Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

created wrappers for new NPP functions

Browse Source 
removed void integral(const GpuMat& src, GpuMat& sum, GpuMat& sqsum, Stream& stream) - it fails with NPP_NOT_IMPLEMENTED error
updated docs, accuracy and performance tests
pull/13383/head
Vladislav Vinogradov 13 years ago
parent e426dfc396
commit 2d30480982
37 changed files with 1993 additions and 575 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 193
      modules/core/src/gpumat.cpp
  2. 56
      modules/gpu/doc/image_processing.rst
  3. 3
      modules/gpu/doc/matrix_reductions.rst
  4. 2
      modules/gpu/doc/operations_on_matrices.rst
  5. 119
      modules/gpu/doc/per_element_operations.rst
  6. 77
      modules/gpu/include/opencv2/gpu/gpu.hpp
  7. 32
      modules/gpu/perf/perf_arithm.cpp
  8. 90
      modules/gpu/perf/perf_imgproc.cpp
  9. 2
      modules/gpu/perf/perf_main.cpp
  10. 1
      modules/gpu/perf/perf_utility.hpp
  11. 127
      modules/gpu/src/arithm.cpp
  12. 16
      modules/gpu/src/color.cpp
  13. 2
      modules/gpu/src/cuda/bilateral_filter.cu
  14. 4
      modules/gpu/src/cuda/element_operations.cu
  15. 1213
      modules/gpu/src/element_operations.cpp
  16. 2
      modules/gpu/src/filtering.cpp
  17. 101
      modules/gpu/src/graphcuts.cpp
  18. 161
      modules/gpu/src/imgproc.cpp
  19. 13
      modules/gpu/src/initialization.cpp
  20. 17
      modules/gpu/src/matrix_reductions.cpp
  21. 2
      modules/gpu/src/optical_flow.cpp
  22. 2
      modules/gpu/src/optical_flow_farneback.cpp
  23. 6
      modules/gpu/src/orb.cpp
  24. 4
      modules/gpu/src/precomp.hpp
  25. 185
      modules/gpu/test/test_arithm.cpp
  26. 43
      modules/gpu/test/test_imgproc.cpp
  27. 2
      modules/gpu/test/test_main.cpp
  28. 1
      modules/highgui/src/window.cpp
  29. 2
      modules/stitching/include/opencv2/stitching/detail/seam_finders.hpp
  30. 2
      modules/stitching/src/seam_finders.cpp
  31. 2
      samples/cpp/point_cloud.cpp
  32. 68
      samples/gpu/alpha_comp.cpp
  33. 2
      samples/gpu/brox_optical_flow.cpp
  34. 6
      samples/gpu/farneback_optical_flow.cpp
  35. 2
      samples/gpu/performance/performance.cpp
  36. 6
      samples/gpu/performance/performance.h
  37. 2
      samples/gpu/performance/tests.cpp

193
modules/core/src/gpumat.cpp
Unescape View File

@ -48,6 +48,17 @@
#ifdef HAVE_CUDA
#include <cuda_runtime.h>
#include <npp.h>
#define CUDART_MINIMUM_REQUIRED_VERSION 4010
#define NPP_MINIMUM_REQUIRED_VERSION 4100
#if (CUDART_VERSION < CUDART_MINIMUM_REQUIRED_VERSION)
#error "Insufficient Cuda Runtime library version, please update it."
#endif
#if (NPP_VERSION_MAJOR * 1000 + NPP_VERSION_MINOR * 100 + NPP_VERSION_BUILD < NPP_MINIMUM_REQUIRED_VERSION)
#error "Insufficient NPP version, please update it."
#endif
#endif
using namespace std;
@ -460,15 +471,17 @@ namespace cv { namespace gpu
namespace
{
//////////////////////////////////////////////////////////////////////////
// Convert
template<int n> struct NPPTypeTraits;
template<> struct NPPTypeTraits<CV_8U> { typedef Npp8u npp_type; };
template<> struct NPPTypeTraits<CV_8S> { typedef Npp8s npp_type; };
template<> struct NPPTypeTraits<CV_16U> { typedef Npp16u npp_type; };
template<> struct NPPTypeTraits<CV_16S> { typedef Npp16s npp_type; };
template<> struct NPPTypeTraits<CV_32S> { typedef Npp32s npp_type; };
template<> struct NPPTypeTraits<CV_32F> { typedef Npp32f npp_type; };
template<> struct NPPTypeTraits<CV_64F> { typedef Npp64f npp_type; };
//////////////////////////////////////////////////////////////////////////
// Convert
template<int SDEPTH, int DDEPTH> struct NppConvertFunc
{
@ -494,6 +507,7 @@ namespace
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), dst.ptr<dst_t>(), static_cast<int>(dst.step), sz) );
cudaSafeCall( cudaDeviceSynchronize() );
@ -508,6 +522,7 @@ namespace
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( func(src.ptr<Npp32f>(), static_cast<int>(src.step), dst.ptr<dst_t>(), static_cast<int>(dst.step), sz, NPP_RND_NEAR) );
cudaSafeCall( cudaDeviceSynchronize() );
@ -529,6 +544,14 @@ namespace
typedef NppStatus (*func_ptr)(src_t val, src_t* pSrc, int nSrcStep, NppiSize oSizeROI);
};
template<int SCN> struct NppSetFunc<CV_8S, SCN>
{
typedef NppStatus (*func_ptr)(Npp8s values[], Npp8s* pSrc, int nSrcStep, NppiSize oSizeROI);
};
template<> struct NppSetFunc<CV_8S, 1>
{
typedef NppStatus (*func_ptr)(Npp8s val, Npp8s* pSrc, int nSrcStep, NppiSize oSizeROI);
};
template<int SDEPTH, int SCN, typename NppSetFunc<SDEPTH, SCN>::func_ptr func> struct NppSet
{
@ -613,6 +636,35 @@ namespace
}
};
//////////////////////////////////////////////////////////////////////////
// CopyMasked
template<int SDEPTH> struct NppCopyMaskedFunc
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef NppStatus (*func_ptr)(const src_t* pSrc, int nSrcStep, src_t* pDst, int nDstStep, NppiSize oSizeROI, const Npp8u* pMask, int nMaskStep);
};
template<int SDEPTH, typename NppCopyMaskedFunc<SDEPTH>::func_ptr func> struct NppCopyMasked
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
static void copyMasked(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t /*stream*/)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), dst.ptr<src_t>(), static_cast<int>(dst.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
//////////////////////////////////////////////////////////////////////////
// CudaFuncTable
class CudaFuncTable : public GpuFuncTable
{
public:
@ -631,7 +683,26 @@ namespace
void copyWithMask(const GpuMat& src, GpuMat& dst, const GpuMat& mask) const
{
::cv::gpu::copyWithMask(src, dst, mask);
CV_Assert(src.size() == dst.size() && src.type() == dst.type());
CV_Assert(src.size() == mask.size() && mask.depth() == CV_8U && (mask.channels() == 1 || mask.channels() == src.channels()));
typedef void (*caller_t)(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream);
static const caller_t callers[7][4] =
{
/* 8U */ {NppCopyMasked<CV_8U, nppiCopy_8u_C1MR>::copyMasked, cv::gpu::copyWithMask, NppCopyMasked<CV_8U, nppiCopy_8u_C3MR>::copyMasked, NppCopyMasked<CV_8U, nppiCopy_8u_C4MR>::copyMasked},
/* 8S */ {cv::gpu::copyWithMask, cv::gpu::copyWithMask, cv::gpu::copyWithMask, cv::gpu::copyWithMask},
/* 16U */ {NppCopyMasked<CV_16U, nppiCopy_16u_C1MR>::copyMasked, cv::gpu::copyWithMask, NppCopyMasked<CV_16U, nppiCopy_16u_C3MR>::copyMasked, NppCopyMasked<CV_16U, nppiCopy_16u_C4MR>::copyMasked},
/* 16S */ {NppCopyMasked<CV_16S, nppiCopy_16s_C1MR>::copyMasked, cv::gpu::copyWithMask, NppCopyMasked<CV_16S, nppiCopy_16s_C3MR>::copyMasked, NppCopyMasked<CV_16S, nppiCopy_16s_C4MR>::copyMasked},
/* 32S */ {NppCopyMasked<CV_32S, nppiCopy_32s_C1MR>::copyMasked, cv::gpu::copyWithMask, NppCopyMasked<CV_32S, nppiCopy_32s_C3MR>::copyMasked, NppCopyMasked<CV_32S, nppiCopy_32s_C4MR>::copyMasked},
/* 32F */ {NppCopyMasked<CV_32F, nppiCopy_32f_C1MR>::copyMasked, cv::gpu::copyWithMask, NppCopyMasked<CV_32F, nppiCopy_32f_C3MR>::copyMasked, NppCopyMasked<CV_32F, nppiCopy_32f_C4MR>::copyMasked},
/* 64F */ {cv::gpu::copyWithMask, cv::gpu::copyWithMask, cv::gpu::copyWithMask, cv::gpu::copyWithMask}
};
caller_t func = mask.channels() == src.channels() ? callers[src.depth()][src.channels()] : cv::gpu::copyWithMask;
CV_DbgAssert(func != 0);
func(src, dst, mask, 0);
}
void convert(const GpuMat& src, GpuMat& dst) const
@ -641,65 +712,65 @@ namespace
{
{
/* 8U -> 8U */ {0, 0, 0, 0},
/* 8U -> 8S */ {::cv::gpu::convertTo, ::cv::gpu::convertTo, ::cv::gpu::convertTo, ::cv::gpu::convertTo},
/* 8U -> 16U */ {NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C1R>::cvt,::cv::gpu::convertTo,::cv::gpu::convertTo,NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C4R>::cvt},
/* 8U -> 16S */ {NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C1R>::cvt,::cv::gpu::convertTo,::cv::gpu::convertTo,NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C4R>::cvt},
/* 8U -> 32S */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 8U -> 32F */ {NppCvt<CV_8U, CV_32F, nppiConvert_8u32f_C1R>::cvt,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 8U -> 64F */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo}
/* 8U -> 8S */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 8U -> 16U */ {NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C1R>::cvt,cv::gpu::convertTo,cv::gpu::convertTo,NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C4R>::cvt},
/* 8U -> 16S */ {NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C1R>::cvt,cv::gpu::convertTo,cv::gpu::convertTo,NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C4R>::cvt},
/* 8U -> 32S */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 8U -> 32F */ {NppCvt<CV_8U, CV_32F, nppiConvert_8u32f_C1R>::cvt,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 8U -> 64F */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo}
},
{
/* 8S -> 8U */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 8S -> 8U */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 8S -> 8S */ {0,0,0,0},
/* 8S -> 16U */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 8S -> 16S */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 8S -> 32S */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 8S -> 32F */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 8S -> 64F */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo}
/* 8S -> 16U */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 8S -> 16S */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 8S -> 32S */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 8S -> 32F */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 8S -> 64F */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo}
},
{
/* 16U -> 8U */ {NppCvt<CV_16U, CV_8U, nppiConvert_16u8u_C1R>::cvt,::cv::gpu::convertTo,::cv::gpu::convertTo,NppCvt<CV_16U, CV_8U, nppiConvert_16u8u_C4R>::cvt},
/* 16U -> 8S */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 16U -> 8U */ {NppCvt<CV_16U, CV_8U, nppiConvert_16u8u_C1R>::cvt,cv::gpu::convertTo,cv::gpu::convertTo,NppCvt<CV_16U, CV_8U, nppiConvert_16u8u_C4R>::cvt},
/* 16U -> 8S */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 16U -> 16U */ {0,0,0,0},
/* 16U -> 16S */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 16U -> 32S */ {NppCvt<CV_16U, CV_32S, nppiConvert_16u32s_C1R>::cvt,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 16U -> 32F */ {NppCvt<CV_16U, CV_32F, nppiConvert_16u32f_C1R>::cvt,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 16U -> 64F */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo}
/* 16U -> 16S */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 16U -> 32S */ {NppCvt<CV_16U, CV_32S, nppiConvert_16u32s_C1R>::cvt,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 16U -> 32F */ {NppCvt<CV_16U, CV_32F, nppiConvert_16u32f_C1R>::cvt,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 16U -> 64F */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo}
},
{
/* 16S -> 8U */ {NppCvt<CV_16S, CV_8U, nppiConvert_16s8u_C1R>::cvt,::cv::gpu::convertTo,::cv::gpu::convertTo,NppCvt<CV_16S, CV_8U, nppiConvert_16s8u_C4R>::cvt},
/* 16S -> 8S */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 16S -> 16U */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 16S -> 8U */ {NppCvt<CV_16S, CV_8U, nppiConvert_16s8u_C1R>::cvt,cv::gpu::convertTo,cv::gpu::convertTo,NppCvt<CV_16S, CV_8U, nppiConvert_16s8u_C4R>::cvt},
/* 16S -> 8S */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 16S -> 16U */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 16S -> 16S */ {0,0,0,0},
/* 16S -> 32S */ {NppCvt<CV_16S, CV_32S, nppiConvert_16s32s_C1R>::cvt,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 16S -> 32F */ {NppCvt<CV_16S, CV_32F, nppiConvert_16s32f_C1R>::cvt,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 16S -> 64F */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo}
/* 16S -> 32S */ {NppCvt<CV_16S, CV_32S, nppiConvert_16s32s_C1R>::cvt,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 16S -> 32F */ {NppCvt<CV_16S, CV_32F, nppiConvert_16s32f_C1R>::cvt,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 16S -> 64F */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo}
},
{
/* 32S -> 8U */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 32S -> 8S */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 32S -> 16U */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 32S -> 16S */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 32S -> 8U */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 32S -> 8S */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 32S -> 16U */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 32S -> 16S */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 32S -> 32S */ {0,0,0,0},
/* 32S -> 32F */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 32S -> 64F */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo}
/* 32S -> 32F */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 32S -> 64F */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo}
},
{
/* 32F -> 8U */ {NppCvt<CV_32F, CV_8U, nppiConvert_32f8u_C1R>::cvt,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 32F -> 8S */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 32F -> 16U */ {NppCvt<CV_32F, CV_16U, nppiConvert_32f16u_C1R>::cvt,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 32F -> 16S */ {NppCvt<CV_32F, CV_16S, nppiConvert_32f16s_C1R>::cvt,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 32F -> 32S */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 32F -> 8U */ {NppCvt<CV_32F, CV_8U, nppiConvert_32f8u_C1R>::cvt,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 32F -> 8S */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 32F -> 16U */ {NppCvt<CV_32F, CV_16U, nppiConvert_32f16u_C1R>::cvt,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 32F -> 16S */ {NppCvt<CV_32F, CV_16S, nppiConvert_32f16s_C1R>::cvt,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 32F -> 32S */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 32F -> 32F */ {0,0,0,0},
/* 32F -> 64F */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo}
/* 32F -> 64F */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo}
},
{
/* 64F -> 8U */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 64F -> 8S */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 64F -> 16U */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 64F -> 16S */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 64F -> 32S */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 64F -> 32F */ {::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo,::cv::gpu::convertTo},
/* 64F -> 8U */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 64F -> 8S */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 64F -> 16U */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 64F -> 16S */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 64F -> 32S */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 64F -> 32F */ {cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo,cv::gpu::convertTo},
/* 64F -> 64F */ {0,0,0,0}
}
};
@ -712,7 +783,7 @@ namespace
void convert(const GpuMat& src, GpuMat& dst, double alpha, double beta) const
{
::cv::gpu::convertTo(src, dst, alpha, beta);
cv::gpu::convertTo(src, dst, alpha, beta);
}
void setTo(GpuMat& m, Scalar s, const GpuMat& mask) const
@ -744,13 +815,13 @@ namespace
typedef void (*caller_t)(GpuMat& src, Scalar s);
static const caller_t callers[7][4] =
{
{NppSet<CV_8U, 1, nppiSet_8u_C1R>::set, ::cv::gpu::setTo, ::cv::gpu::setTo, NppSet<CV_8U, 4, nppiSet_8u_C4R>::set},
{::cv::gpu::setTo, ::cv::gpu::setTo, ::cv::gpu::setTo, ::cv::gpu::setTo},
{NppSet<CV_16U, 1, nppiSet_16u_C1R>::set, NppSet<CV_16U, 2, nppiSet_16u_C2R>::set, ::cv::gpu::setTo, NppSet<CV_16U, 4, nppiSet_16u_C4R>::set},
{NppSet<CV_16S, 1, nppiSet_16s_C1R>::set, NppSet<CV_16S, 2, nppiSet_16s_C2R>::set, ::cv::gpu::setTo, NppSet<CV_16S, 4, nppiSet_16s_C4R>::set},
{NppSet<CV_32S, 1, nppiSet_32s_C1R>::set, ::cv::gpu::setTo, ::cv::gpu::setTo, NppSet<CV_32S, 4, nppiSet_32s_C4R>::set},
{NppSet<CV_32F, 1, nppiSet_32f_C1R>::set, ::cv::gpu::setTo, ::cv::gpu::setTo, NppSet<CV_32F, 4, nppiSet_32f_C4R>::set},
{::cv::gpu::setTo, ::cv::gpu::setTo, ::cv::gpu::setTo, ::cv::gpu::setTo}
{NppSet<CV_8U, 1, nppiSet_8u_C1R>::set, cv::gpu::setTo, cv::gpu::setTo, NppSet<CV_8U, 4, nppiSet_8u_C4R>::set},
{NppSet<CV_8S, 1, nppiSet_8s_C1R>::set, NppSet<CV_8S, 2, nppiSet_8s_C2R>::set, NppSet<CV_8S, 3, nppiSet_8s_C3R>::set, NppSet<CV_8S, 4, nppiSet_8s_C4R>::set},
{NppSet<CV_16U, 1, nppiSet_16u_C1R>::set, NppSet<CV_16U, 2, nppiSet_16u_C2R>::set, cv::gpu::setTo, NppSet<CV_16U, 4, nppiSet_16u_C4R>::set},
{NppSet<CV_16S, 1, nppiSet_16s_C1R>::set, NppSet<CV_16S, 2, nppiSet_16s_C2R>::set, cv::gpu::setTo, NppSet<CV_16S, 4, nppiSet_16s_C4R>::set},
{NppSet<CV_32S, 1, nppiSet_32s_C1R>::set, cv::gpu::setTo, cv::gpu::setTo, NppSet<CV_32S, 4, nppiSet_32s_C4R>::set},
{NppSet<CV_32F, 1, nppiSet_32f_C1R>::set, cv::gpu::setTo, cv::gpu::setTo, NppSet<CV_32F, 4, nppiSet_32f_C4R>::set},
{cv::gpu::setTo, cv::gpu::setTo, cv::gpu::setTo, cv::gpu::setTo}
};
callers[m.depth()][m.channels() - 1](m, s);
@ -761,13 +832,13 @@ namespace
static const caller_t callers[7][4] =
{
{NppSetMask<CV_8U, 1, nppiSet_8u_C1MR>::set, ::cv::gpu::setTo, ::cv::gpu::setTo, NppSetMask<CV_8U, 4, nppiSet_8u_C4MR>::set},
{::cv::gpu::setTo, ::cv::gpu::setTo, ::cv::gpu::setTo, ::cv::gpu::setTo},
{NppSetMask<CV_16U, 1, nppiSet_16u_C1MR>::set, ::cv::gpu::setTo, ::cv::gpu::setTo, NppSetMask<CV_16U, 4, nppiSet_16u_C4MR>::set},
{NppSetMask<CV_16S, 1, nppiSet_16s_C1MR>::set, ::cv::gpu::setTo, ::cv::gpu::setTo, NppSetMask<CV_16S, 4, nppiSet_16s_C4MR>::set},
{NppSetMask<CV_32S, 1, nppiSet_32s_C1MR>::set, ::cv::gpu::setTo, ::cv::gpu::setTo, NppSetMask<CV_32S, 4, nppiSet_32s_C4MR>::set},
{NppSetMask<CV_32F, 1, nppiSet_32f_C1MR>::set, ::cv::gpu::setTo, ::cv::gpu::setTo, NppSetMask<CV_32F, 4, nppiSet_32f_C4MR>::set},
{::cv::gpu::setTo, ::cv::gpu::setTo, ::cv::gpu::setTo, ::cv::gpu::setTo}
{NppSetMask<CV_8U, 1, nppiSet_8u_C1MR>::set, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_8U, 4, nppiSet_8u_C4MR>::set},
{cv::gpu::setTo, cv::gpu::setTo, cv::gpu::setTo, cv::gpu::setTo},
{NppSetMask<CV_16U, 1, nppiSet_16u_C1MR>::set, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_16U, 4, nppiSet_16u_C4MR>::set},
{NppSetMask<CV_16S, 1, nppiSet_16s_C1MR>::set, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_16S, 4, nppiSet_16s_C4MR>::set},
{NppSetMask<CV_32S, 1, nppiSet_32s_C1MR>::set, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_32S, 4, nppiSet_32s_C4MR>::set},
{NppSetMask<CV_32F, 1, nppiSet_32f_C1MR>::set, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_32F, 4, nppiSet_32f_C4MR>::set},
{cv::gpu::setTo, cv::gpu::setTo, cv::gpu::setTo, cv::gpu::setTo}
};
callers[m.depth()][m.channels() - 1](m, s, mask);

56
modules/gpu/doc/image_processing.rst
Unescape View File

@ -69,18 +69,14 @@ Performs a mean-shift segmentation of the source image and eliminates small segm
gpu::integral
-----------------
Computes an integral image and a squared integral image.
Computes an integral image.
.. ocv:function:: void gpu::integral(const GpuMat& src, GpuMat& sum, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::integral(const GpuMat& src, GpuMat& sum, GpuMat& sqsum, Stream& stream = Stream::Null())
:param src: Source image. Only ``CV_8UC1`` images are supported for now.
:param sum: Integral image containing 32-bit unsigned integer values packed into ``CV_32SC1`` .
:param sqsum: Squared integral image of the ``CV_32FC1`` type.
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`integral`
@ -380,6 +376,22 @@ Converts an image from one color space to another.
gpu::swapChannels
-----------------
Exchanges the color channels of an image in-place.
.. ocv:function:: void gpu::swapChannels(GpuMat& image, const int dstOrder[4], Stream& stream = Stream::Null())
:param src: Source image. Supports only ``CV_8UC4`` type.
:param dstOrder: Integer array describing how channel values are permutated. The n-th entry of the array contains the number of the channel that is stored in the n-th channel of the output image. E.g. Given an RGBA image, aDstOrder = [3,2,1,0] converts this to ABGR channel order.
:param stream: Stream for the asynchronous version.
The methods support arbitrary permutations of the original channels, including replication.
gpu::threshold
------------------
Applies a fixed-level threshold to each array element.
@ -489,7 +501,7 @@ Rotates an image around the origin (0,0) and then shifts it.
.. ocv:function:: void gpu::rotate(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift = 0, double yShift = 0, int interpolation = INTER_LINEAR, Stream& stream = Stream::Null())
:param src: Source image. ``CV_8UC1`` and ``CV_8UC4`` types are supported.
:param src: Source image. Supports 1, 3 or 4 channels images with ``CV_8U`` , ``CV_16U`` or ``CV_32F`` depth.
:param dst: Destination image with the same type as ``src`` . The size is ``dsize`` .
@ -751,6 +763,38 @@ Performs linear blending of two images.
gpu::alphaComp
-------------------
Composites two images using alpha opacity values contained in each image.
.. ocv:function:: void gpu::alphaComp(const GpuMat& img1, const GpuMat& img2, GpuMat& dst, int alpha_op, Stream& stream = Stream::Null())
:param img1: First image. Supports ``CV_8UC4`` , ``CV_16UC4`` , ``CV_32SC4`` and ``CV_32FC4`` types.
:param img1: Second image. Must have the same size and the same type as ``img1`` .
:param dst: Destination image.
:param alpha_op: Flag specifying the alpha-blending operation:
* **ALPHA_OVER**
* **ALPHA_IN**
* **ALPHA_OUT**
* **ALPHA_ATOP**
* **ALPHA_XOR**
* **ALPHA_PLUS**
* **ALPHA_OVER_PREMUL**
* **ALPHA_IN_PREMUL**
* **ALPHA_OUT_PREMUL**
* **ALPHA_ATOP_PREMUL**
* **ALPHA_XOR_PREMUL**
* **ALPHA_PLUS_PREMUL**
* **ALPHA_PREMUL**
:param stream: Stream for the asynchronous version.
gpu::Canny
-------------------
Finds edges in an image using the [Canny86]_ algorithm.

3
modules/gpu/doc/matrix_reductions.rst
Unescape View File

@ -10,6 +10,7 @@ gpu::meanStdDev
Computes a mean value and a standard deviation of matrix elements.
.. ocv:function:: void gpu::meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev)
.. ocv:function:: void gpu::meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev, GpuMat& buf);
:param mtx: Source matrix. ``CV_8UC1`` matrices are supported for now.
@ -17,6 +18,8 @@ Computes a mean value and a standard deviation of matrix elements.
:param stddev: Standard deviation value.
:param buf: Optional buffer to avoid extra memory allocations. It is resized automatically.
.. seealso:: :ocv:func:`meanStdDev`

2
modules/gpu/doc/operations_on_matrices.rst
Unescape View File

@ -63,7 +63,7 @@ Flips a 2D matrix around vertical, horizontal, or both axes.
.. ocv:function:: void gpu::flip(const GpuMat& src, GpuMat& dst, int flipCode, Stream& stream = Stream::Null())
:param src: Source matrix. Only ``CV_8UC1`` and ``CV_8UC4`` matrices are supported for now.
:param src: Source matrix. Supports 1, 3 and 4 channels images with ``CV_8U``, ``CV_16U``, ``CV_32S`` or ``CV_32F`` depth.
:param dst: Destination matrix.

119
modules/gpu/doc/per_element_operations.rst
Unescape View File

@ -139,45 +139,65 @@ where ``I`` is a multi-dimensional index of array elements. In case of multi-cha
gpu::exp
gpu::abs
------------
Computes an exponent of each matrix element.
Computes an absolute value of each matrix element.
.. ocv:function:: void gpu::exp(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::abs(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null())
:param src: Source matrix. ``CV_32FC1`` matrixes are supported for now.
:param src: Source matrix. Supports ``CV_16S`` and ``CV_32F`` depth.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`exp`
.. seealso:: :ocv:func:`abs`
gpu::pow
gpu::sqr
------------
Raises every matrix element to a power.
Computes a square value of each matrix element.
.. ocv:function:: void gpu::pow(const GpuMat& src, double power, GpuMat& dst, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::sqr(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null())
:param src: Source matrix. Supports ``CV_8U`` , ``CV_16U`` , ``CV_16S`` and ``CV_32F`` depth.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
:param src: Source matrix. Supports all type, except ``CV_64F`` depth.
:param power: Exponent of power.
gpu::sqrt
------------
Computes a square root of each matrix element.
.. ocv:function:: void gpu::sqrt(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null())
:param src: Source matrix. Supports ``CV_8U`` , ``CV_16U`` , ``CV_16S`` and ``CV_32F`` depth.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
The function ``pow`` raises every element of the input matrix to ``p`` :
.. seealso:: :ocv:func:`sqrt`
.. math::
\texttt{dst} (I) = \fork{\texttt{src}(I)^p}{if \texttt{p} is integer}{|\texttt{src}(I)|^p}{otherwise}
.. seealso:: :ocv:func:`pow`
gpu::exp
------------
Computes an exponent of each matrix element.
.. ocv:function:: void gpu::exp(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null())
:param src: Source matrix. Supports ``CV_8U`` , ``CV_16U`` , ``CV_16S`` and ``CV_32F`` depth.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`exp`
@ -187,7 +207,7 @@ Computes a natural logarithm of absolute value of each matrix element.
.. ocv:function:: void gpu::log(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null())
:param src: Source matrix. ``CV_32FC1`` matrixes are supported for now.
:param src: Source matrix. Supports ``CV_8U`` , ``CV_16U`` , ``CV_16S`` and ``CV_32F`` depth.
:param dst: Destination matrix with the same size and type as ``src`` .
@ -197,6 +217,30 @@ Computes a natural logarithm of absolute value of each matrix element.
gpu::pow
------------
Raises every matrix element to a power.
.. ocv:function:: void gpu::pow(const GpuMat& src, double power, GpuMat& dst, Stream& stream = Stream::Null())
:param src: Source matrix. Supports all type, except ``CV_64F`` depth.
:param power: Exponent of power.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
The function ``pow`` raises every element of the input matrix to ``p`` :
.. math::
\texttt{dst} (I) = \fork{\texttt{src}(I)^p}{if \texttt{p} is integer}{|\texttt{src}(I)|^p}{otherwise}
.. seealso:: :ocv:func:`pow`
gpu::absdiff
----------------
Computes per-element absolute difference of two matrices (or of a matrix and scalar).
@ -262,9 +306,10 @@ Performs a per-element bitwise inversion.
gpu::bitwise_or
-------------------
Performs a per-element bitwise disjunction of two matrices.
Performs a per-element bitwise disjunction of two matrices or of matrix and scalar.
.. ocv:function:: void gpu::bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null())
.. ocv:function:: void gpu::bitwise_or(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null())
:param src1: First source matrix.
@ -280,9 +325,10 @@ Performs a per-element bitwise disjunction of two matrices.
gpu::bitwise_and
--------------------
Performs a per-element bitwise conjunction of two matrices.
Performs a per-element bitwise conjunction of two matrices or of matrix and scalar.
.. ocv:function:: void gpu::bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null())
.. ocv:function:: void gpu::bitwise_and(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null())
:param src1: First source matrix.
@ -298,9 +344,10 @@ Performs a per-element bitwise conjunction of two matrices.
gpu::bitwise_xor
--------------------
Performs a per-element bitwise ``exclusive or`` operation of two matrices.
Performs a per-element bitwise ``exclusive or`` operation of two matrices of matrix and scalar.
.. ocv:function:: void gpu::bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null())
.. ocv:function:: void gpu::bitwise_xor(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null())
:param src1: First source matrix.
@ -314,6 +361,38 @@ Performs a per-element bitwise ``exclusive or`` operation of two matrices.
gpu::rshift
--------------------
Performs pixel by pixel right shift of an image by a constant value.
.. ocv:function:: void gpu::rshift(const GpuMat& src, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null())
:param src: Source matrix. Supports 1, 3 and 4 channels images with integers elements.
:param sc: Constant values, one per channel.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
gpu::lshift
--------------------
Performs pixel by pixel right left of an image by a constant value.
.. ocv:function:: void gpu::lshift(const GpuMat& src, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null())
:param src: Source matrix. Supports 1, 3 and 4 channels images with ``CV_8U`` , ``CV_16U`` or ``CV_32S`` depth.
:param sc: Constant values, one per channel.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
gpu::min
------------
Computes the per-element minimum of two matrices (or a matrix and a scalar).

77
modules/gpu/include/opencv2/gpu/gpu.hpp
Unescape View File

@ -498,7 +498,7 @@ CV_EXPORTS void gemm(const GpuMat& src1, const GpuMat& src2, double alpha,
CV_EXPORTS void transpose(const GpuMat& src1, GpuMat& dst, Stream& stream = Stream::Null());
//! reverses the order of the rows, columns or both in a matrix
//! supports CV_8UC1, CV_8UC4 types
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U, CV_32S or CV_32F depth
CV_EXPORTS void flip(const GpuMat& a, GpuMat& b, int flipCode, Stream& stream = Stream::Null());
//! transforms 8-bit unsigned integers using lookup table: dst(i)=lut(src(i))
@ -586,20 +586,32 @@ CV_EXPORTS void absdiff(const GpuMat& a, const GpuMat& b, GpuMat& c, Stream& str
//! computes element-wise absolute difference of array and scalar (c = abs(a - s))
CV_EXPORTS void absdiff(const GpuMat& a, const Scalar& s, GpuMat& c, Stream& stream = Stream::Null());
//! computes absolute value of each matrix element
//! supports CV_16S and CV_32F depth
CV_EXPORTS void abs(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
//! computes square of each pixel in an image
//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
CV_EXPORTS void sqr(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
//! computes square root of each pixel in an image
//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
CV_EXPORTS void sqrt(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
//! computes exponent of each matrix element (b = e**a)
//! supports only CV_32FC1 type
//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
CV_EXPORTS void exp(const GpuMat& a, GpuMat& b, Stream& stream = Stream::Null());
//! computes natural logarithm of absolute value of each matrix element: b = log(abs(a))
//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
CV_EXPORTS void log(const GpuMat& a, GpuMat& b, Stream& stream = Stream::Null());
//! computes power of each matrix element:
// (dst(i,j) = pow( src(i,j) , power), if src.type() is integer
// (dst(i,j) = pow(fabs(src(i,j)), power), otherwise
//! supports all, except depth == CV_64F
CV_EXPORTS void pow(const GpuMat& src, double power, GpuMat& dst, Stream& stream = Stream::Null());
//! computes natural logarithm of absolute value of each matrix element: b = log(abs(a))
//! supports only CV_32FC1 type
CV_EXPORTS void log(const GpuMat& a, GpuMat& b, Stream& stream = Stream::Null());
//! compares elements of two arrays (c = a <cmpop> b)
CV_EXPORTS void compare(const GpuMat& a, const GpuMat& b, GpuMat& c, int cmpop, Stream& stream = Stream::Null());
@ -608,12 +620,29 @@ CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask=G
//! calculates per-element bit-wise disjunction of two arrays
CV_EXPORTS void bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
//! calculates per-element bit-wise disjunction of array and scalar
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
CV_EXPORTS void bitwise_or(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
//! calculates per-element bit-wise conjunction of two arrays
CV_EXPORTS void bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
//! calculates per-element bit-wise conjunction of array and scalar
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
CV_EXPORTS void bitwise_and(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
//! calculates per-element bit-wise "exclusive or" operation
CV_EXPORTS void bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
//! calculates per-element bit-wise "exclusive or" of array and scalar
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
CV_EXPORTS void bitwise_xor(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
//! pixel by pixel right shift of an image by a constant value
//! supports 1, 3 and 4 channels images with integers elements
CV_EXPORTS void rshift(const GpuMat& src, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
//! pixel by pixel left shift of an image by a constant value
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
CV_EXPORTS void lshift(const GpuMat& src, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
//! computes per-element minimum of two arrays (dst = min(src1, src2))
CV_EXPORTS void min(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null());
@ -627,6 +656,13 @@ CV_EXPORTS void max(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream&
//! computes per-element maximum of array and scalar (dst = max(src1, src2))
CV_EXPORTS void max(const GpuMat& src1, double src2, GpuMat& dst, Stream& stream = Stream::Null());
enum { ALPHA_OVER, ALPHA_IN, ALPHA_OUT, ALPHA_ATOP, ALPHA_XOR, ALPHA_PLUS, ALPHA_OVER_PREMUL, ALPHA_IN_PREMUL, ALPHA_OUT_PREMUL,
ALPHA_ATOP_PREMUL, ALPHA_XOR_PREMUL, ALPHA_PLUS_PREMUL, ALPHA_PREMUL};
//! Composite two images using alpha opacity values contained in each image
//! Supports CV_8UC4, CV_16UC4, CV_32SC4 and CV_32FC4 types
CV_EXPORTS void alphaComp(const GpuMat& img1, const GpuMat& img2, GpuMat& dst, int alpha_op, Stream& stream = Stream::Null());
////////////////////////////// Image processing //////////////////////////////
@ -665,6 +701,13 @@ CV_EXPORTS void reprojectImageTo3D(const GpuMat& disp, GpuMat& xyzw, const Mat&
//! converts image from one color space to another
CV_EXPORTS void cvtColor(const GpuMat& src, GpuMat& dst, int code, int dcn = 0, Stream& stream = Stream::Null());
//! swap channels
//! dstOrder - Integer array describing how channel values are permutated. The n-th entry
//! of the array contains the number of the channel that is stored in the n-th channel of
//! the output image. E.g. Given an RGBA image, aDstOrder = [3,2,1,0] converts this to ABGR
//! channel order.
CV_EXPORTS void swapChannels(GpuMat& image, const int dstOrder[4], Stream& stream = Stream::Null());
//! applies fixed threshold to the image
CV_EXPORTS double threshold(const GpuMat& src, GpuMat& dst, double thresh, double maxval, int type, Stream& stream = Stream::Null());
@ -692,9 +735,9 @@ CV_EXPORTS void buildWarpCylindricalMaps(Size src_size, Rect dst_roi, const Mat
CV_EXPORTS void buildWarpSphericalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, float scale,
GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null());
//! rotate 8bit single or four channel image
//! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
//! supports CV_8UC1, CV_8UC4 types
//! rotates an image around the origin (0,0) and then shifts it
//! supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
//! supports 1, 3 or 4 channels images with CV_8U, CV_16U or CV_32F depth
CV_EXPORTS void rotate(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift = 0, double yShift = 0,
int interpolation = INTER_LINEAR, Stream& stream = Stream::Null());
@ -706,15 +749,9 @@ CV_EXPORTS void copyMakeBorder(const GpuMat& src, GpuMat& dst, int top, int bott
//! sum will have CV_32S type, but will contain unsigned int values
//! supports only CV_8UC1 source type
CV_EXPORTS void integral(const GpuMat& src, GpuMat& sum, Stream& stream = Stream::Null());
//! buffered version
CV_EXPORTS void integralBuffered(const GpuMat& src, GpuMat& sum, GpuMat& buffer, Stream& stream = Stream::Null());
//! computes the integral image and integral for the squared image
//! sum will have CV_32S type, sqsum - CV32F type
//! supports only CV_8UC1 source type
CV_EXPORTS void integral(const GpuMat& src, GpuMat& sum, GpuMat& sqsum, Stream& stream = Stream::Null());
//! computes squared integral image
//! result matrix will have 64F type, but will contain 64U values
//! supports source images of 8UC1 type only
@ -859,6 +896,8 @@ private:
//! computes mean value and standard deviation of all or selected array elements
//! supports only CV_8UC1 type
CV_EXPORTS void meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev);
//! buffered version
CV_EXPORTS void meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev, GpuMat& buf);
//! computes norm of array
//! supports NORM_INF, NORM_L1, NORM_L2
@ -939,10 +978,16 @@ CV_EXPORTS void solvePnPRansac(const Mat& object, const Mat& image, const Mat& c
//////////////////////////////// Image Labeling ////////////////////////////////
//!performs labeling via graph cuts
//!performs labeling via graph cuts of a 2D regular 4-connected graph.
CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& bottom, GpuMat& labels,
GpuMat& buf, Stream& stream = Stream::Null());
//!performs labeling via graph cuts of a 2D regular 8-connected graph.
CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& topLeft, GpuMat& topRight,
GpuMat& bottom, GpuMat& bottomLeft, GpuMat& bottomRight,
GpuMat& labels,
GpuMat& buf, Stream& stream = Stream::Null());
////////////////////////////////// Histograms //////////////////////////////////
//! Compute levels with even distribution. levels will have 1 row and nLevels cols and CV_32SC1 type.

32
modules/gpu/perf/perf_arithm.cpp
Unescape View File

@ -59,7 +59,7 @@ GPU_PERF_TEST(Flip, cv::gpu::DeviceInfo, cv::Size, perf::MatType, FlipCode)
INSTANTIATE_TEST_CASE_P(Arithm, Flip, testing::Combine(
ALL_DEVICES,
GPU_TYPICAL_MAT_SIZES,
testing::Values(CV_8UC1, CV_8UC4),
testing::Values(CV_8UC1, CV_8UC4, CV_32FC1, CV_32FC4),
testing::Values((int) HORIZONTAL_AXIS, (int) VERTICAL_AXIS, (int) BOTH_AXIS)));
//////////////////////////////////////////////////////////////////////
@ -363,6 +363,33 @@ INSTANTIATE_TEST_CASE_P(Arithm, BitwiseAnd, testing::Combine(
GPU_TYPICAL_MAT_SIZES,
testing::Values(CV_8UC1, CV_16UC1, CV_32SC1)));
GPU_PERF_TEST(BitwiseScalarAnd, cv::gpu::DeviceInfo, cv::Size, perf::MatType)
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::Size size = GET_PARAM(1);
int type = GET_PARAM(2);
cv::gpu::setDevice(devInfo.deviceID());
cv::Mat src_host(size, type);
declare.in(src_host, WARMUP_RNG);
cv::gpu::GpuMat src(src_host);
cv::gpu::GpuMat dst;
cv::Scalar sc = cv::Scalar(123, 123, 123, 123);
TEST_CYCLE()
{
cv::gpu::bitwise_and(src, sc, dst);
}
}
INSTANTIATE_TEST_CASE_P(Arithm, BitwiseScalarAnd, testing::Combine(
ALL_DEVICES,
GPU_TYPICAL_MAT_SIZES,
testing::Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_16UC1, CV_16UC3, CV_16UC4, CV_32SC1, CV_32SC3, CV_32SC4)));
//////////////////////////////////////////////////////////////////////
// Min
@ -411,10 +438,11 @@ GPU_PERF_TEST(MeanStdDev, cv::gpu::DeviceInfo, cv::Size)
cv::gpu::GpuMat src(src_host);
cv::Scalar mean;
cv::Scalar stddev;
cv::gpu::GpuMat buf;
TEST_CYCLE()
{
cv::gpu::meanStdDev(src, mean, stddev);
cv::gpu::meanStdDev(src, mean, stddev, buf);
}
}

90
modules/gpu/perf/perf_imgproc.cpp
Unescape View File

@ -201,7 +201,7 @@ GPU_PERF_TEST(CvtColor, cv::gpu::DeviceInfo, cv::Size, perf::MatType, CvtColorIn
declare.in(src_host, WARMUP_RNG);
cv::gpu::GpuMat src(src_host);
cv::gpu::GpuMat dst(size, CV_MAKETYPE(type, info.dcn));
cv::gpu::GpuMat dst;
TEST_CYCLE()
{
@ -218,6 +218,32 @@ INSTANTIATE_TEST_CASE_P(ImgProc, CvtColor, testing::Combine(
CvtColorInfo(4, 4, cv::COLOR_BGR2XYZ), CvtColorInfo(4, 4, cv::COLOR_BGR2YCrCb), CvtColorInfo(4, 4, cv::COLOR_YCrCb2BGR),
CvtColorInfo(4, 4, cv::COLOR_BGR2HSV), CvtColorInfo(4, 4, cv::COLOR_HSV2BGR))));
//////////////////////////////////////////////////////////////////////
// SwapChannels
GPU_PERF_TEST(SwapChannels, cv::gpu::DeviceInfo, cv::Size)
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::Size size = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
cv::Mat src_host(size, CV_8UC4);
declare.in(src_host, WARMUP_RNG);
cv::gpu::GpuMat src(src_host);
const int dstOrder[] = {2, 1, 0, 3};
TEST_CYCLE()
{
cv::gpu::swapChannels(src, dstOrder);
}
}
INSTANTIATE_TEST_CASE_P(ImgProc, SwapChannels, testing::Combine(ALL_DEVICES, GPU_TYPICAL_MAT_SIZES));
//////////////////////////////////////////////////////////////////////
// Threshold
@ -457,7 +483,7 @@ GPU_PERF_TEST(Rotate, cv::gpu::DeviceInfo, cv::Size, perf::MatType, Interpolatio
INSTANTIATE_TEST_CASE_P(ImgProc, Rotate, testing::Combine(
ALL_DEVICES,
GPU_TYPICAL_MAT_SIZES,
testing::Values(CV_8UC1, CV_8UC4),
testing::Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_16UC1, CV_16UC3, CV_16UC4, CV_32FC1, CV_32FC3, CV_32FC4),
testing::Values((int) cv::INTER_NEAREST, (int) cv::INTER_LINEAR, (int) cv::INTER_CUBIC)));
//////////////////////////////////////////////////////////////////////
@ -519,33 +545,6 @@ INSTANTIATE_TEST_CASE_P(ImgProc, Integral, testing::Combine(
ALL_DEVICES,
GPU_TYPICAL_MAT_SIZES));
//////////////////////////////////////////////////////////////////////
// IntegralBoth
GPU_PERF_TEST(IntegralBoth, cv::gpu::DeviceInfo, cv::Size)
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::Size size = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
cv::Mat src_host(size, CV_8UC1);
declare.in(src_host, WARMUP_RNG);
cv::gpu::GpuMat src(src_host);
cv::gpu::GpuMat sum, sqsum;
TEST_CYCLE()
{
cv::gpu::integral(src, sum, sqsum);
}
}
INSTANTIATE_TEST_CASE_P(ImgProc, IntegralBoth, testing::Combine(
ALL_DEVICES,
GPU_TYPICAL_MAT_SIZES));
//////////////////////////////////////////////////////////////////////
// IntegralSqr
@ -849,6 +848,39 @@ INSTANTIATE_TEST_CASE_P(ImgProc, BlendLinear, testing::Combine(
GPU_TYPICAL_MAT_SIZES,
testing::Values(CV_8UC1, CV_32FC1)));
//////////////////////////////////////////////////////////////////////
// AlphaComp
GPU_PERF_TEST(AlphaComp, cv::gpu::DeviceInfo, cv::Size, perf::MatType, AlphaOp)
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::Size size = GET_PARAM(1);
int type = GET_PARAM(2);
int alpha_op = GET_PARAM(3);
cv::gpu::setDevice(devInfo.deviceID());
cv::Mat img1_host(size, type);
cv::Mat img2_host(size, type);
declare.in(img1_host, img2_host, WARMUP_RNG);
cv::gpu::GpuMat img1(img1_host);
cv::gpu::GpuMat img2(img2_host);
cv::gpu::GpuMat dst;
TEST_CYCLE()
{
cv::gpu::alphaComp(img1, img2, dst, alpha_op);
}
}
INSTANTIATE_TEST_CASE_P(ImgProc, AlphaComp, testing::Combine(
ALL_DEVICES,
GPU_TYPICAL_MAT_SIZES,
testing::Values(CV_8UC4, CV_16UC4, CV_32SC4, CV_32FC4),
testing::Values((int)cv::gpu::ALPHA_OVER, (int)cv::gpu::ALPHA_IN, (int)cv::gpu::ALPHA_OUT, (int)cv::gpu::ALPHA_ATOP, (int)cv::gpu::ALPHA_XOR, (int)cv::gpu::ALPHA_PLUS, (int)cv::gpu::ALPHA_OVER_PREMUL, (int)cv::gpu::ALPHA_IN_PREMUL, (int)cv::gpu::ALPHA_OUT_PREMUL, (int)cv::gpu::ALPHA_ATOP_PREMUL, (int)cv::gpu::ALPHA_XOR_PREMUL, (int)cv::gpu::ALPHA_PLUS_PREMUL, (int)cv::gpu::ALPHA_PREMUL)));
//////////////////////////////////////////////////////////////////////
// Canny

2
modules/gpu/perf/perf_main.cpp
Unescape View File

@ -11,7 +11,7 @@ int main(int argc, char **argv)
#else
int main(int argc, char** argv)
int main()
{
printf("OpenCV was built without CUDA support\n");
return 0;

1
modules/gpu/perf/perf_utility.hpp
Unescape View File

@ -11,6 +11,7 @@ CV_ENUM(FlipCode, HORIZONTAL_AXIS, VERTICAL_AXIS, BOTH_AXIS)
CV_ENUM(Interpolation, cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_CUBIC)
CV_ENUM(MatchMethod, cv::TM_SQDIFF, cv::TM_SQDIFF_NORMED, cv::TM_CCORR, cv::TM_CCORR_NORMED, cv::TM_CCOEFF, cv::TM_CCOEFF_NORMED)
CV_ENUM(NormType, cv::NORM_INF, cv::NORM_L1, cv::NORM_L2)
CV_ENUM(AlphaOp, cv::gpu::ALPHA_OVER, cv::gpu::ALPHA_IN, cv::gpu::ALPHA_OUT, cv::gpu::ALPHA_ATOP, cv::gpu::ALPHA_XOR, cv::gpu::ALPHA_PLUS, cv::gpu::ALPHA_OVER_PREMUL, cv::gpu::ALPHA_IN_PREMUL, cv::gpu::ALPHA_OUT_PREMUL, cv::gpu::ALPHA_ATOP_PREMUL, cv::gpu::ALPHA_XOR_PREMUL, cv::gpu::ALPHA_PLUS_PREMUL, cv::gpu::ALPHA_PREMUL)
struct CvtColorInfo
{

127
modules/gpu/src/arithm.cpp
Unescape View File

@ -52,8 +52,6 @@ void cv::gpu::gemm(const GpuMat&, const GpuMat&, double, const GpuMat&, double,
void cv::gpu::transpose(const GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::flip(const GpuMat&, GpuMat&, int, Stream&) { throw_nogpu(); }
void cv::gpu::LUT(const GpuMat&, const Mat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::exp(const GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::log(const GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::magnitude(const GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::magnitudeSqr(const GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::magnitude(const GpuMat&, const GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
@ -89,9 +87,9 @@ void cv::gpu::gemm(const GpuMat& src1, const GpuMat& src2, double alpha, const G
CV_Assert(src1.type() == CV_32FC1 || src1.type() == CV_32FC2 || src1.type() == CV_64FC1 || src1.type() == CV_64FC2);
CV_Assert(src2.type() == src1.type() && (src3.empty() || src3.type() == src1.type()));
bool tr1 = flags & GEMM_1_T;
bool tr2 = flags & GEMM_2_T;
bool tr3 = flags & GEMM_3_T;
bool tr1 = (flags & GEMM_1_T) != 0;
bool tr2 = (flags & GEMM_2_T) != 0;
bool tr3 = (flags & GEMM_3_T) != 0;
Size src1Size = tr1 ? Size(src1.rows, src1.cols) : src1.size();
Size src2Size = tr2 ? Size(src2.rows, src2.cols) : src2.size();
@ -243,35 +241,66 @@ void cv::gpu::transpose(const GpuMat& src, GpuMat& dst, Stream& s)
////////////////////////////////////////////////////////////////////////
// flip
void cv::gpu::flip(const GpuMat& src, GpuMat& dst, int flipCode, Stream& s)
namespace
{
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC4);
template<int DEPTH> struct NppTypeTraits;
template<> struct NppTypeTraits<CV_8U> { typedef Npp8u npp_t; };
template<> struct NppTypeTraits<CV_8S> { typedef Npp8s npp_t; };
template<> struct NppTypeTraits<CV_16U> { typedef Npp16u npp_t; };
template<> struct NppTypeTraits<CV_16S> { typedef Npp16s npp_t; };
template<> struct NppTypeTraits<CV_32S> { typedef Npp32s npp_t; };
template<> struct NppTypeTraits<CV_32F> { typedef Npp32f npp_t; };
template<> struct NppTypeTraits<CV_64F> { typedef Npp64f npp_t; };
template <int DEPTH> struct NppMirrorFunc
{
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
dst.create( src.size(), src.type() );
typedef NppStatus (*func_t)(const npp_t* pSrc, int nSrcStep, npp_t* pDst, int nDstStep, NppiSize oROI, NppiAxis flip);
};
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
template <int DEPTH, typename NppMirrorFunc<DEPTH>::func_t func> struct NppMirror
{
typedef typename NppMirrorFunc<DEPTH>::npp_t npp_t;
cudaStream_t stream = StreamAccessor::getStream(s);
static void call(const GpuMat& src, GpuMat& dst, int flipCode, cudaStream_t stream)
{
NppStreamHandler h(stream);
NppStreamHandler h(stream);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
if (src.type() == CV_8UC1)
{
nppSafeCall( nppiMirror_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz,
(flipCode == 0 ? NPP_HORIZONTAL_AXIS : (flipCode > 0 ? NPP_VERTICAL_AXIS : NPP_BOTH_AXIS))) );
}
else
nppSafeCall( func(src.ptr<npp_t>(), static_cast<int>(src.step),
dst.ptr<npp_t>(), static_cast<int>(dst.step), sz,
(flipCode == 0 ? NPP_HORIZONTAL_AXIS : (flipCode > 0 ? NPP_VERTICAL_AXIS : NPP_BOTH_AXIS))) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
}
void cv::gpu::flip(const GpuMat& src, GpuMat& dst, int flipCode, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, GpuMat& dst, int flipCode, cudaStream_t stream);
static const func_t funcs[6][4] =
{
nppSafeCall( nppiMirror_8u_C4R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz,
(flipCode == 0 ? NPP_HORIZONTAL_AXIS : (flipCode > 0 ? NPP_VERTICAL_AXIS : NPP_BOTH_AXIS))) );
}
{NppMirror<CV_8U, nppiMirror_8u_C1R>::call, 0, NppMirror<CV_8U, nppiMirror_8u_C3R>::call, NppMirror<CV_8U, nppiMirror_8u_C4R>::call},
{0,0,0,0},
{NppMirror<CV_16U, nppiMirror_16u_C1R>::call, 0, NppMirror<CV_16U, nppiMirror_16u_C3R>::call, NppMirror<CV_16U, nppiMirror_16u_C4R>::call},
{0,0,0,0},
{NppMirror<CV_32S, nppiMirror_32s_C1R>::call, 0, NppMirror<CV_32S, nppiMirror_32s_C3R>::call, NppMirror<CV_32S, nppiMirror_32s_C4R>::call},
{NppMirror<CV_32F, nppiMirror_32f_C1R>::call, 0, NppMirror<CV_32F, nppiMirror_32f_C3R>::call, NppMirror<CV_32F, nppiMirror_32f_C4R>::call}
};
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
CV_Assert(src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_32S || src.depth() == CV_32F);
CV_Assert(src.channels() == 1 || src.channels() == 3 || src.channels() == 4);
dst.create(src.size(), src.type());
funcs[src.depth()][src.channels() - 1](src, dst, flipCode, StreamAccessor::getStream(stream));
}
////////////////////////////////////////////////////////////////////////
@ -340,52 +369,6 @@ void cv::gpu::LUT(const GpuMat& src, const Mat& lut, GpuMat& dst, Stream& s)
cudaSafeCall( cudaDeviceSynchronize() );
}
////////////////////////////////////////////////////////////////////////
// exp
void cv::gpu::exp(const GpuMat& src, GpuMat& dst, Stream& s)
{
CV_Assert(src.type() == CV_32FC1);
dst.create(src.size(), src.type());
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
cudaStream_t stream = StreamAccessor::getStream(s);
NppStreamHandler h(stream);
nppSafeCall( nppiExp_32f_C1R(src.ptr<Npp32f>(), static_cast<int>(src.step), dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
////////////////////////////////////////////////////////////////////////
// log
void cv::gpu::log(const GpuMat& src, GpuMat& dst, Stream& s)
{
CV_Assert(src.type() == CV_32FC1);
dst.create(src.size(), src.type());
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
cudaStream_t stream = StreamAccessor::getStream(s);
NppStreamHandler h(stream);
nppSafeCall( nppiLn_32f_C1R(src.ptr<Npp32f>(), static_cast<int>(src.step), dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
////////////////////////////////////////////////////////////////////////
// NPP magnitide

16
modules/gpu/src/color.cpp
Unescape View File

@ -48,6 +48,7 @@ using namespace cv::gpu;
#if !defined (HAVE_CUDA)
void cv::gpu::cvtColor(const GpuMat&, GpuMat&, int, int, Stream&) { throw_nogpu(); }
void cv::gpu::swapChannels(GpuMat&, const int[], Stream&) { throw_nogpu(); }
#else /* !defined (HAVE_CUDA) */
@ -1423,4 +1424,19 @@ void cv::gpu::cvtColor(const GpuMat& src, GpuMat& dst, int code, int dcn, Stream
func(src, dst, dcn, stream);
}
void cv::gpu::swapChannels(GpuMat& image, const int dstOrder[4], Stream& s)
{
CV_Assert(image.type() == CV_8UC4);
cudaStream_t stream = StreamAccessor::getStream(s);
NppStreamHandler h(stream);
NppiSize sz;
sz.width = image.cols;
sz.height = image.rows;
nppSafeCall( nppiSwapChannels_8u_C4IR(image.ptr<Npp8u>(), static_cast<int>(image.step), sz, dstOrder) );
}
#endif /* !defined (HAVE_CUDA) */

2
modules/gpu/src/cuda/bilateral_filter.cu
Unescape View File

@ -209,7 +209,7 @@ namespace cv { namespace gpu { namespace device
cv::gpu::error("Unsupported channels count", __FILE__, __LINE__, "bilateral_filter_caller");
}
if (stream != 0)
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}

4
modules/gpu/src/cuda/element_operations.cu
Unescape View File

@ -1104,9 +1104,9 @@ namespace cv { namespace gpu { namespace device
cv::gpu::device::transform((DevMem2D_<T>)src1, (DevMem2D_<T>)dst, op, WithOutMask(), stream);
}
template void absdiff_gpu<uchar >(const DevMem2Db& src1, double src2, const DevMem2Db& dst, cudaStream_t stream);
//template void absdiff_gpu<uchar >(const DevMem2Db& src1, double src2, const DevMem2Db& dst, cudaStream_t stream);
template void absdiff_gpu<schar >(const DevMem2Db& src1, double src2, const DevMem2Db& dst, cudaStream_t stream);
template void absdiff_gpu<ushort>(const DevMem2Db& src1, double src2, const DevMem2Db& dst, cudaStream_t stream);
//template void absdiff_gpu<ushort>(const DevMem2Db& src1, double src2, const DevMem2Db& dst, cudaStream_t stream);
template void absdiff_gpu<short >(const DevMem2Db& src1, double src2, const DevMem2Db& dst, cudaStream_t stream);
template void absdiff_gpu<int >(const DevMem2Db& src1, double src2, const DevMem2Db& dst, cudaStream_t stream);
//template void absdiff_gpu<float >(const DevMem2Db& src1, double src2, const DevMem2Db& dst, cudaStream_t stream);

1213
modules/gpu/src/element_operations.cpp
View File

File diff suppressed because it is too large Load Diff

2
modules/gpu/src/filtering.cpp
Unescape View File

@ -50,7 +50,7 @@ using namespace cv::gpu;
Ptr<FilterEngine_GPU> cv::gpu::createFilter2D_GPU(const Ptr<BaseFilter_GPU>&, int, int) { throw_nogpu(); return Ptr<FilterEngine_GPU>(0); }
Ptr<FilterEngine_GPU> cv::gpu::createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>&, const Ptr<BaseColumnFilter_GPU>&, int, int, int) { throw_nogpu(); return Ptr<FilterEngine_GPU>(0); }
Ptr<FilterEngine_GPU> cv::gpu::createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>&, const Ptr<BaseColumnFilter_GPU>&, int, int, int, GpuMat& buf) { throw_nogpu(); return Ptr<FilterEngine_GPU>(0); }
Ptr<FilterEngine_GPU> cv::gpu::createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>&, const Ptr<BaseColumnFilter_GPU>&, int, int, int, GpuMat&) { throw_nogpu(); return Ptr<FilterEngine_GPU>(0); }
Ptr<BaseRowFilter_GPU> cv::gpu::getRowSumFilter_GPU(int, int, int, int) { throw_nogpu(); return Ptr<BaseRowFilter_GPU>(0); }
Ptr<BaseColumnFilter_GPU> cv::gpu::getColumnSumFilter_GPU(int, int, int, int) { throw_nogpu(); return Ptr<BaseColumnFilter_GPU>(0); }
Ptr<BaseFilter_GPU> cv::gpu::getBoxFilter_GPU(int, int, const Size&, Point) { throw_nogpu(); return Ptr<BaseFilter_GPU>(0); }

101
modules/gpu/src/graphcuts.cpp
Unescape View File

@ -45,12 +45,41 @@
#if !defined (HAVE_CUDA)
void cv::gpu::graphcut(GpuMat&, GpuMat&, GpuMat&, GpuMat&, GpuMat&, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::graphcut(GpuMat&, GpuMat&, GpuMat&, GpuMat&, GpuMat&, GpuMat&, GpuMat&, GpuMat&, GpuMat&, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
#else /* !defined (HAVE_CUDA) */
namespace
{
typedef NppStatus (*init_func_t)(NppiSize oSize, NppiGraphcutState** ppState, Npp8u* pDeviceMem);
class NppiGraphcutStateHandler
{
public:
NppiGraphcutStateHandler(NppiSize sznpp, Npp8u* pDeviceMem, const init_func_t func)
{
nppSafeCall( func(sznpp, &pState, pDeviceMem) );
}
~NppiGraphcutStateHandler()
{
nppSafeCall( nppiGraphcutFree(pState) );
}
operator NppiGraphcutState*()
{
return pState;
}
private:
NppiGraphcutState* pState;
};
}
void cv::gpu::graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& bottom, GpuMat& labels, GpuMat& buf, Stream& s)
{
Size src_size = terminals.size();
CV_Assert(terminals.type() == CV_32S);
CV_Assert(leftTransp.size() == Size(src_size.height, src_size.width));
CV_Assert(leftTransp.type() == CV_32S);
@ -70,30 +99,76 @@ void cv::gpu::graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTrans
int bufsz;
nppSafeCall( nppiGraphcutGetSize(sznpp, &bufsz) );
if ((size_t)bufsz > buf.cols * buf.rows * buf.elemSize())
buf.create(1, bufsz, CV_8U);
ensureSizeIsEnough(1, bufsz, CV_8U, buf);
cudaStream_t stream = StreamAccessor::getStream(s);
NppStreamHandler h(stream);
#if CUDART_VERSION > 4000
NppiGraphcutState* pState;
nppSafeCall( nppiGraphcutInitAlloc(sznpp, &pState, buf.ptr<Npp8u>()) );
NppiGraphcutStateHandler state(sznpp, buf.ptr<Npp8u>(), nppiGraphcutInitAlloc);
nppSafeCall( nppiGraphcut_32s8u(terminals.ptr<Npp32s>(), leftTransp.ptr<Npp32s>(), rightTransp.ptr<Npp32s>(), top.ptr<Npp32s>(), bottom.ptr<Npp32s>(),
static_cast<int>(terminals.step), static_cast<int>(leftTransp.step), sznpp, labels.ptr<Npp8u>(), static_cast<int>(labels.step), pState) );
nppSafeCall( nppiGraphcutFree(pState) );
#else
nppSafeCall( nppiGraphcut_32s8u(terminals.ptr<Npp32s>(), leftTransp.ptr<Npp32s>(), rightTransp.ptr<Npp32s>(), top.ptr<Npp32s>(), bottom.ptr<Npp32s>(),
static_cast<int>(terminals.step), static_cast<int>(leftTransp.step), sznpp, labels.ptr<Npp8u>(), static_cast<int>(labels.step), buf.ptr<Npp8u>()) );
#endif
static_cast<int>(terminals.step), static_cast<int>(leftTransp.step), sznpp, labels.ptr<Npp8u>(), static_cast<int>(labels.step), state) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
void cv::gpu::graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& topLeft, GpuMat& topRight,
GpuMat& bottom, GpuMat& bottomLeft, GpuMat& bottomRight, GpuMat& labels, GpuMat& buf, Stream& s)
{
Size src_size = terminals.size();
#endif /* !defined (HAVE_CUDA) */
CV_Assert(terminals.type() == CV_32S);
CV_Assert(leftTransp.size() == Size(src_size.height, src_size.width));
CV_Assert(leftTransp.type() == CV_32S);
CV_Assert(rightTransp.size() == Size(src_size.height, src_size.width));
CV_Assert(rightTransp.type() == CV_32S);
CV_Assert(top.size() == src_size);
CV_Assert(top.type() == CV_32S);
CV_Assert(topLeft.size() == src_size);
CV_Assert(topLeft.type() == CV_32S);
CV_Assert(topRight.size() == src_size);
CV_Assert(topRight.type() == CV_32S);
CV_Assert(bottom.size() == src_size);
CV_Assert(bottom.type() == CV_32S);
CV_Assert(bottomLeft.size() == src_size);
CV_Assert(bottomLeft.type() == CV_32S);
CV_Assert(bottomRight.size() == src_size);
CV_Assert(bottomRight.type() == CV_32S);
labels.create(src_size, CV_8U);
NppiSize sznpp;
sznpp.width = src_size.width;
sznpp.height = src_size.height;
int bufsz;
nppSafeCall( nppiGraphcut8GetSize(sznpp, &bufsz) );
ensureSizeIsEnough(1, bufsz, CV_8U, buf);
cudaStream_t stream = StreamAccessor::getStream(s);
NppStreamHandler h(stream);
NppiGraphcutStateHandler state(sznpp, buf.ptr<Npp8u>(), nppiGraphcut8InitAlloc);
nppSafeCall( nppiGraphcut8_32s8u(terminals.ptr<Npp32s>(), leftTransp.ptr<Npp32s>(), rightTransp.ptr<Npp32s>(),
top.ptr<Npp32s>(), topLeft.ptr<Npp32s>(), topRight.ptr<Npp32s>(),
bottom.ptr<Npp32s>(), bottomLeft.ptr<Npp32s>(), bottomRight.ptr<Npp32s>(),
static_cast<int>(terminals.step), static_cast<int>(leftTransp.step), sznpp, labels.ptr<Npp8u>(), static_cast<int>(labels.step), state) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
#endif /* !defined (HAVE_CUDA) */

161
modules/gpu/src/imgproc.cpp
Unescape View File

@ -62,7 +62,6 @@ void cv::gpu::buildWarpSphericalMaps(Size, Rect, const Mat&, const Mat&, float,
void cv::gpu::rotate(const GpuMat&, GpuMat&, Size, double, double, double, int, Stream&) { throw_nogpu(); }
void cv::gpu::integral(const GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::integralBuffered(const GpuMat&, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::integral(const GpuMat&, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::sqrIntegral(const GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::columnSum(const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::rectStdDev(const GpuMat&, const GpuMat&, GpuMat&, const Rect&, Stream&) { throw_nogpu(); }
@ -91,7 +90,7 @@ void cv::gpu::mulAndScaleSpectrums(const GpuMat&, const GpuMat&, GpuMat&, int, f
void cv::gpu::dft(const GpuMat&, GpuMat&, Size, int, Stream&) { throw_nogpu(); }
void cv::gpu::ConvolveBuf::create(Size, Size) { throw_nogpu(); }
void cv::gpu::convolve(const GpuMat&, const GpuMat&, GpuMat&, bool) { throw_nogpu(); }
void cv::gpu::convolve(const GpuMat&, const GpuMat&, GpuMat&, bool, ConvolveBuf&, Stream& stream) { throw_nogpu(); }
void cv::gpu::convolve(const GpuMat&, const GpuMat&, GpuMat&, bool, ConvolveBuf&, Stream&) { throw_nogpu(); }
void cv::gpu::pyrDown(const GpuMat&, GpuMat&, int, Stream&) { throw_nogpu(); }
void cv::gpu::pyrUp(const GpuMat&, GpuMat&, int, Stream&) { throw_nogpu(); }
void cv::gpu::Canny(const GpuMat&, GpuMat&, double, double, int, bool) { throw_nogpu(); }
@ -780,44 +779,78 @@ void cv::gpu::buildWarpSphericalMaps(Size src_size, Rect dst_roi, const Mat &K,
////////////////////////////////////////////////////////////////////////
// rotate
void cv::gpu::rotate(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift, double yShift, int interpolation, Stream& s)
{
static const int npp_inter[] = {NPPI_INTER_NN, NPPI_INTER_LINEAR, NPPI_INTER_CUBIC};
namespace
{
template<int DEPTH> struct NppTypeTraits;
template<> struct NppTypeTraits<CV_8U> { typedef Npp8u npp_t; };
template<> struct NppTypeTraits<CV_8S> { typedef Npp8s npp_t; };
template<> struct NppTypeTraits<CV_16U> { typedef Npp16u npp_t; };
template<> struct NppTypeTraits<CV_16S> { typedef Npp16s npp_t; };
template<> struct NppTypeTraits<CV_32S> { typedef Npp32s npp_t; };
template<> struct NppTypeTraits<CV_32F> { typedef Npp32f npp_t; };
template<> struct NppTypeTraits<CV_64F> { typedef Npp64f npp_t; };
template <int DEPTH> struct NppRotateFunc
{
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
typedef NppStatus (*func_t)(const npp_t* pSrc, NppiSize oSrcSize, int nSrcStep, NppiRect oSrcROI,
npp_t* pDst, int nDstStep, NppiRect oDstROI,
double nAngle, double nShiftX, double nShiftY, int eInterpolation);
};
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC4);
CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC);
template <int DEPTH, typename NppRotateFunc<DEPTH>::func_t func> struct NppRotate
{
typedef typename NppRotateFunc<DEPTH>::npp_t npp_t;
dst.create(dsize, src.type());
static void call(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift, double yShift, int interpolation, cudaStream_t stream)
{
static const int npp_inter[] = {NPPI_INTER_NN, NPPI_INTER_LINEAR, NPPI_INTER_CUBIC};
NppiSize srcsz;
srcsz.height = src.rows;
srcsz.width = src.cols;
NppiRect srcroi;
srcroi.x = srcroi.y = 0;
srcroi.height = src.rows;
srcroi.width = src.cols;
NppiRect dstroi;
dstroi.x = dstroi.y = 0;
dstroi.height = dst.rows;
dstroi.width = dst.cols;
NppStreamHandler h(stream);
cudaStream_t stream = StreamAccessor::getStream(s);
NppiSize srcsz;
srcsz.height = src.rows;
srcsz.width = src.cols;
NppiRect srcroi;
srcroi.x = srcroi.y = 0;
srcroi.height = src.rows;
srcroi.width = src.cols;
NppiRect dstroi;
dstroi.x = dstroi.y = 0;
dstroi.height = dst.rows;
dstroi.width = dst.cols;
nppSafeCall( func(src.ptr<npp_t>(), srcsz, static_cast<int>(src.step), srcroi,
dst.ptr<npp_t>(), static_cast<int>(dst.step), dstroi, angle, xShift, yShift, npp_inter[interpolation]) );
NppStreamHandler h(stream);
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
}
if (src.type() == CV_8UC1)
{
nppSafeCall( nppiRotate_8u_C1R(src.ptr<Npp8u>(), srcsz, static_cast<int>(src.step), srcroi,
dst.ptr<Npp8u>(), static_cast<int>(dst.step), dstroi, angle, xShift, yShift, npp_inter[interpolation]) );
}
else
void cv::gpu::rotate(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift, double yShift, int interpolation, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift, double yShift, int interpolation, cudaStream_t stream);
static const func_t funcs[6][4] =
{
nppSafeCall( nppiRotate_8u_C4R(src.ptr<Npp8u>(), srcsz, static_cast<int>(src.step), srcroi,
dst.ptr<Npp8u>(), static_cast<int>(dst.step), dstroi, angle, xShift, yShift, npp_inter[interpolation]) );
}
{NppRotate<CV_8U, nppiRotate_8u_C1R>::call, 0, NppRotate<CV_8U, nppiRotate_8u_C3R>::call, NppRotate<CV_8U, nppiRotate_8u_C4R>::call},
{0,0,0,0},
{NppRotate<CV_16U, nppiRotate_16u_C1R>::call, 0, NppRotate<CV_16U, nppiRotate_16u_C3R>::call, NppRotate<CV_16U, nppiRotate_16u_C4R>::call},
{0,0,0,0},
{0,0,0,0},
{NppRotate<CV_32F, nppiRotate_32f_C1R>::call, 0, NppRotate<CV_32F, nppiRotate_32f_C3R>::call, NppRotate<CV_32F, nppiRotate_32f_C4R>::call}
};
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
CV_Assert(src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_32F);
CV_Assert(src.channels() == 1 || src.channels() == 3 || src.channels() == 4);
CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC);
dst.create(dsize, src.type());
funcs[src.depth()][src.channels() - 1](src, dst, dsize, angle, xShift, yShift, interpolation, StreamAccessor::getStream(stream));
}
////////////////////////////////////////////////////////////////////////
@ -857,30 +890,6 @@ void cv::gpu::integralBuffered(const GpuMat& src, GpuMat& sum, GpuMat& buffer, S
cudaSafeCall( cudaDeviceSynchronize() );
}
void cv::gpu::integral(const GpuMat& src, GpuMat& sum, GpuMat& sqsum, Stream& s)
{
CV_Assert(src.type() == CV_8UC1);
int width = src.cols + 1, height = src.rows + 1;
sum.create(height, width, CV_32S);
sqsum.create(height, width, CV_32F);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
cudaStream_t stream = StreamAccessor::getStream(s);
NppStreamHandler h(stream);
nppSafeCall( nppiSqrIntegral_8u32s32f_C1R(const_cast<Npp8u*>(src.ptr<Npp8u>()), static_cast<int>(src.step),
sum.ptr<Npp32s>(), static_cast<int>(sum.step), sqsum.ptr<Npp32f>(), static_cast<int>(sqsum.step), sz, 0, 0.0f, height) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
//////////////////////////////////////////////////////////////////////////////
// sqrIntegral
@ -935,7 +944,6 @@ void cv::gpu::columnSum(const GpuMat& src, GpuMat& dst)
void cv::gpu::rectStdDev(const GpuMat& src, const GpuMat& sqr, GpuMat& dst, const Rect& rect, Stream& s)
{
#if CUDART_VERSION > 4000
CV_Assert(src.type() == CV_32SC1 && sqr.type() == CV_64FC1);
dst.create(src.size(), CV_32FC1);
@ -959,31 +967,6 @@ void cv::gpu::rectStdDev(const GpuMat& src, const GpuMat& sqr, GpuMat& dst, cons
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
#else
CV_Assert(src.type() == CV_32SC1 && sqr.type() == CV_32FC1);
dst.create(src.size(), CV_32FC1);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
NppiRect nppRect;
nppRect.height = rect.height;
nppRect.width = rect.width;
nppRect.x = rect.x;
nppRect.y = rect.y;
cudaStream_t stream = StreamAccessor::getStream(s);
NppStreamHandler h(stream);
nppSafeCall( nppiRectStdDev_32s32f_C1R(src.ptr<Npp32s>(), static_cast<int>(src.step), sqr.ptr<Npp32f>(), static_cast<int>(sqr.step),
dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz, nppRect) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
#endif
}
@ -992,25 +975,19 @@ void cv::gpu::rectStdDev(const GpuMat& src, const GpuMat& sqr, GpuMat& dst, cons
namespace
{
template<int n> struct NPPTypeTraits;
template<> struct NPPTypeTraits<CV_8U> { typedef Npp8u npp_type; };
template<> struct NPPTypeTraits<CV_16U> { typedef Npp16u npp_type; };
template<> struct NPPTypeTraits<CV_16S> { typedef Npp16s npp_type; };
template<> struct NPPTypeTraits<CV_32F> { typedef Npp32f npp_type; };
typedef NppStatus (*get_buf_size_c1_t)(NppiSize oSizeROI, int nLevels, int* hpBufferSize);
typedef NppStatus (*get_buf_size_c4_t)(NppiSize oSizeROI, int nLevels[], int* hpBufferSize);
template<int SDEPTH> struct NppHistogramEvenFuncC1
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef typename NppTypeTraits<SDEPTH>::npp_t src_t;
typedef NppStatus (*func_ptr)(const src_t* pSrc, int nSrcStep, NppiSize oSizeROI, Npp32s * pHist,
int nLevels, Npp32s nLowerLevel, Npp32s nUpperLevel, Npp8u * pBuffer);
};
template<int SDEPTH> struct NppHistogramEvenFuncC4
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef typename NppTypeTraits<SDEPTH>::npp_t src_t;
typedef NppStatus (*func_ptr)(const src_t* pSrc, int nSrcStep, NppiSize oSizeROI,
Npp32s * pHist[4], int nLevels[4], Npp32s nLowerLevel[4], Npp32s nUpperLevel[4], Npp8u * pBuffer);
@ -1079,7 +1056,7 @@ namespace
template<int SDEPTH> struct NppHistogramRangeFuncC1
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef typename NppTypeTraits<SDEPTH>::npp_t src_t;
typedef Npp32s level_t;
enum {LEVEL_TYPE_CODE=CV_32SC1};
@ -1097,7 +1074,7 @@ namespace
};
template<int SDEPTH> struct NppHistogramRangeFuncC4
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef typename NppTypeTraits<SDEPTH>::npp_t src_t;
typedef Npp32s level_t;
enum {LEVEL_TYPE_CODE=CV_32SC1};

13
modules/gpu/src/initialization.cpp
Unescape View File

@ -171,8 +171,8 @@ bool cv::gpu::DeviceInfo::supports(cv::gpu::FeatureSet) const { throw_nogpu(); r
bool cv::gpu::DeviceInfo::isCompatible() const { throw_nogpu(); return false; }
void cv::gpu::DeviceInfo::query() { throw_nogpu(); }
void cv::gpu::DeviceInfo::queryMemory(size_t&, size_t&) const { throw_nogpu(); }
void cv::gpu::printCudaDeviceInfo(int device) { throw_nogpu(); }
void cv::gpu::printShortCudaDeviceInfo(int device) { throw_nogpu(); }
void cv::gpu::printCudaDeviceInfo(int) { throw_nogpu(); }
void cv::gpu::printShortCudaDeviceInfo(int) { throw_nogpu(); }
#else /* !defined (HAVE_CUDA) */
@ -346,7 +346,6 @@ void cv::gpu::printCudaDeviceInfo(int device)
convertSMVer2Cores(prop.major, prop.minor) * prop.multiProcessorCount);
printf(" GPU Clock Speed: %.2f GHz\n", prop.clockRate * 1e-6f);
#if (CUDART_VERSION >= 4000)
// This is not available in the CUDA Runtime API, so we make the necessary calls the driver API to support this for output
int memoryClock, memBusWidth, L2CacheSize;
getCudaAttribute<int>( &memoryClock, CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE, dev );
@ -364,7 +363,7 @@ void cv::gpu::printCudaDeviceInfo(int device)
printf(" Max Layered Texture Size (dim) x layers 1D=(%d) x %d, 2D=(%d,%d) x %d\n",
prop.maxTexture1DLayered[0], prop.maxTexture1DLayered[1],
prop.maxTexture2DLayered[0], prop.maxTexture2DLayered[1], prop.maxTexture2DLayered[2]);
#endif
printf(" Total amount of constant memory: %u bytes\n", (int)prop.totalConstMem);
printf(" Total amount of shared memory per block: %u bytes\n", (int)prop.sharedMemPerBlock);
printf(" Total number of registers available per block: %d\n", prop.regsPerBlock);
@ -375,11 +374,7 @@ void cv::gpu::printCudaDeviceInfo(int device)
printf(" Maximum memory pitch: %u bytes\n", (int)prop.memPitch);
printf(" Texture alignment: %u bytes\n", (int)prop.textureAlignment);
#if CUDART_VERSION >= 4000
printf(" Concurrent copy and execution: %s with %d copy engine(s)\n", (prop.deviceOverlap ? "Yes" : "No"), prop.asyncEngineCount);
#else
printf(" Concurrent copy and execution: %s\n", prop.deviceOverlap ? "Yes" : "No");
#endif
printf(" Run time limit on kernels: %s\n", prop.kernelExecTimeoutEnabled ? "Yes" : "No");
printf(" Integrated GPU sharing Host Memory: %s\n", prop.integrated ? "Yes" : "No");
printf(" Support host page-locked memory mapping: %s\n", prop.canMapHostMemory ? "Yes" : "No");
@ -388,10 +383,8 @@ void cv::gpu::printCudaDeviceInfo(int device)
printf(" Alignment requirement for Surfaces: %s\n", prop.surfaceAlignment ? "Yes" : "No");
printf(" Device has ECC support enabled: %s\n", prop.ECCEnabled ? "Yes" : "No");
printf(" Device is using TCC driver mode: %s\n", prop.tccDriver ? "Yes" : "No");
#if CUDART_VERSION >= 4000
printf(" Device supports Unified Addressing (UVA): %s\n", prop.unifiedAddressing ? "Yes" : "No");
printf(" Device PCI Bus ID / PCI location ID: %d / %d\n", prop.pciBusID, prop.pciDeviceID );
#endif
printf(" Compute Mode:\n");
printf(" %s \n", computeMode[prop.computeMode]);
}

17
modules/gpu/src/matrix_reductions.cpp
Unescape View File

@ -48,6 +48,7 @@ using namespace cv::gpu;
#if !defined (HAVE_CUDA)
void cv::gpu::meanStdDev(const GpuMat&, Scalar&, Scalar&) { throw_nogpu(); }
void cv::gpu::meanStdDev(const GpuMat&, Scalar&, Scalar&, GpuMat&) { throw_nogpu(); }
double cv::gpu::norm(const GpuMat&, int) { throw_nogpu(); return 0.0; }
double cv::gpu::norm(const GpuMat&, int, GpuMat&) { throw_nogpu(); return 0.0; }
double cv::gpu::norm(const GpuMat&, const GpuMat&, int) { throw_nogpu(); return 0.0; }
@ -108,6 +109,12 @@ namespace
// meanStdDev
void cv::gpu::meanStdDev(const GpuMat& src, Scalar& mean, Scalar& stddev)
{
GpuMat buf;
meanStdDev(src, mean, stddev, buf);
}
void cv::gpu::meanStdDev(const GpuMat& src, Scalar& mean, Scalar& stddev, GpuMat& buf)
{
CV_Assert(src.type() == CV_8UC1);
@ -117,15 +124,12 @@ void cv::gpu::meanStdDev(const GpuMat& src, Scalar& mean, Scalar& stddev)
DeviceBuffer dbuf(2);
#if CUDART_VERSION > 4000
int bufSize;
nppSafeCall( nppiMeanStdDev8uC1RGetBufferHostSize(sz, &bufSize) );
GpuMat buf(1, bufSize, CV_8UC1);
ensureSizeIsEnough(1, bufSize, CV_8UC1, buf);
nppSafeCall( nppiMean_StdDev_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step), sz, buf.ptr<Npp8u>(), dbuf, (double*)dbuf + 1) );
#else
nppSafeCall( nppiMean_StdDev_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step), sz, dbuf, (double*)dbuf + 1) );
#endif
cudaSafeCall( cudaDeviceSynchronize() );
@ -133,7 +137,6 @@ void cv::gpu::meanStdDev(const GpuMat& src, Scalar& mean, Scalar& stddev)
dbuf.download(ptrs);
}
////////////////////////////////////////////////////////////////////////
// norm
@ -151,7 +154,7 @@ double cv::gpu::norm(const GpuMat& src, int normType, GpuMat& buf)
return absSum(src_single_channel, buf)[0];
if (normType == NORM_L2)
return sqrt(sqrSum(src_single_channel, buf)[0]);
return std::sqrt(sqrSum(src_single_channel, buf)[0]);
if (normType == NORM_INF)
{

2
modules/gpu/src/optical_flow.cpp
Unescape View File

@ -228,7 +228,7 @@ void cv::gpu::createOpticalFlowNeedleMap(const GpuMat& u, const GpuMat& v, GpuMa
minMax(u_avg, 0, &uMax);
minMax(v_avg, 0, &vMax);
float max_flow = static_cast<float>(sqrt(uMax * uMax + vMax * vMax));
float max_flow = static_cast<float>(std::sqrt(uMax * uMax + vMax * vMax));
CreateOpticalFlowNeedleMap_gpu(u_avg, v_avg, vertex.ptr<float>(), colors.ptr<float>(), max_flow, 1.0f / u.cols, 1.0f / u.rows);

2
modules/gpu/src/optical_flow_farneback.cpp
Unescape View File

@ -160,7 +160,7 @@ void cv::gpu::FarnebackOpticalFlow::setPolynomialExpansionConsts(int n, double s
double ig11, ig03, ig33, ig55;
prepareGaussian(n, sigma, g, xg, xxg, ig11, ig03, ig33, ig55);
device::optflow_farneback::setPolynomialExpansionConsts(n, g, xg, xxg, ig11, ig03, ig33, ig55);
device::optflow_farneback::setPolynomialExpansionConsts(n, g, xg, xxg, static_cast<float>(ig11), static_cast<float>(ig03), static_cast<float>(ig33), static_cast<float>(ig55));
}

6
modules/gpu/src/orb.cpp
Unescape View File

@ -429,11 +429,11 @@ void cv::gpu::ORB_GPU::setParams(size_t n_features, const ORB::CommonParams& det
// pre-compute the end of a row in a circular patch
int half_patch_size = params_.patch_size_ / 2;
vector<int> u_max(half_patch_size + 1);
for (int v = 0; v <= half_patch_size * sqrt(2.f) / 2 + 1; ++v)
u_max[v] = cvRound(sqrt(static_cast<float>(half_patch_size * half_patch_size - v * v)));
for (int v = 0; v <= half_patch_size * std::sqrt(2.f) / 2 + 1; ++v)
u_max[v] = cvRound(std::sqrt(static_cast<float>(half_patch_size * half_patch_size - v * v)));
// Make sure we are symmetric
for (int v = half_patch_size, v_0 = 0; v >= half_patch_size * sqrt(2.f) / 2; --v)
for (int v = half_patch_size, v_0 = 0; v >= half_patch_size * std::sqrt(2.f) / 2; --v)
{
while (u_max[v_0] == u_max[v_0 + 1])
++v_0;

4
modules/gpu/src/precomp.hpp
Unescape View File

@ -91,8 +91,8 @@
#include "nvidia/NCVHaarObjectDetection.hpp"
#include "nvidia/NCVBroxOpticalFlow.hpp"
#define CUDART_MINIMUM_REQUIRED_VERSION 4000
#define NPP_MINIMUM_REQUIRED_VERSION 4000
#define CUDART_MINIMUM_REQUIRED_VERSION 4010
#define NPP_MINIMUM_REQUIRED_VERSION 4100
#if (CUDART_VERSION < CUDART_MINIMUM_REQUIRED_VERSION)
#error "Insufficient Cuda Runtime library version, please update it."

185
modules/gpu/test/test_arithm.cpp
Unescape View File

@ -69,10 +69,10 @@ PARAM_TEST_CASE(ArithmTestBase, cv::gpu::DeviceInfo, MatType, UseRoi)
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat1 = randomMat(rng, size, type, 1, 16, false);
mat2 = randomMat(rng, size, type, 1, 16, false);
mat1 = randomMat(rng, size, type, 5, 16, false);
mat2 = randomMat(rng, size, type, 5, 16, false);
val = cv::Scalar(rng.uniform(0.1, 3.0), rng.uniform(0.1, 3.0), rng.uniform(0.1, 3.0), rng.uniform(0.1, 3.0));
val = cv::Scalar(rng.uniform(1, 3), rng.uniform(1, 3), rng.uniform(1, 3), rng.uniform(1, 3));
}
};
@ -115,7 +115,8 @@ TEST_P(Add, Scalar)
INSTANTIATE_TEST_CASE_P(Arithm, Add, Combine(
ALL_DEVICES,
Values(CV_8UC1, CV_16UC1, CV_32SC1, CV_32FC1),
Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_16UC1, CV_16UC3, CV_16UC4, CV_16SC1, CV_16SC2, CV_16SC3, CV_16SC4,
CV_32SC1, CV_32SC2, CV_32SC3, CV_32FC1, CV_32FC2, CV_32FC3, CV_32FC4),
USE_ROI));
////////////////////////////////////////////////////////////////////////////////
@ -157,7 +158,8 @@ TEST_P(Subtract, Scalar)
INSTANTIATE_TEST_CASE_P(Arithm, Subtract, Combine(
ALL_DEVICES,
Values(CV_8UC1, CV_16UC1, CV_32SC1, CV_32FC1),
Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_16UC1, CV_16UC3, CV_16UC4, CV_16SC1, CV_16SC2, CV_16SC3, CV_16SC4,
CV_32SC1, CV_32SC2, CV_32SC3, CV_32FC1, CV_32FC2, CV_32FC3, CV_32FC4),
USE_ROI));
////////////////////////////////////////////////////////////////////////////////
@ -199,7 +201,8 @@ TEST_P(Multiply, Scalar)
INSTANTIATE_TEST_CASE_P(Arithm, Multiply, Combine(
ALL_DEVICES,
Values(CV_8UC1, CV_16UC1, CV_32SC1, CV_32FC1),
Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_16UC1, CV_16UC3, CV_16UC4, CV_16SC1, CV_16SC3, CV_16SC4,
CV_32SC1, CV_32SC3, CV_32FC1, CV_32FC3, CV_32FC4),
USE_ROI));
////////////////////////////////////////////////////////////////////////////////
@ -220,7 +223,7 @@ TEST_P(Divide, Array)
gpuRes.download(dst);
EXPECT_MAT_NEAR(dst_gold, dst, 1.0);
EXPECT_MAT_NEAR(dst_gold, dst, mat1.depth() == CV_32F ? 1e-5 : 1);
}
TEST_P(Divide, Scalar)
@ -236,12 +239,13 @@ TEST_P(Divide, Scalar)
gpuRes.download(dst);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-5);
EXPECT_MAT_NEAR(dst_gold, dst, mat1.depth() == CV_32F ? 1e-5 : 1);
}
INSTANTIATE_TEST_CASE_P(Arithm, Divide, Combine(
ALL_DEVICES,
Values(CV_8UC1, CV_16UC1, CV_32SC1, CV_32FC1),
Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_16UC1, CV_16UC3, CV_16UC4, CV_16SC1, CV_16SC3, CV_16SC4,
CV_32SC1, CV_32SC3, CV_32FC1, CV_32FC3, CV_32FC4),
USE_ROI));
////////////////////////////////////////////////////////////////////////////////
@ -312,6 +316,83 @@ INSTANTIATE_TEST_CASE_P(Arithm, Absdiff, Combine(
Values(CV_8UC1, CV_16UC1, CV_32SC1, CV_32FC1),
USE_ROI));
////////////////////////////////////////////////////////////////////////////////
// abs
struct Abs : ArithmTestBase {};
TEST_P(Abs, Array)
{
cv::Mat dst_gold = cv::abs(mat1);
cv::Mat dst;
cv::gpu::GpuMat gpuRes;
cv::gpu::abs(loadMat(mat1, useRoi), gpuRes);
gpuRes.download(dst);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(Arithm, Abs, Combine(
ALL_DEVICES,
Values(CV_16SC1, CV_32FC1),
USE_ROI));
////////////////////////////////////////////////////////////////////////////////
// Sqr
struct Sqr : ArithmTestBase {};
TEST_P(Sqr, Array)
{
cv::Mat dst_gold;
cv::multiply(mat1, mat1, dst_gold);
cv::Mat dst;
cv::gpu::GpuMat gpuRes;
cv::gpu::sqr(loadMat(mat1, useRoi), gpuRes);
gpuRes.download(dst);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(Arithm, Sqr, Combine(
ALL_DEVICES,
Values(CV_8UC1, CV_16UC1, CV_16SC1, CV_32FC1),
USE_ROI));
////////////////////////////////////////////////////////////////////////////////
// Sqrt
struct Sqrt : ArithmTestBase {};
TEST_P(Sqrt, Array)
{
cv::Mat dst_gold;
cv::sqrt(mat1, dst_gold);
cv::Mat dst;
cv::gpu::GpuMat gpuRes;
cv::gpu::sqrt(loadMat(mat1, useRoi), gpuRes);
gpuRes.download(dst);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(Arithm, Sqrt, Combine(
ALL_DEVICES,
Values(MatType(CV_32FC1)),
USE_ROI));
////////////////////////////////////////////////////////////////////////////////
// compare
@ -513,7 +594,7 @@ TEST_P(Flip, Accuracy)
INSTANTIATE_TEST_CASE_P(Arithm, Flip, Combine(
ALL_DEVICES,
Values(CV_8UC1, CV_8UC4),
Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_16UC1, CV_16UC3, CV_16UC4, CV_32SC1, CV_32SC3, CV_32SC4, CV_32FC1, CV_32FC3, CV_32FC4),
Values((int)FLIP_BOTH, (int)FLIP_X, (int)FLIP_Y),
USE_ROI));
@ -1329,6 +1410,90 @@ INSTANTIATE_TEST_CASE_P(Arithm, Bitwise, Combine(
ALL_DEVICES,
ALL_TYPES));
PARAM_TEST_CASE(BitwiseScalar, cv::gpu::DeviceInfo, MatType)
{
cv::gpu::DeviceInfo devInfo;
int type;
cv::Size size;
cv::Mat mat;
cv::Scalar sc;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
type = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat.create(size, type);
for (int i = 0; i < mat.rows; ++i)
{
cv::Mat row(1, static_cast<int>(mat.cols * mat.elemSize()), CV_8U, (void*)mat.ptr(i));
rng.fill(row, cv::RNG::UNIFORM, cv::Scalar(0), cv::Scalar(255));
}
sc = cv::Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
}
};
TEST_P(BitwiseScalar, Or)
{
cv::Mat dst_gold;
cv::bitwise_or(mat, sc, dst_gold);
cv::Mat dst;
cv::gpu::GpuMat dev_dst;
cv::gpu::bitwise_or(loadMat(mat), sc, dev_dst);
dev_dst.download(dst);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
TEST_P(BitwiseScalar, And)
{
cv::Mat dst_gold;
cv::bitwise_and(mat, sc, dst_gold);
cv::Mat dst;
cv::gpu::GpuMat dev_dst;
cv::gpu::bitwise_and(loadMat(mat), sc, dev_dst);
dev_dst.download(dst);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
TEST_P(BitwiseScalar, Xor)
{
cv::Mat dst_gold;
cv::bitwise_xor(mat, sc, dst_gold);
cv::Mat dst;
cv::gpu::GpuMat dev_dst;
cv::gpu::bitwise_xor(loadMat(mat), sc, dev_dst);
dev_dst.download(dst);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(Arithm, BitwiseScalar, Combine(
ALL_DEVICES,
Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_16UC1, CV_16UC3, CV_16UC4, CV_32SC1, CV_32SC3, CV_32SC4)));
//////////////////////////////////////////////////////////////////////////////
// addWeighted

43
modules/gpu/test/test_imgproc.cpp
Unescape View File

@ -2377,6 +2377,49 @@ INSTANTIATE_TEST_CASE_P(ImgProc, CvtColor, Combine(
Values(CV_8U, CV_16U, CV_32F),
USE_ROI));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// swapChannels
PARAM_TEST_CASE(SwapChannels, cv::gpu::DeviceInfo, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
bool useRoi;
cv::Mat img;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
useRoi = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
cv::Mat imgBase = readImage("stereobm/aloe-L.png");
ASSERT_FALSE(imgBase.empty());
cv::cvtColor(imgBase, img, cv::COLOR_BGR2BGRA);
cv::cvtColor(img, dst_gold, cv::COLOR_BGRA2RGBA);
}
};
TEST_P(SwapChannels, Accuracy)
{
cv::gpu::GpuMat gpuImage = loadMat(img, useRoi);
const int dstOrder[] = {2, 1, 0, 3};
cv::gpu::swapChannels(gpuImage, dstOrder);
cv::Mat dst;
gpuImage.download(dst);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(ImgProc, SwapChannels, Combine(ALL_DEVICES, USE_ROI));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// histograms

2
modules/gpu/test/test_main.cpp
Unescape View File

@ -136,7 +136,7 @@ int main(int argc, char** argv)
#else // HAVE_CUDA
int main(int argc, char** argv)
int main()
{
printf("OpenCV was built without CUDA support\n");
return 0;

1
modules/highgui/src/window.cpp
Unescape View File

@ -146,7 +146,6 @@ CV_IMPL double cvGetWindowProperty(const char* name, int prop_id)
default:
return -1;
}
return -1;
}
void cv::namedWindow( const string& winname, int flags )

2
modules/stitching/include/opencv2/stitching/detail/seam_finders.hpp
Unescape View File

@ -105,6 +105,8 @@ public:
GraphCutSeamFinder(int cost_type = COST_COLOR_GRAD, float terminal_cost = 10000.f,
float bad_region_penalty = 1000.f);
~GraphCutSeamFinder();
void find(const std::vector<Mat> &src, const std::vector<Point> &corners,
std::vector<Mat> &masks);

2
modules/stitching/src/seam_finders.cpp
Unescape View File

@ -411,6 +411,8 @@ void GraphCutSeamFinder::Impl::findInPair(size_t first, size_t second, Rect roi)
GraphCutSeamFinder::GraphCutSeamFinder(int cost_type, float terminal_cost, float bad_region_penalty)
: impl_(new Impl(cost_type, terminal_cost, bad_region_penalty)) {}
GraphCutSeamFinder::~GraphCutSeamFinder() {}
void GraphCutSeamFinder::find(const vector<Mat> &src, const vector<Point> &corners,
vector<Mat> &masks)

2
samples/cpp/point_cloud.cpp
Unescape View File

@ -247,7 +247,7 @@ inline int clamp(int val, int minVal, int maxVal)
return max(min(val, maxVal), minVal);
}
void PointCloudRenderer::onMouseEvent(int event, int x, int y, int flags)
void PointCloudRenderer::onMouseEvent(int event, int x, int y, int /*flags*/)
{
static int oldx = x;
static int oldy = y;

68
samples/gpu/alpha_comp.cpp
Unescape View File

@ -0,0 +1,68 @@
#include <iostream>
#include "opencv2/core/opengl_interop.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/gpu/gpu.hpp"
using namespace std;
using namespace cv;
using namespace cv::gpu;
int main()
{
cout << "This program demonstrates using alphaComp" << endl;
cout << "Press SPACE to change compositing operation" << endl;
cout << "Press ESC to exit" << endl;
namedWindow("First Image", WINDOW_NORMAL);
namedWindow("Second Image", WINDOW_NORMAL);
namedWindow("Result", WINDOW_OPENGL);
setGlDevice();
Mat src1(640, 480, CV_8UC4, Scalar::all(0));
Mat src2(640, 480, CV_8UC4, Scalar::all(0));
rectangle(src1, Rect(50, 50, 200, 200), Scalar(0, 0, 255, 128), 30);
rectangle(src2, Rect(100, 100, 200, 200), Scalar(255, 0, 0, 128), 30);
GpuMat d_src1(src1);
GpuMat d_src2(src2);
GpuMat d_res;
imshow("First Image", src1);
imshow("Second Image", src2);
int alpha_op = ALPHA_OVER;
const char* op_names[] =
{
"ALPHA_OVER", "ALPHA_IN", "ALPHA_OUT", "ALPHA_ATOP", "ALPHA_XOR", "ALPHA_PLUS", "ALPHA_OVER_PREMUL", "ALPHA_IN_PREMUL", "ALPHA_OUT_PREMUL",
"ALPHA_ATOP_PREMUL", "ALPHA_XOR_PREMUL", "ALPHA_PLUS_PREMUL", "ALPHA_PREMUL"
};
while (true)
{
cout << op_names[alpha_op] << endl;
alphaComp(d_src1, d_src2, d_res, alpha_op);
imshow("Result", d_res);
char key = static_cast<char>(waitKey());
if (key == 27)
break;
if (key == 32)
{
++alpha_op;
if (alpha_op > ALPHA_PREMUL)
alpha_op = ALPHA_OVER;
}
}
return 0;
}

2
samples/gpu/brox_optical_flow.cpp
Unescape View File

@ -226,7 +226,7 @@ int main(int argc, const char* argv[])
break;
case 'S':
if (currentFrame < frames.size() - 1)
if (currentFrame < static_cast<int>(frames.size()) - 1)
++currentFrame;
imshow("Interpolated frame", frames[currentFrame]);

6
samples/gpu/farneback_optical_flow.cpp
Unescape View File

@ -26,7 +26,7 @@ void colorizeFlow(const Mat &u, const Mat &v, Mat &dst)
minMaxLoc(v, &vMin, &vMax, 0, 0);
uMin = ::abs(uMin); uMax = ::abs(uMax);
vMin = ::abs(vMin); vMax = ::abs(vMax);
float dMax = ::max(::max(uMin, uMax), ::max(vMin, vMax));
float dMax = static_cast<float>(::max(::max(uMin, uMax), ::max(vMin, vMax)));
dst.create(u.size(), CV_8UC3);
for (int y = 0; y < u.rows; ++y)
@ -111,11 +111,11 @@ int main(int argc, char **argv)
s.str("");
s << "opt. flow FPS: " << cvRound((getTickFrequency()/(tc1-tc0)));
putText(image, s.str(), Point(5, 65), FONT_HERSHEY_SIMPLEX, 1., Scalar(255,0,255), 2.);
putText(image, s.str(), Point(5, 65), FONT_HERSHEY_SIMPLEX, 1., Scalar(255,0,255), 2);
s.str("");
s << "total FPS: " << cvRound((getTickFrequency()/(t1-t0)));
putText(image, s.str(), Point(5, 105), FONT_HERSHEY_SIMPLEX, 1., Scalar(255,0,255), 2.);
putText(image, s.str(), Point(5, 105), FONT_HERSHEY_SIMPLEX, 1., Scalar(255,0,255), 2);
imshow("flow", image);

2
samples/gpu/performance/performance.cpp
Unescape View File

@ -63,7 +63,7 @@ void TestSystem::finishCurrentSubtest()
double cpu_time = cpu_elapsed_ / getTickFrequency() * 1000.0;
double gpu_time = gpu_elapsed_ / getTickFrequency() * 1000.0;
double speedup = static_cast<double>(cpu_elapsed_) / std::max((int64)1, gpu_elapsed_);
double speedup = static_cast<double>(cpu_elapsed_) / std::max(1.0, gpu_elapsed_);
speedup_total_ += speedup;
printMetrics(cpu_time, gpu_time, speedup);

6
samples/gpu/performance/performance.h
Unescape View File

@ -127,8 +127,10 @@ private:
std::stringstream cur_subtest_description_;
bool cur_subtest_is_empty_;
int64 cpu_started_, cpu_elapsed_;
int64 gpu_started_, gpu_elapsed_;
int64 cpu_started_;
int64 gpu_started_;
double cpu_elapsed_;
double gpu_elapsed_;
double speedup_total_;
int num_subtests_called_;

2
samples/gpu/performance/tests.cpp
Unescape View File

@ -1199,7 +1199,7 @@ TEST(FarnebackOpticalFlow)
if (frame1.empty()) throw runtime_error("can't open " + datasets[i] + "2.png");
gpu::FarnebackOpticalFlow calc;
calc.fastPyramids = fastPyramids;
calc.fastPyramids = fastPyramids != 0;
calc.flags |= useGaussianBlur ? OPTFLOW_FARNEBACK_GAUSSIAN : 0;
gpu::GpuMat d_frame0(frame0), d_frame1(frame1), d_flowx, d_flowy;

Write Preview
Loading…
Cancel
Save