opencv/modules/gpu/src/matrix_reductions.cpp

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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 GpuMaterials 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 bpied warranties, including, but not limited to, the bpied
// 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"
using namespace cv;
using namespace cv::gpu;
#if !defined (HAVE_CUDA)
void cv::gpu::meanStdDev(const GpuMat&, Scalar&, Scalar&) { 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; }
Scalar cv::gpu::sum(const GpuMat&) { throw_nogpu(); return Scalar(); }
Scalar cv::gpu::sum(const GpuMat&, GpuMat&) { throw_nogpu(); return Scalar(); }
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Scalar cv::gpu::absSum(const GpuMat&) { throw_nogpu(); return Scalar(); }
Scalar cv::gpu::absSum(const GpuMat&, GpuMat&) { throw_nogpu(); return Scalar(); }
Scalar cv::gpu::sqrSum(const GpuMat&) { throw_nogpu(); return Scalar(); }
Scalar cv::gpu::sqrSum(const GpuMat&, GpuMat&) { throw_nogpu(); return Scalar(); }
void cv::gpu::minMax(const GpuMat&, double*, double*, const GpuMat&) { throw_nogpu(); }
void cv::gpu::minMax(const GpuMat&, double*, double*, const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::minMaxLoc(const GpuMat&, double*, double*, Point*, Point*, const GpuMat&) { throw_nogpu(); }
void cv::gpu::minMaxLoc(const GpuMat&, double*, double*, Point*, Point*, const GpuMat&, GpuMat&, GpuMat&) { throw_nogpu(); }
int cv::gpu::countNonZero(const GpuMat&) { throw_nogpu(); return 0; }
int cv::gpu::countNonZero(const GpuMat&, GpuMat&) { throw_nogpu(); return 0; }
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void cv::gpu::reduce(const GpuMat&, GpuMat&, int, int, int, Stream&) { throw_nogpu(); }
#else
namespace
{
class DeviceBuffer
{
public:
explicit DeviceBuffer(int count_ = 1) : count(count_)
{
cudaSafeCall( cudaMalloc(&pdev, count * sizeof(double)) );
}
~DeviceBuffer()
{
cudaSafeCall( cudaFree(pdev) );
}
operator double*() {return pdev;}
void download(double* hptr)
{
double hbuf;
cudaSafeCall( cudaMemcpy(&hbuf, pdev, sizeof(double), cudaMemcpyDeviceToHost) );
*hptr = hbuf;
}
void download(double** hptrs)
{
AutoBuffer<double, 2 * sizeof(double)> hbuf(count);
cudaSafeCall( cudaMemcpy((void*)hbuf, pdev, count * sizeof(double), cudaMemcpyDeviceToHost) );
for (int i = 0; i < count; ++i)
*hptrs[i] = hbuf[i];
}
private:
double* pdev;
int count;
};
}
////////////////////////////////////////////////////////////////////////
// meanStdDev
void cv::gpu::meanStdDev(const GpuMat& src, Scalar& mean, Scalar& stddev)
{
CV_Assert(src.type() == CV_8UC1);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
DeviceBuffer dbuf(2);
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nppSafeCall( nppiMean_StdDev_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step), sz, dbuf, (double*)dbuf + 1) );
cudaSafeCall( cudaDeviceSynchronize() );
double* ptrs[2] = {mean.val, stddev.val};
dbuf.download(ptrs);
}
////////////////////////////////////////////////////////////////////////
// norm
double cv::gpu::norm(const GpuMat& src, int normType)
{
GpuMat buf;
return norm(src, normType, buf);
}
double cv::gpu::norm(const GpuMat& src, int normType, GpuMat& buf)
{
GpuMat src_single_channel = src.reshape(1);
if (normType == NORM_L1)
return absSum(src_single_channel, buf)[0];
if (normType == NORM_L2)
return sqrt(sqrSum(src_single_channel, buf)[0]);
if (normType == NORM_INF)
{
double min_val, max_val;
minMax(src_single_channel, &min_val, &max_val, GpuMat(), buf);
return std::max(std::abs(min_val), std::abs(max_val));
}
CV_Error(CV_StsBadArg, "norm: unsupported norm type");
return 0;
}
double cv::gpu::norm(const GpuMat& src1, const GpuMat& src2, int normType)
{
CV_DbgAssert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(src1.type() == CV_8UC1);
CV_Assert(normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2);
typedef NppStatus (*npp_norm_diff_func_t)(const Npp8u* pSrc1, int nSrcStep1, const Npp8u* pSrc2, int nSrcStep2,
NppiSize oSizeROI, Npp64f* pRetVal);
static const npp_norm_diff_func_t npp_norm_diff_func[] = {nppiNormDiff_Inf_8u_C1R, nppiNormDiff_L1_8u_C1R, nppiNormDiff_L2_8u_C1R};
NppiSize sz;
sz.width = src1.cols;
sz.height = src1.rows;
int funcIdx = normType >> 1;
double retVal;
DeviceBuffer dbuf;
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nppSafeCall( npp_norm_diff_func[funcIdx](src1.ptr<Npp8u>(), static_cast<int>(src1.step), src2.ptr<Npp8u>(), static_cast<int>(src2.step), sz, dbuf) );
cudaSafeCall( cudaDeviceSynchronize() );
dbuf.download(&retVal);
return retVal;
}
////////////////////////////////////////////////////////////////////////
// Sum
BEGIN_OPENCV_DEVICE_NAMESPACE
namespace matrix_reductions
{
namespace sum
{
template <typename T>
void sumCaller(const DevMem2Db src, PtrStepb buf, double* sum, int cn);
template <typename T>
void sumMultipassCaller(const DevMem2Db src, PtrStepb buf, double* sum, int cn);
template <typename T>
void absSumCaller(const DevMem2Db src, PtrStepb buf, double* sum, int cn);
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template <typename T>
void absSumMultipassCaller(const DevMem2Db src, PtrStepb buf, double* sum, int cn);
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template <typename T>
void sqrSumCaller(const DevMem2Db src, PtrStepb buf, double* sum, int cn);
template <typename T>
void sqrSumMultipassCaller(const DevMem2Db src, PtrStepb buf, double* sum, int cn);
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void getBufSizeRequired(int cols, int rows, int cn, int& bufcols, int& bufrows);
}
}
END_OPENCV_DEVICE_NAMESPACE
Scalar cv::gpu::sum(const GpuMat& src)
{
GpuMat buf;
return sum(src, buf);
}
Scalar cv::gpu::sum(const GpuMat& src, GpuMat& buf)
{
using namespace OPENCV_DEVICE_NAMESPACE_ matrix_reductions::sum;
typedef void (*Caller)(const DevMem2Db, PtrStepb, double*, int);
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static Caller multipass_callers[7] =
{
sumMultipassCaller<unsigned char>, sumMultipassCaller<char>,
sumMultipassCaller<unsigned short>, sumMultipassCaller<short>,
sumMultipassCaller<int>, sumMultipassCaller<float>, 0
};
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static Caller singlepass_callers[7] = {
sumCaller<unsigned char>, sumCaller<char>,
sumCaller<unsigned short>, sumCaller<short>,
sumCaller<int>, sumCaller<float>, 0
};
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Size buf_size;
getBufSizeRequired(src.cols, src.rows, src.channels(), buf_size.width, buf_size.height);
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ensureSizeIsEnough(buf_size, CV_8U, buf);
Caller* callers = multipass_callers;
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if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
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callers = singlepass_callers;
Caller caller = callers[src.depth()];
if (!caller) CV_Error(CV_StsBadArg, "sum: unsupported type");
double result[4];
caller(src, buf, result, src.channels());
return Scalar(result[0], result[1], result[2], result[3]);
}
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Scalar cv::gpu::absSum(const GpuMat& src)
{
GpuMat buf;
return absSum(src, buf);
}
Scalar cv::gpu::absSum(const GpuMat& src, GpuMat& buf)
{
using namespace OPENCV_DEVICE_NAMESPACE_ matrix_reductions::sum;
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typedef void (*Caller)(const DevMem2Db, PtrStepb, double*, int);
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static Caller multipass_callers[7] =
{
absSumMultipassCaller<unsigned char>, absSumMultipassCaller<char>,
absSumMultipassCaller<unsigned short>, absSumMultipassCaller<short>,
absSumMultipassCaller<int>, absSumMultipassCaller<float>, 0
};
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static Caller singlepass_callers[7] =
{
absSumCaller<unsigned char>, absSumCaller<char>,
absSumCaller<unsigned short>, absSumCaller<short>,
absSumCaller<int>, absSumCaller<float>, 0
};
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Size buf_size;
getBufSizeRequired(src.cols, src.rows, src.channels(), buf_size.width, buf_size.height);
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ensureSizeIsEnough(buf_size, CV_8U, buf);
Caller* callers = multipass_callers;
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if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
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callers = singlepass_callers;
Caller caller = callers[src.depth()];
if (!caller) CV_Error(CV_StsBadArg, "absSum: unsupported type");
double result[4];
caller(src, buf, result, src.channels());
return Scalar(result[0], result[1], result[2], result[3]);
}
Scalar cv::gpu::sqrSum(const GpuMat& src)
{
GpuMat buf;
return sqrSum(src, buf);
}
Scalar cv::gpu::sqrSum(const GpuMat& src, GpuMat& buf)
{
using namespace OPENCV_DEVICE_NAMESPACE_ matrix_reductions::sum;
typedef void (*Caller)(const DevMem2Db, PtrStepb, double*, int);
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static Caller multipass_callers[7] =
{
sqrSumMultipassCaller<unsigned char>, sqrSumMultipassCaller<char>,
sqrSumMultipassCaller<unsigned short>, sqrSumMultipassCaller<short>,
sqrSumMultipassCaller<int>, sqrSumMultipassCaller<float>, 0
};
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static Caller singlepass_callers[7] =
{
sqrSumCaller<unsigned char>, sqrSumCaller<char>,
sqrSumCaller<unsigned short>, sqrSumCaller<short>,
sqrSumCaller<int>, sqrSumCaller<float>, 0
};
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Caller* callers = multipass_callers;
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if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
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callers = singlepass_callers;
Size buf_size;
getBufSizeRequired(src.cols, src.rows, src.channels(), buf_size.width, buf_size.height);
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ensureSizeIsEnough(buf_size, CV_8U, buf);
Caller caller = callers[src.depth()];
if (!caller) CV_Error(CV_StsBadArg, "sqrSum: unsupported type");
double result[4];
caller(src, buf, result, src.channels());
return Scalar(result[0], result[1], result[2], result[3]);
}
////////////////////////////////////////////////////////////////////////
// Find min or max
BEGIN_OPENCV_DEVICE_NAMESPACE
namespace matrix_reductions
{
namespace minmax
{
void getBufSizeRequired(int cols, int rows, int elem_size, int& bufcols, int& bufrows);
template <typename T>
void minMaxCaller(const DevMem2Db src, double* minval, double* maxval, PtrStepb buf);
template <typename T>
void minMaxMaskCaller(const DevMem2Db src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
template <typename T>
void minMaxMultipassCaller(const DevMem2Db src, double* minval, double* maxval, PtrStepb buf);
template <typename T>
void minMaxMaskMultipassCaller(const DevMem2Db src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
}
}
END_OPENCV_DEVICE_NAMESPACE
void cv::gpu::minMax(const GpuMat& src, double* minVal, double* maxVal, const GpuMat& mask)
{
GpuMat buf;
minMax(src, minVal, maxVal, mask, buf);
}
void cv::gpu::minMax(const GpuMat& src, double* minVal, double* maxVal, const GpuMat& mask, GpuMat& buf)
{
using namespace OPENCV_DEVICE_NAMESPACE_ matrix_reductions::minmax;
typedef void (*Caller)(const DevMem2Db, double*, double*, PtrStepb);
typedef void (*MaskedCaller)(const DevMem2Db, const PtrStepb, double*, double*, PtrStepb);
static Caller multipass_callers[7] =
{
minMaxMultipassCaller<unsigned char>, minMaxMultipassCaller<char>,
minMaxMultipassCaller<unsigned short>, minMaxMultipassCaller<short>,
minMaxMultipassCaller<int>, minMaxMultipassCaller<float>, 0
};
static Caller singlepass_callers[7] =
{
minMaxCaller<unsigned char>, minMaxCaller<char>,
minMaxCaller<unsigned short>, minMaxCaller<short>,
minMaxCaller<int>, minMaxCaller<float>, minMaxCaller<double>
};
static MaskedCaller masked_multipass_callers[7] =
{
minMaxMaskMultipassCaller<unsigned char>, minMaxMaskMultipassCaller<char>,
minMaxMaskMultipassCaller<unsigned short>, minMaxMaskMultipassCaller<short>,
minMaxMaskMultipassCaller<int>, minMaxMaskMultipassCaller<float>, 0
};
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static MaskedCaller masked_singlepass_callers[7] =
{
minMaxMaskCaller<unsigned char>, minMaxMaskCaller<char>,
minMaxMaskCaller<unsigned short>, minMaxMaskCaller<short>,
minMaxMaskCaller<int>, minMaxMaskCaller<float>, minMaxMaskCaller<double>
};
CV_Assert(src.channels() == 1);
CV_Assert(mask.empty() || (mask.type() == CV_8U && src.size() == mask.size()));
double minVal_; if (!minVal) minVal = &minVal_;
double maxVal_; if (!maxVal) maxVal = &maxVal_;
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Size buf_size;
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getBufSizeRequired(src.cols, src.rows, static_cast<int>(src.elemSize()), buf_size.width, buf_size.height);
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ensureSizeIsEnough(buf_size, CV_8U, buf);
if (mask.empty())
{
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Caller* callers = multipass_callers;
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if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
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callers = singlepass_callers;
Caller caller = callers[src.type()];
if (!caller) CV_Error(CV_StsBadArg, "minMax: unsupported type");
caller(src, minVal, maxVal, buf);
}
else
{
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MaskedCaller* callers = masked_multipass_callers;
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if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
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callers = masked_singlepass_callers;
MaskedCaller caller = callers[src.type()];
if (!caller) CV_Error(CV_StsBadArg, "minMax: unsupported type");
caller(src, mask, minVal, maxVal, buf);
}
}
////////////////////////////////////////////////////////////////////////
// Locate min and max
BEGIN_OPENCV_DEVICE_NAMESPACE
namespace matrix_reductions
{
namespace minmaxloc
{
void getBufSizeRequired(int cols, int rows, int elem_size, int& b1cols,
int& b1rows, int& b2cols, int& b2rows);
template <typename T>
void minMaxLocCaller(const DevMem2Db src, double* minval, double* maxval,
int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf);
template <typename T>
void minMaxLocMaskCaller(const DevMem2Db src, const PtrStepb mask, double* minval, double* maxval,
int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf);
template <typename T>
void minMaxLocMultipassCaller(const DevMem2Db src, double* minval, double* maxval,
int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf);
template <typename T>
void minMaxLocMaskMultipassCaller(const DevMem2Db src, const PtrStepb mask, double* minval, double* maxval,
int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf);
}
}
END_OPENCV_DEVICE_NAMESPACE
void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc, const GpuMat& mask)
{
GpuMat valBuf, locBuf;
minMaxLoc(src, minVal, maxVal, minLoc, maxLoc, mask, valBuf, locBuf);
}
void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
const GpuMat& mask, GpuMat& valBuf, GpuMat& locBuf)
{
using namespace OPENCV_DEVICE_NAMESPACE_ matrix_reductions::minmaxloc;
typedef void (*Caller)(const DevMem2Db, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
typedef void (*MaskedCaller)(const DevMem2Db, const PtrStepb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
static Caller multipass_callers[7] =
{
minMaxLocMultipassCaller<unsigned char>, minMaxLocMultipassCaller<char>,
minMaxLocMultipassCaller<unsigned short>, minMaxLocMultipassCaller<short>,
minMaxLocMultipassCaller<int>, minMaxLocMultipassCaller<float>, 0
};
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static Caller singlepass_callers[7] =
{
minMaxLocCaller<unsigned char>, minMaxLocCaller<char>,
minMaxLocCaller<unsigned short>, minMaxLocCaller<short>,
minMaxLocCaller<int>, minMaxLocCaller<float>, minMaxLocCaller<double>
};
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static MaskedCaller masked_multipass_callers[7] =
{
minMaxLocMaskMultipassCaller<unsigned char>, minMaxLocMaskMultipassCaller<char>,
minMaxLocMaskMultipassCaller<unsigned short>, minMaxLocMaskMultipassCaller<short>,
minMaxLocMaskMultipassCaller<int>, minMaxLocMaskMultipassCaller<float>, 0
};
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static MaskedCaller masked_singlepass_callers[7] =
{
minMaxLocMaskCaller<unsigned char>, minMaxLocMaskCaller<char>,
minMaxLocMaskCaller<unsigned short>, minMaxLocMaskCaller<short>,
minMaxLocMaskCaller<int>, minMaxLocMaskCaller<float>, minMaxLocMaskCaller<double>
};
CV_Assert(src.channels() == 1);
CV_Assert(mask.empty() || (mask.type() == CV_8U && src.size() == mask.size()));
double minVal_; if (!minVal) minVal = &minVal_;
double maxVal_; if (!maxVal) maxVal = &maxVal_;
int minLoc_[2];
int maxLoc_[2];
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Size valbuf_size, locbuf_size;
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getBufSizeRequired(src.cols, src.rows, static_cast<int>(src.elemSize()), valbuf_size.width,
valbuf_size.height, locbuf_size.width, locbuf_size.height);
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ensureSizeIsEnough(valbuf_size, CV_8U, valBuf);
ensureSizeIsEnough(locbuf_size, CV_8U, locBuf);
if (mask.empty())
{
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Caller* callers = multipass_callers;
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if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
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callers = singlepass_callers;
Caller caller = callers[src.type()];
if (!caller) CV_Error(CV_StsBadArg, "minMaxLoc: unsupported type");
caller(src, minVal, maxVal, minLoc_, maxLoc_, valBuf, locBuf);
}
else
{
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MaskedCaller* callers = masked_multipass_callers;
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if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
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callers = masked_singlepass_callers;
MaskedCaller caller = callers[src.type()];
if (!caller) CV_Error(CV_StsBadArg, "minMaxLoc: unsupported type");
caller(src, mask, minVal, maxVal, minLoc_, maxLoc_, valBuf, locBuf);
}
if (minLoc) { minLoc->x = minLoc_[0]; minLoc->y = minLoc_[1]; }
if (maxLoc) { maxLoc->x = maxLoc_[0]; maxLoc->y = maxLoc_[1]; }
}
//////////////////////////////////////////////////////////////////////////////
// Count non-zero elements
BEGIN_OPENCV_DEVICE_NAMESPACE
namespace matrix_reductions
{
namespace countnonzero
{
void getBufSizeRequired(int cols, int rows, int& bufcols, int& bufrows);
template <typename T>
int countNonZeroCaller(const DevMem2Db src, PtrStepb buf);
template <typename T>
int countNonZeroMultipassCaller(const DevMem2Db src, PtrStepb buf);
}
}
END_OPENCV_DEVICE_NAMESPACE
int cv::gpu::countNonZero(const GpuMat& src)
{
GpuMat buf;
return countNonZero(src, buf);
}
int cv::gpu::countNonZero(const GpuMat& src, GpuMat& buf)
{
using namespace OPENCV_DEVICE_NAMESPACE_ matrix_reductions::countnonzero;
typedef int (*Caller)(const DevMem2Db src, PtrStepb buf);
static Caller multipass_callers[7] =
{
countNonZeroMultipassCaller<unsigned char>, countNonZeroMultipassCaller<char>,
countNonZeroMultipassCaller<unsigned short>, countNonZeroMultipassCaller<short>,
countNonZeroMultipassCaller<int>, countNonZeroMultipassCaller<float>, 0
};
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static Caller singlepass_callers[7] =
{
countNonZeroCaller<unsigned char>, countNonZeroCaller<char>,
countNonZeroCaller<unsigned short>, countNonZeroCaller<short>,
countNonZeroCaller<int>, countNonZeroCaller<float>, countNonZeroCaller<double> };
CV_Assert(src.channels() == 1);
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Size buf_size;
getBufSizeRequired(src.cols, src.rows, buf_size.width, buf_size.height);
ensureSizeIsEnough(buf_size, CV_8U, buf);
Caller* callers = multipass_callers;
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if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
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callers = singlepass_callers;
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Caller caller = callers[src.type()];
if (!caller) CV_Error(CV_StsBadArg, "countNonZero: unsupported type");
return caller(src, buf);
}
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//////////////////////////////////////////////////////////////////////////////
// reduce
BEGIN_OPENCV_DEVICE_NAMESPACE
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namespace matrix_reductions
{
template <typename T, typename S, typename D> void reduceRows_gpu(const DevMem2Db& src, const DevMem2Db& dst, int reduceOp, cudaStream_t stream);
template <typename T, typename S, typename D> void reduceCols_gpu(const DevMem2Db& src, int cn, const DevMem2Db& dst, int reduceOp, cudaStream_t stream);
}
END_OPENCV_DEVICE_NAMESPACE
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void cv::gpu::reduce(const GpuMat& src, GpuMat& dst, int dim, int reduceOp, int dtype, Stream& stream)
{
using namespace OPENCV_DEVICE_NAMESPACE_ matrix_reductions;
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CV_Assert(src.depth() <= CV_32F && src.channels() <= 4 && dtype <= CV_32F);
CV_Assert(dim == 0 || dim == 1);
CV_Assert(reduceOp == CV_REDUCE_SUM || reduceOp == CV_REDUCE_AVG || reduceOp == CV_REDUCE_MAX || reduceOp == CV_REDUCE_MIN);
if (dtype < 0)
dtype = src.depth();
dst.create(1, dim == 0 ? src.cols : src.rows, CV_MAKETYPE(dtype, src.channels()));
if (dim == 0)
{
typedef void (*caller_t)(const DevMem2Db& src, const DevMem2Db& dst, int reduceOp, cudaStream_t stream);
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static const caller_t callers[6][6] =
{
{
reduceRows_gpu<unsigned char, int, unsigned char>,
0/*reduceRows_gpu<unsigned char, int, signed char>*/,
0/*reduceRows_gpu<unsigned char, int, unsigned short>*/,
0/*reduceRows_gpu<unsigned char, int, short>*/,
reduceRows_gpu<unsigned char, int, int>,
reduceRows_gpu<unsigned char, int, float>
},
{
0/*reduceRows_gpu<signed char, int, unsigned char>*/,
0/*reduceRows_gpu<signed char, int, signed char>*/,
0/*reduceRows_gpu<signed char, int, unsigned short>*/,
0/*reduceRows_gpu<signed char, int, short>*/,
0/*reduceRows_gpu<signed char, int, int>*/,
0/*reduceRows_gpu<signed char, int, float>*/
},
{
0/*reduceRows_gpu<unsigned short, int, unsigned char>*/,
0/*reduceRows_gpu<unsigned short, int, signed char>*/,
reduceRows_gpu<unsigned short, int, unsigned short>,
0/*reduceRows_gpu<unsigned short, int, short>*/,
reduceRows_gpu<unsigned short, int, int>,
reduceRows_gpu<unsigned short, int, float>
},
{
0/*reduceRows_gpu<short, int, unsigned char>*/,
0/*reduceRows_gpu<short, int, signed char>*/,
0/*reduceRows_gpu<short, int, unsigned short>*/,
reduceRows_gpu<short, int, short>,
reduceRows_gpu<short, int, int>,
reduceRows_gpu<short, int, float>
},
{
0/*reduceRows_gpu<int, int, unsigned char>*/,
0/*reduceRows_gpu<int, int, signed char>*/,
0/*reduceRows_gpu<int, int, unsigned short>*/,
0/*reduceRows_gpu<int, int, short>*/,
reduceRows_gpu<int, int, int>,
reduceRows_gpu<int, int, float>
},
{
0/*reduceRows_gpu<float, float, unsigned char>*/,
0/*reduceRows_gpu<float, float, signed char>*/,
0/*reduceRows_gpu<float, float, unsigned short>*/,
0/*reduceRows_gpu<float, float, short>*/,
0/*reduceRows_gpu<float, float, int>*/,
reduceRows_gpu<float, float, float>
}
};
const caller_t func = callers[src.depth()][dst.depth()];
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of input and output array formats");
func(src.reshape(1), dst.reshape(1), reduceOp, StreamAccessor::getStream(stream));
}
else
{
typedef void (*caller_t)(const DevMem2Db& src, int cn, const DevMem2Db& dst, int reduceOp, cudaStream_t stream);
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static const caller_t callers[6][6] =
{
{
reduceCols_gpu<unsigned char, int, unsigned char>,
0/*reduceCols_gpu<unsigned char, int, signed char>*/,
0/*reduceCols_gpu<unsigned char, int, unsigned short>*/,
0/*reduceCols_gpu<unsigned char, int, short>*/,
reduceCols_gpu<unsigned char, int, int>,
reduceCols_gpu<unsigned char, int, float>
},
{
0/*reduceCols_gpu<signed char, int, unsigned char>*/,
0/*reduceCols_gpu<signed char, int, signed char>*/,
0/*reduceCols_gpu<signed char, int, unsigned short>*/,
0/*reduceCols_gpu<signed char, int, short>*/,
0/*reduceCols_gpu<signed char, int, int>*/,
0/*reduceCols_gpu<signed char, int, float>*/
},
{
0/*reduceCols_gpu<unsigned short, int, unsigned char>*/,
0/*reduceCols_gpu<unsigned short, int, signed char>*/,
reduceCols_gpu<unsigned short, int, unsigned short>,
0/*reduceCols_gpu<unsigned short, int, short>*/,
reduceCols_gpu<unsigned short, int, int>,
reduceCols_gpu<unsigned short, int, float>
},
{
0/*reduceCols_gpu<short, int, unsigned char>*/,
0/*reduceCols_gpu<short, int, signed char>*/,
0/*reduceCols_gpu<short, int, unsigned short>*/,
reduceCols_gpu<short, int, short>,
reduceCols_gpu<short, int, int>,
reduceCols_gpu<short, int, float>
},
{
0/*reduceCols_gpu<int, int, unsigned char>*/,
0/*reduceCols_gpu<int, int, signed char>*/,
0/*reduceCols_gpu<int, int, unsigned short>*/,
0/*reduceCols_gpu<int, int, short>*/,
reduceCols_gpu<int, int, int>,
reduceCols_gpu<int, int, float>
},
{
0/*reduceCols_gpu<float, unsigned char>*/,
0/*reduceCols_gpu<float, signed char>*/,
0/*reduceCols_gpu<float, unsigned short>*/,
0/*reduceCols_gpu<float, short>*/,
0/*reduceCols_gpu<float, int>*/,
reduceCols_gpu<float, float, float>
}
};
const caller_t func = callers[src.depth()][dst.depth()];
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of input and output array formats");
func(src, src.channels(), dst, reduceOp, StreamAccessor::getStream(stream));
}
}
#endif