}
#ifdef HAVE_TEGRA_OPTIMIZATION
- if(sigma1 == 0 && sigma2 == 0 && tegra::gaussian(_src.getMat(), _dst.getMat(), ksize, borderType))
+ Mat src = _src.getMat();
+ Mat dst = _dst.getMat();
+ if(sigma1 == 0 && sigma2 == 0 && tegra::gaussian(src, dst, ksize, borderType))
return;
#endif
Ptr<BaseCascadeClassifier::MaskGenerator> createFaceDetectionMaskGenerator()
{
#ifdef HAVE_TEGRA_OPTIMIZATION
- return tegra::getCascadeClassifierMaskGenerator(*this);
+ return tegra::getCascadeClassifierMaskGenerator();
#else
return Ptr<BaseCascadeClassifier::MaskGenerator>();
#endif
void normalizeUsingWeightMap(InputArray _weight, InputOutputArray _src)
{
#ifdef HAVE_TEGRA_OPTIMIZATION
+ Mat weight = _weight.getMat();
+ Mat src = _src.getMat();
if(tegra::normalizeUsingWeightMap(weight, src))
return;
#endif
!ocl_normalizeUsingWeightMap(_weight, _src) )
#endif
{
- Mat weight = _weight.getMat();
- Mat src = _src.getMat();
-
CV_Assert(src.type() == CV_16SC3);
if(weight.type() == CV_32FC1)
void createLaplacePyr(InputArray img, int num_levels, std::vector<UMat> &pyr)
{
#ifdef HAVE_TEGRA_OPTIMIZATION
- if(tegra::createLaplacePyr(img, num_levels, pyr))
+ cv::Mat imgMat = img.getMat();
+ if(tegra::createLaplacePyr(imgMat, num_levels, pyr))
return;
#endif