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Merge pull request #26458 from asmorkalov:as/drop_c_core

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Drop C API in core
This commit is contained in:
Alexander Smorkalov 2024-11-14 17:27:36 +03:00 committed by GitHub
commit 77b45145d1
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25 changed files with 60 additions and 12104 deletions
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4
modules/core/CMakeLists.txt
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@ -176,10 +176,6 @@ ocv_target_link_libraries(${the_module} PRIVATE
"${OPENCV_HAL_LINKER_LIBS}"
)
if(OPENCV_CORE_EXCLUDE_C_API)
ocv_target_compile_definitions(${the_module} PRIVATE "OPENCV_EXCLUDE_C_API=1")
endif()
if(OPENCV_DISABLE_THREAD_SUPPORT)
ocv_target_compile_definitions(${the_module} PUBLIC "OPENCV_DISABLE_THREAD_SUPPORT=1")
endif()

2263
modules/core/include/opencv2/core/core_c.h
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1910
modules/core/include/opencv2/core/types_c.h
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11
modules/core/src/alloc.cpp
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@ -236,15 +236,4 @@ void fastFree(void* ptr)
#endif // OPENCV_ALLOC_ENABLE_STATISTICS
} // namespace
CV_IMPL void* cvAlloc( size_t size )
{
return cv::fastMalloc( size );
}
CV_IMPL void cvFree_( void* ptr )
{
cv::fastFree( ptr );
}
/* End of file. */

164
modules/core/src/arithm.cpp
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@ -2233,168 +2233,4 @@ void cv::inRange(InputArray _src, InputArray _lowerb,
}
}
}
#ifndef OPENCV_EXCLUDE_C_API
/****************************************************************************************\
* Earlier API: cvAdd etc. *
\****************************************************************************************/
CV_IMPL void
cvNot( const CvArr* srcarr, CvArr* dstarr )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
CV_Assert( src.size == dst.size && src.type() == dst.type() );
cv::bitwise_not( src, dst );
}
CV_IMPL void
cvAnd( const CvArr* srcarr1, const CvArr* srcarr2, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), src2 = cv::cvarrToMat(srcarr2),
dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src1.size == dst.size && src1.type() == dst.type() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::bitwise_and( src1, src2, dst, mask );
}
CV_IMPL void
cvOr( const CvArr* srcarr1, const CvArr* srcarr2, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), src2 = cv::cvarrToMat(srcarr2),
dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src1.size == dst.size && src1.type() == dst.type() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::bitwise_or( src1, src2, dst, mask );
}
CV_IMPL void
cvXor( const CvArr* srcarr1, const CvArr* srcarr2, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), src2 = cv::cvarrToMat(srcarr2),
dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src1.size == dst.size && src1.type() == dst.type() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::bitwise_xor( src1, src2, dst, mask );
}
CV_IMPL void
cvAndS( const CvArr* srcarr, CvScalar s, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src.size == dst.size && src.type() == dst.type() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::bitwise_and( src, (const cv::Scalar&)s, dst, mask );
}
CV_IMPL void
cvOrS( const CvArr* srcarr, CvScalar s, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src.size == dst.size && src.type() == dst.type() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::bitwise_or( src, (const cv::Scalar&)s, dst, mask );
}
CV_IMPL void
cvXorS( const CvArr* srcarr, CvScalar s, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src.size == dst.size && src.type() == dst.type() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::bitwise_xor( src, (const cv::Scalar&)s, dst, mask );
}
CV_IMPL void cvAdd( const CvArr* srcarr1, const CvArr* srcarr2, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), src2 = cv::cvarrToMat(srcarr2),
dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src1.size == dst.size && src1.channels() == dst.channels() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::add( src1, src2, dst, mask, dst.type() );
}
CV_IMPL void cvSub( const CvArr* srcarr1, const CvArr* srcarr2, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), src2 = cv::cvarrToMat(srcarr2),
dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src1.size == dst.size && src1.channels() == dst.channels() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::subtract( src1, src2, dst, mask, dst.type() );
}
CV_IMPL void cvAddS( const CvArr* srcarr1, CvScalar value, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1),
dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src1.size == dst.size && src1.channels() == dst.channels() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::add( src1, (const cv::Scalar&)value, dst, mask, dst.type() );
}
CV_IMPL void cvSubRS( const CvArr* srcarr1, CvScalar value, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1),
dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src1.size == dst.size && src1.channels() == dst.channels() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::subtract( (const cv::Scalar&)value, src1, dst, mask, dst.type() );
}
CV_IMPL void cvMul( const CvArr* srcarr1, const CvArr* srcarr2,
CvArr* dstarr, double scale )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), src2 = cv::cvarrToMat(srcarr2),
dst = cv::cvarrToMat(dstarr);
CV_Assert( src1.size == dst.size && src1.channels() == dst.channels() );
cv::multiply( src1, src2, dst, scale, dst.type() );
}
CV_IMPL void cvDiv( const CvArr* srcarr1, const CvArr* srcarr2,
CvArr* dstarr, double scale )
{
cv::Mat src2 = cv::cvarrToMat(srcarr2),
dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src2.size == dst.size && src2.channels() == dst.channels() );
if( srcarr1 )
cv::divide( cv::cvarrToMat(srcarr1), src2, dst, scale, dst.type() );
else
cv::divide( scale, src2, dst, dst.type() );
}
CV_IMPL void
cvCmpS( const void* srcarr1, double value, void* dstarr, int cmp_op )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), dst = cv::cvarrToMat(dstarr);
CV_Assert( src1.size == dst.size && dst.type() == CV_8U );
cv::compare( src1, value, dst, cmp_op );
}
#endif // OPENCV_EXCLUDE_C_API
/* End of file. */

2787
modules/core/src/array.cpp
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137
modules/core/src/convert_c.cpp
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@ -1,137 +0,0 @@
// 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"
#ifndef OPENCV_EXCLUDE_C_API
CV_IMPL void
cvSplit( const void* srcarr, void* dstarr0, void* dstarr1, void* dstarr2, void* dstarr3 )
{
void* dptrs[] = { dstarr0, dstarr1, dstarr2, dstarr3 };
cv::Mat src = cv::cvarrToMat(srcarr);
int i, j, nz = 0;
for( i = 0; i < 4; i++ )
nz += dptrs[i] != 0;
CV_Assert( nz > 0 );
std::vector<cv::Mat> dvec(nz);
std::vector<int> pairs(nz*2);
for( i = j = 0; i < 4; i++ )
{
if( dptrs[i] != 0 )
{
dvec[j] = cv::cvarrToMat(dptrs[i]);
CV_Assert( dvec[j].size() == src.size() );
CV_Assert( dvec[j].depth() == src.depth() );
CV_Assert( dvec[j].channels() == 1 );
CV_Assert( i < src.channels() );
pairs[j*2] = i;
pairs[j*2+1] = j;
j++;
}
}
if( nz == src.channels() )
cv::split( src, dvec );
else
{
cv::mixChannels( &src, 1, &dvec[0], nz, &pairs[0], nz );
}
}
CV_IMPL void
cvMerge( const void* srcarr0, const void* srcarr1, const void* srcarr2,
const void* srcarr3, void* dstarr )
{
const void* sptrs[] = { srcarr0, srcarr1, srcarr2, srcarr3 };
cv::Mat dst = cv::cvarrToMat(dstarr);
int i, j, nz = 0;
for( i = 0; i < 4; i++ )
nz += sptrs[i] != 0;
CV_Assert( nz > 0 );
std::vector<cv::Mat> svec(nz);
std::vector<int> pairs(nz*2);
for( i = j = 0; i < 4; i++ )
{
if( sptrs[i] != 0 )
{
svec[j] = cv::cvarrToMat(sptrs[i]);
CV_Assert( svec[j].size == dst.size &&
svec[j].depth() == dst.depth() &&
svec[j].channels() == 1 && i < dst.channels() );
pairs[j*2] = j;
pairs[j*2+1] = i;
j++;
}
}
if( nz == dst.channels() )
cv::merge( svec, dst );
else
{
cv::mixChannels( &svec[0], nz, &dst, 1, &pairs[0], nz );
}
}
CV_IMPL void
cvMixChannels( const CvArr** src, int src_count,
CvArr** dst, int dst_count,
const int* from_to, int pair_count )
{
cv::AutoBuffer<cv::Mat> buf(src_count + dst_count);
int i;
for( i = 0; i < src_count; i++ )
buf[i] = cv::cvarrToMat(src[i]);
for( i = 0; i < dst_count; i++ )
buf[i+src_count] = cv::cvarrToMat(dst[i]);
cv::mixChannels(&buf[0], src_count, &buf[src_count], dst_count, from_to, pair_count);
}
CV_IMPL void
cvConvertScaleAbs( const void* srcarr, void* dstarr,
double scale, double shift )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
CV_Assert( src.size == dst.size && dst.type() == CV_8UC(src.channels()));
cv::convertScaleAbs( src, dst, scale, shift );
}
CV_IMPL void
cvConvertScale( const void* srcarr, void* dstarr,
double scale, double shift )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
CV_Assert( src.size == dst.size && src.channels() == dst.channels() );
src.convertTo(dst, dst.type(), scale, shift);
}
CV_IMPL void cvLUT( const void* srcarr, void* dstarr, const void* lutarr )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr), lut = cv::cvarrToMat(lutarr);
CV_Assert( dst.size() == src.size() && dst.type() == CV_MAKETYPE(lut.depth(), src.channels()) );
cv::LUT( src, lut, dst );
}
CV_IMPL void cvNormalize( const CvArr* srcarr, CvArr* dstarr,
double a, double b, int norm_type, const CvArr* maskarr )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr), mask;
if( maskarr )
mask = cv::cvarrToMat(maskarr);
CV_Assert( dst.size() == src.size() && src.channels() == dst.channels() );
cv::normalize( src, dst, a, b, norm_type, dst.type(), mask );
}
#endif // OPENCV_EXCLUDE_C_API

112
modules/core/src/copy.cpp
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@ -49,7 +49,6 @@
#include "precomp.hpp"
#include "opencl_kernels_core.hpp"
namespace cv
{
@ -1129,115 +1128,4 @@ void cv::copyMakeBorder( InputArray _src, OutputArray _dst, int top, int bottom,
top, left, (int)src.elemSize(), (uchar*)buf.data() );
}
}
#ifndef OPENCV_EXCLUDE_C_API
/* dst = src */
CV_IMPL void
cvCopy( const void* srcarr, void* dstarr, const void* maskarr )
{
if( CV_IS_SPARSE_MAT(srcarr) && CV_IS_SPARSE_MAT(dstarr))
{
CV_Assert( maskarr == 0 );
CvSparseMat* src1 = (CvSparseMat*)srcarr;
CvSparseMat* dst1 = (CvSparseMat*)dstarr;
CvSparseMatIterator iterator;
CvSparseNode* node;
dst1->dims = src1->dims;
memcpy( dst1->size, src1->size, src1->dims*sizeof(src1->size[0]));
dst1->valoffset = src1->valoffset;
dst1->idxoffset = src1->idxoffset;
cvClearSet( dst1->heap );
if( src1->heap->active_count >= dst1->hashsize*CV_SPARSE_HASH_RATIO )
{
cvFree( &dst1->hashtable );
dst1->hashsize = src1->hashsize;
dst1->hashtable =
(void**)cvAlloc( dst1->hashsize*sizeof(dst1->hashtable[0]));
}
memset( dst1->hashtable, 0, dst1->hashsize*sizeof(dst1->hashtable[0]));
for( node = cvInitSparseMatIterator( src1, &iterator );
node != 0; node = cvGetNextSparseNode( &iterator ))
{
CvSparseNode* node_copy = (CvSparseNode*)cvSetNew( dst1->heap );
int tabidx = node->hashval & (dst1->hashsize - 1);
memcpy( node_copy, node, dst1->heap->elem_size );
node_copy->next = (CvSparseNode*)dst1->hashtable[tabidx];
dst1->hashtable[tabidx] = node_copy;
}
return;
}
cv::Mat src = cv::cvarrToMat(srcarr, false, true, 1), dst = cv::cvarrToMat(dstarr, false, true, 1);
CV_Assert( src.depth() == dst.depth() && src.size == dst.size );
int coi1 = 0, coi2 = 0;
if( CV_IS_IMAGE(srcarr) )
coi1 = cvGetImageCOI((const IplImage*)srcarr);
if( CV_IS_IMAGE(dstarr) )
coi2 = cvGetImageCOI((const IplImage*)dstarr);
if( coi1 || coi2 )
{
CV_Assert( (coi1 != 0 || src.channels() == 1) &&
(coi2 != 0 || dst.channels() == 1) );
int pair[] = { std::max(coi1-1, 0), std::max(coi2-1, 0) };
cv::mixChannels( &src, 1, &dst, 1, pair, 1 );
return;
}
else
CV_Assert( src.channels() == dst.channels() );
if( !maskarr )
src.copyTo(dst);
else
src.copyTo(dst, cv::cvarrToMat(maskarr));
}
CV_IMPL void
cvSet( void* arr, CvScalar value, const void* maskarr )
{
cv::Mat m = cv::cvarrToMat(arr);
if( !maskarr )
m = value;
else
m.setTo(cv::Scalar(value), cv::cvarrToMat(maskarr));
}
CV_IMPL void
cvSetZero( CvArr* arr )
{
if( CV_IS_SPARSE_MAT(arr) )
{
CvSparseMat* mat1 = (CvSparseMat*)arr;
cvClearSet( mat1->heap );
if( mat1->hashtable )
memset( mat1->hashtable, 0, mat1->hashsize*sizeof(mat1->hashtable[0]));
return;
}
cv::Mat m = cv::cvarrToMat(arr);
m = cv::Scalar(0);
}
CV_IMPL void
cvFlip( const CvArr* srcarr, CvArr* dstarr, int flip_mode )
{
cv::Mat src = cv::cvarrToMat(srcarr);
cv::Mat dst;
if (!dstarr)
dst = src;
else
dst = cv::cvarrToMat(dstarr);
CV_Assert( src.type() == dst.type() && src.size() == dst.size() );
cv::flip( src, dst, flip_mode );
}
#endif // OPENCV_EXCLUDE_C_API
/* End of file. */

3589
modules/core/src/datastructs.cpp
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60
modules/core/src/dxt.cpp
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@ -4639,64 +4639,4 @@ int cv::getOptimalDFTSize( int size0 )
return optimalDFTSizeTab[b];
}
#ifndef OPENCV_EXCLUDE_C_API
CV_IMPL void
cvDFT( const CvArr* srcarr, CvArr* dstarr, int flags, int nonzero_rows )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst0 = cv::cvarrToMat(dstarr), dst = dst0;
int _flags = ((flags & CV_DXT_INVERSE) ? cv::DFT_INVERSE : 0) |
((flags & CV_DXT_SCALE) ? cv::DFT_SCALE : 0) |
((flags & CV_DXT_ROWS) ? cv::DFT_ROWS : 0);
CV_Assert( src.size == dst.size );
if( src.type() != dst.type() )
{
if( dst.channels() == 2 )
_flags |= cv::DFT_COMPLEX_OUTPUT;
else
_flags |= cv::DFT_REAL_OUTPUT;
}
cv::dft( src, dst, _flags, nonzero_rows );
CV_Assert( dst.data == dst0.data ); // otherwise it means that the destination size or type was incorrect
}
CV_IMPL void
cvMulSpectrums( const CvArr* srcAarr, const CvArr* srcBarr,
CvArr* dstarr, int flags )
{
cv::Mat srcA = cv::cvarrToMat(srcAarr),
srcB = cv::cvarrToMat(srcBarr),
dst = cv::cvarrToMat(dstarr);
CV_Assert( srcA.size == dst.size && srcA.type() == dst.type() );
cv::mulSpectrums(srcA, srcB, dst,
(flags & CV_DXT_ROWS) ? cv::DFT_ROWS : 0,
(flags & CV_DXT_MUL_CONJ) != 0 );
}
CV_IMPL void
cvDCT( const CvArr* srcarr, CvArr* dstarr, int flags )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
CV_Assert( src.size == dst.size && src.type() == dst.type() );
int _flags = ((flags & CV_DXT_INVERSE) ? cv::DCT_INVERSE : 0) |
((flags & CV_DXT_ROWS) ? cv::DCT_ROWS : 0);
cv::dct( src, dst, _flags );
}
CV_IMPL int
cvGetOptimalDFTSize( int size0 )
{
return cv::getOptimalDFTSize(size0);
}
#endif // OPENCV_EXCLUDE_C_API
/* End of file. */

197
modules/core/src/lapack.cpp
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@ -1548,200 +1548,3 @@ void cv::SVBackSubst(InputArray w, InputArray u, InputArray vt, InputArray rhs,
SVD::backSubst(w, u, vt, rhs, dst);
}
#ifndef OPENCV_EXCLUDE_C_API
CV_IMPL double
cvDet( const CvArr* arr )
{
if( CV_IS_MAT(arr) && ((CvMat*)arr)->rows <= 3 )
{
CvMat* mat = (CvMat*)arr;
int type = CV_MAT_TYPE(mat->type);
int rows = mat->rows;
uchar* m = mat->data.ptr;
int step = mat->step;
CV_Assert( rows == mat->cols );
#define Mf(y, x) ((float*)(m + y*step))[x]
#define Md(y, x) ((double*)(m + y*step))[x]
if( type == CV_32F )
{
if( rows == 2 )
return det2(Mf);
if( rows == 3 )
return det3(Mf);
}
else if( type == CV_64F )
{
if( rows == 2 )
return det2(Md);
if( rows == 3 )
return det3(Md);
}
}
return cv::determinant(cv::cvarrToMat(arr));
}
CV_IMPL double
cvInvert( const CvArr* srcarr, CvArr* dstarr, int method )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
CV_Assert( src.type() == dst.type() && src.rows == dst.cols && src.cols == dst.rows );
return cv::invert( src, dst, method == CV_CHOLESKY ? cv::DECOMP_CHOLESKY :
method == CV_SVD ? cv::DECOMP_SVD :
method == CV_SVD_SYM ? cv::DECOMP_EIG : cv::DECOMP_LU );
}
CV_IMPL int
cvSolve( const CvArr* Aarr, const CvArr* barr, CvArr* xarr, int method )
{
cv::Mat A = cv::cvarrToMat(Aarr), b = cv::cvarrToMat(barr), x = cv::cvarrToMat(xarr);
CV_Assert( A.type() == x.type() && A.cols == x.rows && x.cols == b.cols );
bool is_normal = (method & CV_NORMAL) != 0;
method &= ~CV_NORMAL;
return cv::solve( A, b, x, (method == CV_CHOLESKY ? cv::DECOMP_CHOLESKY :
method == CV_SVD ? cv::DECOMP_SVD :
method == CV_SVD_SYM ? cv::DECOMP_EIG :
A.rows > A.cols ? cv::DECOMP_QR : cv::DECOMP_LU) + (is_normal ? cv::DECOMP_NORMAL : 0) );
}
CV_IMPL void
cvEigenVV( CvArr* srcarr, CvArr* evectsarr, CvArr* evalsarr, double,
int, int )
{
cv::Mat src = cv::cvarrToMat(srcarr), evals0 = cv::cvarrToMat(evalsarr), evals = evals0;
if( evectsarr )
{
cv::Mat evects0 = cv::cvarrToMat(evectsarr), evects = evects0;
eigen(src, evals, evects);
if( evects0.data != evects.data )
{
const uchar* p = evects0.ptr();
evects.convertTo(evects0, evects0.type());
CV_Assert( p == evects0.ptr() );
}
}
else
eigen(src, evals);
if( evals0.data != evals.data )
{
const uchar* p = evals0.ptr();
if( evals0.size() == evals.size() )
evals.convertTo(evals0, evals0.type());
else if( evals0.type() == evals.type() )
cv::transpose(evals, evals0);
else
cv::Mat(evals.t()).convertTo(evals0, evals0.type());
CV_Assert( p == evals0.ptr() );
}
}
CV_IMPL void
cvSVD( CvArr* aarr, CvArr* warr, CvArr* uarr, CvArr* varr, int flags )
{
cv::Mat a = cv::cvarrToMat(aarr), w = cv::cvarrToMat(warr), u, v;
int m = a.rows, n = a.cols, type = a.type(), mn = std::max(m, n), nm = std::min(m, n);
CV_Assert( w.type() == type &&
(w.size() == cv::Size(nm,1) || w.size() == cv::Size(1, nm) ||
w.size() == cv::Size(nm, nm) || w.size() == cv::Size(n, m)) );
cv::SVD svd;
if( w.size() == cv::Size(nm, 1) )
svd.w = cv::Mat(nm, 1, type, w.ptr() );
else if( w.isContinuous() )
svd.w = w;
if( uarr )
{
u = cv::cvarrToMat(uarr);
CV_Assert( u.type() == type );
svd.u = u;
}
if( varr )
{
v = cv::cvarrToMat(varr);
CV_Assert( v.type() == type );
svd.vt = v;
}
svd(a, ((flags & CV_SVD_MODIFY_A) ? cv::SVD::MODIFY_A : 0) |
((!svd.u.data && !svd.vt.data) ? cv::SVD::NO_UV : 0) |
((m != n && (svd.u.size() == cv::Size(mn, mn) ||
svd.vt.size() == cv::Size(mn, mn))) ? cv::SVD::FULL_UV : 0));
if( !u.empty() )
{
if( flags & CV_SVD_U_T )
cv::transpose( svd.u, u );
else if( u.data != svd.u.data )
{
CV_Assert( u.size() == svd.u.size() );
svd.u.copyTo(u);
}
}
if( !v.empty() )
{
if( !(flags & CV_SVD_V_T) )
cv::transpose( svd.vt, v );
else if( v.data != svd.vt.data )
{
CV_Assert( v.size() == svd.vt.size() );
svd.vt.copyTo(v);
}
}
if( w.data != svd.w.data )
{
if( w.size() == svd.w.size() )
svd.w.copyTo(w);
else
{
w = cv::Scalar(0);
cv::Mat wd = w.diag();
svd.w.copyTo(wd);
}
}
}
CV_IMPL void
cvSVBkSb( const CvArr* warr, const CvArr* uarr,
const CvArr* varr, const CvArr* rhsarr,
CvArr* dstarr, int flags )
{
cv::Mat w = cv::cvarrToMat(warr), u = cv::cvarrToMat(uarr),
v = cv::cvarrToMat(varr), rhs,
dst = cv::cvarrToMat(dstarr), dst0 = dst;
if( flags & CV_SVD_U_T )
{
cv::Mat tmp;
transpose(u, tmp);
u = tmp;
}
if( !(flags & CV_SVD_V_T) )
{
cv::Mat tmp;
transpose(v, tmp);
v = tmp;
}
if( rhsarr )
rhs = cv::cvarrToMat(rhsarr);
cv::SVD::backSubst(w, u, v, rhs, dst);
CV_Assert( dst.data == dst0.data );
}
#endif // OPENCV_EXCLUDE_C_API

42
modules/core/src/mathfuncs.cpp
View File

@ -1607,33 +1607,6 @@ bool checkRange(InputArray _src, bool quiet, Point* pt, double minVal, double ma
} // namespace cv
#ifndef OPENCV_EXCLUDE_C_API
CV_IMPL void cvExp( const CvArr* srcarr, CvArr* dstarr )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
CV_Assert( src.type() == dst.type() && src.size == dst.size );
cv::exp( src, dst );
}
CV_IMPL void cvPow( const CvArr* srcarr, CvArr* dstarr, double power )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
CV_Assert( src.type() == dst.type() && src.size == dst.size );
cv::pow( src, power, dst );
}
CV_IMPL int cvCheckArr( const CvArr* arr, int flags,
double minVal, double maxVal )
{
if( (flags & CV_CHECK_RANGE) == 0 )
minVal = -DBL_MAX, maxVal = DBL_MAX;
return cv::checkRange(cv::cvarrToMat(arr), (flags & CV_CHECK_QUIET) != 0, 0, minVal, maxVal );
}
#endif // OPENCV_EXCLUDE_C_API
/*
Finds real roots of cubic, quadratic or linear equation.
The original code has been taken from Ken Turkowski web page
@ -1927,21 +1900,6 @@ double cv::solvePoly( InputArray _coeffs0, OutputArray _roots0, int maxIters )
return maxDiff;
}
#ifndef OPENCV_EXCLUDE_C_API
void cvSolvePoly(const CvMat* a, CvMat *r, int maxiter, int)
{
cv::Mat _a = cv::cvarrToMat(a);
cv::Mat _r = cv::cvarrToMat(r);
cv::Mat _r0 = _r;
cv::solvePoly(_a, _r, maxiter);
CV_Assert( _r.data == _r0.data ); // check that the array of roots was not reallocated
}
#endif // OPENCV_EXCLUDE_C_API
// Common constants for dispatched code
namespace cv { namespace details {

251
modules/core/src/matmul.dispatch.cpp
View File

@ -696,7 +696,7 @@ void calcCovarMatrix( const Mat* data, int nsamples, Mat& covar, Mat& _mean, int
Mat mean;
ctype = std::max(std::max(CV_MAT_DEPTH(ctype >= 0 ? ctype : type), _mean.depth()), CV_32F);
if( (flags & CV_COVAR_USE_AVG) != 0 )
if( (flags & cv::COVAR_USE_AVG) != 0 )
{
CV_Assert( _mean.size() == size );
if( _mean.isContinuous() && _mean.type() == ctype )
@ -722,8 +722,8 @@ void calcCovarMatrix( const Mat* data, int nsamples, Mat& covar, Mat& _mean, int
}
}
calcCovarMatrix( _data, covar, mean, (flags & ~(CV_COVAR_ROWS|CV_COVAR_COLS)) | CV_COVAR_ROWS, ctype );
if( (flags & CV_COVAR_USE_AVG) == 0 )
calcCovarMatrix( _data, covar, mean, (flags & ~(cv::COVAR_ROWS|cv::COVAR_COLS)) | cv::COVAR_ROWS, ctype );
if( (flags & cv::COVAR_USE_AVG) == 0 )
_mean = mean.reshape(1, size.height);
}
@ -754,7 +754,7 @@ void calcCovarMatrix( InputArray _src, OutputArray _covar, InputOutputArray _mea
}
Mat mean;
if( (flags & CV_COVAR_USE_AVG) != 0 )
if( (flags & cv::COVAR_USE_AVG) != 0 )
{
CV_Assert( _mean.size() == size );
@ -770,9 +770,9 @@ void calcCovarMatrix( InputArray _src, OutputArray _covar, InputOutputArray _mea
mean = _mean.getMat().reshape(1, 1);
}
calcCovarMatrix( _data, _covar, mean, (flags & ~(CV_COVAR_ROWS|CV_COVAR_COLS)) | CV_COVAR_ROWS, ctype );
calcCovarMatrix( _data, _covar, mean, (flags & ~(cv::COVAR_ROWS|cv::COVAR_COLS)) | cv::COVAR_ROWS, ctype );
if( (flags & CV_COVAR_USE_AVG) == 0 )
if( (flags & cv::COVAR_USE_AVG) == 0 )
{
mean = mean.reshape(1, size.height);
mean.copyTo(_mean);
@ -781,14 +781,14 @@ void calcCovarMatrix( InputArray _src, OutputArray _covar, InputOutputArray _mea
}
Mat data = _src.getMat(), mean;
CV_Assert( ((flags & CV_COVAR_ROWS) != 0) ^ ((flags & CV_COVAR_COLS) != 0) );
bool takeRows = (flags & CV_COVAR_ROWS) != 0;
CV_Assert( ((flags & cv::COVAR_ROWS) != 0) ^ ((flags & cv::COVAR_COLS) != 0) );
bool takeRows = (flags & cv::COVAR_ROWS) != 0;
int type = data.type();
int nsamples = takeRows ? data.rows : data.cols;
CV_Assert( nsamples > 0 );
Size size = takeRows ? Size(data.cols, 1) : Size(1, data.rows);
if( (flags & CV_COVAR_USE_AVG) != 0 )
if( (flags & cv::COVAR_USE_AVG) != 0 )
{
mean = _mean.getMat();
ctype = std::max(std::max(CV_MAT_DEPTH(ctype >= 0 ? ctype : type), mean.depth()), CV_32F);
@ -808,8 +808,8 @@ void calcCovarMatrix( InputArray _src, OutputArray _covar, InputOutputArray _mea
mean = _mean.getMat();
}
mulTransposed( data, _covar, ((flags & CV_COVAR_NORMAL) == 0) ^ takeRows,
mean, (flags & CV_COVAR_SCALE) != 0 ? 1./nsamples : 1, ctype );
mulTransposed( data, _covar, ((flags & cv::COVAR_NORMAL) == 0) ^ takeRows,
mean, (flags & cv::COVAR_SCALE) != 0 ? 1./nsamples : 1, ctype );
}
@ -1097,233 +1097,4 @@ double UMat::dot(InputArray m) const
}
} // namespace cv::
#ifndef OPENCV_EXCLUDE_C_API
/****************************************************************************************\
* Earlier API *
\****************************************************************************************/
CV_IMPL void cvGEMM( const CvArr* Aarr, const CvArr* Barr, double alpha,
const CvArr* Carr, double beta, CvArr* Darr, int flags )
{
cv::Mat A = cv::cvarrToMat(Aarr), B = cv::cvarrToMat(Barr);
cv::Mat C, D = cv::cvarrToMat(Darr);
if( Carr )
C = cv::cvarrToMat(Carr);
CV_Assert_N( (D.rows == ((flags & CV_GEMM_A_T) == 0 ? A.rows : A.cols)),
(D.cols == ((flags & CV_GEMM_B_T) == 0 ? B.cols : B.rows)),
D.type() == A.type() );
gemm( A, B, alpha, C, beta, D, flags );
}
CV_IMPL void
cvTransform( const CvArr* srcarr, CvArr* dstarr,
const CvMat* transmat, const CvMat* shiftvec )
{
cv::Mat m = cv::cvarrToMat(transmat), src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
if( shiftvec )
{
cv::Mat v = cv::cvarrToMat(shiftvec).reshape(1,m.rows),
_m(m.rows, m.cols + 1, m.type()), m1 = _m.colRange(0,m.cols), v1 = _m.col(m.cols);
m.convertTo(m1, m1.type());
v.convertTo(v1, v1.type());
m = _m;
}
CV_Assert_N( dst.depth() == src.depth(), dst.channels() == m.rows );
cv::transform( src, dst, m );
}
CV_IMPL void
cvPerspectiveTransform( const CvArr* srcarr, CvArr* dstarr, const CvMat* mat )
{
cv::Mat m = cv::cvarrToMat(mat), src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
CV_Assert_N( dst.type() == src.type(), dst.channels() == m.rows-1 );
cv::perspectiveTransform( src, dst, m );
}
CV_IMPL void cvScaleAdd( const CvArr* srcarr1, CvScalar scale,
const CvArr* srcarr2, CvArr* dstarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), dst = cv::cvarrToMat(dstarr);
CV_Assert_N( src1.size == dst.size, src1.type() == dst.type() );
cv::scaleAdd( src1, scale.val[0], cv::cvarrToMat(srcarr2), dst );
}
CV_IMPL void
cvCalcCovarMatrix( const CvArr** vecarr, int count,
CvArr* covarr, CvArr* avgarr, int flags )
{
cv::Mat cov0 = cv::cvarrToMat(covarr), cov = cov0, mean0, mean;
CV_Assert_N( vecarr != 0, count >= 1 );
if( avgarr )
mean = mean0 = cv::cvarrToMat(avgarr);
if( (flags & CV_COVAR_COLS) != 0 || (flags & CV_COVAR_ROWS) != 0 )
{
cv::Mat data = cv::cvarrToMat(vecarr[0]);
cv::calcCovarMatrix( data, cov, mean, flags, cov.type() );
}
else
{
std::vector<cv::Mat> data(count);
for( int i = 0; i < count; i++ )
data[i] = cv::cvarrToMat(vecarr[i]);
cv::calcCovarMatrix( &data[0], count, cov, mean, flags, cov.type() );
}
if( mean.data != mean0.data && mean0.data )
mean.convertTo(mean0, mean0.type());
if( cov.data != cov0.data )
cov.convertTo(cov0, cov0.type());
}
CV_IMPL double
cvMahalanobis( const CvArr* srcAarr, const CvArr* srcBarr, const CvArr* matarr )
{
return cv::Mahalanobis(cv::cvarrToMat(srcAarr),
cv::cvarrToMat(srcBarr), cv::cvarrToMat(matarr));
}
CV_IMPL void
cvMulTransposed( const CvArr* srcarr, CvArr* dstarr,
int order, const CvArr* deltaarr, double scale )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst0 = cv::cvarrToMat(dstarr), dst = dst0, delta;
if( deltaarr )
delta = cv::cvarrToMat(deltaarr);
cv::mulTransposed( src, dst, order != 0, delta, scale, dst.type());
if( dst.data != dst0.data )
dst.convertTo(dst0, dst0.type());
}
CV_IMPL double cvDotProduct( const CvArr* srcAarr, const CvArr* srcBarr )
{
return cv::cvarrToMat(srcAarr).dot(cv::cvarrToMat(srcBarr));
}
CV_IMPL void
cvCalcPCA( const CvArr* data_arr, CvArr* avg_arr, CvArr* eigenvals, CvArr* eigenvects, int flags )
{
cv::Mat data = cv::cvarrToMat(data_arr), mean0 = cv::cvarrToMat(avg_arr);
cv::Mat evals0 = cv::cvarrToMat(eigenvals), evects0 = cv::cvarrToMat(eigenvects);
cv::Mat mean = mean0, evals = evals0, evects = evects0;
cv::PCA pca;
pca.mean = mean;
pca.eigenvalues = evals;
pca.eigenvectors = evects;
pca(data, (flags & cv::PCA::USE_AVG) ? mean : cv::Mat(),
flags, !evals.empty() ? evals.rows + evals.cols - 1 : 0);
if( pca.mean.size() == mean.size() )
pca.mean.convertTo( mean, mean.type() );
else
{
cv::Mat temp; pca.mean.convertTo( temp, mean.type() );
transpose( temp, mean );
}
evals = pca.eigenvalues;
evects = pca.eigenvectors;
int ecount0 = evals0.cols + evals0.rows - 1;
int ecount = evals.cols + evals.rows - 1;
CV_Assert_N( (evals0.cols == 1 || evals0.rows == 1),
ecount0 <= ecount,
evects0.cols == evects.cols,
evects0.rows == ecount0 );
cv::Mat temp = evals0;
if( evals.rows == 1 )
evals.colRange(0, ecount0).convertTo(temp, evals0.type());
else
evals.rowRange(0, ecount0).convertTo(temp, evals0.type());
if( temp.data != evals0.data )
transpose(temp, evals0);
evects.rowRange(0, ecount0).convertTo( evects0, evects0.type() );
// otherwise some datatype's or size's were incorrect, so the output arrays have been reallocated
CV_Assert( mean0.data == mean.data );
}
CV_IMPL void
cvProjectPCA( const CvArr* data_arr, const CvArr* avg_arr,
const CvArr* eigenvects, CvArr* result_arr )
{
cv::Mat data = cv::cvarrToMat(data_arr), mean = cv::cvarrToMat(avg_arr);
cv::Mat evects = cv::cvarrToMat(eigenvects), dst0 = cv::cvarrToMat(result_arr), dst = dst0;
cv::PCA pca;
pca.mean = mean;
int n;
if( mean.rows == 1 )
{
CV_Assert_N(dst.cols <= evects.rows, dst.rows == data.rows);
n = dst.cols;
}
else
{
CV_Assert_N(dst.rows <= evects.rows, dst.cols == data.cols);
n = dst.rows;
}
pca.eigenvectors = evects.rowRange(0, n);
cv::Mat result = pca.project(data);
if( result.cols != dst.cols )
result = result.reshape(1, 1);
result.convertTo(dst, dst.type());
CV_Assert(dst0.data == dst.data);
}
CV_IMPL void
cvBackProjectPCA( const CvArr* proj_arr, const CvArr* avg_arr,
const CvArr* eigenvects, CvArr* result_arr )
{
cv::Mat data = cv::cvarrToMat(proj_arr), mean = cv::cvarrToMat(avg_arr);
cv::Mat evects = cv::cvarrToMat(eigenvects), dst0 = cv::cvarrToMat(result_arr), dst = dst0;
cv::PCA pca;
pca.mean = mean;
int n;
if( mean.rows == 1 )
{
CV_Assert_N(data.cols <= evects.rows, dst.rows == data.rows);
n = data.cols;
}
else
{
CV_Assert_N(data.rows <= evects.rows, dst.cols == data.cols);
n = data.rows;
}
pca.eigenvectors = evects.rowRange(0, n);
cv::Mat result = pca.backProject(data);
result.convertTo(dst, dst.type());
CV_Assert(dst0.data == dst.data);
}
#endif // OPENCV_EXCLUDE_C_API
/* End of file. */

328
modules/core/src/matrix_c.cpp
View File

@ -1,328 +0,0 @@
// 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 "opencv2/core/mat.hpp"
#include "opencv2/core/types_c.h"
#ifndef OPENCV_EXCLUDE_C_API
// glue
CvMatND cvMatND(const cv::Mat& m)
{
CvMatND self;
cvInitMatNDHeader(&self, m.dims, m.size, m.type(), m.data );
int i, d = m.dims;
for( i = 0; i < d; i++ )
self.dim[i].step = (int)m.step[i];
self.type |= m.flags & cv::Mat::CONTINUOUS_FLAG;
return self;
}
_IplImage cvIplImage(const cv::Mat& m)
{
_IplImage self;
CV_Assert( m.dims <= 2 );
cvInitImageHeader(&self, cvSize(m.size()), cvIplDepth(m.flags), m.channels());
cvSetData(&self, m.data, (int)m.step[0]);
return self;
}
namespace cv {
static Mat cvMatToMat(const CvMat* m, bool copyData)
{
Mat thiz;
if( !m )
return thiz;
if( !copyData )
{
thiz.flags = Mat::MAGIC_VAL + (m->type & (CV_MAT_TYPE_MASK|CV_MAT_CONT_FLAG));
thiz.dims = 2;
thiz.rows = m->rows;
thiz.cols = m->cols;
thiz.datastart = thiz.data = m->data.ptr;
size_t esz = CV_ELEM_SIZE(m->type), minstep = thiz.cols*esz, _step = m->step;
if( _step == 0 )
_step = minstep;
thiz.datalimit = thiz.datastart + _step*thiz.rows;
thiz.dataend = thiz.datalimit - _step + minstep;
thiz.step[0] = _step; thiz.step[1] = esz;
}
else
{
thiz.datastart = thiz.dataend = thiz.data = 0;
Mat(m->rows, m->cols, m->type, m->data.ptr, m->step).copyTo(thiz);
}
return thiz;
}
static Mat cvMatNDToMat(const CvMatND* m, bool copyData)
{
Mat thiz;
if( !m )
return thiz;
thiz.datastart = thiz.data = m->data.ptr;
thiz.flags |= CV_MAT_TYPE(m->type);
int _sizes[CV_MAX_DIM];
size_t _steps[CV_MAX_DIM];
int d = m->dims;
for( int i = 0; i < d; i++ )
{
_sizes[i] = m->dim[i].size;
_steps[i] = m->dim[i].step;
}
setSize(thiz, d, _sizes, _steps);
finalizeHdr(thiz);
if( copyData )
{
Mat temp(thiz);
thiz.release();
temp.copyTo(thiz);
}
return thiz;
}
static Mat iplImageToMat(const IplImage* img, bool copyData)
{
Mat m;
if( !img )
return m;
m.dims = 2;
CV_DbgAssert(CV_IS_IMAGE(img) && img->imageData != 0);
int imgdepth = IPL2CV_DEPTH(img->depth);
size_t esz;
m.step[0] = img->widthStep;
if(!img->roi)
{
CV_Assert(img->dataOrder == IPL_DATA_ORDER_PIXEL);
m.flags = Mat::MAGIC_VAL + CV_MAKETYPE(imgdepth, img->nChannels);
m.rows = img->height;
m.cols = img->width;
m.datastart = m.data = (uchar*)img->imageData;
esz = CV_ELEM_SIZE(m.flags);
}
else
{
CV_Assert(img->dataOrder == IPL_DATA_ORDER_PIXEL || img->roi->coi != 0);
bool selectedPlane = img->roi->coi && img->dataOrder == IPL_DATA_ORDER_PLANE;
m.flags = Mat::MAGIC_VAL + CV_MAKETYPE(imgdepth, selectedPlane ? 1 : img->nChannels);
m.rows = img->roi->height;
m.cols = img->roi->width;
esz = CV_ELEM_SIZE(m.flags);
m.datastart = m.data = (uchar*)img->imageData +
(selectedPlane ? (img->roi->coi - 1)*m.step*img->height : 0) +
img->roi->yOffset*m.step[0] + img->roi->xOffset*esz;
}
m.datalimit = m.datastart + m.step.p[0]*m.rows;
m.dataend = m.datastart + m.step.p[0]*(m.rows-1) + esz*m.cols;
m.step[1] = esz;
m.updateContinuityFlag();
if( copyData )
{
Mat m2 = m;
m.release();
if( !img->roi || !img->roi->coi ||
img->dataOrder == IPL_DATA_ORDER_PLANE)
m2.copyTo(m);
else
{
int ch[] = {img->roi->coi - 1, 0};
m.create(m2.rows, m2.cols, m2.type());
mixChannels(&m2, 1, &m, 1, ch, 1);
}
}
return m;
}
Mat cvarrToMat(const CvArr* arr, bool copyData,
bool /*allowND*/, int coiMode, AutoBuffer<double>* abuf )
{
if( !arr )
return Mat();
if( CV_IS_MAT_HDR_Z(arr) )
return cvMatToMat((const CvMat*)arr, copyData);
if( CV_IS_MATND(arr) )
return cvMatNDToMat((const CvMatND*)arr, copyData );
if( CV_IS_IMAGE(arr) )
{
const IplImage* iplimg = (const IplImage*)arr;
if( coiMode == 0 && iplimg->roi && iplimg->roi->coi > 0 )
CV_Error(cv::Error::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->data();
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::Error::StsBadArg, "Unknown array type");
}
void extractImageCOI(const CvArr* arr, OutputArray _ch, int coi)
{
Mat mat = cvarrToMat(arr, false, true, 1);
_ch.create(mat.dims, mat.size, mat.depth());
Mat ch = _ch.getMat();
if(coi < 0)
{
CV_Assert( CV_IS_IMAGE(arr) );
coi = cvGetImageCOI((const IplImage*)arr)-1;
}
CV_Assert(0 <= coi && coi < mat.channels());
int _pairs[] = { coi, 0 };
mixChannels( &mat, 1, &ch, 1, _pairs, 1 );
}
} // cv::
// operations
CV_IMPL void cvSetIdentity( CvArr* arr, CvScalar value )
{
cv::Mat m = cv::cvarrToMat(arr);
cv::setIdentity(m, value);
}
CV_IMPL CvScalar cvTrace( const CvArr* arr )
{
return cvScalar(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::Error::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::Error::StsBadSize, "The output array size is incorrect" );
if( src.channels() != dst.channels() )
CV_Error( cv::Error::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::Error::StsUnsupportedFormat, "The function only supports 32sC1 and 32fC1 datatypes" );
return arr;
}
#endif // OPENCV_EXCLUDE_C_API

54
modules/core/src/matrix_expressions.cpp
View File

@ -554,7 +554,7 @@ void MatOp::matmul(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const
if( isT(e1) )
{
flags = CV_GEMM_A_T;
flags = cv::GEMM_1_T;
scale = e1.alpha;
m1 = e1.a;
}
@ -568,7 +568,7 @@ void MatOp::matmul(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const
if( isT(e2) )
{
flags |= CV_GEMM_B_T;
flags |= cv::GEMM_2_T;
scale *= e2.alpha;
m2 = e2.a;
}
@ -946,7 +946,7 @@ MatExpr operator < (const Mat& a, const Mat& b)
{
checkOperandsExist(a, b);
MatExpr e;
MatOp_Cmp::makeExpr(e, CV_CMP_LT, a, b);
MatOp_Cmp::makeExpr(e, cv::CMP_LT, a, b);
return e;
}
@ -954,7 +954,7 @@ MatExpr operator < (const Mat& a, double s)
{
checkOperandsExist(a);
MatExpr e;
MatOp_Cmp::makeExpr(e, CV_CMP_LT, a, s);
MatOp_Cmp::makeExpr(e, cv::CMP_LT, a, s);
return e;
}
@ -962,7 +962,7 @@ MatExpr operator < (double s, const Mat& a)
{
checkOperandsExist(a);
MatExpr e;
MatOp_Cmp::makeExpr(e, CV_CMP_GT, a, s);
MatOp_Cmp::makeExpr(e, cv::CMP_GT, a, s);
return e;
}
@ -970,7 +970,7 @@ MatExpr operator <= (const Mat& a, const Mat& b)
{
checkOperandsExist(a, b);
MatExpr e;
MatOp_Cmp::makeExpr(e, CV_CMP_LE, a, b);
MatOp_Cmp::makeExpr(e, cv::CMP_LE, a, b);
return e;
}
@ -978,7 +978,7 @@ MatExpr operator <= (const Mat& a, double s)
{
checkOperandsExist(a);
MatExpr e;
MatOp_Cmp::makeExpr(e, CV_CMP_LE, a, s);
MatOp_Cmp::makeExpr(e, cv::CMP_LE, a, s);
return e;
}
@ -986,7 +986,7 @@ MatExpr operator <= (double s, const Mat& a)
{
checkOperandsExist(a);
MatExpr e;
MatOp_Cmp::makeExpr(e, CV_CMP_GE, a, s);
MatOp_Cmp::makeExpr(e, cv::CMP_GE, a, s);
return e;
}
@ -994,7 +994,7 @@ MatExpr operator == (const Mat& a, const Mat& b)
{
checkOperandsExist(a, b);
MatExpr e;
MatOp_Cmp::makeExpr(e, CV_CMP_EQ, a, b);
MatOp_Cmp::makeExpr(e, cv::CMP_EQ, a, b);
return e;
}
@ -1002,7 +1002,7 @@ MatExpr operator == (const Mat& a, double s)
{
checkOperandsExist(a);
MatExpr e;
MatOp_Cmp::makeExpr(e, CV_CMP_EQ, a, s);
MatOp_Cmp::makeExpr(e, cv::CMP_EQ, a, s);
return e;
}
@ -1010,7 +1010,7 @@ MatExpr operator == (double s, const Mat& a)
{
checkOperandsExist(a);
MatExpr e;
MatOp_Cmp::makeExpr(e, CV_CMP_EQ, a, s);
MatOp_Cmp::makeExpr(e, cv::CMP_EQ, a, s);
return e;
}
@ -1018,7 +1018,7 @@ MatExpr operator != (const Mat& a, const Mat& b)
{
checkOperandsExist(a, b);
MatExpr e;
MatOp_Cmp::makeExpr(e, CV_CMP_NE, a, b);
MatOp_Cmp::makeExpr(e, cv::CMP_NE, a, b);
return e;
}
@ -1026,7 +1026,7 @@ MatExpr operator != (const Mat& a, double s)
{
checkOperandsExist(a);
MatExpr e;
MatOp_Cmp::makeExpr(e, CV_CMP_NE, a, s);
MatOp_Cmp::makeExpr(e, cv::CMP_NE, a, s);
return e;
}
@ -1034,7 +1034,7 @@ MatExpr operator != (double s, const Mat& a)
{
checkOperandsExist(a);
MatExpr e;
MatOp_Cmp::makeExpr(e, CV_CMP_NE, a, s);
MatOp_Cmp::makeExpr(e, cv::CMP_NE, a, s);
return e;
}
@ -1042,7 +1042,7 @@ MatExpr operator >= (const Mat& a, const Mat& b)
{
checkOperandsExist(a, b);
MatExpr e;
MatOp_Cmp::makeExpr(e, CV_CMP_GE, a, b);
MatOp_Cmp::makeExpr(e, cv::CMP_GE, a, b);
return e;
}
@ -1050,7 +1050,7 @@ MatExpr operator >= (const Mat& a, double s)
{
checkOperandsExist(a);
MatExpr e;
MatOp_Cmp::makeExpr(e, CV_CMP_GE, a, s);
MatOp_Cmp::makeExpr(e, cv::CMP_GE, a, s);
return e;
}
@ -1058,7 +1058,7 @@ MatExpr operator >= (double s, const Mat& a)
{
checkOperandsExist(a);
MatExpr e;
MatOp_Cmp::makeExpr(e, CV_CMP_LE, a, s);
MatOp_Cmp::makeExpr(e, cv::CMP_LE, a, s);
return e;
}
@ -1066,7 +1066,7 @@ MatExpr operator > (const Mat& a, const Mat& b)
{
checkOperandsExist(a, b);
MatExpr e;
MatOp_Cmp::makeExpr(e, CV_CMP_GT, a, b);
MatOp_Cmp::makeExpr(e, cv::CMP_GT, a, b);
return e;
}
@ -1074,7 +1074,7 @@ MatExpr operator > (const Mat& a, double s)
{
checkOperandsExist(a);
MatExpr e;
MatOp_Cmp::makeExpr(e, CV_CMP_GT, a, s);
MatOp_Cmp::makeExpr(e, cv::CMP_GT, a, s);
return e;
}
@ -1082,7 +1082,7 @@ MatExpr operator > (double s, const Mat& a)
{
checkOperandsExist(a);
MatExpr e;
MatOp_Cmp::makeExpr(e, CV_CMP_LT, a, s);
MatOp_Cmp::makeExpr(e, cv::CMP_LT, a, s);
return e;
}
@ -1574,10 +1574,10 @@ void MatOp_GEMM::add(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const
double alpha1 = i1 ? 1 : e1.alpha, alpha2 = i2 ? 1 : e2.alpha;
if( isMatProd(e1) && (i2 || isScaled(e2) || isT(e2)) )
MatOp_GEMM::makeExpr(res, (e1.flags & ~CV_GEMM_C_T)|(isT(e2) ? CV_GEMM_C_T : 0),
MatOp_GEMM::makeExpr(res, (e1.flags & ~cv::GEMM_3_T)|(isT(e2) ? cv::GEMM_3_T : 0),
e1.a, e1.b, alpha1, e2.a, alpha2);
else if( isMatProd(e2) && (i1 || isScaled(e1) || isT(e1)) )
MatOp_GEMM::makeExpr(res, (e2.flags & ~CV_GEMM_C_T)|(isT(e1) ? CV_GEMM_C_T : 0),
MatOp_GEMM::makeExpr(res, (e2.flags & ~cv::GEMM_3_T)|(isT(e1) ? cv::GEMM_3_T : 0),
e2.a, e2.b, alpha2, e1.a, alpha1);
else if( this == e2.op )
MatOp::add(e1, e2, res);
@ -1593,10 +1593,10 @@ void MatOp_GEMM::subtract(const MatExpr& e1, const MatExpr& e2, MatExpr& res) co
double alpha1 = i1 ? 1 : e1.alpha, alpha2 = i2 ? 1 : e2.alpha;
if( isMatProd(e1) && (i2 || isScaled(e2) || isT(e2)) )
MatOp_GEMM::makeExpr(res, (e1.flags & ~CV_GEMM_C_T)|(isT(e2) ? CV_GEMM_C_T : 0),
MatOp_GEMM::makeExpr(res, (e1.flags & ~cv::GEMM_3_T)|(isT(e2) ? cv::GEMM_3_T : 0),
e1.a, e1.b, alpha1, e2.a, -alpha2);
else if( isMatProd(e2) && (i1 || isScaled(e1) || isT(e1)) )
MatOp_GEMM::makeExpr(res, (e2.flags & ~CV_GEMM_C_T)|(isT(e1) ? CV_GEMM_C_T : 0),
MatOp_GEMM::makeExpr(res, (e2.flags & ~cv::GEMM_3_T)|(isT(e1) ? cv::GEMM_3_T : 0),
e2.a, e2.b, -alpha2, e1.a, alpha1);
else if( this == e2.op )
MatOp::subtract(e1, e2, res);
@ -1618,9 +1618,9 @@ void MatOp_GEMM::transpose(const MatExpr& e, MatExpr& res) const
CV_INSTRUMENT_REGION();
res = e;
res.flags = (!(e.flags & CV_GEMM_A_T) ? CV_GEMM_B_T : 0) |
(!(e.flags & CV_GEMM_B_T) ? CV_GEMM_A_T : 0) |
(!(e.flags & CV_GEMM_C_T) ? CV_GEMM_C_T : 0);
res.flags = (!(e.flags & cv::GEMM_1_T) ? cv::GEMM_2_T : 0) |
(!(e.flags & cv::GEMM_2_T) ? cv::GEMM_1_T : 0) |
(!(e.flags & cv::GEMM_3_T) ? cv::GEMM_3_T : 0);
swap(res.a, res.b);
}

1
modules/core/src/matrix_operations.cpp
View File

@ -4,7 +4,6 @@
#include "precomp.hpp"
#include "opencv2/core/mat.hpp"
#include "opencv2/core/types_c.h"
#include "opencl_kernels_core.hpp"
#undef HAVE_IPP

39
modules/core/src/matrix_sparse.cpp
View File

@ -4,7 +4,6 @@
#include "precomp.hpp"
#include "opencv2/core/mat.hpp"
#include "opencv2/core/types_c.h"
namespace cv {
@ -851,41 +850,3 @@ void normalize( const SparseMat& src, SparseMat& dst, double a, int norm_type )
}
} // cv::
//
// C-API glue
//
CvSparseMat* cvCreateSparseMat(const cv::SparseMat& sm)
{
if( !sm.hdr || sm.hdr->dims > (int)cv::SparseMat::MAX_DIM)
return 0;
CvSparseMat* m = cvCreateSparseMat(sm.hdr->dims, sm.hdr->size, sm.type());
cv::SparseMatConstIterator from = sm.begin();
size_t i, N = sm.nzcount(), esz = sm.elemSize();
for( i = 0; i < N; i++, ++from )
{
const cv::SparseMat::Node* n = from.node();
uchar* to = cvPtrND(m, n->idx, 0, -2, 0);
cv::copyElem(from.ptr, to, esz);
}
return m;
}
void CvSparseMat::copyToSparseMat(cv::SparseMat& m) const
{
m.create( dims, &size[0], type );
CvSparseMatIterator it;
CvSparseNode* n = cvInitSparseMatIterator(this, &it);
size_t esz = m.elemSize();
for( ; n != 0; n = cvGetNextSparseNode(&it) )
{
const int* idx = CV_NODE_IDX(this, n);
uchar* to = m.newNode(idx, m.hash(idx));
cv::copyElem((const uchar*)CV_NODE_VAL(this, n), to, esz);
}
}

6
modules/core/src/norm.cpp
View File

@ -1026,7 +1026,7 @@ static bool ipp_norm(InputArray _src1, InputArray _src2, int normType, InputArra
#if IPP_VERSION_X100 >= 700
Mat src1 = _src1.getMat(), src2 = _src2.getMat(), mask = _mask.getMat();
if( normType & CV_RELATIVE )
if( normType & NORM_RELATIVE )
{
normType &= NORM_TYPE_MASK;
@ -1257,9 +1257,9 @@ double norm( InputArray _src1, InputArray _src2, int normType, InputArray _mask
CV_IPP_RUN(IPP_VERSION_X100 >= 700, ipp_norm(_src1, _src2, normType, _mask, _result), _result);
if( normType & CV_RELATIVE )
if( normType & NORM_RELATIVE )
{
return norm(_src1, _src2, normType & ~CV_RELATIVE, _mask)/(norm(_src2, normType, _mask) + DBL_EPSILON);
return norm(_src1, _src2, normType & ~NORM_RELATIVE, _mask)/(norm(_src2, normType, _mask) + DBL_EPSILON);
}
Mat src1 = _src1.getMat(), src2 = _src2.getMat(), mask = _mask.getMat();

28
modules/core/src/pca.cpp
View File

@ -65,7 +65,7 @@ PCA::PCA(InputArray data, InputArray _mean, int flags, double retainedVariance)
PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, int maxComponents)
{
Mat data = _data.getMat(), _mean = __mean.getMat();
int covar_flags = CV_COVAR_SCALE;
int covar_flags = cv::COVAR_SCALE;
int len, in_count;
Size mean_sz;
@ -74,14 +74,14 @@ PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, int maxComp
{
len = data.rows;
in_count = data.cols;
covar_flags |= CV_COVAR_COLS;
covar_flags |= cv::COVAR_COLS;
mean_sz = Size(1, len);
}
else
{
len = data.cols;
in_count = data.rows;
covar_flags |= CV_COVAR_ROWS;
covar_flags |= cv::COVAR_ROWS;
mean_sz = Size(len, 1);
}
@ -92,7 +92,7 @@ PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, int maxComp
// "scrambled" way to compute PCA (when cols(A)>rows(A)):
// B = A'A; B*x=b*x; C = AA'; C*y=c*y -> AA'*y=c*y -> A'A*(A'*y)=c*(A'*y) -> c = b, x=A'*y
if( len <= in_count )
covar_flags |= CV_COVAR_NORMAL;
covar_flags |= cv::COVAR_NORMAL;
int ctype = std::max(CV_32F, data.depth());
mean.create( mean_sz, ctype );
@ -103,13 +103,13 @@ PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, int maxComp
{
CV_Assert( _mean.size() == mean_sz );
_mean.convertTo(mean, ctype);
covar_flags |= CV_COVAR_USE_AVG;
covar_flags |= cv::COVAR_USE_AVG;
}
calcCovarMatrix( data, covar, mean, covar_flags, ctype );
eigen( covar, eigenvalues, eigenvectors );
if( !(covar_flags & CV_COVAR_NORMAL) )
if( !(covar_flags & cv::COVAR_NORMAL) )
{
// PCA::DATA_AS_ROW: cols(A)>rows(A). x=A'*y -> x'=y'*A
// PCA::DATA_AS_COL: rows(A)>cols(A). x=A''*y -> x'=y'*A'
@ -127,7 +127,7 @@ PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, int maxComp
Mat evects1(count, len, ctype);
gemm( eigenvectors, tmp_data, 1, Mat(), 0, evects1,
(flags & PCA::DATA_AS_COL) ? CV_GEMM_B_T : 0);
(flags & PCA::DATA_AS_COL) ? cv::GEMM_2_T : 0);
eigenvectors = evects1;
// normalize eigenvectors
@ -201,7 +201,7 @@ int computeCumulativeEnergy(const Mat& eigenvalues, double retainedVariance)
PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, double retainedVariance)
{
Mat data = _data.getMat(), _mean = __mean.getMat();
int covar_flags = CV_COVAR_SCALE;
int covar_flags = cv::COVAR_SCALE;
int len, in_count;
Size mean_sz;
@ -210,14 +210,14 @@ PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, double reta
{
len = data.rows;
in_count = data.cols;
covar_flags |= CV_COVAR_COLS;
covar_flags |= cv::COVAR_COLS;
mean_sz = Size(1, len);
}
else
{
len = data.cols;
in_count = data.rows;
covar_flags |= CV_COVAR_ROWS;
covar_flags |= cv::COVAR_ROWS;
mean_sz = Size(len, 1);
}
@ -228,7 +228,7 @@ PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, double reta
// "scrambled" way to compute PCA (when cols(A)>rows(A)):
// B = A'A; B*x=b*x; C = AA'; C*y=c*y -> AA'*y=c*y -> A'A*(A'*y)=c*(A'*y) -> c = b, x=A'*y
if( len <= in_count )
covar_flags |= CV_COVAR_NORMAL;
covar_flags |= cv::COVAR_NORMAL;
int ctype = std::max(CV_32F, data.depth());
mean.create( mean_sz, ctype );
@ -239,13 +239,13 @@ PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, double reta
{
CV_Assert( _mean.size() == mean_sz );
_mean.convertTo(mean, ctype);
covar_flags |= CV_COVAR_USE_AVG;
covar_flags |= cv::COVAR_USE_AVG;
}
calcCovarMatrix( data, covar, mean, covar_flags, ctype );
eigen( covar, eigenvalues, eigenvectors );
if( !(covar_flags & CV_COVAR_NORMAL) )
if( !(covar_flags & cv::COVAR_NORMAL) )
{
// PCA::DATA_AS_ROW: cols(A)>rows(A). x=A'*y -> x'=y'*A
// PCA::DATA_AS_COL: rows(A)>cols(A). x=A''*y -> x'=y'*A'
@ -263,7 +263,7 @@ PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, double reta
Mat evects1(count, len, ctype);
gemm( eigenvectors, tmp_data, 1, Mat(), 0, evects1,
(flags & PCA::DATA_AS_COL) ? CV_GEMM_B_T : 0);
(flags & PCA::DATA_AS_COL) ? cv::GEMM_2_T : 0);
eigenvectors = evects1;
// normalize all eigenvectors

2
modules/core/src/persistence.cpp
View File

@ -11,6 +11,8 @@
#include <opencv2/core/utils/logger.hpp>
#define CV_SEQ_ELTYPE_PTR CV_MAKE_TYPE(CV_8U, 8 /*sizeof(void*)*/)
namespace cv
{

1
modules/core/src/persistence.hpp
View File

@ -6,7 +6,6 @@
#ifndef SRC_PERSISTENCE_HPP
#define SRC_PERSISTENCE_HPP
#include "opencv2/core/types_c.h"
#include <deque>
#include <sstream>
#include <string>

1
modules/core/src/precomp.hpp
View File

@ -56,7 +56,6 @@
#include "cvconfig.h"
#include "opencv2/core/utility.hpp"
#include "opencv2/core/core_c.h"
#include "opencv2/core/cuda.hpp"
#include "opencv2/core/opengl.hpp"
#include "opencv2/core/va_intel.hpp"

2
modules/core/src/rand.cpp
View File

@ -47,6 +47,8 @@ namespace cv
///////////////////////////// Functions Declaration //////////////////////////////////////
#define CV_RNG_COEFF 4164903690U
/*
Multiply-with-carry generator is used here:
temp = ( A*X(n) + carry )

123
modules/core/src/stat_c.cpp
View File

@ -1,123 +0,0 @@
// 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"
#ifndef OPENCV_EXCLUDE_C_API
CV_IMPL CvScalar cvSum( const CvArr* srcarr )
{
cv::Scalar sum = cv::sum(cv::cvarrToMat(srcarr, false, true, 1));
if( CV_IS_IMAGE(srcarr) )
{
int coi = cvGetImageCOI((IplImage*)srcarr);
if( coi )
{
CV_Assert( 0 < coi && coi <= 4 );
sum = cv::Scalar(sum[coi-1]);
}
}
return cvScalar(sum);
}
CV_IMPL int cvCountNonZero( const CvArr* imgarr )
{
cv::Mat img = cv::cvarrToMat(imgarr, false, true, 1);
if( img.channels() > 1 )
cv::extractImageCOI(imgarr, img);
return countNonZero(img);
}
CV_IMPL CvScalar
cvAvg( const void* imgarr, const void* maskarr )
{
cv::Mat img = cv::cvarrToMat(imgarr, false, true, 1);
cv::Scalar mean = !maskarr ? cv::mean(img) : cv::mean(img, cv::cvarrToMat(maskarr));
if( CV_IS_IMAGE(imgarr) )
{
int coi = cvGetImageCOI((IplImage*)imgarr);
if( coi )
{
CV_Assert( 0 < coi && coi <= 4 );
mean = cv::Scalar(mean[coi-1]);
}
}
return cvScalar(mean);
}
CV_IMPL void
cvAvgSdv( const CvArr* imgarr, CvScalar* _mean, CvScalar* _sdv, const void* maskarr )
{
cv::Scalar mean, sdv;
cv::Mat mask;
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::meanStdDev(cv::cvarrToMat(imgarr, false, true, 1), mean, sdv, mask );
if( CV_IS_IMAGE(imgarr) )
{
int coi = cvGetImageCOI((IplImage*)imgarr);
if( coi )
{
CV_Assert( 0 < coi && coi <= 4 );
mean = cv::Scalar(mean[coi-1]);
sdv = cv::Scalar(sdv[coi-1]);
}
}
if( _mean )
*(cv::Scalar*)_mean = mean;
if( _sdv )
*(cv::Scalar*)_sdv = sdv;
}
CV_IMPL void
cvMinMaxLoc( const void* imgarr, double* _minVal, double* _maxVal,
CvPoint* _minLoc, CvPoint* _maxLoc, const void* maskarr )
{
cv::Mat mask, img = cv::cvarrToMat(imgarr, false, true, 1);
if( maskarr )
mask = cv::cvarrToMat(maskarr);
if( img.channels() > 1 )
cv::extractImageCOI(imgarr, img);
cv::minMaxLoc( img, _minVal, _maxVal,
(cv::Point*)_minLoc, (cv::Point*)_maxLoc, mask );
}
CV_IMPL double
cvNorm( const void* imgA, const void* imgB, int normType, const void* maskarr )
{
cv::Mat a, mask;
if( !imgA )
{
imgA = imgB;
imgB = 0;
}
a = cv::cvarrToMat(imgA, false, true, 1);
if( maskarr )
mask = cv::cvarrToMat(maskarr);
if( a.channels() > 1 && CV_IS_IMAGE(imgA) && cvGetImageCOI((const IplImage*)imgA) > 0 )
cv::extractImageCOI(imgA, a);
if( !imgB )
return !maskarr ? cv::norm(a, normType) : cv::norm(a, normType, mask);
cv::Mat b = cv::cvarrToMat(imgB, false, true, 1);
if( b.channels() > 1 && CV_IS_IMAGE(imgB) && cvGetImageCOI((const IplImage*)imgB) > 0 )
cv::extractImageCOI(imgB, b);
return !maskarr ? cv::norm(a, b, normType) : cv::norm(a, b, normType, mask);
}
#endif // OPENCV_EXCLUDE_C_API

52
modules/core/src/system.cpp
View File

@ -42,6 +42,7 @@
//M*/
#include "precomp.hpp"
//#include "opencv2/core/core_c.h"
#include <atomic>
#include <iostream>
#include <ostream>
@ -1380,57 +1381,6 @@ void terminate(Error::Code _code, const String& _err, const char* _func, const c
}
CV_IMPL int cvGetErrMode(void)
{
return 0;
}
CV_IMPL int cvSetErrMode(int)
{
return 0;
}
CV_IMPL int cvGetErrStatus(void)
{
return 0;
}
CV_IMPL void cvSetErrStatus(int)
{
}
/* function, which converts int to int */
CV_IMPL int
cvErrorFromIppStatus( int status )
{
switch (status)
{
case CV_BADSIZE_ERR: return cv::Error::StsBadSize;
case CV_BADMEMBLOCK_ERR: return cv::Error::StsBadMemBlock;
case CV_NULLPTR_ERR: return cv::Error::StsNullPtr;
case CV_DIV_BY_ZERO_ERR: return cv::Error::StsDivByZero;
case CV_BADSTEP_ERR: return cv::Error::BadStep;
case CV_OUTOFMEM_ERR: return cv::Error::StsNoMem;
case CV_BADARG_ERR: return cv::Error::StsBadArg;
case CV_NOTDEFINED_ERR: return cv::Error::StsError;
case CV_INPLACE_NOT_SUPPORTED_ERR: return cv::Error::StsInplaceNotSupported;
case CV_NOTFOUND_ERR: return cv::Error::StsObjectNotFound;
case CV_BADCONVERGENCE_ERR: return cv::Error::StsNoConv;
case CV_BADDEPTH_ERR: return cv::Error::BadDepth;
case CV_UNMATCHED_FORMATS_ERR: return cv::Error::StsUnmatchedFormats;
case CV_UNSUPPORTED_COI_ERR: return cv::Error::BadCOI;
case CV_UNSUPPORTED_CHANNELS_ERR: return cv::Error::BadNumChannels;
case CV_BADFLAG_ERR: return cv::Error::StsBadFlag;
case CV_BADRANGE_ERR: return cv::Error::StsBadArg;
case CV_BADCOEF_ERR: return cv::Error::StsBadArg;
case CV_BADFACTOR_ERR: return cv::Error::StsBadArg;
case CV_BADPOINT_ERR: return cv::Error::StsBadPoint;
default:
return cv::Error::StsError;
}
}
namespace cv {
bool __termination = false;