void repeat(InputArray _src, int ny, int nx, OutputArray _dst)
{
CV_Assert( _src.dims() <= 2 );
CV_Assert( ny > 0 && nx > 0 );
Size ssize = _src.size();
_dst.create(ssize.height*ny, ssize.width*nx, _src.type());
CV_OCL_RUN(_dst.isUMat(),
ocl_repeat(_src, ny, nx, _dst))
Mat src = _src.getMat(), dst = _dst.getMat();
Size dsize = dst.size();
int esz = (int)src.elemSize();
int x, y;
ssize.width *= esz; dsize.width *= esz;
for( y = 0; y < ssize.height; y++ )
{
for( x = 0; x < dsize.width; x += ssize.width )
memcpy( dst.data + y*dst.step + x, src.data + y*src.step, ssize.width );
}
for( ; y < dsize.height; y++ )
memcpy( dst.data + y*dst.step, dst.data + (y - ssize.height)*dst.step, dsize.width );
}
inline bool checkScalar(InputArray sc, int atype, int sckind, int akind)
{
if( sc.dims() > 2 || !sc.isContinuous() )
return false;
Size sz = sc.size();
if(sz.width != 1 && sz.height != 1)
return false;
int cn = CV_MAT_CN(atype);
if( akind == _InputArray::MATX && sckind != _InputArray::MATX )
return false;
return sz == Size(1, 1) || sz == Size(1, cn) || sz == Size(cn, 1) ||
(sz == Size(1, 4) && sc.type() == CV_64F && cn <= 4);
}
void flip( InputArray _src, OutputArray _dst, int flip_mode )
{
CV_Assert( _src.dims() <= 2 );
CV_OCL_RUN( _dst.isUMat(), ocl_flip(_src,_dst, flip_mode))
Mat src = _src.getMat();
_dst.create( src.size(), src.type() );
Mat dst = _dst.getMat();
size_t esz = src.elemSize();
if( flip_mode <= 0 )
flipVert( src.data, src.step, dst.data, dst.step, src.size(), esz );
else
flipHoriz( src.data, src.step, dst.data, dst.step, src.size(), esz );
if( flip_mode < 0 )
flipHoriz( dst.data, dst.step, dst.data, dst.step, dst.size(), esz );
}
void cv::fastNlMeansDenoisingColored( InputArray _src, OutputArray _dst,
float h, float hForColorComponents,
int templateWindowSize, int searchWindowSize)
{
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
if (type != CV_8UC3 && type != CV_8UC4)
{
CV_Error(Error::StsBadArg, "Type of input image should be CV_8UC3!");
return;
}
CV_OCL_RUN(_src.dims() <= 2 && (_dst.isUMat() || _src.isUMat()),
ocl_fastNlMeansDenoisingColored(_src, _dst, h, hForColorComponents,
templateWindowSize, searchWindowSize))
Mat src = _src.getMat();
_dst.create(src.size(), type);
Mat dst = _dst.getMat();
Mat src_lab;
cvtColor(src, src_lab, COLOR_LBGR2Lab);
Mat l(src.size(), CV_8U);
Mat ab(src.size(), CV_8UC2);
Mat l_ab[] = { l, ab };
int from_to[] = { 0,0, 1,1, 2,2 };
mixChannels(&src_lab, 1, l_ab, 2, from_to, 3);
fastNlMeansDenoising(l, l, h, templateWindowSize, searchWindowSize);
fastNlMeansDenoising(ab, ab, hForColorComponents, templateWindowSize, searchWindowSize);
Mat l_ab_denoised[] = { l, ab };
Mat dst_lab(src.size(), CV_MAKE_TYPE(depth, 3));
mixChannels(l_ab_denoised, 2, &dst_lab, 1, from_to, 3);
cvtColor(dst_lab, dst, COLOR_Lab2LBGR, cn);
}
void cv::fastNlMeansDenoising( InputArray _src, OutputArray _dst, float h,
int templateWindowSize, int searchWindowSize)
{
CV_OCL_RUN(_src.dims() <= 2 && (_src.isUMat() || _dst.isUMat()),
ocl_fastNlMeansDenoising(_src, _dst, h, templateWindowSize, searchWindowSize))
Mat src = _src.getMat();
_dst.create(src.size(), src.type());
Mat dst = _dst.getMat();
#ifdef HAVE_TEGRA_OPTIMIZATION
if(tegra::fastNlMeansDenoising(src, dst, h, templateWindowSize, searchWindowSize))
return;
#endif
switch (src.type()) {
case CV_8U:
parallel_for_(cv::Range(0, src.rows),
FastNlMeansDenoisingInvoker<uchar>(
src, dst, templateWindowSize, searchWindowSize, h));
break;
case CV_8UC2:
parallel_for_(cv::Range(0, src.rows),
FastNlMeansDenoisingInvoker<cv::Vec2b>(
src, dst, templateWindowSize, searchWindowSize, h));
break;
case CV_8UC3:
parallel_for_(cv::Range(0, src.rows),
FastNlMeansDenoisingInvoker<cv::Vec3b>(
src, dst, templateWindowSize, searchWindowSize, h));
break;
default:
CV_Error(Error::StsBadArg,
"Unsupported image format! Only CV_8UC1, CV_8UC2 and CV_8UC3 are supported");
}
}
