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opencv/modules/gpu/src/imgproc_gpu.cpp
Anatoly Baksheev 6b6a63ba38 fixed some warnings 
GPU: TargetArchs -> added FEATURE_SET prefix.
2011-02-15 10:54:49 +00:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include <utility>
using namespace cv;
using namespace cv::gpu;
#if !defined (HAVE_CUDA)
void cv::gpu::remap(const GpuMat&, GpuMat&, const GpuMat&, const GpuMat&){ throw_nogpu(); }
void cv::gpu::meanShiftFiltering(const GpuMat&, GpuMat&, int, int, TermCriteria) { throw_nogpu(); }
void cv::gpu::meanShiftProc(const GpuMat&, GpuMat&, GpuMat&, int, int, TermCriteria) { throw_nogpu(); }
void cv::gpu::drawColorDisp(const GpuMat&, GpuMat&, int) { throw_nogpu(); }
void cv::gpu::drawColorDisp(const GpuMat&, GpuMat&, int, const Stream&) { throw_nogpu(); }
void cv::gpu::reprojectImageTo3D(const GpuMat&, GpuMat&, const Mat&) { throw_nogpu(); }
void cv::gpu::reprojectImageTo3D(const GpuMat&, GpuMat&, const Mat&, const Stream&) { throw_nogpu(); }
void cv::gpu::resize(const GpuMat&, GpuMat&, Size, double, double, int) { throw_nogpu(); }
void cv::gpu::copyMakeBorder(const GpuMat&, GpuMat&, int, int, int, int, const Scalar&) { throw_nogpu(); }
void cv::gpu::warpAffine(const GpuMat&, GpuMat&, const Mat&, Size, int) { throw_nogpu(); }
void cv::gpu::warpPerspective(const GpuMat&, GpuMat&, const Mat&, Size, int) { throw_nogpu(); }
void cv::gpu::rotate(const GpuMat&, GpuMat&, Size, double, double, double, int) { throw_nogpu(); }
void cv::gpu::integral(const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::integralBuffered(const GpuMat&, GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::integral(const GpuMat&, GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::sqrIntegral(const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::columnSum(const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::rectStdDev(const GpuMat&, const GpuMat&, GpuMat&, const Rect&) { throw_nogpu(); }
//void cv::gpu::Canny(const GpuMat&, GpuMat&, double, double, int) { throw_nogpu(); }
//void cv::gpu::Canny(const GpuMat&, GpuMat&, GpuMat&, double, double, int) { throw_nogpu(); }
//void cv::gpu::Canny(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, double, double, int) { throw_nogpu(); }
//void cv::gpu::Canny(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, GpuMat&, double, double, int) { throw_nogpu(); }
void cv::gpu::evenLevels(GpuMat&, int, int, int) { throw_nogpu(); }
void cv::gpu::histEven(const GpuMat&, GpuMat&, int, int, int) { throw_nogpu(); }
void cv::gpu::histEven(const GpuMat&, GpuMat*, int*, int*, int*) { throw_nogpu(); }
void cv::gpu::histRange(const GpuMat&, GpuMat&, const GpuMat&) { throw_nogpu(); }
void cv::gpu::histRange(const GpuMat&, GpuMat*, const GpuMat*) { throw_nogpu(); }
void cv::gpu::cornerHarris(const GpuMat&, GpuMat&, int, int, double, int) { throw_nogpu(); }
void cv::gpu::cornerMinEigenVal(const GpuMat&, GpuMat&, int, int, int) { throw_nogpu(); }
void cv::gpu::mulSpectrums(const GpuMat&, const GpuMat&, GpuMat&, int, bool) { throw_nogpu(); }
void cv::gpu::mulAndScaleSpectrums(const GpuMat&, const GpuMat&, GpuMat&, int, float, bool) { throw_nogpu(); }
void cv::gpu::dft(const GpuMat&, GpuMat&, Size, int) { 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&) { throw_nogpu(); }
#else /* !defined (HAVE_CUDA) */
namespace cv { namespace gpu { namespace imgproc
{
void remap_gpu_1c(const DevMem2D& src, const DevMem2Df& xmap, const DevMem2Df& ymap, DevMem2D dst);
void remap_gpu_3c(const DevMem2D& src, const DevMem2Df& xmap, const DevMem2Df& ymap, DevMem2D dst);
extern "C" void meanShiftFiltering_gpu(const DevMem2D& src, DevMem2D dst, int sp, int sr, int maxIter, float eps);
extern "C" void meanShiftProc_gpu(const DevMem2D& src, DevMem2D dstr, DevMem2D dstsp, int sp, int sr, int maxIter, float eps);
void drawColorDisp_gpu(const DevMem2D& src, const DevMem2D& dst, int ndisp, const cudaStream_t& stream);
void drawColorDisp_gpu(const DevMem2D_<short>& src, const DevMem2D& dst, int ndisp, const cudaStream_t& stream);
void reprojectImageTo3D_gpu(const DevMem2D& disp, const DevMem2Df& xyzw, const float* q, const cudaStream_t& stream);
void reprojectImageTo3D_gpu(const DevMem2D_<short>& disp, const DevMem2Df& xyzw, const float* q, const cudaStream_t& stream);
}}}
////////////////////////////////////////////////////////////////////////
// remap
void cv::gpu::remap(const GpuMat& src, GpuMat& dst, const GpuMat& xmap, const GpuMat& ymap)
{
typedef void (*remap_gpu_t)(const DevMem2D& src, const DevMem2Df& xmap, const DevMem2Df& ymap, DevMem2D dst);
static const remap_gpu_t callers[] = {imgproc::remap_gpu_1c, 0, imgproc::remap_gpu_3c};
CV_Assert((src.type() == CV_8U || src.type() == CV_8UC3) && xmap.type() == CV_32F && ymap.type() == CV_32F);
GpuMat out;
if (dst.data != src.data)
out = dst;
out.create(xmap.size(), src.type());
callers[src.channels() - 1](src, xmap, ymap, out);
dst = out;
}
////////////////////////////////////////////////////////////////////////
// meanShiftFiltering_GPU
void cv::gpu::meanShiftFiltering(const GpuMat& src, GpuMat& dst, int sp, int sr, TermCriteria criteria)
{
CV_Assert(TargetArchs::builtWith(FEATURE_SET_COMPUTE_12) && DeviceInfo().supports(FEATURE_SET_COMPUTE_12));
if( src.empty() )
CV_Error( CV_StsBadArg, "The input image is empty" );
if( src.depth() != CV_8U || src.channels() != 4 )
CV_Error( CV_StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported" );
dst.create( src.size(), CV_8UC4 );
if( !(criteria.type & TermCriteria::MAX_ITER) )
criteria.maxCount = 5;
int maxIter = std::min(std::max(criteria.maxCount, 1), 100);
float eps;
if( !(criteria.type & TermCriteria::EPS) )
eps = 1.f;
eps = (float)std::max(criteria.epsilon, 0.0);
imgproc::meanShiftFiltering_gpu(src, dst, sp, sr, maxIter, eps);
}
////////////////////////////////////////////////////////////////////////
// meanShiftProc_GPU
void cv::gpu::meanShiftProc(const GpuMat& src, GpuMat& dstr, GpuMat& dstsp, int sp, int sr, TermCriteria criteria)
{
CV_Assert(TargetArchs::builtWith(FEATURE_SET_COMPUTE_12) && DeviceInfo().supports(FEATURE_SET_COMPUTE_12));
if( src.empty() )
CV_Error( CV_StsBadArg, "The input image is empty" );
if( src.depth() != CV_8U || src.channels() != 4 )
CV_Error( CV_StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported" );
dstr.create( src.size(), CV_8UC4 );
dstsp.create( src.size(), CV_16SC2 );
if( !(criteria.type & TermCriteria::MAX_ITER) )
criteria.maxCount = 5;
int maxIter = std::min(std::max(criteria.maxCount, 1), 100);
float eps;
if( !(criteria.type & TermCriteria::EPS) )
eps = 1.f;
eps = (float)std::max(criteria.epsilon, 0.0);
imgproc::meanShiftProc_gpu(src, dstr, dstsp, sp, sr, maxIter, eps);
}
////////////////////////////////////////////////////////////////////////
// drawColorDisp
namespace
{
template <typename T>
void drawColorDisp_caller(const GpuMat& src, GpuMat& dst, int ndisp, const cudaStream_t& stream)
{
GpuMat out;
if (dst.data != src.data)
out = dst;
out.create(src.size(), CV_8UC4);
imgproc::drawColorDisp_gpu((DevMem2D_<T>)src, out, ndisp, stream);
dst = out;
}
typedef void (*drawColorDisp_caller_t)(const GpuMat& src, GpuMat& dst, int ndisp, const cudaStream_t& stream);
const drawColorDisp_caller_t drawColorDisp_callers[] = {drawColorDisp_caller<unsigned char>, 0, 0, drawColorDisp_caller<short>, 0, 0, 0, 0};
}
void cv::gpu::drawColorDisp(const GpuMat& src, GpuMat& dst, int ndisp)
{
CV_Assert(src.type() == CV_8U || src.type() == CV_16S);
drawColorDisp_callers[src.type()](src, dst, ndisp, 0);
}
void cv::gpu::drawColorDisp(const GpuMat& src, GpuMat& dst, int ndisp, const Stream& stream)
{
CV_Assert(src.type() == CV_8U || src.type() == CV_16S);
drawColorDisp_callers[src.type()](src, dst, ndisp, StreamAccessor::getStream(stream));
}
////////////////////////////////////////////////////////////////////////
// reprojectImageTo3D
namespace
{
template <typename T>
void reprojectImageTo3D_caller(const GpuMat& disp, GpuMat& xyzw, const Mat& Q, const cudaStream_t& stream)
{
xyzw.create(disp.rows, disp.cols, CV_32FC4);
imgproc::reprojectImageTo3D_gpu((DevMem2D_<T>)disp, xyzw, Q.ptr<float>(), stream);
}
typedef void (*reprojectImageTo3D_caller_t)(const GpuMat& disp, GpuMat& xyzw, const Mat& Q, const cudaStream_t& stream);
const reprojectImageTo3D_caller_t reprojectImageTo3D_callers[] = {reprojectImageTo3D_caller<unsigned char>, 0, 0, reprojectImageTo3D_caller<short>, 0, 0, 0, 0};
}
void cv::gpu::reprojectImageTo3D(const GpuMat& disp, GpuMat& xyzw, const Mat& Q)
{
CV_Assert((disp.type() == CV_8U || disp.type() == CV_16S) && Q.type() == CV_32F && Q.rows == 4 && Q.cols == 4);
reprojectImageTo3D_callers[disp.type()](disp, xyzw, Q, 0);
}
void cv::gpu::reprojectImageTo3D(const GpuMat& disp, GpuMat& xyzw, const Mat& Q, const Stream& stream)
{
CV_Assert((disp.type() == CV_8U || disp.type() == CV_16S) && Q.type() == CV_32F && Q.rows == 4 && Q.cols == 4);
reprojectImageTo3D_callers[disp.type()](disp, xyzw, Q, StreamAccessor::getStream(stream));
}
////////////////////////////////////////////////////////////////////////
// resize
void cv::gpu::resize(const GpuMat& src, GpuMat& dst, Size dsize, double fx, double fy, int interpolation)
{
static const int npp_inter[] = {NPPI_INTER_NN, NPPI_INTER_LINEAR/*, NPPI_INTER_CUBIC, 0, NPPI_INTER_LANCZOS*/};
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC4);
CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR/* || interpolation == INTER_CUBIC || interpolation == INTER_LANCZOS4*/);
CV_Assert( src.size().area() > 0 );
CV_Assert( !(dsize == Size()) || (fx > 0 && fy > 0) );
if( dsize == Size() )
{
dsize = Size(saturate_cast<int>(src.cols * fx), saturate_cast<int>(src.rows * fy));
}
else
{
fx = (double)dsize.width / src.cols;
fy = (double)dsize.height / src.rows;
}
dst.create(dsize, src.type());
NppiSize srcsz;
srcsz.width = src.cols;
srcsz.height = src.rows;
NppiRect srcrect;
srcrect.x = srcrect.y = 0;
srcrect.width = src.cols;
srcrect.height = src.rows;
NppiSize dstsz;
dstsz.width = dst.cols;
dstsz.height = dst.rows;
if (src.type() == CV_8UC1)
{
nppSafeCall( nppiResize_8u_C1R(src.ptr<Npp8u>(), srcsz, src.step, srcrect,
dst.ptr<Npp8u>(), dst.step, dstsz, fx, fy, npp_inter[interpolation]) );
}
else
{
nppSafeCall( nppiResize_8u_C4R(src.ptr<Npp8u>(), srcsz, src.step, srcrect,
dst.ptr<Npp8u>(), dst.step, dstsz, fx, fy, npp_inter[interpolation]) );
}
cudaSafeCall( cudaThreadSynchronize() );
}
////////////////////////////////////////////////////////////////////////
// copyMakeBorder
void cv::gpu::copyMakeBorder(const GpuMat& src, GpuMat& dst, int top, int bottom, int left, int right, const Scalar& value)
{
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC4 || src.type() == CV_32SC1 || src.type() == CV_32FC1);
dst.create(src.rows + top + bottom, src.cols + left + right, src.type());
NppiSize srcsz;
srcsz.width = src.cols;
srcsz.height = src.rows;
NppiSize dstsz;
dstsz.width = dst.cols;
dstsz.height = dst.rows;
switch (src.type())
{
case CV_8UC1:
{
Npp8u nVal = static_cast<Npp8u>(value[0]);
nppSafeCall( nppiCopyConstBorder_8u_C1R(src.ptr<Npp8u>(), src.step, srcsz,
dst.ptr<Npp8u>(), dst.step, dstsz, top, left, nVal) );
break;
}
case CV_8UC4:
{
Npp8u nVal[] = {static_cast<Npp8u>(value[0]), static_cast<Npp8u>(value[1]), static_cast<Npp8u>(value[2]), static_cast<Npp8u>(value[3])};
nppSafeCall( nppiCopyConstBorder_8u_C4R(src.ptr<Npp8u>(), src.step, srcsz,
dst.ptr<Npp8u>(), dst.step, dstsz, top, left, nVal) );
break;
}
case CV_32SC1:
{
Npp32s nVal = static_cast<Npp32s>(value[0]);
nppSafeCall( nppiCopyConstBorder_32s_C1R(src.ptr<Npp32s>(), src.step, srcsz,
dst.ptr<Npp32s>(), dst.step, dstsz, top, left, nVal) );
break;
}
case CV_32FC1:
{
Npp32f val = static_cast<Npp32f>(value[0]);
Npp32s nVal = *(reinterpret_cast<Npp32s*>(&val));
nppSafeCall( nppiCopyConstBorder_32s_C1R(src.ptr<Npp32s>(), src.step, srcsz,
dst.ptr<Npp32s>(), dst.step, dstsz, top, left, nVal) );
break;
}
default:
CV_Assert(!"Unsupported source type");
}
cudaSafeCall( cudaThreadSynchronize() );
}
////////////////////////////////////////////////////////////////////////
// warp
namespace
{
typedef NppStatus (*npp_warp_8u_t)(const Npp8u* pSrc, NppiSize srcSize, int srcStep, NppiRect srcRoi, Npp8u* pDst,
int dstStep, NppiRect dstRoi, const double coeffs[][3],
int interpolation);
typedef NppStatus (*npp_warp_16u_t)(const Npp16u* pSrc, NppiSize srcSize, int srcStep, NppiRect srcRoi, Npp16u* pDst,
int dstStep, NppiRect dstRoi, const double coeffs[][3],
int interpolation);
typedef NppStatus (*npp_warp_32s_t)(const Npp32s* pSrc, NppiSize srcSize, int srcStep, NppiRect srcRoi, Npp32s* pDst,
int dstStep, NppiRect dstRoi, const double coeffs[][3],
int interpolation);
typedef NppStatus (*npp_warp_32f_t)(const Npp32f* pSrc, NppiSize srcSize, int srcStep, NppiRect srcRoi, Npp32f* pDst,
int dstStep, NppiRect dstRoi, const double coeffs[][3],
int interpolation);
void nppWarpCaller(const GpuMat& src, GpuMat& dst, double coeffs[][3], const Size& dsize, int flags,
npp_warp_8u_t npp_warp_8u[][2], npp_warp_16u_t npp_warp_16u[][2],
npp_warp_32s_t npp_warp_32s[][2], npp_warp_32f_t npp_warp_32f[][2])
{
static const int npp_inter[] = {NPPI_INTER_NN, NPPI_INTER_LINEAR, NPPI_INTER_CUBIC};
int interpolation = flags & INTER_MAX;
CV_Assert((src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_32S || src.depth() == CV_32F) && src.channels() != 2);
CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC);
dst.create(dsize, src.type());
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;
int warpInd = (flags & WARP_INVERSE_MAP) >> 4;
switch (src.depth())
{
case CV_8U:
nppSafeCall( npp_warp_8u[src.channels()][warpInd](src.ptr<Npp8u>(), srcsz, src.step, srcroi,
dst.ptr<Npp8u>(), dst.step, dstroi, coeffs, npp_inter[interpolation]) );
break;
case CV_16U:
nppSafeCall( npp_warp_16u[src.channels()][warpInd](src.ptr<Npp16u>(), srcsz, src.step, srcroi,
dst.ptr<Npp16u>(), dst.step, dstroi, coeffs, npp_inter[interpolation]) );
break;
case CV_32S:
nppSafeCall( npp_warp_32s[src.channels()][warpInd](src.ptr<Npp32s>(), srcsz, src.step, srcroi,
dst.ptr<Npp32s>(), dst.step, dstroi, coeffs, npp_inter[interpolation]) );
break;
case CV_32F:
nppSafeCall( npp_warp_32f[src.channels()][warpInd](src.ptr<Npp32f>(), srcsz, src.step, srcroi,
dst.ptr<Npp32f>(), dst.step, dstroi, coeffs, npp_inter[interpolation]) );
break;
default:
CV_Assert(!"Unsupported source type");
}
cudaSafeCall( cudaThreadSynchronize() );
}
}
void cv::gpu::warpAffine(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags)
{
static npp_warp_8u_t npp_warpAffine_8u[][2] =
{
{0, 0},
{nppiWarpAffine_8u_C1R, nppiWarpAffineBack_8u_C1R},
{0, 0},
{nppiWarpAffine_8u_C3R, nppiWarpAffineBack_8u_C3R},
{nppiWarpAffine_8u_C4R, nppiWarpAffineBack_8u_C4R}
};
static npp_warp_16u_t npp_warpAffine_16u[][2] =
{
{0, 0},
{nppiWarpAffine_16u_C1R, nppiWarpAffineBack_16u_C1R},
{0, 0},
{nppiWarpAffine_16u_C3R, nppiWarpAffineBack_16u_C3R},
{nppiWarpAffine_16u_C4R, nppiWarpAffineBack_16u_C4R}
};
static npp_warp_32s_t npp_warpAffine_32s[][2] =
{
{0, 0},
{nppiWarpAffine_32s_C1R, nppiWarpAffineBack_32s_C1R},
{0, 0},
{nppiWarpAffine_32s_C3R, nppiWarpAffineBack_32s_C3R},
{nppiWarpAffine_32s_C4R, nppiWarpAffineBack_32s_C4R}
};
static npp_warp_32f_t npp_warpAffine_32f[][2] =
{
{0, 0},
{nppiWarpAffine_32f_C1R, nppiWarpAffineBack_32f_C1R},
{0, 0},
{nppiWarpAffine_32f_C3R, nppiWarpAffineBack_32f_C3R},
{nppiWarpAffine_32f_C4R, nppiWarpAffineBack_32f_C4R}
};
CV_Assert(M.rows == 2 && M.cols == 3);
double coeffs[2][3];
Mat coeffsMat(2, 3, CV_64F, (void*)coeffs);
M.convertTo(coeffsMat, coeffsMat.type());
nppWarpCaller(src, dst, coeffs, dsize, flags, npp_warpAffine_8u, npp_warpAffine_16u, npp_warpAffine_32s, npp_warpAffine_32f);
}
void cv::gpu::warpPerspective(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags)
{
static npp_warp_8u_t npp_warpPerspective_8u[][2] =
{
{0, 0},
{nppiWarpPerspective_8u_C1R, nppiWarpPerspectiveBack_8u_C1R},
{0, 0},
{nppiWarpPerspective_8u_C3R, nppiWarpPerspectiveBack_8u_C3R},
{nppiWarpPerspective_8u_C4R, nppiWarpPerspectiveBack_8u_C4R}
};
static npp_warp_16u_t npp_warpPerspective_16u[][2] =
{
{0, 0},
{nppiWarpPerspective_16u_C1R, nppiWarpPerspectiveBack_16u_C1R},
{0, 0},
{nppiWarpPerspective_16u_C3R, nppiWarpPerspectiveBack_16u_C3R},
{nppiWarpPerspective_16u_C4R, nppiWarpPerspectiveBack_16u_C4R}
};
static npp_warp_32s_t npp_warpPerspective_32s[][2] =
{
{0, 0},
{nppiWarpPerspective_32s_C1R, nppiWarpPerspectiveBack_32s_C1R},
{0, 0},
{nppiWarpPerspective_32s_C3R, nppiWarpPerspectiveBack_32s_C3R},
{nppiWarpPerspective_32s_C4R, nppiWarpPerspectiveBack_32s_C4R}
};
static npp_warp_32f_t npp_warpPerspective_32f[][2] =
{
{0, 0},
{nppiWarpPerspective_32f_C1R, nppiWarpPerspectiveBack_32f_C1R},
{0, 0},
{nppiWarpPerspective_32f_C3R, nppiWarpPerspectiveBack_32f_C3R},
{nppiWarpPerspective_32f_C4R, nppiWarpPerspectiveBack_32f_C4R}
};
CV_Assert(M.rows == 3 && M.cols == 3);
double coeffs[3][3];
Mat coeffsMat(3, 3, CV_64F, (void*)coeffs);
M.convertTo(coeffsMat, coeffsMat.type());
nppWarpCaller(src, dst, coeffs, dsize, flags, npp_warpPerspective_8u, npp_warpPerspective_16u, npp_warpPerspective_32s, npp_warpPerspective_32f);
}
////////////////////////////////////////////////////////////////////////
// rotate
void cv::gpu::rotate(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift, double yShift, int interpolation)
{
static const int npp_inter[] = {NPPI_INTER_NN, NPPI_INTER_LINEAR, NPPI_INTER_CUBIC};
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC4);
CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC);
dst.create(dsize, src.type());
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;
if (src.type() == CV_8UC1)
{
nppSafeCall( nppiRotate_8u_C1R(src.ptr<Npp8u>(), srcsz, src.step, srcroi,
dst.ptr<Npp8u>(), dst.step, dstroi, angle, xShift, yShift, npp_inter[interpolation]) );
}
else
{
nppSafeCall( nppiRotate_8u_C4R(src.ptr<Npp8u>(), srcsz, src.step, srcroi,
dst.ptr<Npp8u>(), dst.step, dstroi, angle, xShift, yShift, npp_inter[interpolation]) );
}
cudaSafeCall( cudaThreadSynchronize() );
}
////////////////////////////////////////////////////////////////////////
// integral
void cv::gpu::integral(const GpuMat& src, GpuMat& sum)
{
GpuMat buffer;
integralBuffered(src, sum, buffer);
}
void cv::gpu::integralBuffered(const GpuMat& src, GpuMat& sum, GpuMat& buffer)
{
CV_Assert(src.type() == CV_8UC1);
sum.create(src.rows + 1, src.cols + 1, CV_32S);
NcvSize32u roiSize;
roiSize.width = src.cols;
roiSize.height = src.rows;
cudaDeviceProp prop;
cudaSafeCall( cudaGetDeviceProperties(&prop, cv::gpu::getDevice()) );
Ncv32u bufSize;
nppSafeCall( nppiStIntegralGetSize_8u32u(roiSize, &bufSize, prop) );
ensureSizeIsEnough(1, bufSize, CV_8UC1, buffer);
nppSafeCall( nppiStIntegral_8u32u_C1R(const_cast<Ncv8u*>(src.ptr<Ncv8u>()), src.step,
sum.ptr<Ncv32u>(), sum.step, roiSize, buffer.ptr<Ncv8u>(), bufSize, prop) );
cudaSafeCall( cudaThreadSynchronize() );
}
void cv::gpu::integral(const GpuMat& src, GpuMat& sum, GpuMat& sqsum)
{
CV_Assert(src.type() == CV_8UC1);
int w = src.cols + 1, h = src.rows + 1;
sum.create(h, w, CV_32S);
sqsum.create(h, w, CV_32F);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( nppiSqrIntegral_8u32s32f_C1R(const_cast<Npp8u*>(src.ptr<Npp8u>()), src.step, sum.ptr<Npp32s>(),
sum.step, sqsum.ptr<Npp32f>(), sqsum.step, sz, 0, 0.0f, h) );
cudaSafeCall( cudaThreadSynchronize() );
}
//////////////////////////////////////////////////////////////////////////////
// sqrIntegral
void cv::gpu::sqrIntegral(const GpuMat& src, GpuMat& sqsum)
{
CV_Assert(src.type() == CV_8U);
NcvSize32u roiSize;
roiSize.width = src.cols;
roiSize.height = src.rows;
cudaDeviceProp prop;
cudaSafeCall( cudaGetDeviceProperties(&prop, cv::gpu::getDevice()) );
Ncv32u bufSize;
nppSafeCall(nppiStSqrIntegralGetSize_8u64u(roiSize, &bufSize, prop));
GpuMat buf(1, bufSize, CV_8U);
sqsum.create(src.rows + 1, src.cols + 1, CV_64F);
nppSafeCall(nppiStSqrIntegral_8u64u_C1R(const_cast<Ncv8u*>(src.ptr<Ncv8u>(0)), src.step,
sqsum.ptr<Ncv64u>(0), sqsum.step, roiSize, buf.ptr<Ncv8u>(0), bufSize, prop));
cudaSafeCall( cudaThreadSynchronize() );
}
//////////////////////////////////////////////////////////////////////////////
// columnSum
namespace cv { namespace gpu { namespace imgproc
{
void columnSum_32F(const DevMem2D src, const DevMem2D dst);
}}}
void cv::gpu::columnSum(const GpuMat& src, GpuMat& dst)
{
CV_Assert(src.type() == CV_32F);
dst.create(src.size(), CV_32F);
imgproc::columnSum_32F(src, dst);
}
void cv::gpu::rectStdDev(const GpuMat& src, const GpuMat& sqr, GpuMat& dst, const Rect& rect)
{
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;
nppSafeCall( nppiRectStdDev_32s32f_C1R(src.ptr<Npp32s>(), src.step, sqr.ptr<Npp32f>(), sqr.step,
dst.ptr<Npp32f>(), dst.step, sz, nppRect) );
cudaSafeCall( cudaThreadSynchronize() );
}
////////////////////////////////////////////////////////////////////////
// Canny
//void cv::gpu::Canny(const GpuMat& image, GpuMat& edges, double threshold1, double threshold2, int apertureSize)
//{
// CV_Assert(!"disabled until fix crash");
//
// GpuMat srcDx, srcDy;
//
// Sobel(image, srcDx, CV_32F, 1, 0, apertureSize);
// Sobel(image, srcDy, CV_32F, 0, 1, apertureSize);
//
// GpuMat buf;
//
// Canny(srcDx, srcDy, edges, buf, threshold1, threshold2, apertureSize);
//}
//
//void cv::gpu::Canny(const GpuMat& image, GpuMat& edges, GpuMat& buf, double threshold1, double threshold2, int apertureSize)
//{
// CV_Assert(!"disabled until fix crash");
//
// GpuMat srcDx, srcDy;
//
// Sobel(image, srcDx, CV_32F, 1, 0, apertureSize);
// Sobel(image, srcDy, CV_32F, 0, 1, apertureSize);
//
// Canny(srcDx, srcDy, edges, buf, threshold1, threshold2, apertureSize);
//}
//
//void cv::gpu::Canny(const GpuMat& srcDx, const GpuMat& srcDy, GpuMat& edges, double threshold1, double threshold2, int apertureSize)
//{
// CV_Assert(!"disabled until fix crash");
//
// GpuMat buf;
// Canny(srcDx, srcDy, edges, buf, threshold1, threshold2, apertureSize);
//}
//
//void cv::gpu::Canny(const GpuMat& srcDx, const GpuMat& srcDy, GpuMat& edges, GpuMat& buf, double threshold1, double threshold2, int apertureSize)
//{
// CV_Assert(!"disabled until fix crash");
// CV_Assert(srcDx.type() == CV_32FC1 && srcDy.type() == CV_32FC1 && srcDx.size() == srcDy.size());
//
// edges.create(srcDx.size(), CV_8UC1);
//
// NppiSize sz;
// sz.height = srcDx.rows;
// sz.width = srcDx.cols;
//
// int bufsz;
// nppSafeCall( nppiCannyGetBufferSize(sz, &bufsz) );
// ensureSizeIsEnough(1, bufsz, CV_8UC1, buf);
//
// nppSafeCall( nppiCanny_32f8u_C1R(srcDx.ptr<Npp32f>(), srcDx.step, srcDy.ptr<Npp32f>(), srcDy.step,
// edges.ptr<Npp8u>(), edges.step, sz, (Npp32f)threshold1, (Npp32f)threshold2, buf.ptr<Npp8u>()) );
//
// cudaSafeCall( cudaThreadSynchronize() );
//}
////////////////////////////////////////////////////////////////////////
// Histogram
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 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 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);
};
template<int SDEPTH, typename NppHistogramEvenFuncC1<SDEPTH>::func_ptr func, get_buf_size_c1_t get_buf_size>
struct NppHistogramEvenC1
{
typedef typename NppHistogramEvenFuncC1<SDEPTH>::src_t src_t;
static void hist(const GpuMat& src, GpuMat& hist, int histSize, int lowerLevel, int upperLevel)
{
int levels = histSize + 1;
hist.create(1, histSize, CV_32S);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
GpuMat buffer;
int buf_size;
get_buf_size(sz, levels, &buf_size);
buffer.create(1, buf_size, CV_8U);
nppSafeCall( func(src.ptr<src_t>(), src.step, sz, hist.ptr<Npp32s>(), levels,
lowerLevel, upperLevel, buffer.ptr<Npp8u>()) );
cudaSafeCall( cudaThreadSynchronize() );
}
};
template<int SDEPTH, typename NppHistogramEvenFuncC4<SDEPTH>::func_ptr func, get_buf_size_c4_t get_buf_size>
struct NppHistogramEvenC4
{
typedef typename NppHistogramEvenFuncC4<SDEPTH>::src_t src_t;
static void hist(const GpuMat& src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4])
{
int levels[] = {histSize[0] + 1, histSize[1] + 1, histSize[2] + 1, histSize[3] + 1};
hist[0].create(1, histSize[0], CV_32S);
hist[1].create(1, histSize[1], CV_32S);
hist[2].create(1, histSize[2], CV_32S);
hist[3].create(1, histSize[3], CV_32S);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Npp32s* pHist[] = {hist[0].ptr<Npp32s>(), hist[1].ptr<Npp32s>(), hist[2].ptr<Npp32s>(), hist[3].ptr<Npp32s>()};
GpuMat buffer;
int buf_size;
get_buf_size(sz, levels, &buf_size);
buffer.create(1, buf_size, CV_8U);
nppSafeCall( func(src.ptr<src_t>(), src.step, sz, pHist, levels, lowerLevel, upperLevel, buffer.ptr<Npp8u>()) );
cudaSafeCall( cudaThreadSynchronize() );
}
};
template<int SDEPTH> struct NppHistogramRangeFuncC1
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef Npp32s level_t;
enum {LEVEL_TYPE_CODE=CV_32SC1};
typedef NppStatus (*func_ptr)(const src_t* pSrc, int nSrcStep, NppiSize oSizeROI, Npp32s* pHist,
const Npp32s* pLevels, int nLevels, Npp8u* pBuffer);
};
template<> struct NppHistogramRangeFuncC1<CV_32F>
{
typedef Npp32f src_t;
typedef Npp32f level_t;
enum {LEVEL_TYPE_CODE=CV_32FC1};
typedef NppStatus (*func_ptr)(const Npp32f* pSrc, int nSrcStep, NppiSize oSizeROI, Npp32s* pHist,
const Npp32f* pLevels, int nLevels, Npp8u* pBuffer);
};
template<int SDEPTH> struct NppHistogramRangeFuncC4
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef Npp32s level_t;
enum {LEVEL_TYPE_CODE=CV_32SC1};
typedef NppStatus (*func_ptr)(const src_t* pSrc, int nSrcStep, NppiSize oSizeROI, Npp32s* pHist[4],
const Npp32s* pLevels[4], int nLevels[4], Npp8u* pBuffer);
};
template<> struct NppHistogramRangeFuncC4<CV_32F>
{
typedef Npp32f src_t;
typedef Npp32f level_t;
enum {LEVEL_TYPE_CODE=CV_32FC1};
typedef NppStatus (*func_ptr)(const Npp32f* pSrc, int nSrcStep, NppiSize oSizeROI, Npp32s* pHist[4],
const Npp32f* pLevels[4], int nLevels[4], Npp8u* pBuffer);
};
template<int SDEPTH, typename NppHistogramRangeFuncC1<SDEPTH>::func_ptr func, get_buf_size_c1_t get_buf_size>
struct NppHistogramRangeC1
{
typedef typename NppHistogramRangeFuncC1<SDEPTH>::src_t src_t;
typedef typename NppHistogramRangeFuncC1<SDEPTH>::level_t level_t;
enum {LEVEL_TYPE_CODE=NppHistogramRangeFuncC1<SDEPTH>::LEVEL_TYPE_CODE};
static void hist(const GpuMat& src, GpuMat& hist, const GpuMat& levels)
{
CV_Assert(levels.type() == LEVEL_TYPE_CODE && levels.rows == 1);
hist.create(1, levels.cols - 1, CV_32S);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
GpuMat buffer;
int buf_size;
get_buf_size(sz, levels.cols, &buf_size);
buffer.create(1, buf_size, CV_8U);
nppSafeCall( func(src.ptr<src_t>(), src.step, sz, hist.ptr<Npp32s>(), levels.ptr<level_t>(), levels.cols, buffer.ptr<Npp8u>()) );
cudaSafeCall( cudaThreadSynchronize() );
}
};
template<int SDEPTH, typename NppHistogramRangeFuncC4<SDEPTH>::func_ptr func, get_buf_size_c4_t get_buf_size>
struct NppHistogramRangeC4
{
typedef typename NppHistogramRangeFuncC4<SDEPTH>::src_t src_t;
typedef typename NppHistogramRangeFuncC1<SDEPTH>::level_t level_t;
enum {LEVEL_TYPE_CODE=NppHistogramRangeFuncC1<SDEPTH>::LEVEL_TYPE_CODE};
static void hist(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4])
{
CV_Assert(levels[0].type() == LEVEL_TYPE_CODE && levels[0].rows == 1);
CV_Assert(levels[1].type() == LEVEL_TYPE_CODE && levels[1].rows == 1);
CV_Assert(levels[2].type() == LEVEL_TYPE_CODE && levels[2].rows == 1);
CV_Assert(levels[3].type() == LEVEL_TYPE_CODE && levels[3].rows == 1);
hist[0].create(1, levels[0].cols - 1, CV_32S);
hist[1].create(1, levels[1].cols - 1, CV_32S);
hist[2].create(1, levels[2].cols - 1, CV_32S);
hist[3].create(1, levels[3].cols - 1, CV_32S);
Npp32s* pHist[] = {hist[0].ptr<Npp32s>(), hist[1].ptr<Npp32s>(), hist[2].ptr<Npp32s>(), hist[3].ptr<Npp32s>()};
int nLevels[] = {levels[0].cols, levels[1].cols, levels[2].cols, levels[3].cols};
const level_t* pLevels[] = {levels[0].ptr<level_t>(), levels[1].ptr<level_t>(), levels[2].ptr<level_t>(), levels[3].ptr<level_t>()};
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
GpuMat buffer;
int buf_size;
get_buf_size(sz, nLevels, &buf_size);
buffer.create(1, buf_size, CV_8U);
nppSafeCall( func(src.ptr<src_t>(), src.step, sz, pHist, pLevels, nLevels, buffer.ptr<Npp8u>()) );
cudaSafeCall( cudaThreadSynchronize() );
}
};
}
void cv::gpu::evenLevels(GpuMat& levels, int nLevels, int lowerLevel, int upperLevel)
{
Mat host_levels(1, nLevels, CV_32SC1);
nppSafeCall( nppiEvenLevelsHost_32s(host_levels.ptr<Npp32s>(), nLevels, lowerLevel, upperLevel) );
levels.upload(host_levels);
}
void cv::gpu::histEven(const GpuMat& src, GpuMat& hist, int histSize, int lowerLevel, int upperLevel)
{
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_16UC1 || src.type() == CV_16SC1 );
typedef void (*hist_t)(const GpuMat& src, GpuMat& hist, int levels, int lowerLevel, int upperLevel);
static const hist_t hist_callers[] =
{
NppHistogramEvenC1<CV_8U , nppiHistogramEven_8u_C1R , nppiHistogramEvenGetBufferSize_8u_C1R >::hist,
0,
NppHistogramEvenC1<CV_16U, nppiHistogramEven_16u_C1R, nppiHistogramEvenGetBufferSize_16u_C1R>::hist,
NppHistogramEvenC1<CV_16S, nppiHistogramEven_16s_C1R, nppiHistogramEvenGetBufferSize_16s_C1R>::hist
};
hist_callers[src.depth()](src, hist, histSize, lowerLevel, upperLevel);
}
void cv::gpu::histEven(const GpuMat& src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4])
{
CV_Assert(src.type() == CV_8UC4 || src.type() == CV_16UC4 || src.type() == CV_16SC4 );
typedef void (*hist_t)(const GpuMat& src, GpuMat hist[4], int levels[4], int lowerLevel[4], int upperLevel[4]);
static const hist_t hist_callers[] =
{
NppHistogramEvenC4<CV_8U , nppiHistogramEven_8u_C4R , nppiHistogramEvenGetBufferSize_8u_C4R >::hist,
0,
NppHistogramEvenC4<CV_16U, nppiHistogramEven_16u_C4R, nppiHistogramEvenGetBufferSize_16u_C4R>::hist,
NppHistogramEvenC4<CV_16S, nppiHistogramEven_16s_C4R, nppiHistogramEvenGetBufferSize_16s_C4R>::hist
};
hist_callers[src.depth()](src, hist, histSize, lowerLevel, upperLevel);
}
void cv::gpu::histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels)
{
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_16UC1 || src.type() == CV_16SC1 || src.type() == CV_32FC1);
typedef void (*hist_t)(const GpuMat& src, GpuMat& hist, const GpuMat& levels);
static const hist_t hist_callers[] =
{
NppHistogramRangeC1<CV_8U , nppiHistogramRange_8u_C1R , nppiHistogramRangeGetBufferSize_8u_C1R >::hist,
0,
NppHistogramRangeC1<CV_16U, nppiHistogramRange_16u_C1R, nppiHistogramRangeGetBufferSize_16u_C1R>::hist,
NppHistogramRangeC1<CV_16S, nppiHistogramRange_16s_C1R, nppiHistogramRangeGetBufferSize_16s_C1R>::hist,
0,
NppHistogramRangeC1<CV_32F, nppiHistogramRange_32f_C1R, nppiHistogramRangeGetBufferSize_32f_C1R>::hist
};
hist_callers[src.depth()](src, hist, levels);
}
void cv::gpu::histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4])
{
CV_Assert(src.type() == CV_8UC4 || src.type() == CV_16UC4 || src.type() == CV_16SC4 || src.type() == CV_32FC4);
typedef void (*hist_t)(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4]);
static const hist_t hist_callers[] =
{
NppHistogramRangeC4<CV_8U , nppiHistogramRange_8u_C4R , nppiHistogramRangeGetBufferSize_8u_C4R >::hist,
0,
NppHistogramRangeC4<CV_16U, nppiHistogramRange_16u_C4R, nppiHistogramRangeGetBufferSize_16u_C4R>::hist,
NppHistogramRangeC4<CV_16S, nppiHistogramRange_16s_C4R, nppiHistogramRangeGetBufferSize_16s_C4R>::hist,
0,
NppHistogramRangeC4<CV_32F, nppiHistogramRange_32f_C4R, nppiHistogramRangeGetBufferSize_32f_C4R>::hist
};
hist_callers[src.depth()](src, hist, levels);
}
////////////////////////////////////////////////////////////////////////
// cornerHarris & minEgenVal
namespace cv { namespace gpu { namespace imgproc {
void extractCovData_caller(const DevMem2Df Dx, const DevMem2Df Dy, PtrStepf dst);
void cornerHarris_caller(const int block_size, const float k, const DevMem2D Dx, const DevMem2D Dy, DevMem2D dst, int border_type);
void cornerMinEigenVal_caller(const int block_size, const DevMem2D Dx, const DevMem2D Dy, DevMem2D dst, int border_type);
}}}
namespace
{
template <typename T>
void extractCovData(const GpuMat& src, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, int borderType)
{
double scale = (double)(1 << ((ksize > 0 ? ksize : 3) - 1)) * blockSize;
if (ksize < 0)
scale *= 2.;
if (src.depth() == CV_8U)
scale *= 255.;
scale = 1./scale;
GpuMat tmp_buf(src.size(), CV_32F);
Dx.create(src.size(), CV_32F);
Dy.create(src.size(), CV_32F);
if (ksize > 0)
{
Sobel(src, Dx, CV_32F, 1, 0, ksize, scale, borderType);
Sobel(src, Dy, CV_32F, 0, 1, ksize, scale, borderType);
}
else
{
Scharr(src, Dx, CV_32F, 1, 0, scale, borderType);
Scharr(src, Dy, CV_32F, 0, 1, scale, borderType);
}
}
void extractCovData(const GpuMat& src, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, int borderType)
{
switch (src.type())
{
case CV_8U:
extractCovData<unsigned char>(src, Dx, Dy, blockSize, ksize, borderType);
break;
case CV_32F:
extractCovData<float>(src, Dx, Dy, blockSize, ksize, borderType);
break;
default:
CV_Error(CV_StsBadArg, "extractCovData: unsupported type of the source matrix");
}
}
} // Anonymous namespace
bool cv::gpu::tryConvertToGpuBorderType(int cpuBorderType, int& gpuBorderType)
{
if (cpuBorderType == cv::BORDER_REFLECT101)
{
gpuBorderType = cv::gpu::BORDER_REFLECT101_GPU;
return true;
}
if (cpuBorderType == cv::BORDER_REPLICATE)
{
gpuBorderType = cv::gpu::BORDER_REPLICATE_GPU;
return true;
}
if (cpuBorderType == cv::BORDER_CONSTANT)
{
gpuBorderType = cv::gpu::BORDER_CONSTANT_GPU;
return true;
}
return false;
}
void cv::gpu::cornerHarris(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, double k, int borderType)
{
CV_Assert(borderType == cv::BORDER_REFLECT101 ||
borderType == cv::BORDER_REPLICATE);
int gpuBorderType;
CV_Assert(tryConvertToGpuBorderType(borderType, gpuBorderType));
GpuMat Dx, Dy;
extractCovData(src, Dx, Dy, blockSize, ksize, borderType);
dst.create(src.size(), CV_32F);
imgproc::cornerHarris_caller(blockSize, (float)k, Dx, Dy, dst, gpuBorderType);
}
void cv::gpu::cornerMinEigenVal(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, int borderType)
{
CV_Assert(borderType == cv::BORDER_REFLECT101 ||
borderType == cv::BORDER_REPLICATE);
int gpuBorderType;
CV_Assert(tryConvertToGpuBorderType(borderType, gpuBorderType));
GpuMat Dx, Dy;
extractCovData(src, Dx, Dy, blockSize, ksize, borderType);
dst.create(src.size(), CV_32F);
imgproc::cornerMinEigenVal_caller(blockSize, Dx, Dy, dst, gpuBorderType);
}
//////////////////////////////////////////////////////////////////////////////
// mulSpectrums
namespace cv { namespace gpu { namespace imgproc
{
void mulSpectrums(const PtrStep_<cufftComplex> a, const PtrStep_<cufftComplex> b,
DevMem2D_<cufftComplex> c);
void mulSpectrums_CONJ(const PtrStep_<cufftComplex> a, const PtrStep_<cufftComplex> b,
DevMem2D_<cufftComplex> c);
}}}
void cv::gpu::mulSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c,
int flags, bool conjB)
{
typedef void (*Caller)(const PtrStep_<cufftComplex>, const PtrStep_<cufftComplex>,
DevMem2D_<cufftComplex>);
static Caller callers[] = { imgproc::mulSpectrums,
imgproc::mulSpectrums_CONJ };
CV_Assert(a.type() == b.type() && a.type() == CV_32FC2);
CV_Assert(a.size() == b.size());
c.create(a.size(), CV_32FC2);
Caller caller = callers[(int)conjB];
caller(a, b, c);
}
//////////////////////////////////////////////////////////////////////////////
// mulAndScaleSpectrums
namespace cv { namespace gpu { namespace imgproc
{
void mulAndScaleSpectrums(const PtrStep_<cufftComplex> a, const PtrStep_<cufftComplex> b,
float scale, DevMem2D_<cufftComplex> c);
void mulAndScaleSpectrums_CONJ(const PtrStep_<cufftComplex> a, const PtrStep_<cufftComplex> b,
float scale, DevMem2D_<cufftComplex> c);
}}}
void cv::gpu::mulAndScaleSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c,
int flags, float scale, bool conjB)
{
typedef void (*Caller)(const PtrStep_<cufftComplex>, const PtrStep_<cufftComplex>,
float scale, DevMem2D_<cufftComplex>);
static Caller callers[] = { imgproc::mulAndScaleSpectrums,
imgproc::mulAndScaleSpectrums_CONJ };
CV_Assert(a.type() == b.type() && a.type() == CV_32FC2);
CV_Assert(a.size() == b.size());
c.create(a.size(), CV_32FC2);
Caller caller = callers[(int)conjB];
caller(a, b, scale, c);
}
//////////////////////////////////////////////////////////////////////////////
// dft
void cv::gpu::dft(const GpuMat& src, GpuMat& dst, Size dft_size, int flags)
{
CV_Assert(src.type() == CV_32F || src.type() == CV_32FC2);
// We don't support unpacked output (in the case of real input)
CV_Assert(!(flags & DFT_COMPLEX_OUTPUT));
bool is_1d_input = (dft_size.height == 1) || (dft_size.width == 1);
int is_row_dft = flags & DFT_ROWS;
int is_scaled_dft = flags & DFT_SCALE;
int is_inverse = flags & DFT_INVERSE;
bool is_complex_input = src.channels() == 2;
bool is_complex_output = !(flags & DFT_REAL_OUTPUT);
// We don't support real-to-real transform
CV_Assert(is_complex_input || is_complex_output);
GpuMat src_data;
// Make sure here we work with the continuous input,
// as CUFFT can't handle gaps
src_data = src;
createContinuous(src.rows, src.cols, src.type(), src_data);
if (src_data.data != src.data)
src.copyTo(src_data);
Size dft_size_opt = dft_size;
if (is_1d_input && !is_row_dft)
{
// If the source matrix is single column handle it as single row
dft_size_opt.width = std::max(dft_size.width, dft_size.height);
dft_size_opt.height = std::min(dft_size.width, dft_size.height);
}
cufftType dft_type = CUFFT_R2C;
if (is_complex_input)
dft_type = is_complex_output ? CUFFT_C2C : CUFFT_C2R;
CV_Assert(dft_size_opt.width > 1);
cufftHandle plan;
if (is_1d_input || is_row_dft)
cufftPlan1d(&plan, dft_size_opt.width, dft_type, dft_size_opt.height);
else
cufftPlan2d(&plan, dft_size_opt.height, dft_size_opt.width, dft_type);
if (is_complex_input)
{
if (is_complex_output)
{
createContinuous(dft_size, CV_32FC2, dst);
cufftSafeCall(cufftExecC2C(
plan, src_data.ptr<cufftComplex>(), dst.ptr<cufftComplex>(),
is_inverse ? CUFFT_INVERSE : CUFFT_FORWARD));
}
else
{
createContinuous(dft_size, CV_32F, dst);
cufftSafeCall(cufftExecC2R(
plan, src_data.ptr<cufftComplex>(), dst.ptr<cufftReal>()));
}
}
else
{
// We could swap dft_size for efficiency. Here we must reflect it
if (dft_size == dft_size_opt)
createContinuous(Size(dft_size.width / 2 + 1, dft_size.height), CV_32FC2, dst);
else
createContinuous(Size(dft_size.width, dft_size.height / 2 + 1), CV_32FC2, dst);
cufftSafeCall(cufftExecR2C(
plan, src_data.ptr<cufftReal>(), dst.ptr<cufftComplex>()));
}
cufftSafeCall(cufftDestroy(plan));
if (is_scaled_dft)
multiply(dst, Scalar::all(1. / dft_size.area()), dst);
}
//////////////////////////////////////////////////////////////////////////////
// convolve
void cv::gpu::ConvolveBuf::create(Size image_size, Size templ_size)
{
result_size = Size(image_size.width - templ_size.width + 1,
image_size.height - templ_size.height + 1);
block_size = estimateBlockSize(result_size, templ_size);
dft_size.width = getOptimalDFTSize(block_size.width + templ_size.width - 1);
dft_size.height = getOptimalDFTSize(block_size.width + templ_size.height - 1);
createContinuous(dft_size, CV_32F, image_block);
createContinuous(dft_size, CV_32F, templ_block);
createContinuous(dft_size, CV_32F, result_data);
spect_len = dft_size.height * (dft_size.width / 2 + 1);
createContinuous(1, spect_len, CV_32FC2, image_spect);
createContinuous(1, spect_len, CV_32FC2, templ_spect);
createContinuous(1, spect_len, CV_32FC2, result_spect);
block_size.width = std::min(dft_size.width - templ_size.width + 1, result_size.width);
block_size.height = std::min(dft_size.height - templ_size.height + 1, result_size.height);
}
Size cv::gpu::ConvolveBuf::estimateBlockSize(Size result_size, Size templ_size)
{
int scale = 40;
Size bsize_min(1024, 1024);
// Check whether we use Fermi generation or newer GPU
if (DeviceInfo().majorVersion() >= 2)
{
bsize_min.width = 2048;
bsize_min.height = 2048;
}
Size bsize(std::max(templ_size.width * scale, bsize_min.width),
std::max(templ_size.height * scale, bsize_min.height));
bsize.width = std::min(bsize.width, result_size.width);
bsize.height = std::min(bsize.height, result_size.height);
return bsize;
}
void cv::gpu::convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result,
bool ccorr)
{
ConvolveBuf buf;
convolve(image, templ, result, ccorr, buf);
}
void cv::gpu::convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result,
bool ccorr, ConvolveBuf& buf)
{
StaticAssert<sizeof(float) == sizeof(cufftReal)>::check();
StaticAssert<sizeof(float) * 2 == sizeof(cufftComplex)>::check();
CV_Assert(image.type() == CV_32F);
CV_Assert(templ.type() == CV_32F);
buf.create(image.size(), templ.size());
result.create(buf.result_size, CV_32F);
Size& block_size = buf.block_size;
Size& dft_size = buf.dft_size;
GpuMat& image_block = buf.image_block;
GpuMat& templ_block = buf.templ_block;
GpuMat& result_data = buf.result_data;
GpuMat& image_spect = buf.image_spect;
GpuMat& templ_spect = buf.templ_spect;
GpuMat& result_spect = buf.result_spect;
cufftHandle planR2C, planC2R;
cufftSafeCall(cufftPlan2d(&planC2R, dft_size.height, dft_size.width, CUFFT_C2R));
cufftSafeCall(cufftPlan2d(&planR2C, dft_size.height, dft_size.width, CUFFT_R2C));
GpuMat templ_roi(templ.size(), CV_32F, templ.data, templ.step);
copyMakeBorder(templ_roi, templ_block, 0, templ_block.rows - templ_roi.rows, 0,
templ_block.cols - templ_roi.cols, 0);
cufftSafeCall(cufftExecR2C(planR2C, templ_block.ptr<cufftReal>(),
templ_spect.ptr<cufftComplex>()));
// Process all blocks of the result matrix
for (int y = 0; y < result.rows; y += block_size.height)
{
for (int x = 0; x < result.cols; x += block_size.width)
{
Size image_roi_size(std::min(x + dft_size.width, image.cols) - x,
std::min(y + dft_size.height, image.rows) - y);
GpuMat image_roi(image_roi_size, CV_32F, (void*)(image.ptr<float>(y) + x),
image.step);
copyMakeBorder(image_roi, image_block, 0, image_block.rows - image_roi.rows,
0, image_block.cols - image_roi.cols, 0);
cufftSafeCall(cufftExecR2C(planR2C, image_block.ptr<cufftReal>(),
image_spect.ptr<cufftComplex>()));
mulAndScaleSpectrums(image_spect, templ_spect, result_spect, 0,
1.f / dft_size.area(), ccorr);
cufftSafeCall(cufftExecC2R(planC2R, result_spect.ptr<cufftComplex>(),
result_data.ptr<cufftReal>()));
Size result_roi_size(std::min(x + block_size.width, result.cols) - x,
std::min(y + block_size.height, result.rows) - y);
GpuMat result_roi(result_roi_size, result.type(),
(void*)(result.ptr<float>(y) + x), result.step);
GpuMat result_block(result_roi_size, result_data.type(),
result_data.ptr(), result_data.step);
result_block.copyTo(result_roi);
}
}
cufftSafeCall(cufftDestroy(planR2C));
cufftSafeCall(cufftDestroy(planC2R));
}
#endif /* !defined (HAVE_CUDA) */
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