)obj;
+        if( allowTransposed )
+        {
+            if( !m.isContinuous() )
+            {
+                CV_Assert(!fixedType() && !fixedSize());
+                m.release();
+            }
+
+            if( d == 2 && m.dims == 2 && !m.empty() &&
+                m.type() == mtype && m.rows == sizes[1] && m.cols == sizes[0] )
+                return;
+        }
+
+        if(fixedType())
+        {
+            if(CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 )
+                mtype = m.type();
+            else
+                CV_Assert(CV_MAT_TYPE(mtype) == m.type());
+        }
+        if(fixedSize())
+        {
+            CV_Assert(m.dims == d);
+            for(int j = 0; j < d; ++j)
+                CV_Assert(m.size[j] == sizes[j]);
+        }
+        m.create(d, sizes, mtype);
+        return;
+    }
+
+    if( k == MATX )
+    {
+        CV_Assert( i < 0 );
+        int type0 = CV_MAT_TYPE(flags);
+        CV_Assert( mtype == type0 || (CV_MAT_CN(mtype) == 1 && ((1 << type0) & fixedDepthMask) != 0) );
+        CV_Assert( d == 2 && ((sizes[0] == sz.height && sizes[1] == sz.width) ||
+                                 (allowTransposed && sizes[0] == sz.width && sizes[1] == sz.height)));
+        return;
+    }
+
+    if( k == STD_ARRAY )
+    {
+        int type0 = CV_MAT_TYPE(flags);
+        CV_Assert( mtype == type0 || (CV_MAT_CN(mtype) == 1 && ((1 << type0) & fixedDepthMask) != 0) );
+        CV_Assert( d == 2 && sz.area() == sizes[0]*sizes[1]);
+        return;
+    }
+
+    if( k == STD_VECTOR || k == STD_VECTOR_VECTOR )
+    {
+        CV_Assert( d == 2 && (sizes[0] == 1 || sizes[1] == 1 || sizes[0]*sizes[1] == 0) );
+        size_t len = sizes[0]*sizes[1] > 0 ? sizes[0] + sizes[1] - 1 : 0;
+        std::vector<uchar>* v = (std::vector<uchar>*)obj;
+
+        if( k == STD_VECTOR_VECTOR )
+        {
+            std::vector<std::vector<uchar> >& vv = *(std::vector<std::vector<uchar> >*)obj;
+            if( i < 0 )
+            {
+                CV_Assert(!fixedSize() || len == vv.size());
+                vv.resize(len);
+                return;
+            }
+            CV_Assert( i < (int)vv.size() );
+            v = &vv[i];
+        }
+        else
+            CV_Assert( i < 0 );
+
+        int type0 = CV_MAT_TYPE(flags);
+        CV_Assert( mtype == type0 || (CV_MAT_CN(mtype) == CV_MAT_CN(type0) && ((1 << type0) & fixedDepthMask) != 0) );
+
+        int esz = CV_ELEM_SIZE(type0);
+        CV_Assert(!fixedSize() || len == ((std::vector<uchar>*)v)->size() / esz);
+        switch( esz )
+        {
+        case 1:
+            ((std::vector<uchar>*)v)->resize(len);
+            break;
+        case 2:
+            ((std::vector<Vec2b>*)v)->resize(len);
+            break;
+        case 3:
+            ((std::vector<Vec3b>*)v)->resize(len);
+            break;
+        case 4:
+            ((std::vector<int>*)v)->resize(len);
+            break;
+        case 6:
+            ((std::vector<Vec3s>*)v)->resize(len);
+            break;
+        case 8:
+            ((std::vector<Vec2i>*)v)->resize(len);
+            break;
+        case 12:
+            ((std::vector<Vec3i>*)v)->resize(len);
+            break;
+        case 16:
+            ((std::vector<Vec4i>*)v)->resize(len);
+            break;
+        case 24:
+            ((std::vector<Vec6i>*)v)->resize(len);
+            break;
+        case 32:
+            ((std::vector<Vec8i>*)v)->resize(len);
+            break;
+        case 36:
+            ((std::vector<Vec<int, 9> >*)v)->resize(len);
+            break;
+        case 48:
+            ((std::vector<Vec<int, 12> >*)v)->resize(len);
+            break;
+        case 64:
+            ((std::vector<Vec<int, 16> >*)v)->resize(len);
+            break;
+        case 128:
+            ((std::vector<Vec<int, 32> >*)v)->resize(len);
+            break;
+        case 256:
+            ((std::vector<Vec<int, 64> >*)v)->resize(len);
+            break;
+        case 512:
+            ((std::vector<Vec<int, 128> >*)v)->resize(len);
+            break;
+        default:
+            CV_Error_(CV_StsBadArg, ("Vectors with element size %d are not supported. Please, modify OutputArray::create()\n", esz));
+        }
+        return;
+    }
+
+    if( k == NONE )
+    {
+        CV_Error(CV_StsNullPtr, "create() called for the missing output array" );
+        return;
+    }
+
+    if( k == STD_VECTOR_MAT )
+    {
+        std::vector<Mat>& v = *(std::vector<Mat>*)obj;
+
+        if( i < 0 )
+        {
+            CV_Assert( d == 2 && (sizes[0] == 1 || sizes[1] == 1 || sizes[0]*sizes[1] == 0) );
+            size_t len = sizes[0]*sizes[1] > 0 ? sizes[0] + sizes[1] - 1 : 0, len0 = v.size();
+
+            CV_Assert(!fixedSize() || len == len0);
+            v.resize(len);
+            if( fixedType() )
+            {
+                int _type = CV_MAT_TYPE(flags);
+                for( size_t j = len0; j < len; j++ )
+                {
+                    if( v[j].type() == _type )
+                        continue;
+                    CV_Assert( v[j].empty() );
+                    v[j].flags = (v[j].flags & ~CV_MAT_TYPE_MASK) | _type;
+                }
+            }
+            return;
+        }
+
+        CV_Assert( i < (int)v.size() );
+        Mat& m = v[i];
+
+        if( allowTransposed )
+        {
+            if( !m.isContinuous() )
+            {
+                CV_Assert(!fixedType() && !fixedSize());
+                m.release();
+            }
+
+            if( d == 2 && m.dims == 2 && m.data &&
+                m.type() == mtype && m.rows == sizes[1] && m.cols == sizes[0] )
+                return;
+        }
+
+        if(fixedType())
+        {
+            if(CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 )
+                mtype = m.type();
+            else
+                CV_Assert(CV_MAT_TYPE(mtype) == m.type());
+        }
+        if(fixedSize())
+        {
+            CV_Assert(m.dims == d);
+            for(int j = 0; j < d; ++j)
+                CV_Assert(m.size[j] == sizes[j]);
+        }
+
+        m.create(d, sizes, mtype);
+        return;
+    }
+
+    if( k == STD_ARRAY_MAT )
+    {
+        Mat* v = (Mat*)obj;
+
+        if( i < 0 )
+        {
+            CV_Assert( d == 2 && (sizes[0] == 1 || sizes[1] == 1 || sizes[0]*sizes[1] == 0) );
+            size_t len = sizes[0]*sizes[1] > 0 ? sizes[0] + sizes[1] - 1 : 0, len0 = sz.height;
+
+            CV_Assert(len == len0);
+            if( fixedType() )
+            {
+                int _type = CV_MAT_TYPE(flags);
+                for( size_t j = len0; j < len; j++ )
+                {
+                    if( v[j].type() == _type )
+                        continue;
+                    CV_Assert( v[j].empty() );
+                    v[j].flags = (v[j].flags & ~CV_MAT_TYPE_MASK) | _type;
+                }
+            }
+            return;
+        }
+
+        CV_Assert( i < sz.height );
+        Mat& m = v[i];
+
+        if( allowTransposed )
+        {
+            if( !m.isContinuous() )
+            {
+                CV_Assert(!fixedType() && !fixedSize());
+                m.release();
+            }
+
+            if( d == 2 && m.dims == 2 && m.data &&
+                m.type() == mtype && m.rows == sizes[1] && m.cols == sizes[0] )
+                return;
+        }
+
+        if(fixedType())
+        {
+            if(CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 )
+                mtype = m.type();
+            else
+                CV_Assert(CV_MAT_TYPE(mtype) == m.type());
+        }
+
+        if(fixedSize())
+        {
+            CV_Assert(m.dims == d);
+            for(int j = 0; j < d; ++j)
+                CV_Assert(m.size[j] == sizes[j]);
+        }
+
+        m.create(d, sizes, mtype);
+        return;
+    }
+
+    if( k == STD_VECTOR_UMAT )
+    {
+        std::vector<UMat>& v = *(std::vector<UMat>*)obj;
+
+        if( i < 0 )
+        {
+            CV_Assert( d == 2 && (sizes[0] == 1 || sizes[1] == 1 || sizes[0]*sizes[1] == 0) );
+            size_t len = sizes[0]*sizes[1] > 0 ? sizes[0] + sizes[1] - 1 : 0, len0 = v.size();
+
+            CV_Assert(!fixedSize() || len == len0);
+            v.resize(len);
+            if( fixedType() )
+            {
+                int _type = CV_MAT_TYPE(flags);
+                for( size_t j = len0; j < len; j++ )
+                {
+                    if( v[j].type() == _type )
+                        continue;
+                    CV_Assert( v[j].empty() );
+                    v[j].flags = (v[j].flags & ~CV_MAT_TYPE_MASK) | _type;
+                }
+            }
+            return;
+        }
+
+        CV_Assert( i < (int)v.size() );
+        UMat& m = v[i];
+
+        if( allowTransposed )
+        {
+            if( !m.isContinuous() )
+            {
+                CV_Assert(!fixedType() && !fixedSize());
+                m.release();
+            }
+
+            if( d == 2 && m.dims == 2 && m.u &&
+                m.type() == mtype && m.rows == sizes[1] && m.cols == sizes[0] )
+                return;
+        }
+
+        if(fixedType())
+        {
+            if(CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 )
+                mtype = m.type();
+            else
+                CV_Assert(CV_MAT_TYPE(mtype) == m.type());
+        }
+        if(fixedSize())
+        {
+            CV_Assert(m.dims == d);
+            for(int j = 0; j < d; ++j)
+                CV_Assert(m.size[j] == sizes[j]);
+        }
+
+        m.create(d, sizes, mtype);
+        return;
+    }
+
+    CV_Error(Error::StsNotImplemented, "Unknown/unsupported array type");
+}
+
+void _OutputArray::createSameSize(const _InputArray& arr, int mtype) const
+{
+    int arrsz[CV_MAX_DIM], d = arr.sizend(arrsz);
+    create(d, arrsz, mtype);
+}
+
+void _OutputArray::release() const
+{
+    CV_Assert(!fixedSize());
+
+    int k = kind();
+
+    if( k == MAT )
+    {
+        ((Mat*)obj)->release();
+        return;
+    }
+
+    if( k == UMAT )
+    {
+        ((UMat*)obj)->release();
+        return;
+    }
+
+    if( k == CUDA_GPU_MAT )
+    {
+        ((cuda::GpuMat*)obj)->release();
+        return;
+    }
+
+    if( k == CUDA_HOST_MEM )
+    {
+        ((cuda::HostMem*)obj)->release();
+        return;
+    }
+
+    if( k == OPENGL_BUFFER )
+    {
+        ((ogl::Buffer*)obj)->release();
+        return;
+    }
+
+    if( k == NONE )
+        return;
+
+    if( k == STD_VECTOR )
+    {
+        create(Size(), CV_MAT_TYPE(flags));
+        return;
+    }
+
+    if( k == STD_VECTOR_VECTOR )
+    {
+        ((std::vector<std::vector<uchar> >*)obj)->clear();
+        return;
+    }
+
+    if( k == STD_VECTOR_MAT )
+    {
+        ((std::vector<Mat>*)obj)->clear();
+        return;
+    }
+
+    if( k == STD_VECTOR_UMAT )
+    {
+        ((std::vector<UMat>*)obj)->clear();
+        return;
+    }
+    if (k == STD_VECTOR_CUDA_GPU_MAT)
+    {
+        ((std::vector<cuda::GpuMat>*)obj)->clear();
+        return;
+    }
+    CV_Error(Error::StsNotImplemented, "Unknown/unsupported array type");
+}
+
+void _OutputArray::clear() const
+{
+    int k = kind();
+
+    if( k == MAT )
+    {
+        CV_Assert(!fixedSize());
+        ((Mat*)obj)->resize(0);
+        return;
+    }
+
+    release();
+}
+
+bool _OutputArray::needed() const
+{
+    return kind() != NONE;
+}
+
+Mat& _OutputArray::getMatRef(int i) const
+{
+    int k = kind();
+    if( i < 0 )
+    {
+        CV_Assert( k == MAT );
+        return *(Mat*)obj;
+    }
+
+    CV_Assert( k == STD_VECTOR_MAT || k == STD_ARRAY_MAT );
+
+    if( k == STD_VECTOR_MAT )
+    {
+        std::vector<Mat>& v = *(std::vector<Mat>*)obj;
+        CV_Assert( i < (int)v.size() );
+        return v[i];
+    }
+    else
+    {
+        Mat* v = (Mat*)obj;
+        CV_Assert( 0 <= i && i < sz.height );
+        return v[i];
+    }
+}
+
+UMat& _OutputArray::getUMatRef(int i) const
+{
+    int k = kind();
+    if( i < 0 )
+    {
+        CV_Assert( k == UMAT );
+        return *(UMat*)obj;
+    }
+    else
+    {
+        CV_Assert( k == STD_VECTOR_UMAT );
+        std::vector<UMat>& v = *(std::vector<UMat>*)obj;
+        CV_Assert( i < (int)v.size() );
+        return v[i];
+    }
+}
+
+cuda::GpuMat& _OutputArray::getGpuMatRef() const
+{
+    int k = kind();
+    CV_Assert( k == CUDA_GPU_MAT );
+    return *(cuda::GpuMat*)obj;
+}
+std::vector<cuda::GpuMat>& _OutputArray::getGpuMatVecRef() const
+{
+    int k = kind();
+    CV_Assert(k == STD_VECTOR_CUDA_GPU_MAT);
+    return *(std::vector<cuda::GpuMat>*)obj;
+}
+
+ogl::Buffer& _OutputArray::getOGlBufferRef() const
+{
+    int k = kind();
+    CV_Assert( k == OPENGL_BUFFER );
+    return *(ogl::Buffer*)obj;
+}
+
+cuda::HostMem& _OutputArray::getHostMemRef() const
+{
+    int k = kind();
+    CV_Assert( k == CUDA_HOST_MEM );
+    return *(cuda::HostMem*)obj;
+}
+
+void _OutputArray::setTo(const _InputArray& arr, const _InputArray & mask) const
+{
+    int k = kind();
+
+    if( k == NONE )
+        ;
+    else if( k == MAT || k == MATX || k == STD_VECTOR || k == STD_ARRAY )
+    {
+        Mat m = getMat();
+        m.setTo(arr, mask);
+    }
+    else if( k == UMAT )
+        ((UMat*)obj)->setTo(arr, mask);
+    else if( k == CUDA_GPU_MAT )
+    {
+        Mat value = arr.getMat();
+        CV_Assert( checkScalar(value, type(), arr.kind(), _InputArray::CUDA_GPU_MAT) );
+        ((cuda::GpuMat*)obj)->setTo(Scalar(Vec<double, 4>(value.ptr<double>())), mask);
+    }
+    else
+        CV_Error(Error::StsNotImplemented, "");
+}
+
+
+void _OutputArray::assign(const UMat& u) const
+{
+    int k = kind();
+    if (k == UMAT)
+    {
+        *(UMat*)obj = u;
+    }
+    else if (k == MAT)
+    {
+        u.copyTo(*(Mat*)obj); // TODO check u.getMat()
+    }
+    else if (k == MATX)
+    {
+        u.copyTo(getMat()); // TODO check u.getMat()
+    }
+    else
+    {
+        CV_Error(Error::StsNotImplemented, "");
+    }
+}
+
+
+void _OutputArray::assign(const Mat& m) const
+{
+    int k = kind();
+    if (k == UMAT)
+    {
+        m.copyTo(*(UMat*)obj); // TODO check m.getUMat()
+    }
+    else if (k == MAT)
+    {
+        *(Mat*)obj = m;
+    }
+    else if (k == MATX)
+    {
+        m.copyTo(getMat());
+    }
+    else
+    {
+        CV_Error(Error::StsNotImplemented, "");
+    }
+}
+
+
+void _OutputArray::assign(const std::vector<UMat>& v) const
+{
+    int k = kind();
+    if (k == STD_VECTOR_UMAT)
+    {
+        std::vector<UMat>& this_v = *(std::vector<UMat>*)obj;
+        CV_Assert(this_v.size() == v.size());
+
+        for (size_t i = 0; i < v.size(); i++)
+        {
+            const UMat& m = v[i];
+            UMat& this_m = this_v[i];
+            if (this_m.u != NULL && this_m.u == m.u)
+                continue; // same object (see dnn::Layer::forward_fallback)
+            m.copyTo(this_m);
+        }
+    }
+    else if (k == STD_VECTOR_MAT)
+    {
+        std::vector<Mat>& this_v = *(std::vector<Mat>*)obj;
+        CV_Assert(this_v.size() == v.size());
+
+        for (size_t i = 0; i < v.size(); i++)
+        {
+            const UMat& m = v[i];
+            Mat& this_m = this_v[i];
+            if (this_m.u != NULL && this_m.u == m.u)
+                continue; // same object (see dnn::Layer::forward_fallback)
+            m.copyTo(this_m);
+        }
+    }
+    else
+    {
+        CV_Error(Error::StsNotImplemented, "");
+    }
+}
+
+
+void _OutputArray::assign(const std::vector<Mat>& v) const
+{
+    int k = kind();
+    if (k == STD_VECTOR_UMAT)
+    {
+        std::vector<UMat>& this_v = *(std::vector<UMat>*)obj;
+        CV_Assert(this_v.size() == v.size());
+
+        for (size_t i = 0; i < v.size(); i++)
+        {
+            const Mat& m = v[i];
+            UMat& this_m = this_v[i];
+            if (this_m.u != NULL && this_m.u == m.u)
+                continue; // same object (see dnn::Layer::forward_fallback)
+            m.copyTo(this_m);
+        }
+    }
+    else if (k == STD_VECTOR_MAT)
+    {
+        std::vector<Mat>& this_v = *(std::vector<Mat>*)obj;
+        CV_Assert(this_v.size() == v.size());
+
+        for (size_t i = 0; i < v.size(); i++)
+        {
+            const Mat& m = v[i];
+            Mat& this_m = this_v[i];
+            if (this_m.u != NULL && this_m.u == m.u)
+                continue; // same object (see dnn::Layer::forward_fallback)
+            m.copyTo(this_m);
+        }
+    }
+    else
+    {
+        CV_Error(Error::StsNotImplemented, "");
+    }
+}
+
+
+static _InputOutputArray _none;
+InputOutputArray noArray() { return _none; }
+
+} // cv::

modules/core/src/precomp.hpp

@@ -190,6 +190,9 @@ inline Size getContinuousSize( const Mat& m1, const Mat& m2,
                               m1.cols, m1.rows, widthScale);
 }
 
+void setSize( Mat& m, int _dims, const int* _sz, const size_t* _steps, bool autoSteps=false );
+void finalizeHdr(Mat& m);
+
 struct NoVec
 {
     size_t operator()(const void*, const void*, void*, size_t) const { return 0; }

modules/core/src/types.cpp

@@ -46,6 +46,8 @@
 namespace cv
 {
 
+////////////////////// KeyPoint //////////////////////
+
 size_t KeyPoint::hash() const
 {
     size_t _Val = 2166136261U, scale = 16777619U;
@@ -140,4 +142,69 @@ float KeyPoint::overlap( const KeyPoint& kp1, const KeyPoint& kp2 )
     return ovrl;
 }
 
+////////////////////// RotatedRect //////////////////////
+
+RotatedRect::RotatedRect(const Point2f& _point1, const Point2f& _point2, const Point2f& _point3)
+{
+    Point2f _center = 0.5f * (_point1 + _point3);
+    Vec2f vecs[2];
+    vecs[0] = Vec2f(_point1 - _point2);
+    vecs[1] = Vec2f(_point2 - _point3);
+    // check that given sides are perpendicular
+    CV_Assert( abs(vecs[0].dot(vecs[1])) / (norm(vecs[0]) * norm(vecs[1])) <= FLT_EPSILON );
+
+    // wd_i stores which vector (0,1) or (1,2) will make the width
+    // One of them will definitely have slope within -1 to 1
+    int wd_i = 0;
+    if( abs(vecs[1][1]) < abs(vecs[1][0]) ) wd_i = 1;
+    int ht_i = (wd_i + 1) % 2;
+
+    float _angle = atan(vecs[wd_i][1] / vecs[wd_i][0]) * 180.0f / (float) CV_PI;
+    float _width = (float) norm(vecs[wd_i]);
+    float _height = (float) norm(vecs[ht_i]);
+
+    center = _center;
+    size = Size2f(_width, _height);
+    angle = _angle;
+}
+
+void RotatedRect::points(Point2f pt[]) const
+{
+    double _angle = angle*CV_PI/180.;
+    float b = (float)cos(_angle)*0.5f;
+    float a = (float)sin(_angle)*0.5f;
+
+    pt[0].x = center.x - a*size.height - b*size.width;
+    pt[0].y = center.y + b*size.height - a*size.width;
+    pt[1].x = center.x + a*size.height - b*size.width;
+    pt[1].y = center.y - b*size.height - a*size.width;
+    pt[2].x = 2*center.x - pt[0].x;
+    pt[2].y = 2*center.y - pt[0].y;
+    pt[3].x = 2*center.x - pt[1].x;
+    pt[3].y = 2*center.y - pt[1].y;
+}
+
+Rect RotatedRect::boundingRect() const
+{
+    Point2f pt[4];
+    points(pt);
+    Rect r(cvFloor(std::min(std::min(std::min(pt[0].x, pt[1].x), pt[2].x), pt[3].x)),
+           cvFloor(std::min(std::min(std::min(pt[0].y, pt[1].y), pt[2].y), pt[3].y)),
+           cvCeil(std::max(std::max(std::max(pt[0].x, pt[1].x), pt[2].x), pt[3].x)),
+           cvCeil(std::max(std::max(std::max(pt[0].y, pt[1].y), pt[2].y), pt[3].y)));
+    r.width -= r.x - 1;
+    r.height -= r.y - 1;
+    return r;
+}
+
+
+Rect_<float> RotatedRect::boundingRect2f() const
+{
+    Point2f pt[4];
+    points(pt);
+    Rect_<float> r(Point_<float>(min(min(min(pt[0].x, pt[1].x), pt[2].x), pt[3].x), min(min(min(pt[0].y, pt[1].y), pt[2].y), pt[3].y)),
+                   Point_<float>(max(max(max(pt[0].x, pt[1].x), pt[2].x), pt[3].x), max(max(max(pt[0].y, pt[1].y), pt[2].y), pt[3].y)));
+    return r;
+}
+
 } // cv