-/*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, 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"
-#include "opencl_kernels_core.hpp"
-
#include "bufferpool.impl.hpp"
-/****************************************************************************************\
-* [scaled] Identity matrix initialization *
-\****************************************************************************************/
-
namespace cv {
void MatAllocator::map(UMatData*, int) const
delete u;
}
};
+
namespace
{
MatAllocator* volatile g_matAllocator = NULL;
}
-
MatAllocator* Mat::getDefaultAllocator()
{
if (g_matAllocator == NULL)
CV_SINGLETON_LAZY_INIT(MatAllocator, new StdMatAllocator())
}
-void 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;
- }
-}
-
+//==================================================================================================
-static inline void setSize( Mat& m, int _dims, const int* _sz,
- const size_t* _steps, bool autoSteps=false )
+void setSize( Mat& m, int _dims, const int* _sz, const size_t* _steps, bool autoSteps)
{
CV_Assert( 0 <= _dims && _dims <= CV_MAX_DIM );
if( m.dims != _dims )
m.flags &= ~Mat::CONTINUOUS_FLAG;
}
-static void finalizeHdr(Mat& m)
+void finalizeHdr(Mat& m)
{
updateContinuityFlag(m);
int d = m.dims;
m.dataend = m.datalimit = 0;
}
+//==================================================================================================
void Mat::create(int d, const int* _sizes, int _type)
{
updateContinuityFlag(*this);
}
-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 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 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 Mat::diag(int d) const
{
return m;
}
+
void Mat::pop_back(size_t nelems)
{
CV_Assert( nelems <= (size_t)size.p[0] );
flags &= ~CONTINUOUS_FLAG;
}
+
void Mat::reserve(size_t nelems)
{
const size_t MIN_SIZE = 64;
dataend = data + step.p[0]*r;
}
+
void Mat::reserveBuffer(size_t nbytes)
{
size_t esz = 1;
}
-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 Mat::locateROI( Size& wholeSize, Point& ofs ) const
{
CV_Assert( dims <= 2 && step[0] > 0 );
return *this;
}
-}
-
-void cv::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 cv::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 );
-}
-
-namespace cv
-{
-
Mat Mat::reshape(int new_cn, int new_rows) const
{
int cn = channels();
return hdr;
}
-Mat Mat::diag(const Mat& d)
-{
- CV_Assert( d.cols == 1 || d.rows == 1 );
- int len = d.rows + d.cols - 1;
- Mat m(len, len, d.type(), Scalar(0));
- Mat md = m.diag();
- if( d.cols == 1 )
- d.copyTo(md);
- else
- transpose(d, md);
- return m;
-}
-
-int Mat::checkVector(int _elemChannels, int _depth, bool _requireContinuous) const
-{
- return data && (depth() == _depth || _depth <= 0) &&
- (isContinuous() || !_requireContinuous) &&
- ((dims == 2 && (((rows == 1 || cols == 1) && channels() == _elemChannels) ||
- (cols == _elemChannels && channels() == 1))) ||
- (dims == 3 && channels() == 1 && size.p[2] == _elemChannels && (size.p[0] == 1 || size.p[1] == 1) &&
- (isContinuous() || step.p[1] == step.p[2]*size.p[2])))
- ? (int)(total()*channels()/_elemChannels) : -1;
-}
-
-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,"");
- }
-}
-
-
-/*************************************************************************************************\
- Input/Output Array
-\*************************************************************************************************/
-
-Mat _InputArray::getMat_(int i) const
+Mat Mat::reshape(int _cn, int _newndims, const int* _newsz) 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 )
+ if(_newndims == dims)
{
- 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(_newsz == 0)
+ return reshape(_cn);
+ if(_newndims == 2)
+ return reshape(_cn, _newsz[0]);
}
- if( k == STD_VECTOR_MAT )
+ if (isContinuous())
{
- const std::vector<Mat>& v = *(const std::vector<Mat>*)obj;
- CV_Assert( 0 <= i && i < (int)v.size() );
-
- return v[i];
- }
+ CV_Assert(_cn >= 0 && _newndims > 0 && _newndims <= CV_MAX_DIM && _newsz);
- if( k == STD_ARRAY_MAT )
- {
- const Mat* v = (const Mat*)obj;
- CV_Assert( 0 <= i && i < sz.height );
+ if (_cn == 0)
+ _cn = this->channels();
+ else
+ CV_Assert(_cn <= CV_CN_MAX);
- return v[i];
- }
+ size_t total_elem1_ref = this->total() * this->channels();
+ size_t total_elem1 = _cn;
- if( k == STD_VECTOR_UMAT )
- {
- const std::vector<UMat>& v = *(const std::vector<UMat>*)obj;
- CV_Assert( 0 <= i && i < (int)v.size() );
+ AutoBuffer<int, 4> newsz_buf( (size_t)_newndims );
- return v[i].getMat(accessFlags);
- }
+ for (int i = 0; i < _newndims; i++)
+ {
+ CV_Assert(_newsz[i] >= 0);
- 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 (_newsz[i] > 0)
+ newsz_buf[i] = _newsz[i];
+ else if (i < dims)
+ newsz_buf[i] = this->size[i];
+ else
+ CV_Error(CV_StsOutOfRange, "Copy dimension (which has zero size) is not present in source matrix");
- 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();
- }
+ total_elem1 *= (size_t)newsz_buf[i];
+ }
- if( k == CUDA_HOST_MEM )
- {
- CV_Assert( i < 0 );
+ if (total_elem1 != total_elem1_ref)
+ CV_Error(CV_StsUnmatchedSizes, "Requested and source matrices have different count of elements");
- const cuda::HostMem* cuda_mem = (const cuda::HostMem*)obj;
+ Mat hdr = *this;
+ hdr.flags = (hdr.flags & ~CV_MAT_CN_MASK) | ((_cn-1) << CV_CN_SHIFT);
+ setSize(hdr, _newndims, (int*)newsz_buf, NULL, true);
- return cuda_mem->createMatHeader();
+ return hdr;
}
- CV_Error(Error::StsNotImplemented, "Unknown/unsupported array type");
+ CV_Error(CV_StsNotImplemented, "Reshaping of n-dimensional non-continuous matrices is not supported yet");
+ // TBD
return Mat();
}
-UMat _InputArray::getUMat(int i) const
+Mat Mat::reshape(int _cn, const std::vector<int>& _newshape) 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 )
+ if(_newshape.empty())
{
- const Mat* m = (const Mat*)obj;
- if( i < 0 )
- return m->getUMat(accessFlags);
- return m->row(i).getUMat(accessFlags);
+ CV_Assert(empty());
+ return *this;
}
- return getMat(i).getUMat(accessFlags);
+ return reshape(_cn, (int)_newshape.size(), &_newshape[0]);
}
-void _InputArray::getMatVector(std::vector<Mat>& mv) const
+Mat Mat::diag(const Mat& d)
{
- 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);
+ CV_Assert( d.cols == 1 || d.rows == 1 );
+ int len = d.rows + d.cols - 1;
+ Mat m(len, len, d.type(), Scalar(0));
+ Mat md = m.diag();
+ if( d.cols == 1 )
+ d.copyTo(md);
+ else
+ transpose(d, md);
+ return m;
+}
- 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; }
-
-}
-
-/*************************************************************************************************\
- Matrix Operations
-\*************************************************************************************************/
-
-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 );
-}
-
-
-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;
-}
-
-///////////////////////////// n-dimensional matrices ////////////////////////////
-
-namespace cv
-{
-
-Mat Mat::reshape(int _cn, int _newndims, const int* _newsz) const
-{
- if(_newndims == dims)
- {
- if(_newsz == 0)
- return reshape(_cn);
- if(_newndims == 2)
- return reshape(_cn, _newsz[0]);
- }
-
- if (isContinuous())
- {
- CV_Assert(_cn >= 0 && _newndims > 0 && _newndims <= CV_MAX_DIM && _newsz);
-
- if (_cn == 0)
- _cn = this->channels();
- else
- CV_Assert(_cn <= CV_CN_MAX);
-
- size_t total_elem1_ref = this->total() * this->channels();
- size_t total_elem1 = _cn;
-
- AutoBuffer<int, 4> newsz_buf( (size_t)_newndims );
-
- for (int i = 0; i < _newndims; i++)
- {
- CV_Assert(_newsz[i] >= 0);
-
- if (_newsz[i] > 0)
- newsz_buf[i] = _newsz[i];
- else if (i < dims)
- newsz_buf[i] = this->size[i];
- else
- CV_Error(CV_StsOutOfRange, "Copy dimension (which has zero size) is not present in source matrix");
-
- total_elem1 *= (size_t)newsz_buf[i];
- }
-
- if (total_elem1 != total_elem1_ref)
- CV_Error(CV_StsUnmatchedSizes, "Requested and source matrices have different count of elements");
-
- Mat hdr = *this;
- hdr.flags = (hdr.flags & ~CV_MAT_CN_MASK) | ((_cn-1) << CV_CN_SHIFT);
- setSize(hdr, _newndims, (int*)newsz_buf, NULL, true);
-
- return hdr;
- }
-
- CV_Error(CV_StsNotImplemented, "Reshaping of n-dimensional non-continuous matrices is not supported yet");
- // TBD
- return Mat();
-}
-
-Mat Mat::reshape(int _cn, const std::vector<int>& _newshape) const
-{
- if(_newshape.empty())
- {
- CV_Assert(empty());
- return *this;
- }
-
- return reshape(_cn, (int)_newshape.size(), &_newshape[0]);
-}
-
-
-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;
-}
-
-///////////////////////////////////////////////////////////////////////////
-// MatConstIterator //
-///////////////////////////////////////////////////////////////////////////
-
-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);
-}
-
-//////////////////////////////// SparseMat ////////////////////////////////
-
-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;
-}
-
-
-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;
-}
-
-
-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 );
-}
-
-////////////////////// 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;
-}
-
-}
-
-// 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]);
-}
-
-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
+int Mat::checkVector(int _elemChannels, int _depth, bool _requireContinuous) 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);
- }
+ return data && (depth() == _depth || _depth <= 0) &&
+ (isContinuous() || !_requireContinuous) &&
+ ((dims == 2 && (((rows == 1 || cols == 1) && channels() == _elemChannels) ||
+ (cols == _elemChannels && channels() == 1))) ||
+ (dims == 3 && channels() == 1 && size.p[2] == _elemChannels && (size.p[0] == 1 || size.p[1] == 1) &&
+ (isContinuous() || step.p[1] == step.p[2]*size.p[2])))
+ ? (int)(total()*channels()/_elemChannels) : -1;
}
-
-/* End of file. */
+} // cv::
--- /dev/null
+// 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 );
+}
--- /dev/null
+// 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::
projects / platform / upstream / opencv.git / commitdiff