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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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This commit is contained in:
Alexander Alekhin
2018-08-22 17:38:51 +03:00
parent e8a14a775e 6700fdb81f
commit 6356403964
36 changed files with 316 additions and 277 deletions
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modules/core/src/matmul.cpp
+19 -19
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@@ -796,7 +796,7 @@ static bool ocl_gemm( InputArray matA, InputArray matB, double alpha,
int depth = matA.depth(), cn = matA.channels();
int type = CV_MAKETYPE(depth, cn);
CV_Assert( type == matB.type(), (type == CV_32FC1 || type == CV_64FC1 || type == CV_32FC2 || type == CV_64FC2) );
CV_Assert_N( type == matB.type(), (type == CV_32FC1 || type == CV_64FC1 || type == CV_32FC2 || type == CV_64FC2) );
const ocl::Device & dev = ocl::Device::getDefault();
bool doubleSupport = dev.doubleFPConfig() > 0;
@@ -1555,7 +1555,7 @@ void cv::gemm( InputArray matA, InputArray matB, double alpha,
Size a_size = A.size(), d_size;
int len = 0, type = A.type();
CV_Assert( type == B.type(), (type == CV_32FC1 || type == CV_64FC1 || type == CV_32FC2 || type == CV_64FC2) );
CV_Assert_N( type == B.type(), (type == CV_32FC1 || type == CV_64FC1 || type == CV_32FC2 || type == CV_64FC2) );
switch( flags & (GEMM_1_T|GEMM_2_T) )
{
@@ -1583,7 +1583,7 @@ void cv::gemm( InputArray matA, InputArray matB, double alpha,
if( !C.empty() )
{
CV_Assert( C.type() == type,
CV_Assert_N( C.type() == type,
(((flags&GEMM_3_T) == 0 && C.rows == d_size.height && C.cols == d_size.width) ||
((flags&GEMM_3_T) != 0 && C.rows == d_size.width && C.cols == d_size.height)));
}
@@ -2457,7 +2457,7 @@ void cv::calcCovarMatrix( const Mat* data, int nsamples, Mat& covar, Mat& _mean,
{
CV_INSTRUMENT_REGION()
CV_Assert( data, nsamples > 0 );
CV_Assert_N( data, nsamples > 0 );
Size size = data[0].size();
int sz = size.width * size.height, esz = (int)data[0].elemSize();
int type = data[0].type();
@@ -2480,7 +2480,7 @@ void cv::calcCovarMatrix( const Mat* data, int nsamples, Mat& covar, Mat& _mean,
for( int i = 0; i < nsamples; i++ )
{
CV_Assert( data[i].size() == size, data[i].type() == type );
CV_Assert_N( data[i].size() == size, data[i].type() == type );
if( data[i].isContinuous() )
memcpy( _data.ptr(i), data[i].ptr(), sz*esz );
else
@@ -2516,7 +2516,7 @@ void cv::calcCovarMatrix( InputArray _src, OutputArray _covar, InputOutputArray
int i = 0;
for(std::vector<cv::Mat>::iterator each = src.begin(); each != src.end(); ++each, ++i )
{
CV_Assert( (*each).size() == size, (*each).type() == type );
CV_Assert_N( (*each).size() == size, (*each).type() == type );
Mat dataRow(size.height, size.width, type, _data.ptr(i));
(*each).copyTo(dataRow);
}
@@ -2595,7 +2595,7 @@ double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar )
AutoBuffer<double> buf(len);
double result = 0;
CV_Assert( type == v2.type(), type == icovar.type(),
CV_Assert_N( type == v2.type(), type == icovar.type(),
sz == v2.size(), len == icovar.rows && len == icovar.cols );
sz.width *= v1.channels();
@@ -2888,7 +2888,7 @@ void cv::mulTransposed( InputArray _src, OutputArray _dst, bool ata,
if( !delta.empty() )
{
CV_Assert( delta.channels() == 1,
CV_Assert_N( delta.channels() == 1,
(delta.rows == src.rows || delta.rows == 1),
(delta.cols == src.cols || delta.cols == 1));
if( delta.type() != dtype )
@@ -3291,7 +3291,7 @@ double Mat::dot(InputArray _mat) const
Mat mat = _mat.getMat();
int cn = channels();
DotProdFunc func = getDotProdFunc(depth());
CV_Assert( mat.type() == type(), mat.size == size, func != 0 );
CV_Assert_N( mat.type() == type(), mat.size == size, func != 0 );
if( isContinuous() && mat.isContinuous() )
{
@@ -3327,7 +3327,7 @@ CV_IMPL void cvGEMM( const CvArr* Aarr, const CvArr* Barr, double alpha,
if( Carr )
C = cv::cvarrToMat(Carr);
CV_Assert( (D.rows == ((flags & CV_GEMM_A_T) == 0 ? A.rows : A.cols)),
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() );
@@ -3350,7 +3350,7 @@ cvTransform( const CvArr* srcarr, CvArr* dstarr,
m = _m;
}
CV_Assert( dst.depth() == src.depth(), dst.channels() == m.rows );
CV_Assert_N( dst.depth() == src.depth(), dst.channels() == m.rows );
cv::transform( src, dst, m );
}
@@ -3360,7 +3360,7 @@ 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( dst.type() == src.type(), dst.channels() == m.rows-1 );
CV_Assert_N( dst.type() == src.type(), dst.channels() == m.rows-1 );
cv::perspectiveTransform( src, dst, m );
}
@@ -3370,7 +3370,7 @@ CV_IMPL void cvScaleAdd( const CvArr* srcarr1, CvScalar scale,
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), dst = cv::cvarrToMat(dstarr);
CV_Assert( src1.size == dst.size, src1.type() == dst.type() );
CV_Assert_N( src1.size == dst.size, src1.type() == dst.type() );
cv::scaleAdd( src1, scale.val[0], cv::cvarrToMat(srcarr2), dst );
}
@@ -3380,7 +3380,7 @@ cvCalcCovarMatrix( const CvArr** vecarr, int count,
CvArr* covarr, CvArr* avgarr, int flags )
{
cv::Mat cov0 = cv::cvarrToMat(covarr), cov = cov0, mean0, mean;
CV_Assert( vecarr != 0, count >= 1 );
CV_Assert_N( vecarr != 0, count >= 1 );
if( avgarr )
mean = mean0 = cv::cvarrToMat(avgarr);
@@ -3460,7 +3460,7 @@ cvCalcPCA( const CvArr* data_arr, CvArr* avg_arr, CvArr* eigenvals, CvArr* eigen
int ecount0 = evals0.cols + evals0.rows - 1;
int ecount = evals.cols + evals.rows - 1;
CV_Assert( (evals0.cols == 1 || evals0.rows == 1),
CV_Assert_N( (evals0.cols == 1 || evals0.rows == 1),
ecount0 <= ecount,
evects0.cols == evects.cols,
evects0.rows == ecount0 );
@@ -3491,12 +3491,12 @@ cvProjectPCA( const CvArr* data_arr, const CvArr* avg_arr,
int n;
if( mean.rows == 1 )
{
CV_Assert(dst.cols <= evects.rows, dst.rows == data.rows);
CV_Assert_N(dst.cols <= evects.rows, dst.rows == data.rows);
n = dst.cols;
}
else
{
CV_Assert(dst.rows <= evects.rows, dst.cols == data.cols);
CV_Assert_N(dst.rows <= evects.rows, dst.cols == data.cols);
n = dst.rows;
}
pca.eigenvectors = evects.rowRange(0, n);
@@ -3522,12 +3522,12 @@ cvBackProjectPCA( const CvArr* proj_arr, const CvArr* avg_arr,
int n;
if( mean.rows == 1 )
{
CV_Assert(data.cols <= evects.rows, dst.rows == data.rows);
CV_Assert_N(data.cols <= evects.rows, dst.rows == data.rows);
n = data.cols;
}
else
{
CV_Assert(data.rows <= evects.rows, dst.cols == data.cols);
CV_Assert_N(data.rows <= evects.rows, dst.cols == data.cols);
n = data.rows;
}
pca.eigenvectors = evects.rowRange(0, n);