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Open Source Computer Vision Library https://opencv.org/
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opencv/modules/core/src/gpumat.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 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 <limits>
using namespace cv;
using namespace cv::gpu;
//////////////////////////////// Initialization & Info ////////////////////////
#ifndef HAVE_CUDA
int cv::gpu::getCudaEnabledDeviceCount() { return 0; }
void cv::gpu::setDevice(int) { throw_no_cuda(); }
int cv::gpu::getDevice() { throw_no_cuda(); return 0; }
void cv::gpu::resetDevice() { throw_no_cuda(); }
bool cv::gpu::deviceSupports(FeatureSet) { throw_no_cuda(); return false; }
bool cv::gpu::TargetArchs::builtWith(FeatureSet) { throw_no_cuda(); return false; }
bool cv::gpu::TargetArchs::has(int, int) { throw_no_cuda(); return false; }
bool cv::gpu::TargetArchs::hasPtx(int, int) { throw_no_cuda(); return false; }
bool cv::gpu::TargetArchs::hasBin(int, int) { throw_no_cuda(); return false; }
bool cv::gpu::TargetArchs::hasEqualOrLessPtx(int, int) { throw_no_cuda(); return false; }
bool cv::gpu::TargetArchs::hasEqualOrGreater(int, int) { throw_no_cuda(); return false; }
bool cv::gpu::TargetArchs::hasEqualOrGreaterPtx(int, int) { throw_no_cuda(); return false; }
bool cv::gpu::TargetArchs::hasEqualOrGreaterBin(int, int) { throw_no_cuda(); return false; }
size_t cv::gpu::DeviceInfo::sharedMemPerBlock() const { throw_no_cuda(); return 0; }
void cv::gpu::DeviceInfo::queryMemory(size_t&, size_t&) const { throw_no_cuda(); }
size_t cv::gpu::DeviceInfo::freeMemory() const { throw_no_cuda(); return 0; }
size_t cv::gpu::DeviceInfo::totalMemory() const { throw_no_cuda(); return 0; }
bool cv::gpu::DeviceInfo::supports(FeatureSet) const { throw_no_cuda(); return false; }
bool cv::gpu::DeviceInfo::isCompatible() const { throw_no_cuda(); return false; }
void cv::gpu::DeviceInfo::query() { throw_no_cuda(); }
void cv::gpu::printCudaDeviceInfo(int) { throw_no_cuda(); }
void cv::gpu::printShortCudaDeviceInfo(int) { throw_no_cuda(); }
#else // HAVE_CUDA
int cv::gpu::getCudaEnabledDeviceCount()
{
int count;
cudaError_t error = cudaGetDeviceCount( &count );
if (error == cudaErrorInsufficientDriver)
return -1;
if (error == cudaErrorNoDevice)
return 0;
cudaSafeCall( error );
return count;
}
void cv::gpu::setDevice(int device)
{
cudaSafeCall( cudaSetDevice( device ) );
}
int cv::gpu::getDevice()
{
int device;
cudaSafeCall( cudaGetDevice( &device ) );
return device;
}
void cv::gpu::resetDevice()
{
cudaSafeCall( cudaDeviceReset() );
}
namespace
{
class CudaArch
{
public:
CudaArch();
bool builtWith(FeatureSet feature_set) const;
bool hasPtx(int major, int minor) const;
bool hasBin(int major, int minor) const;
bool hasEqualOrLessPtx(int major, int minor) const;
bool hasEqualOrGreaterPtx(int major, int minor) const;
bool hasEqualOrGreaterBin(int major, int minor) const;
private:
static void fromStr(const String& set_as_str, std::vector<int>& arr);
std::vector<int> bin;
std::vector<int> ptx;
std::vector<int> features;
};
const CudaArch cudaArch;
CudaArch::CudaArch()
{
fromStr(CUDA_ARCH_BIN, bin);
fromStr(CUDA_ARCH_PTX, ptx);
fromStr(CUDA_ARCH_FEATURES, features);
}
bool CudaArch::builtWith(FeatureSet feature_set) const
{
return !features.empty() && (features.back() >= feature_set);
}
bool CudaArch::hasPtx(int major, int minor) const
{
return std::find(ptx.begin(), ptx.end(), major * 10 + minor) != ptx.end();
}
bool CudaArch::hasBin(int major, int minor) const
{
return std::find(bin.begin(), bin.end(), major * 10 + minor) != bin.end();
}
bool CudaArch::hasEqualOrLessPtx(int major, int minor) const
{
return !ptx.empty() && (ptx.front() <= major * 10 + minor);
}
bool CudaArch::hasEqualOrGreaterPtx(int major, int minor) const
{
return !ptx.empty() && (ptx.back() >= major * 10 + minor);
}
bool CudaArch::hasEqualOrGreaterBin(int major, int minor) const
{
return !bin.empty() && (bin.back() >= major * 10 + minor);
}
void CudaArch::fromStr(const String& set_as_str, std::vector<int>& arr)
{
arr.clear();
size_t pos = 0;
while (pos < set_as_str.size())
{
if (isspace(set_as_str[pos]))
{
++pos;
}
else
{
int cur_value;
int chars_read;
int args_read = sscanf(set_as_str.c_str() + pos, "%d%n", &cur_value, &chars_read);
CV_Assert(args_read == 1);
arr.push_back(cur_value);
pos += chars_read;
}
}
std::sort(arr.begin(), arr.end());
}
}
bool cv::gpu::TargetArchs::builtWith(cv::gpu::FeatureSet feature_set)
{
return cudaArch.builtWith(feature_set);
}
bool cv::gpu::TargetArchs::has(int major, int minor)
{
return hasPtx(major, minor) || hasBin(major, minor);
}
bool cv::gpu::TargetArchs::hasPtx(int major, int minor)
{
return cudaArch.hasPtx(major, minor);
}
bool cv::gpu::TargetArchs::hasBin(int major, int minor)
{
return cudaArch.hasBin(major, minor);
}
bool cv::gpu::TargetArchs::hasEqualOrLessPtx(int major, int minor)
{
return cudaArch.hasEqualOrLessPtx(major, minor);
}
bool cv::gpu::TargetArchs::hasEqualOrGreater(int major, int minor)
{
return hasEqualOrGreaterPtx(major, minor) || hasEqualOrGreaterBin(major, minor);
}
bool cv::gpu::TargetArchs::hasEqualOrGreaterPtx(int major, int minor)
{
return cudaArch.hasEqualOrGreaterPtx(major, minor);
}
bool cv::gpu::TargetArchs::hasEqualOrGreaterBin(int major, int minor)
{
return cudaArch.hasEqualOrGreaterBin(major, minor);
}
bool cv::gpu::deviceSupports(FeatureSet feature_set)
{
static int versions[] =
{
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1
};
static const int cache_size = static_cast<int>(sizeof(versions) / sizeof(versions[0]));
const int devId = getDevice();
int version;
if (devId < cache_size && versions[devId] >= 0)
version = versions[devId];
else
{
DeviceInfo dev(devId);
version = dev.majorVersion() * 10 + dev.minorVersion();
if (devId < cache_size)
versions[devId] = version;
}
return TargetArchs::builtWith(feature_set) && (version >= feature_set);
}
namespace
{
class DeviceProps
{
public:
DeviceProps();
~DeviceProps();
cudaDeviceProp* get(int devID);
private:
std::vector<cudaDeviceProp*> props_;
};
DeviceProps::DeviceProps()
{
props_.resize(10, 0);
}
DeviceProps::~DeviceProps()
{
for (size_t i = 0; i < props_.size(); ++i)
{
if (props_[i])
delete props_[i];
}
props_.clear();
}
cudaDeviceProp* DeviceProps::get(int devID)
{
if (devID >= (int) props_.size())
props_.resize(devID + 5, 0);
if (!props_[devID])
{
props_[devID] = new cudaDeviceProp;
cudaSafeCall( cudaGetDeviceProperties(props_[devID], devID) );
}
return props_[devID];
}
DeviceProps deviceProps;
}
size_t cv::gpu::DeviceInfo::sharedMemPerBlock() const
{
return deviceProps.get(device_id_)->sharedMemPerBlock;
}
void cv::gpu::DeviceInfo::queryMemory(size_t& _totalMemory, size_t& _freeMemory) const
{
int prevDeviceID = getDevice();
if (prevDeviceID != device_id_)
setDevice(device_id_);
cudaSafeCall( cudaMemGetInfo(&_freeMemory, &_totalMemory) );
if (prevDeviceID != device_id_)
setDevice(prevDeviceID);
}
size_t cv::gpu::DeviceInfo::freeMemory() const
{
size_t _totalMemory, _freeMemory;
queryMemory(_totalMemory, _freeMemory);
return _freeMemory;
}
size_t cv::gpu::DeviceInfo::totalMemory() const
{
size_t _totalMemory, _freeMemory;
queryMemory(_totalMemory, _freeMemory);
return _totalMemory;
}
bool cv::gpu::DeviceInfo::supports(FeatureSet feature_set) const
{
int version = majorVersion() * 10 + minorVersion();
return version >= feature_set;
}
bool cv::gpu::DeviceInfo::isCompatible() const
{
// Check PTX compatibility
if (TargetArchs::hasEqualOrLessPtx(majorVersion(), minorVersion()))
return true;
// Check BIN compatibility
for (int i = minorVersion(); i >= 0; --i)
if (TargetArchs::hasBin(majorVersion(), i))
return true;
return false;
}
void cv::gpu::DeviceInfo::query()
{
const cudaDeviceProp* prop = deviceProps.get(device_id_);
name_ = prop->name;
multi_processor_count_ = prop->multiProcessorCount;
majorVersion_ = prop->major;
minorVersion_ = prop->minor;
}
namespace
{
int convertSMVer2Cores(int major, int minor)
{
// Defines for GPU Architecture types (using the SM version to determine the # of cores per SM
typedef struct {
int SM; // 0xMm (hexidecimal notation), M = SM Major version, and m = SM minor version
int Cores;
} SMtoCores;
SMtoCores gpuArchCoresPerSM[] = { { 0x10, 8 }, { 0x11, 8 }, { 0x12, 8 }, { 0x13, 8 }, { 0x20, 32 }, { 0x21, 48 }, {0x30, 192}, {0x35, 192}, { -1, -1 } };
int index = 0;
while (gpuArchCoresPerSM[index].SM != -1)
{
if (gpuArchCoresPerSM[index].SM == ((major << 4) + minor) )
return gpuArchCoresPerSM[index].Cores;
index++;
}
return -1;
}
}
void cv::gpu::printCudaDeviceInfo(int device)
{
int count = getCudaEnabledDeviceCount();
bool valid = (device >= 0) && (device < count);
int beg = valid ? device : 0;
int end = valid ? device+1 : count;
printf("*** CUDA Device Query (Runtime API) version (CUDART static linking) *** \n\n");
printf("Device count: %d\n", count);
int driverVersion = 0, runtimeVersion = 0;
cudaSafeCall( cudaDriverGetVersion(&driverVersion) );
cudaSafeCall( cudaRuntimeGetVersion(&runtimeVersion) );
const char *computeMode[] = {
"Default (multiple host threads can use ::cudaSetDevice() with device simultaneously)",
"Exclusive (only one host thread in one process is able to use ::cudaSetDevice() with this device)",
"Prohibited (no host thread can use ::cudaSetDevice() with this device)",
"Exclusive Process (many threads in one process is able to use ::cudaSetDevice() with this device)",
"Unknown",
NULL
};
for(int dev = beg; dev < end; ++dev)
{
cudaDeviceProp prop;
cudaSafeCall( cudaGetDeviceProperties(&prop, dev) );
printf("\nDevice %d: \"%s\"\n", dev, prop.name);
printf(" CUDA Driver Version / Runtime Version %d.%d / %d.%d\n", driverVersion/1000, driverVersion%100, runtimeVersion/1000, runtimeVersion%100);
printf(" CUDA Capability Major/Minor version number: %d.%d\n", prop.major, prop.minor);
printf(" Total amount of global memory: %.0f MBytes (%llu bytes)\n", (float)prop.totalGlobalMem/1048576.0f, (unsigned long long) prop.totalGlobalMem);
int cores = convertSMVer2Cores(prop.major, prop.minor);
if (cores > 0)
printf(" (%2d) Multiprocessors x (%2d) CUDA Cores/MP: %d CUDA Cores\n", prop.multiProcessorCount, cores, cores * prop.multiProcessorCount);
printf(" GPU Clock Speed: %.2f GHz\n", prop.clockRate * 1e-6f);
printf(" Max Texture Dimension Size (x,y,z) 1D=(%d), 2D=(%d,%d), 3D=(%d,%d,%d)\n",
prop.maxTexture1D, prop.maxTexture2D[0], prop.maxTexture2D[1],
prop.maxTexture3D[0], prop.maxTexture3D[1], prop.maxTexture3D[2]);
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]);
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);
printf(" Warp size: %d\n", prop.warpSize);
printf(" Maximum number of threads per block: %d\n", prop.maxThreadsPerBlock);
printf(" Maximum sizes of each dimension of a block: %d x %d x %d\n", prop.maxThreadsDim[0], prop.maxThreadsDim[1], prop.maxThreadsDim[2]);
printf(" Maximum sizes of each dimension of a grid: %d x %d x %d\n", prop.maxGridSize[0], prop.maxGridSize[1], prop.maxGridSize[2]);
printf(" Maximum memory pitch: %u bytes\n", (int)prop.memPitch);
printf(" Texture alignment: %u bytes\n", (int)prop.textureAlignment);
printf(" Concurrent copy and execution: %s with %d copy engine(s)\n", (prop.deviceOverlap ? "Yes" : "No"), prop.asyncEngineCount);
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");
printf(" Concurrent kernel execution: %s\n", prop.concurrentKernels ? "Yes" : "No");
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");
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 );
printf(" Compute Mode:\n");
printf(" %s \n", computeMode[prop.computeMode]);
}
printf("\n");
printf("deviceQuery, CUDA Driver = CUDART");
printf(", CUDA Driver Version = %d.%d", driverVersion / 1000, driverVersion % 100);
printf(", CUDA Runtime Version = %d.%d", runtimeVersion/1000, runtimeVersion%100);
printf(", NumDevs = %d\n\n", count);
fflush(stdout);
}
void cv::gpu::printShortCudaDeviceInfo(int device)
{
int count = getCudaEnabledDeviceCount();
bool valid = (device >= 0) && (device < count);
int beg = valid ? device : 0;
int end = valid ? device+1 : count;
int driverVersion = 0, runtimeVersion = 0;
cudaSafeCall( cudaDriverGetVersion(&driverVersion) );
cudaSafeCall( cudaRuntimeGetVersion(&runtimeVersion) );
for(int dev = beg; dev < end; ++dev)
{
cudaDeviceProp prop;
cudaSafeCall( cudaGetDeviceProperties(&prop, dev) );
const char *arch_str = prop.major < 2 ? " (not Fermi)" : "";
printf("Device %d: \"%s\" %.0fMb", dev, prop.name, (float)prop.totalGlobalMem/1048576.0f);
printf(", sm_%d%d%s", prop.major, prop.minor, arch_str);
int cores = convertSMVer2Cores(prop.major, prop.minor);
if (cores > 0)
printf(", %d cores", cores * prop.multiProcessorCount);
printf(", Driver/Runtime ver.%d.%d/%d.%d\n", driverVersion/1000, driverVersion%100, runtimeVersion/1000, runtimeVersion%100);
}
fflush(stdout);
}
#endif // HAVE_CUDA
//////////////////////////////// GpuMat ///////////////////////////////
cv::gpu::GpuMat::GpuMat(const GpuMat& m)
: flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend)
{
if (refcount)
CV_XADD(refcount, 1);
}
cv::gpu::GpuMat::GpuMat(int rows_, int cols_, int type_, void* data_, size_t step_) :
flags(Mat::MAGIC_VAL + (type_ & Mat::TYPE_MASK)), rows(rows_), cols(cols_),
step(step_), data((uchar*)data_), refcount(0),
datastart((uchar*)data_), dataend((uchar*)data_)
{
size_t minstep = cols * elemSize();
if (step == Mat::AUTO_STEP)
{
step = minstep;
flags |= Mat::CONTINUOUS_FLAG;
}
else
{
if (rows == 1)
step = minstep;
CV_DbgAssert(step >= minstep);
flags |= step == minstep ? Mat::CONTINUOUS_FLAG : 0;
}
dataend += step * (rows - 1) + minstep;
}
cv::gpu::GpuMat::GpuMat(Size size_, int type_, void* data_, size_t step_) :
flags(Mat::MAGIC_VAL + (type_ & Mat::TYPE_MASK)), rows(size_.height), cols(size_.width),
step(step_), data((uchar*)data_), refcount(0),
datastart((uchar*)data_), dataend((uchar*)data_)
{
size_t minstep = cols * elemSize();
if (step == Mat::AUTO_STEP)
{
step = minstep;
flags |= Mat::CONTINUOUS_FLAG;
}
else
{
if (rows == 1)
step = minstep;
CV_DbgAssert(step >= minstep);
flags |= step == minstep ? Mat::CONTINUOUS_FLAG : 0;
}
dataend += step * (rows - 1) + minstep;
}
cv::gpu::GpuMat::GpuMat(const GpuMat& m, Range _rowRange, Range _colRange)
{
flags = m.flags;
step = m.step; refcount = m.refcount;
data = m.data; datastart = m.datastart; dataend = m.dataend;
if (_rowRange == Range::all())
rows = m.rows;
else
{
CV_Assert(0 <= _rowRange.start && _rowRange.start <= _rowRange.end && _rowRange.end <= m.rows);
rows = _rowRange.size();
data += step*_rowRange.start;
}
if (_colRange == Range::all())
cols = m.cols;
else
{
CV_Assert(0 <= _colRange.start && _colRange.start <= _colRange.end && _colRange.end <= m.cols);
cols = _colRange.size();
data += _colRange.start*elemSize();
flags &= cols < m.cols ? ~Mat::CONTINUOUS_FLAG : -1;
}
if (rows == 1)
flags |= Mat::CONTINUOUS_FLAG;
if (refcount)
CV_XADD(refcount, 1);
if (rows <= 0 || cols <= 0)
rows = cols = 0;
}
cv::gpu::GpuMat::GpuMat(const GpuMat& m, Rect roi) :
flags(m.flags), rows(roi.height), cols(roi.width),
step(m.step), data(m.data + roi.y*step), refcount(m.refcount),
datastart(m.datastart), dataend(m.dataend)
{
flags &= roi.width < m.cols ? ~Mat::CONTINUOUS_FLAG : -1;
data += roi.x * elemSize();
CV_Assert(0 <= roi.x && 0 <= roi.width && roi.x + roi.width <= m.cols && 0 <= roi.y && 0 <= roi.height && roi.y + roi.height <= m.rows);
if (refcount)
CV_XADD(refcount, 1);
if (rows <= 0 || cols <= 0)
rows = cols = 0;
}
cv::gpu::GpuMat::GpuMat(const Mat& m) :
flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0)
{
upload(m);
}
GpuMat& cv::gpu::GpuMat::operator = (const GpuMat& m)
{
if (this != &m)
{
GpuMat temp(m);
swap(temp);
}
return *this;
}
void cv::gpu::GpuMat::swap(GpuMat& b)
{
std::swap(flags, b.flags);
std::swap(rows, b.rows);
std::swap(cols, b.cols);
std::swap(step, b.step);
std::swap(data, b.data);
std::swap(datastart, b.datastart);
std::swap(dataend, b.dataend);
std::swap(refcount, b.refcount);
}
void cv::gpu::GpuMat::locateROI(Size& wholeSize, Point& ofs) const
{
size_t esz = elemSize();
ptrdiff_t delta1 = data - datastart;
ptrdiff_t delta2 = dataend - datastart;
CV_DbgAssert(step > 0);
if (delta1 == 0)
ofs.x = ofs.y = 0;
else
{
ofs.y = static_cast<int>(delta1 / step);
ofs.x = static_cast<int>((delta1 - step * ofs.y) / esz);
CV_DbgAssert(data == datastart + ofs.y * step + ofs.x * esz);
}
size_t minstep = (ofs.x + cols) * esz;
wholeSize.height = std::max(static_cast<int>((delta2 - minstep) / step + 1), ofs.y + rows);
wholeSize.width = std::max(static_cast<int>((delta2 - step * (wholeSize.height - 1)) / esz), ofs.x + cols);
}
GpuMat& cv::gpu::GpuMat::adjustROI(int dtop, int dbottom, int dleft, int dright)
{
Size wholeSize;
Point ofs;
locateROI(wholeSize, ofs);
size_t esz = elemSize();
int row1 = std::max(ofs.y - dtop, 0);
int row2 = std::min(ofs.y + rows + dbottom, wholeSize.height);
int col1 = std::max(ofs.x - dleft, 0);
int col2 = std::min(ofs.x + cols + dright, wholeSize.width);
data += (row1 - ofs.y) * step + (col1 - ofs.x) * esz;
rows = row2 - row1;
cols = col2 - col1;
if (esz * cols == step || rows == 1)
flags |= Mat::CONTINUOUS_FLAG;
else
flags &= ~Mat::CONTINUOUS_FLAG;
return *this;
}
GpuMat cv::gpu::GpuMat::reshape(int new_cn, int new_rows) const
{
GpuMat hdr = *this;
int cn = channels();
if (new_cn == 0)
new_cn = cn;
int total_width = cols * cn;
if ((new_cn > total_width || total_width % new_cn != 0) && new_rows == 0)
new_rows = rows * total_width / new_cn;
if (new_rows != 0 && new_rows != rows)
{
int total_size = total_width * rows;
if (!isContinuous())
CV_Error(CV_BadStep, "The matrix is not continuous, thus its number of rows can not be changed");
if ((unsigned)new_rows > (unsigned)total_size)
CV_Error(CV_StsOutOfRange, "Bad new number of rows");
total_width = total_size / new_rows;
if (total_width * new_rows != total_size)
CV_Error(CV_StsBadArg, "The total number of matrix elements is not divisible by the new number of rows");
hdr.rows = new_rows;
hdr.step = total_width * elemSize1();
}
int new_width = total_width / new_cn;
if (new_width * new_cn != total_width)
CV_Error(CV_BadNumChannels, "The total width is not divisible by the new number of channels");
hdr.cols = new_width;
hdr.flags = (hdr.flags & ~CV_MAT_CN_MASK) | ((new_cn - 1) << CV_CN_SHIFT);
return hdr;
}
cv::Mat::Mat(const GpuMat& m) : flags(0), dims(0), rows(0), cols(0), data(0), refcount(0), datastart(0), dataend(0), datalimit(0), allocator(0), size(&rows)
{
m.download(*this);
}
void cv::gpu::createContinuous(int rows, int cols, int type, GpuMat& m)
{
int area = rows * cols;
if (m.empty() || m.type() != type || !m.isContinuous() || m.size().area() < area)
m.create(1, area, type);
m.cols = cols;
m.rows = rows;
m.step = m.elemSize() * cols;
m.flags |= Mat::CONTINUOUS_FLAG;
}
void cv::gpu::ensureSizeIsEnough(int rows, int cols, int type, GpuMat& m)
{
if (m.empty() || m.type() != type || m.data != m.datastart)
m.create(rows, cols, type);
else
{
const size_t esz = m.elemSize();
const ptrdiff_t delta2 = m.dataend - m.datastart;
const size_t minstep = m.cols * esz;
Size wholeSize;
wholeSize.height = std::max(static_cast<int>((delta2 - minstep) / m.step + 1), m.rows);
wholeSize.width = std::max(static_cast<int>((delta2 - m.step * (wholeSize.height - 1)) / esz), m.cols);
if (wholeSize.height < rows || wholeSize.width < cols)
m.create(rows, cols, type);
else
{
m.cols = cols;
m.rows = rows;
}
}
}
GpuMat cv::gpu::allocMatFromBuf(int rows, int cols, int type, GpuMat &mat)
{
if (!mat.empty() && mat.type() == type && mat.rows >= rows && mat.cols >= cols)
return mat(Rect(0, 0, cols, rows));
return mat = GpuMat(rows, cols, type);
}
namespace
{
class GpuFuncTable
{
public:
virtual ~GpuFuncTable() {}
virtual void copy(const Mat& src, GpuMat& dst) const = 0;
virtual void copy(const GpuMat& src, Mat& dst) const = 0;
virtual void copy(const GpuMat& src, GpuMat& dst) const = 0;
virtual void copyWithMask(const GpuMat& src, GpuMat& dst, const GpuMat& mask) const = 0;
virtual void convert(const GpuMat& src, GpuMat& dst) const = 0;
virtual void convert(const GpuMat& src, GpuMat& dst, double alpha, double beta) const = 0;
virtual void setTo(GpuMat& m, Scalar s, const GpuMat& mask) const = 0;
virtual void mallocPitch(void** devPtr, size_t* step, size_t width, size_t height) const = 0;
virtual void free(void* devPtr) const = 0;
};
}
#ifndef HAVE_CUDA
namespace
{
class EmptyFuncTable : public GpuFuncTable
{
public:
void copy(const Mat&, GpuMat&) const { throw_no_cuda(); }
void copy(const GpuMat&, Mat&) const { throw_no_cuda(); }
void copy(const GpuMat&, GpuMat&) const { throw_no_cuda(); }
void copyWithMask(const GpuMat&, GpuMat&, const GpuMat&) const { throw_no_cuda(); }
void convert(const GpuMat&, GpuMat&) const { throw_no_cuda(); }
void convert(const GpuMat&, GpuMat&, double, double) const { throw_no_cuda(); }
void setTo(GpuMat&, Scalar, const GpuMat&) const { throw_no_cuda(); }
void mallocPitch(void**, size_t*, size_t, size_t) const { throw_no_cuda(); }
void free(void*) const {}
};
const GpuFuncTable* gpuFuncTable()
{
static EmptyFuncTable empty;
return &empty;
}
}
#else // HAVE_CUDA
namespace cv { namespace gpu { namespace cudev
{
void copyToWithMask_gpu(PtrStepSzb src, PtrStepSzb dst, size_t elemSize1, int cn, PtrStepSzb mask, bool colorMask, cudaStream_t stream);
template <typename T>
void set_to_gpu(PtrStepSzb mat, const T* scalar, int channels, cudaStream_t stream);
template <typename T>
void set_to_gpu(PtrStepSzb mat, const T* scalar, PtrStepSzb mask, int channels, cudaStream_t stream);
void convert_gpu(PtrStepSzb src, int sdepth, PtrStepSzb dst, int ddepth, double alpha, double beta, cudaStream_t stream);
}}}
namespace
{
template <typename T> void kernelSetCaller(GpuMat& src, Scalar s, cudaStream_t stream)
{
Scalar_<T> sf = s;
cv::gpu::cudev::set_to_gpu(src, sf.val, src.channels(), stream);
}
template <typename T> void kernelSetCaller(GpuMat& src, Scalar s, const GpuMat& mask, cudaStream_t stream)
{
Scalar_<T> sf = s;
cv::gpu::cudev::set_to_gpu(src, sf.val, mask, src.channels(), stream);
}
}
namespace cv { namespace gpu
{
CV_EXPORTS void copyWithMask(const cv::gpu::GpuMat&, cv::gpu::GpuMat&, const cv::gpu::GpuMat&, CUstream_st*);
CV_EXPORTS void convertTo(const cv::gpu::GpuMat&, cv::gpu::GpuMat&);
CV_EXPORTS void convertTo(const cv::gpu::GpuMat&, cv::gpu::GpuMat&, double, double, CUstream_st*);
CV_EXPORTS void setTo(cv::gpu::GpuMat&, cv::Scalar, CUstream_st*);
CV_EXPORTS void setTo(cv::gpu::GpuMat&, cv::Scalar, const cv::gpu::GpuMat&, CUstream_st*);
CV_EXPORTS void setTo(cv::gpu::GpuMat&, cv::Scalar);
CV_EXPORTS void setTo(cv::gpu::GpuMat&, cv::Scalar, const cv::gpu::GpuMat&);
}}
namespace cv { namespace gpu
{
void copyWithMask(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream = 0)
{
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()));
cv::gpu::cudev::copyToWithMask_gpu(src.reshape(1), dst.reshape(1), src.elemSize1(), src.channels(), mask.reshape(1), mask.channels() != 1, stream);
}
void convertTo(const GpuMat& src, GpuMat& dst)
{
cv::gpu::cudev::convert_gpu(src.reshape(1), src.depth(), dst.reshape(1), dst.depth(), 1.0, 0.0, 0);
}
void convertTo(const GpuMat& src, GpuMat& dst, double alpha, double beta, cudaStream_t stream = 0)
{
cv::gpu::cudev::convert_gpu(src.reshape(1), src.depth(), dst.reshape(1), dst.depth(), alpha, beta, stream);
}
void setTo(GpuMat& src, Scalar s, cudaStream_t stream)
{
typedef void (*caller_t)(GpuMat& src, Scalar s, cudaStream_t stream);
static const caller_t callers[] =
{
kernelSetCaller<uchar>, kernelSetCaller<schar>, kernelSetCaller<ushort>, kernelSetCaller<short>, kernelSetCaller<int>,
kernelSetCaller<float>, kernelSetCaller<double>
};
callers[src.depth()](src, s, stream);
}
void setTo(GpuMat& src, Scalar s, const GpuMat& mask, cudaStream_t stream)
{
typedef void (*caller_t)(GpuMat& src, Scalar s, const GpuMat& mask, cudaStream_t stream);
static const caller_t callers[] =
{
kernelSetCaller<uchar>, kernelSetCaller<schar>, kernelSetCaller<ushort>, kernelSetCaller<short>, kernelSetCaller<int>,
kernelSetCaller<float>, kernelSetCaller<double>
};
callers[src.depth()](src, s, mask, stream);
}
void setTo(GpuMat& src, Scalar s)
{
setTo(src, s, 0);
}
void setTo(GpuMat& src, Scalar s, const GpuMat& mask)
{
setTo(src, s, mask, 0);
}
}}
namespace
{
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
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t;
typedef NppStatus (*func_ptr)(const src_t* pSrc, int nSrcStep, dst_t* pDst, int nDstStep, NppiSize oSizeROI);
};
template<int DDEPTH> struct NppConvertFunc<CV_32F, DDEPTH>
{
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t;
typedef NppStatus (*func_ptr)(const Npp32f* pSrc, int nSrcStep, dst_t* pDst, int nDstStep, NppiSize oSizeROI, NppRoundMode eRoundMode);
};
template<int SDEPTH, int DDEPTH, typename NppConvertFunc<SDEPTH, DDEPTH>::func_ptr func> struct NppCvt
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t;
static void call(const GpuMat& src, GpuMat& dst)
{
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() );
}
};
template<int DDEPTH, typename NppConvertFunc<CV_32F, DDEPTH>::func_ptr func> struct NppCvt<CV_32F, DDEPTH, func>
{
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t;
static void call(const GpuMat& src, GpuMat& dst)
{
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() );
}
};
//////////////////////////////////////////////////////////////////////////
// Set
template<int SDEPTH, int SCN> struct NppSetFunc
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef NppStatus (*func_ptr)(const src_t values[], src_t* pSrc, int nSrcStep, NppiSize oSizeROI);
};
template<int SDEPTH> struct NppSetFunc<SDEPTH, 1>
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
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
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
static void call(GpuMat& src, Scalar s)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Scalar_<src_t> nppS = s;
nppSafeCall( func(nppS.val, src.ptr<src_t>(), static_cast<int>(src.step), sz) );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template<int SDEPTH, typename NppSetFunc<SDEPTH, 1>::func_ptr func> struct NppSet<SDEPTH, 1, func>
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
static void call(GpuMat& src, Scalar s)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Scalar_<src_t> nppS = s;
nppSafeCall( func(nppS[0], src.ptr<src_t>(), static_cast<int>(src.step), sz) );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template<int SDEPTH, int SCN> struct NppSetMaskFunc
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef NppStatus (*func_ptr)(const src_t values[], src_t* pSrc, int nSrcStep, NppiSize oSizeROI, const Npp8u* pMask, int nMaskStep);
};
template<int SDEPTH> struct NppSetMaskFunc<SDEPTH, 1>
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef NppStatus (*func_ptr)(src_t val, src_t* pSrc, int nSrcStep, NppiSize oSizeROI, const Npp8u* pMask, int nMaskStep);
};
template<int SDEPTH, int SCN, typename NppSetMaskFunc<SDEPTH, SCN>::func_ptr func> struct NppSetMask
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
static void call(GpuMat& src, Scalar s, const GpuMat& mask)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Scalar_<src_t> nppS = s;
nppSafeCall( func(nppS.val, src.ptr<src_t>(), static_cast<int>(src.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template<int SDEPTH, typename NppSetMaskFunc<SDEPTH, 1>::func_ptr func> struct NppSetMask<SDEPTH, 1, func>
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
static void call(GpuMat& src, Scalar s, const GpuMat& mask)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Scalar_<src_t> nppS = s;
nppSafeCall( func(nppS[0], src.ptr<src_t>(), static_cast<int>(src.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
//////////////////////////////////////////////////////////////////////////
// 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 call(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() );
}
};
template <typename T> static inline bool isAligned(const T* ptr, size_t size)
{
return reinterpret_cast<size_t>(ptr) % size == 0;
}
//////////////////////////////////////////////////////////////////////////
// CudaFuncTable
class CudaFuncTable : public GpuFuncTable
{
public:
void copy(const Mat& src, GpuMat& dst) const
{
cudaSafeCall( cudaMemcpy2D(dst.data, dst.step, src.data, src.step, src.cols * src.elemSize(), src.rows, cudaMemcpyHostToDevice) );
}
void copy(const GpuMat& src, Mat& dst) const
{
cudaSafeCall( cudaMemcpy2D(dst.data, dst.step, src.data, src.step, src.cols * src.elemSize(), src.rows, cudaMemcpyDeviceToHost) );
}
void copy(const GpuMat& src, GpuMat& dst) const
{
cudaSafeCall( cudaMemcpy2D(dst.data, dst.step, src.data, src.step, src.cols * src.elemSize(), src.rows, cudaMemcpyDeviceToDevice) );
}
void copyWithMask(const GpuMat& src, GpuMat& dst, const GpuMat& mask) const
{
CV_Assert(src.depth() <= CV_64F && src.channels() <= 4);
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()));
if (src.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
typedef void (*func_t)(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream);
static const func_t funcs[7][4] =
{
/* 8U */ {NppCopyMasked<CV_8U , nppiCopy_8u_C1MR >::call, cv::gpu::copyWithMask, NppCopyMasked<CV_8U , nppiCopy_8u_C3MR >::call, NppCopyMasked<CV_8U , nppiCopy_8u_C4MR >::call},
/* 8S */ {cv::gpu::copyWithMask , cv::gpu::copyWithMask, cv::gpu::copyWithMask , cv::gpu::copyWithMask },
/* 16U */ {NppCopyMasked<CV_16U, nppiCopy_16u_C1MR>::call, cv::gpu::copyWithMask, NppCopyMasked<CV_16U, nppiCopy_16u_C3MR>::call, NppCopyMasked<CV_16U, nppiCopy_16u_C4MR>::call},
/* 16S */ {NppCopyMasked<CV_16S, nppiCopy_16s_C1MR>::call, cv::gpu::copyWithMask, NppCopyMasked<CV_16S, nppiCopy_16s_C3MR>::call, NppCopyMasked<CV_16S, nppiCopy_16s_C4MR>::call},
/* 32S */ {NppCopyMasked<CV_32S, nppiCopy_32s_C1MR>::call, cv::gpu::copyWithMask, NppCopyMasked<CV_32S, nppiCopy_32s_C3MR>::call, NppCopyMasked<CV_32S, nppiCopy_32s_C4MR>::call},
/* 32F */ {NppCopyMasked<CV_32F, nppiCopy_32f_C1MR>::call, cv::gpu::copyWithMask, NppCopyMasked<CV_32F, nppiCopy_32f_C3MR>::call, NppCopyMasked<CV_32F, nppiCopy_32f_C4MR>::call},
/* 64F */ {cv::gpu::copyWithMask , cv::gpu::copyWithMask, cv::gpu::copyWithMask , cv::gpu::copyWithMask }
};
const func_t func = mask.channels() == src.channels() ? funcs[src.depth()][src.channels() - 1] : cv::gpu::copyWithMask;
func(src, dst, mask, 0);
}
void convert(const GpuMat& src, GpuMat& dst) const
{
typedef void (*func_t)(const GpuMat& src, GpuMat& dst);
static const func_t funcs[7][7][4] =
{
{
/* 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>::call, cv::gpu::convertTo, cv::gpu::convertTo, NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C4R>::call},
/* 8U -> 16S */ {NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C1R>::call, cv::gpu::convertTo, cv::gpu::convertTo, NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C4R>::call},
/* 8U -> 32S */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
/* 8U -> 32F */ {NppCvt<CV_8U, CV_32F, nppiConvert_8u32f_C1R>::call, 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 -> 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}
},
{
/* 16U -> 8U */ {NppCvt<CV_16U, CV_8U , nppiConvert_16u8u_C1R >::call, cv::gpu::convertTo, cv::gpu::convertTo, NppCvt<CV_16U, CV_8U, nppiConvert_16u8u_C4R>::call},
/* 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>::call, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
/* 16U -> 32F */ {NppCvt<CV_16U, CV_32F, nppiConvert_16u32f_C1R>::call, 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 >::call, cv::gpu::convertTo, cv::gpu::convertTo, NppCvt<CV_16S, CV_8U, nppiConvert_16s8u_C4R>::call},
/* 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>::call, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
/* 16S -> 32F */ {NppCvt<CV_16S, CV_32F, nppiConvert_16s32f_C1R>::call, 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 -> 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}
},
{
/* 32F -> 8U */ {NppCvt<CV_32F, CV_8U , nppiConvert_32f8u_C1R >::call, 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>::call, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 32F -> 16S */ {NppCvt<CV_32F, CV_16S, nppiConvert_32f16s_C1R>::call, 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}
},
{
/* 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}
}
};
CV_Assert(src.depth() <= CV_64F && src.channels() <= 4);
CV_Assert(dst.depth() <= CV_64F);
CV_Assert(src.size() == dst.size() && src.channels() == dst.channels());
if (src.depth() == CV_64F || dst.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
bool aligned = isAligned(src.data, 16) && isAligned(dst.data, 16);
if (!aligned)
{
cv::gpu::convertTo(src, dst);
return;
}
const func_t func = funcs[src.depth()][dst.depth()][src.channels() - 1];
CV_DbgAssert(func != 0);
func(src, dst);
}
void convert(const GpuMat& src, GpuMat& dst, double alpha, double beta) const
{
CV_Assert(src.depth() <= CV_64F && src.channels() <= 4);
CV_Assert(dst.depth() <= CV_64F);
if (src.depth() == CV_64F || dst.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
cv::gpu::convertTo(src, dst, alpha, beta);
}
void setTo(GpuMat& m, Scalar s, const GpuMat& mask) const
{
if (mask.empty())
{
if (s[0] == 0.0 && s[1] == 0.0 && s[2] == 0.0 && s[3] == 0.0)
{
cudaSafeCall( cudaMemset2D(m.data, m.step, 0, m.cols * m.elemSize(), m.rows) );
return;
}
if (m.depth() == CV_8U)
{
int cn = m.channels();
if (cn == 1 || (cn == 2 && s[0] == s[1]) || (cn == 3 && s[0] == s[1] && s[0] == s[2]) || (cn == 4 && s[0] == s[1] && s[0] == s[2] && s[0] == s[3]))
{
int val = saturate_cast<uchar>(s[0]);
cudaSafeCall( cudaMemset2D(m.data, m.step, val, m.cols * m.elemSize(), m.rows) );
return;
}
}
typedef void (*func_t)(GpuMat& src, Scalar s);
static const func_t funcs[7][4] =
{
{NppSet<CV_8U , 1, nppiSet_8u_C1R >::call, cv::gpu::setTo , cv::gpu::setTo , NppSet<CV_8U , 4, nppiSet_8u_C4R >::call},
{NppSet<CV_8S , 1, nppiSet_8s_C1R >::call, NppSet<CV_8S , 2, nppiSet_8s_C2R >::call, NppSet<CV_8S, 3, nppiSet_8s_C3R>::call, NppSet<CV_8S , 4, nppiSet_8s_C4R >::call},
{NppSet<CV_16U, 1, nppiSet_16u_C1R>::call, NppSet<CV_16U, 2, nppiSet_16u_C2R>::call, cv::gpu::setTo , NppSet<CV_16U, 4, nppiSet_16u_C4R>::call},
{NppSet<CV_16S, 1, nppiSet_16s_C1R>::call, NppSet<CV_16S, 2, nppiSet_16s_C2R>::call, cv::gpu::setTo , NppSet<CV_16S, 4, nppiSet_16s_C4R>::call},
{NppSet<CV_32S, 1, nppiSet_32s_C1R>::call, cv::gpu::setTo , cv::gpu::setTo , NppSet<CV_32S, 4, nppiSet_32s_C4R>::call},
{NppSet<CV_32F, 1, nppiSet_32f_C1R>::call, cv::gpu::setTo , cv::gpu::setTo , NppSet<CV_32F, 4, nppiSet_32f_C4R>::call},
{cv::gpu::setTo , cv::gpu::setTo , cv::gpu::setTo , cv::gpu::setTo }
};
CV_Assert(m.depth() <= CV_64F && m.channels() <= 4);
if (m.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
funcs[m.depth()][m.channels() - 1](m, s);
}
else
{
typedef void (*func_t)(GpuMat& src, Scalar s, const GpuMat& mask);
static const func_t funcs[7][4] =
{
{NppSetMask<CV_8U , 1, nppiSet_8u_C1MR >::call, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_8U , 4, nppiSet_8u_C4MR >::call},
{cv::gpu::setTo , cv::gpu::setTo, cv::gpu::setTo, cv::gpu::setTo },
{NppSetMask<CV_16U, 1, nppiSet_16u_C1MR>::call, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_16U, 4, nppiSet_16u_C4MR>::call},
{NppSetMask<CV_16S, 1, nppiSet_16s_C1MR>::call, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_16S, 4, nppiSet_16s_C4MR>::call},
{NppSetMask<CV_32S, 1, nppiSet_32s_C1MR>::call, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_32S, 4, nppiSet_32s_C4MR>::call},
{NppSetMask<CV_32F, 1, nppiSet_32f_C1MR>::call, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_32F, 4, nppiSet_32f_C4MR>::call},
{cv::gpu::setTo , cv::gpu::setTo, cv::gpu::setTo, cv::gpu::setTo }
};
CV_Assert(m.depth() <= CV_64F && m.channels() <= 4);
if (m.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
funcs[m.depth()][m.channels() - 1](m, s, mask);
}
}
void mallocPitch(void** devPtr, size_t* step, size_t width, size_t height) const
{
cudaSafeCall( cudaMallocPitch(devPtr, step, width, height) );
}
void free(void* devPtr) const
{
cudaFree(devPtr);
}
};
const GpuFuncTable* gpuFuncTable()
{
static CudaFuncTable funcTable;
return &funcTable;
}
}
#endif // HAVE_CUDA
void cv::gpu::GpuMat::upload(const Mat& m)
{
CV_DbgAssert(!m.empty());
create(m.size(), m.type());
gpuFuncTable()->copy(m, *this);
}
void cv::gpu::GpuMat::download(Mat& m) const
{
CV_DbgAssert(!empty());
m.create(size(), type());
gpuFuncTable()->copy(*this, m);
}
void cv::gpu::GpuMat::copyTo(GpuMat& m) const
{
CV_DbgAssert(!empty());
m.create(size(), type());
gpuFuncTable()->copy(*this, m);
}
void cv::gpu::GpuMat::copyTo(GpuMat& mat, const GpuMat& mask) const
{
if (mask.empty())
copyTo(mat);
else
{
mat.create(size(), type());
gpuFuncTable()->copyWithMask(*this, mat, mask);
}
}
void cv::gpu::GpuMat::convertTo(GpuMat& dst, int rtype, double alpha, double beta) const
{
bool noScale = fabs(alpha - 1) < std::numeric_limits<double>::epsilon() && fabs(beta) < std::numeric_limits<double>::epsilon();
if (rtype < 0)
rtype = type();
else
rtype = CV_MAKETYPE(CV_MAT_DEPTH(rtype), channels());
int sdepth = depth();
int ddepth = CV_MAT_DEPTH(rtype);
if (sdepth == ddepth && noScale)
{
copyTo(dst);
return;
}
GpuMat temp;
const GpuMat* psrc = this;
if (sdepth != ddepth && psrc == &dst)
{
temp = *this;
psrc = &temp;
}
dst.create(size(), rtype);
if (noScale)
gpuFuncTable()->convert(*psrc, dst);
else
gpuFuncTable()->convert(*psrc, dst, alpha, beta);
}
GpuMat& cv::gpu::GpuMat::setTo(Scalar s, const GpuMat& mask)
{
CV_Assert(mask.empty() || mask.type() == CV_8UC1);
CV_DbgAssert(!empty());
gpuFuncTable()->setTo(*this, s, mask);
return *this;
}
void cv::gpu::GpuMat::create(int _rows, int _cols, int _type)
{
_type &= Mat::TYPE_MASK;
if (rows == _rows && cols == _cols && type() == _type && data)
return;
if (data)
release();
CV_DbgAssert(_rows >= 0 && _cols >= 0);
if (_rows > 0 && _cols > 0)
{
flags = Mat::MAGIC_VAL + _type;
rows = _rows;
cols = _cols;
size_t esz = elemSize();
void* devPtr;
gpuFuncTable()->mallocPitch(&devPtr, &step, esz * cols, rows);
// Single row must be continuous
if (rows == 1)
step = esz * cols;
if (esz * cols == step)
flags |= Mat::CONTINUOUS_FLAG;
int64 _nettosize = static_cast<int64>(step) * rows;
size_t nettosize = static_cast<size_t>(_nettosize);
datastart = data = static_cast<uchar*>(devPtr);
dataend = data + nettosize;
refcount = static_cast<int*>(fastMalloc(sizeof(*refcount)));
*refcount = 1;
}
}
void cv::gpu::GpuMat::release()
{
if (refcount && CV_XADD(refcount, -1) == 1)
{
fastFree(refcount);
gpuFuncTable()->free(datastart);
}
data = datastart = dataend = 0;
step = rows = cols = 0;
refcount = 0;
}
////////////////////////////////////////////////////////////////////////
// Error handling
#ifdef HAVE_CUDA
namespace
{
#define error_entry(entry) { entry, #entry }
struct ErrorEntry
{
int code;
const char* str;
};
struct ErrorEntryComparer
{
int code;
ErrorEntryComparer(int code_) : code(code_) {}
bool operator()(const ErrorEntry& e) const { return e.code == code; }
};
const ErrorEntry npp_errors [] =
{
error_entry( NPP_NOT_SUPPORTED_MODE_ERROR ),
error_entry( NPP_ROUND_MODE_NOT_SUPPORTED_ERROR ),
error_entry( NPP_RESIZE_NO_OPERATION_ERROR ),
#if defined (_MSC_VER)
error_entry( NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY ),
#endif
error_entry( NPP_BAD_ARG_ERROR ),
error_entry( NPP_LUT_NUMBER_OF_LEVELS_ERROR ),
error_entry( NPP_TEXTURE_BIND_ERROR ),
error_entry( NPP_COEFF_ERROR ),
error_entry( NPP_RECT_ERROR ),
error_entry( NPP_QUAD_ERROR ),
error_entry( NPP_WRONG_INTERSECTION_ROI_ERROR ),
error_entry( NPP_NOT_EVEN_STEP_ERROR ),
error_entry( NPP_INTERPOLATION_ERROR ),
error_entry( NPP_RESIZE_FACTOR_ERROR ),
error_entry( NPP_HAAR_CLASSIFIER_PIXEL_MATCH_ERROR ),
error_entry( NPP_MEMFREE_ERR ),
error_entry( NPP_MEMSET_ERR ),
error_entry( NPP_MEMCPY_ERROR ),
error_entry( NPP_MEM_ALLOC_ERR ),
error_entry( NPP_HISTO_NUMBER_OF_LEVELS_ERROR ),
error_entry( NPP_MIRROR_FLIP_ERR ),
error_entry( NPP_INVALID_INPUT ),
error_entry( NPP_ALIGNMENT_ERROR ),
error_entry( NPP_STEP_ERROR ),
error_entry( NPP_SIZE_ERROR ),
error_entry( NPP_POINTER_ERROR ),
error_entry( NPP_NULL_POINTER_ERROR ),
error_entry( NPP_CUDA_KERNEL_EXECUTION_ERROR ),
error_entry( NPP_NOT_IMPLEMENTED_ERROR ),
error_entry( NPP_ERROR ),
error_entry( NPP_NO_ERROR ),
error_entry( NPP_SUCCESS ),
error_entry( NPP_WARNING ),
error_entry( NPP_WRONG_INTERSECTION_QUAD_WARNING ),
error_entry( NPP_MISALIGNED_DST_ROI_WARNING ),
error_entry( NPP_AFFINE_QUAD_INCORRECT_WARNING ),
error_entry( NPP_DOUBLE_SIZE_WARNING ),
error_entry( NPP_ODD_ROI_WARNING )
};
const size_t npp_error_num = sizeof(npp_errors) / sizeof(npp_errors[0]);
const ErrorEntry cu_errors [] =
{
error_entry( CUDA_SUCCESS ),
error_entry( CUDA_ERROR_INVALID_VALUE ),
error_entry( CUDA_ERROR_OUT_OF_MEMORY ),
error_entry( CUDA_ERROR_NOT_INITIALIZED ),
error_entry( CUDA_ERROR_DEINITIALIZED ),
error_entry( CUDA_ERROR_PROFILER_DISABLED ),
error_entry( CUDA_ERROR_PROFILER_NOT_INITIALIZED ),
error_entry( CUDA_ERROR_PROFILER_ALREADY_STARTED ),
error_entry( CUDA_ERROR_PROFILER_ALREADY_STOPPED ),
error_entry( CUDA_ERROR_NO_DEVICE ),
error_entry( CUDA_ERROR_INVALID_DEVICE ),
error_entry( CUDA_ERROR_INVALID_IMAGE ),
error_entry( CUDA_ERROR_INVALID_CONTEXT ),
error_entry( CUDA_ERROR_CONTEXT_ALREADY_CURRENT ),
error_entry( CUDA_ERROR_MAP_FAILED ),
error_entry( CUDA_ERROR_UNMAP_FAILED ),
error_entry( CUDA_ERROR_ARRAY_IS_MAPPED ),
error_entry( CUDA_ERROR_ALREADY_MAPPED ),
error_entry( CUDA_ERROR_NO_BINARY_FOR_GPU ),
error_entry( CUDA_ERROR_ALREADY_ACQUIRED ),
error_entry( CUDA_ERROR_NOT_MAPPED ),
error_entry( CUDA_ERROR_NOT_MAPPED_AS_ARRAY ),
error_entry( CUDA_ERROR_NOT_MAPPED_AS_POINTER ),
error_entry( CUDA_ERROR_ECC_UNCORRECTABLE ),
error_entry( CUDA_ERROR_UNSUPPORTED_LIMIT ),
error_entry( CUDA_ERROR_CONTEXT_ALREADY_IN_USE ),
error_entry( CUDA_ERROR_INVALID_SOURCE ),
error_entry( CUDA_ERROR_FILE_NOT_FOUND ),
error_entry( CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND ),
error_entry( CUDA_ERROR_SHARED_OBJECT_INIT_FAILED ),
error_entry( CUDA_ERROR_OPERATING_SYSTEM ),
error_entry( CUDA_ERROR_INVALID_HANDLE ),
error_entry( CUDA_ERROR_NOT_FOUND ),
error_entry( CUDA_ERROR_NOT_READY ),
error_entry( CUDA_ERROR_LAUNCH_FAILED ),
error_entry( CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES ),
error_entry( CUDA_ERROR_LAUNCH_TIMEOUT ),
error_entry( CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING ),
error_entry( CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED ),
error_entry( CUDA_ERROR_PEER_ACCESS_NOT_ENABLED ),
error_entry( CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE ),
error_entry( CUDA_ERROR_CONTEXT_IS_DESTROYED ),
error_entry( CUDA_ERROR_ASSERT ),
error_entry( CUDA_ERROR_TOO_MANY_PEERS ),
error_entry( CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED ),
error_entry( CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED ),
error_entry( CUDA_ERROR_UNKNOWN )
};
const size_t cu_errors_num = sizeof(cu_errors) / sizeof(cu_errors[0]);
cv::String getErrorString(int code, const ErrorEntry* errors, size_t n)
{
size_t idx = std::find_if(errors, errors + n, ErrorEntryComparer(code)) - errors;
const char* msg = (idx != n) ? errors[idx].str : "Unknown error code";
cv::String str = cv::format("%s [Code = %d]", msg, code);
return str;
}
}
#endif
String cv::gpu::getNppErrorMessage(int code)
{
#ifndef HAVE_CUDA
(void) code;
return String();
#else
return getErrorString(code, npp_errors, npp_error_num);
#endif
}
String cv::gpu::getCudaDriverApiErrorMessage(int code)
{
#ifndef HAVE_CUDA
(void) code;
return String();
#else
return getErrorString(code, cu_errors, cu_errors_num);
#endif
}
bool cv::gpu::tryConvertToGpuBorderType(int cpuBorderType, int& gpuBorderType)
{
#ifndef HAVE_CUDA
(void) cpuBorderType;
(void) gpuBorderType;
return false;
#else
switch (cpuBorderType)
{
case IPL_BORDER_REFLECT_101:
gpuBorderType = cv::gpu::BORDER_REFLECT101_GPU;
return true;
case IPL_BORDER_REPLICATE:
gpuBorderType = cv::gpu::BORDER_REPLICATE_GPU;
return true;
case IPL_BORDER_CONSTANT:
gpuBorderType = cv::gpu::BORDER_CONSTANT_GPU;
return true;
case IPL_BORDER_REFLECT:
gpuBorderType = cv::gpu::BORDER_REFLECT_GPU;
return true;
case IPL_BORDER_WRAP:
gpuBorderType = cv::gpu::BORDER_WRAP_GPU;
return true;
default:
return false;
};
#endif
}