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Commit b2b1d41d authored by Vladislav Vinogradov's avatar Vladislav Vinogradov
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moved GpuMat class to separate header file

parent f4f38fcc
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Showing with 1189 additions and 1079 deletions
modules/gpu/include/opencv2/gpu/gpu.hpp
View file @ b2b1d41d
......@@ -47,8 +47,8 @@
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/gpu/devmem2d.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/gpu/gpumat.hpp"
namespace cv
{
......@@ -143,182 +143,6 @@ namespace cv
CV_EXPORTS void error(const char *error_string, const char *file, const int line, const char *func);
CV_EXPORTS void nppError( int err, const char *file, const int line, const char *func);
//////////////////////////////// GpuMat ////////////////////////////////
class Stream;
class CudaMem;
//! Smart pointer for GPU memory with reference counting. Its interface is mostly similar with cv::Mat.
class CV_EXPORTS GpuMat
{
public:
//! default constructor
GpuMat();
//! constructs GpuMatrix of the specified size and type (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.)
GpuMat(int rows, int cols, int type);
GpuMat(Size size, int type);
//! constucts GpuMatrix and fills it with the specified value _s.
GpuMat(int rows, int cols, int type, const Scalar& s);
GpuMat(Size size, int type, const Scalar& s);
//! copy constructor
GpuMat(const GpuMat& m);
//! constructor for GpuMatrix headers pointing to user-allocated data
GpuMat(int rows, int cols, int type, void* data, size_t step = Mat::AUTO_STEP);
GpuMat(Size size, int type, void* data, size_t step = Mat::AUTO_STEP);
//! creates a matrix header for a part of the bigger matrix
GpuMat(const GpuMat& m, const Range& rowRange, const Range& colRange);
GpuMat(const GpuMat& m, const Rect& roi);
//! builds GpuMat from Mat. Perfom blocking upload to device.
explicit GpuMat (const Mat& m);
//! destructor - calls release()
~GpuMat();
//! assignment operators
GpuMat& operator = (const GpuMat& m);
//! assignment operator. Perfom blocking upload to device.
GpuMat& operator = (const Mat& m);
//! returns lightweight DevMem2D_ structure for passing to nvcc-compiled code.
// Contains just image size, data ptr and step.
template <class T> operator DevMem2D_<T>() const;
template <class T> operator PtrStep_<T>() const;
//! pefroms blocking upload data to GpuMat.
void upload(const cv::Mat& m);
//! upload async
void upload(const CudaMem& m, Stream& stream);
//! downloads data from device to host memory. Blocking calls.
operator Mat() const;
void download(cv::Mat& m) const;
//! download async
void download(CudaMem& m, Stream& stream) const;
//! returns a new GpuMatrix header for the specified row
GpuMat row(int y) const;
//! returns a new GpuMatrix header for the specified column
GpuMat col(int x) const;
//! ... for the specified row span
GpuMat rowRange(int startrow, int endrow) const;
GpuMat rowRange(const Range& r) const;
//! ... for the specified column span
GpuMat colRange(int startcol, int endcol) const;
GpuMat colRange(const Range& r) const;
//! returns deep copy of the GpuMatrix, i.e. the data is copied
GpuMat clone() const;
//! copies the GpuMatrix content to "m".
// It calls m.create(this->size(), this->type()).
void copyTo( GpuMat& m ) const;
//! copies those GpuMatrix elements to "m" that are marked with non-zero mask elements.
void copyTo( GpuMat& m, const GpuMat& mask ) const;
//! converts GpuMatrix to another datatype with optional scalng. See cvConvertScale.
void convertTo( GpuMat& m, int rtype, double alpha=1, double beta=0 ) const;
void assignTo( GpuMat& m, int type=-1 ) const;
//! sets every GpuMatrix element to s
GpuMat& operator = (const Scalar& s);
//! sets some of the GpuMatrix elements to s, according to the mask
GpuMat& setTo(const Scalar& s, const GpuMat& mask = GpuMat());
//! creates alternative GpuMatrix header for the same data, with different
// number of channels and/or different number of rows. see cvReshape.
GpuMat reshape(int cn, int rows = 0) const;
//! allocates new GpuMatrix data unless the GpuMatrix already has specified size and type.
// previous data is unreferenced if needed.
void create(int rows, int cols, int type);
void create(Size size, int type);
//! decreases reference counter;
// deallocate the data when reference counter reaches 0.
void release();
//! swaps with other smart pointer
void swap(GpuMat& mat);
//! locates GpuMatrix header within a parent GpuMatrix. See below
void locateROI( Size& wholeSize, Point& ofs ) const;
//! moves/resizes the current GpuMatrix ROI inside the parent GpuMatrix.
GpuMat& adjustROI( int dtop, int dbottom, int dleft, int dright );
//! extracts a rectangular sub-GpuMatrix
// (this is a generalized form of row, rowRange etc.)
GpuMat operator()( Range rowRange, Range colRange ) const;
GpuMat operator()( const Rect& roi ) const;
//! returns true iff the GpuMatrix data is continuous
// (i.e. when there are no gaps between successive rows).
// similar to CV_IS_GpuMat_CONT(cvGpuMat->type)
bool isContinuous() const;
//! returns element size in bytes,
// similar to CV_ELEM_SIZE(cvMat->type)
size_t elemSize() const;
//! returns the size of element channel in bytes.
size_t elemSize1() const;
//! returns element type, similar to CV_MAT_TYPE(cvMat->type)
int type() const;
//! returns element type, similar to CV_MAT_DEPTH(cvMat->type)
int depth() const;
//! returns element type, similar to CV_MAT_CN(cvMat->type)
int channels() const;
//! returns step/elemSize1()
size_t step1() const;
//! returns GpuMatrix size:
// width == number of columns, height == number of rows
Size size() const;
//! returns true if GpuMatrix data is NULL
bool empty() const;
//! returns pointer to y-th row
uchar* ptr(int y = 0);
const uchar* ptr(int y = 0) const;
//! template version of the above method
template<typename _Tp> _Tp* ptr(int y = 0);
template<typename _Tp> const _Tp* ptr(int y = 0) const;
//! matrix transposition
GpuMat t() const;
/*! includes several bit-fields:
- the magic signature
- continuity flag
- depth
- number of channels
*/
int flags;
//! the number of rows and columns
int rows, cols;
//! a distance between successive rows in bytes; includes the gap if any
size_t step;
//! pointer to the data
uchar* data;
//! pointer to the reference counter;
// when GpuMatrix points to user-allocated data, the pointer is NULL
int* refcount;
//! helper fields used in locateROI and adjustROI
uchar* datastart;
uchar* dataend;
};
//#define TemplatedGpuMat // experimental now, deprecated to use
#ifdef TemplatedGpuMat
#include "GpuMat_BetaDeprecated.hpp"
#endif
//! Creates continuous GPU matrix
CV_EXPORTS void createContinuous(int rows, int cols, int type, GpuMat& m);
//! Ensures that size of the given matrix is not less than (rows, cols) size
//! and matrix type is match specified one too
CV_EXPORTS void ensureSizeIsEnough(int rows, int cols, int type, GpuMat& m);
//////////////////////////////// CudaMem ////////////////////////////////
// CudaMem is limited cv::Mat with page locked memory allocation.
// Page locked memory is only needed for async and faster coping to GPU.
......
modules/gpu/include/opencv2/gpu/gpumat.hpp 0 → 100644
View file @ b2b1d41d
/*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 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*/
#ifndef __OPENCV_GPUMAT_HPP__
#define __OPENCV_GPUMAT_HPP__
#include "opencv2/core/core.hpp"
#include "opencv2/gpu/devmem2d.hpp"
namespace cv { namespace gpu
{
class Stream;
class CudaMem;
//! Smart pointer for GPU memory with reference counting. Its interface is mostly similar with cv::Mat.
class CV_EXPORTS GpuMat
{
public:
//! default constructor
GpuMat();
//! constructs GpuMatrix of the specified size and type (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.)
GpuMat(int rows, int cols, int type);
GpuMat(Size size, int type);
//! constucts GpuMatrix and fills it with the specified value _s.
GpuMat(int rows, int cols, int type, const Scalar& s);
GpuMat(Size size, int type, const Scalar& s);
//! copy constructor
GpuMat(const GpuMat& m);
//! constructor for GpuMatrix headers pointing to user-allocated data
GpuMat(int rows, int cols, int type, void* data, size_t step = Mat::AUTO_STEP);
GpuMat(Size size, int type, void* data, size_t step = Mat::AUTO_STEP);
//! creates a matrix header for a part of the bigger matrix
GpuMat(const GpuMat& m, const Range& rowRange, const Range& colRange);
GpuMat(const GpuMat& m, const Rect& roi);
//! builds GpuMat from Mat. Perfom blocking upload to device.
explicit GpuMat (const Mat& m);
//! destructor - calls release()
~GpuMat();
//! assignment operators
GpuMat& operator = (const GpuMat& m);
//! assignment operator. Perfom blocking upload to device.
GpuMat& operator = (const Mat& m);
//! returns lightweight DevMem2D_ structure for passing to nvcc-compiled code.
// Contains just image size, data ptr and step.
template <class T> operator DevMem2D_<T>() const;
template <class T> operator PtrStep_<T>() const;
//! pefroms blocking upload data to GpuMat.
void upload(const cv::Mat& m);
//! upload async
void upload(const CudaMem& m, Stream& stream);
//! downloads data from device to host memory. Blocking calls.
operator Mat() const;
void download(cv::Mat& m) const;
//! download async
void download(CudaMem& m, Stream& stream) const;
//! returns a new GpuMatrix header for the specified row
GpuMat row(int y) const;
//! returns a new GpuMatrix header for the specified column
GpuMat col(int x) const;
//! ... for the specified row span
GpuMat rowRange(int startrow, int endrow) const;
GpuMat rowRange(const Range& r) const;
//! ... for the specified column span
GpuMat colRange(int startcol, int endcol) const;
GpuMat colRange(const Range& r) const;
//! returns deep copy of the GpuMatrix, i.e. the data is copied
GpuMat clone() const;
//! copies the GpuMatrix content to "m".
// It calls m.create(this->size(), this->type()).
void copyTo( GpuMat& m ) const;
//! copies those GpuMatrix elements to "m" that are marked with non-zero mask elements.
void copyTo( GpuMat& m, const GpuMat& mask ) const;
//! converts GpuMatrix to another datatype with optional scalng. See cvConvertScale.
void convertTo( GpuMat& m, int rtype, double alpha=1, double beta=0 ) const;
void assignTo( GpuMat& m, int type=-1 ) const;
//! sets every GpuMatrix element to s
GpuMat& operator = (const Scalar& s);
//! sets some of the GpuMatrix elements to s, according to the mask
GpuMat& setTo(const Scalar& s, const GpuMat& mask = GpuMat());
//! creates alternative GpuMatrix header for the same data, with different
// number of channels and/or different number of rows. see cvReshape.
GpuMat reshape(int cn, int rows = 0) const;
//! allocates new GpuMatrix data unless the GpuMatrix already has specified size and type.
// previous data is unreferenced if needed.
void create(int rows, int cols, int type);
void create(Size size, int type);
//! decreases reference counter;
// deallocate the data when reference counter reaches 0.
void release();
//! swaps with other smart pointer
void swap(GpuMat& mat);
//! locates GpuMatrix header within a parent GpuMatrix. See below
void locateROI( Size& wholeSize, Point& ofs ) const;
//! moves/resizes the current GpuMatrix ROI inside the parent GpuMatrix.
GpuMat& adjustROI( int dtop, int dbottom, int dleft, int dright );
//! extracts a rectangular sub-GpuMatrix
// (this is a generalized form of row, rowRange etc.)
GpuMat operator()( Range rowRange, Range colRange ) const;
GpuMat operator()( const Rect& roi ) const;
//! returns true iff the GpuMatrix data is continuous
// (i.e. when there are no gaps between successive rows).
// similar to CV_IS_GpuMat_CONT(cvGpuMat->type)
bool isContinuous() const;
//! returns element size in bytes,
// similar to CV_ELEM_SIZE(cvMat->type)
size_t elemSize() const;
//! returns the size of element channel in bytes.
size_t elemSize1() const;
//! returns element type, similar to CV_MAT_TYPE(cvMat->type)
int type() const;
//! returns element type, similar to CV_MAT_DEPTH(cvMat->type)
int depth() const;
//! returns element type, similar to CV_MAT_CN(cvMat->type)
int channels() const;
//! returns step/elemSize1()
size_t step1() const;
//! returns GpuMatrix size:
// width == number of columns, height == number of rows
Size size() const;
//! returns true if GpuMatrix data is NULL
bool empty() const;
//! returns pointer to y-th row
uchar* ptr(int y = 0);
const uchar* ptr(int y = 0) const;
//! template version of the above method
template<typename _Tp> _Tp* ptr(int y = 0);
template<typename _Tp> const _Tp* ptr(int y = 0) const;
//! matrix transposition
GpuMat t() const;
/*! includes several bit-fields:
- the magic signature
- continuity flag
- depth
- number of channels
*/
int flags;
//! the number of rows and columns
int rows, cols;
//! a distance between successive rows in bytes; includes the gap if any
size_t step;
//! pointer to the data
uchar* data;
//! pointer to the reference counter;
// when GpuMatrix points to user-allocated data, the pointer is NULL
int* refcount;
//! helper fields used in locateROI and adjustROI
uchar* datastart;
uchar* dataend;
};
//! Creates continuous GPU matrix
CV_EXPORTS void createContinuous(int rows, int cols, int type, GpuMat& m);
CV_EXPORTS GpuMat createContinuous(int rows, int cols, int type);
CV_EXPORTS void createContinuous(Size size, int type, GpuMat& m);
CV_EXPORTS GpuMat createContinuous(Size size, int type);
//! Ensures that size of the given matrix is not less than (rows, cols) size
//! and matrix type is match specified one too
CV_EXPORTS void ensureSizeIsEnough(int rows, int cols, int type, GpuMat& m);
CV_EXPORTS void ensureSizeIsEnough(Size size, int type, GpuMat& m);
////////////////////////////////////////////////////////////////////////
//////////////////////////////// GpuMat ////////////////////////////////
////////////////////////////////////////////////////////////////////////
inline GpuMat::GpuMat() : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0) {}
inline GpuMat::GpuMat(int rows_, int cols_, int type_) : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0)
{
if (rows_ > 0 && cols_ > 0)
create(rows_, cols_, type_);
}
inline GpuMat::~GpuMat() { release(); }
template <class T> inline GpuMat::operator DevMem2D_<T>() const { return DevMem2D_<T>(rows, cols, (T*)data, step); }
template <class T> inline GpuMat::operator PtrStep_<T>() const { return PtrStep_<T>(static_cast< DevMem2D_<T> >(*this)); }
inline GpuMat GpuMat::clone() const
{
GpuMat m;
copyTo(m);
return m;
}
inline void GpuMat::assignTo(GpuMat& m, int type) const
{
if( type < 0 )
m = *this;
else
convertTo(m, type);
}
inline size_t GpuMat::step1() const { return step/elemSize1(); }
inline bool GpuMat::empty() const { return data == 0; }
template<typename _Tp> inline _Tp* GpuMat::ptr(int y)
{
return (_Tp*)ptr(y);
}
template<typename _Tp> inline const _Tp* GpuMat::ptr(int y) const
{
return (const _Tp*)ptr(y);
}
inline void swap(GpuMat& a, GpuMat& b) { a.swap(b); }
}}
#endif // __OPENCV_GPUMAT_HPP__
modules/gpu/include/opencv2/gpu/matrix_operations.hpp
View file @ b2b1d41d
......@@ -48,328 +48,6 @@ namespace cv
namespace gpu
{
////////////////////////////////////////////////////////////////////////
//////////////////////////////// GpuMat ////////////////////////////////
////////////////////////////////////////////////////////////////////////
inline GpuMat::GpuMat() : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0) {}
inline GpuMat::GpuMat(int _rows, int _cols, int _type) : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0)
{
if( _rows > 0 && _cols > 0 )
create( _rows, _cols, _type );
}
inline GpuMat::GpuMat(Size _size, int _type) : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0)
{
if( _size.height > 0 && _size.width > 0 )
create( _size.height, _size.width, _type );
}
inline GpuMat::GpuMat(int _rows, int _cols, int _type, const Scalar& _s)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0)
{
if(_rows > 0 && _cols > 0)
{
create(_rows, _cols, _type);
*this = _s;
}
}
inline GpuMat::GpuMat(Size _size, int _type, const Scalar& _s)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0)
{
if( _size.height > 0 && _size.width > 0 )
{
create( _size.height, _size.width, _type );
*this = _s;
}
}
inline 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);
}
inline GpuMat::GpuMat(int _rows, int _cols, int _type, void* _data, size_t _step)
: flags(Mat::MAGIC_VAL + (_type & 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;
}
inline GpuMat::GpuMat(Size _size, int _type, void* _data, size_t _step)
: flags(Mat::MAGIC_VAL + (_type & 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;
}
inline GpuMat::GpuMat(const GpuMat& m, const Range& rowRange, const 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;
}
inline GpuMat::GpuMat(const GpuMat& m, const 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;
}
inline GpuMat::GpuMat(const Mat& m)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0) { upload(m); }
inline GpuMat::~GpuMat() { release(); }
inline GpuMat& GpuMat::operator = (const GpuMat& m)
{
if( this != &m )
{
if( m.refcount )
CV_XADD(m.refcount, 1);
release();
flags = m.flags;
rows = m.rows; cols = m.cols;
step = m.step; data = m.data;
datastart = m.datastart; dataend = m.dataend;
refcount = m.refcount;
}
return *this;
}
inline GpuMat& GpuMat::operator = (const Mat& m) { upload(m); return *this; }
template <class T> inline GpuMat::operator DevMem2D_<T>() const { return DevMem2D_<T>(rows, cols, (T*)data, step); }
template <class T> inline GpuMat::operator PtrStep_<T>() const { return PtrStep_<T>(static_cast< DevMem2D_<T> >(*this)); }
//CPP: void GpuMat::upload(const Mat& m);
inline GpuMat::operator Mat() const
{
Mat m;
download(m);
return m;
}
//CPP void GpuMat::download(cv::Mat& m) const;
inline GpuMat GpuMat::row(int y) const { return GpuMat(*this, Range(y, y+1), Range::all()); }
inline GpuMat GpuMat::col(int x) const { return GpuMat(*this, Range::all(), Range(x, x+1)); }
inline GpuMat GpuMat::rowRange(int startrow, int endrow) const { return GpuMat(*this, Range(startrow, endrow), Range::all()); }
inline GpuMat GpuMat::rowRange(const Range& r) const { return GpuMat(*this, r, Range::all()); }
inline GpuMat GpuMat::colRange(int startcol, int endcol) const { return GpuMat(*this, Range::all(), Range(startcol, endcol)); }
inline GpuMat GpuMat::colRange(const Range& r) const { return GpuMat(*this, Range::all(), r); }
inline GpuMat GpuMat::clone() const
{
GpuMat m;
copyTo(m);
return m;
}
//CPP void GpuMat::copyTo( GpuMat& m ) const;
//CPP void GpuMat::copyTo( GpuMat& m, const GpuMat& mask ) const;
//CPP void GpuMat::convertTo( GpuMat& m, int rtype, double alpha=1, double beta=0 ) const;
inline void GpuMat::assignTo( GpuMat& m, int type ) const
{
if( type < 0 )
m = *this;
else
convertTo(m, type);
}
//CPP GpuMat& GpuMat::operator = (const Scalar& s);
//CPP GpuMat& GpuMat::setTo(const Scalar& s, const GpuMat& mask=GpuMat());
//CPP GpuMat GpuMat::reshape(int _cn, int _rows=0) const;
inline void GpuMat::create(Size _size, int _type) { create(_size.height, _size.width, _type); }
//CPP void GpuMat::create(int _rows, int _cols, int _type);
//CPP void GpuMat::release();
inline void 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 );
}
inline void GpuMat::locateROI( Size& wholeSize, Point& ofs ) const
{
size_t esz = elemSize(), minstep;
ptrdiff_t delta1 = data - datastart, delta2 = dataend - datastart;
CV_DbgAssert( step > 0 );
if( delta1 == 0 )
ofs.x = ofs.y = 0;
else
{
ofs.y = (int)(delta1/step);
ofs.x = (int)((delta1 - step*ofs.y)/esz);
CV_DbgAssert( data == datastart + ofs.y*step + ofs.x*esz );
}
minstep = (ofs.x + cols)*esz;
wholeSize.height = (int)((delta2 - minstep)/step + 1);
wholeSize.height = std::max(wholeSize.height, ofs.y + rows);
wholeSize.width = (int)((delta2 - step*(wholeSize.height-1))/esz);
wholeSize.width = std::max(wholeSize.width, ofs.x + cols);
}
inline GpuMat& GpuMat::adjustROI( int dtop, int dbottom, int dleft, int dright )
{
Size wholeSize; Point ofs;
size_t esz = elemSize();
locateROI( wholeSize, ofs );
int row1 = std::max(ofs.y - dtop, 0), row2 = std::min(ofs.y + rows + dbottom, wholeSize.height);
int col1 = std::max(ofs.x - dleft, 0), 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;
}
inline GpuMat GpuMat::operator()( Range rowRange, Range colRange ) const { return GpuMat(*this, rowRange, colRange); }
inline GpuMat GpuMat::operator()( const Rect& roi ) const { return GpuMat(*this, roi); }
inline bool GpuMat::isContinuous() const { return (flags & Mat::CONTINUOUS_FLAG) != 0; }
inline size_t GpuMat::elemSize() const { return CV_ELEM_SIZE(flags); }
inline size_t GpuMat::elemSize1() const { return CV_ELEM_SIZE1(flags); }
inline int GpuMat::type() const { return CV_MAT_TYPE(flags); }
inline int GpuMat::depth() const { return CV_MAT_DEPTH(flags); }
inline int GpuMat::channels() const { return CV_MAT_CN(flags); }
inline size_t GpuMat::step1() const { return step/elemSize1(); }
inline Size GpuMat::size() const { return Size(cols, rows); }
inline bool GpuMat::empty() const { return data == 0; }
inline uchar* GpuMat::ptr(int y)
{
CV_DbgAssert( (unsigned)y < (unsigned)rows );
return data + step*y;
}
inline const uchar* GpuMat::ptr(int y) const
{
CV_DbgAssert( (unsigned)y < (unsigned)rows );
return data + step*y;
}
template<typename _Tp> inline _Tp* GpuMat::ptr(int y)
{
CV_DbgAssert( (unsigned)y < (unsigned)rows );
return (_Tp*)(data + step*y);
}
template<typename _Tp> inline const _Tp* GpuMat::ptr(int y) const
{
CV_DbgAssert( (unsigned)y < (unsigned)rows );
return (const _Tp*)(data + step*y);
}
inline GpuMat GpuMat::t() const
{
GpuMat tmp;
transpose(*this, tmp);
return tmp;
}
static inline void swap( GpuMat& a, GpuMat& b ) { a.swap(b); }
inline GpuMat createContinuous(int rows, int cols, int type)
{
GpuMat m;
createContinuous(rows, cols, type, m);
return m;
}
inline void createContinuous(Size size, int type, GpuMat& m)
{
createContinuous(size.height, size.width, type, m);
}
inline GpuMat createContinuous(Size size, int type)
{
GpuMat m;
createContinuous(size, type, m);
return m;
}
inline void ensureSizeIsEnough(Size size, int type, GpuMat& m)
{
ensureSizeIsEnough(size.height, size.width, type, m);
}
///////////////////////////////////////////////////////////////////////
//////////////////////////////// CudaMem ////////////////////////////////
///////////////////////////////////////////////////////////////////////
......@@ -457,41 +135,6 @@ inline size_t CudaMem::step1() const { return step/elemSize1(); }
inline Size CudaMem::size() const { return Size(cols, rows); }
inline bool CudaMem::empty() const { return data == 0; }
//////////////////////////////////////////////////////////////////////////////
// Arithmetical operations
inline GpuMat operator ~ (const GpuMat& src)
{
GpuMat dst;
bitwise_not(src, dst);
return dst;
}
inline GpuMat operator | (const GpuMat& src1, const GpuMat& src2)
{
GpuMat dst;
bitwise_or(src1, src2, dst);
return dst;
}
inline GpuMat operator & (const GpuMat& src1, const GpuMat& src2)
{
GpuMat dst;
bitwise_and(src1, src2, dst);
return dst;
}
inline GpuMat operator ^ (const GpuMat& src1, const GpuMat& src2)
{
GpuMat dst;
bitwise_xor(src1, src2, dst);
return dst;
}
} /* end of namespace gpu */
} /* end of namespace cv */
......
modules/gpu/src/gpumat.cpp 0 → 100644
View file @ b2b1d41d
/*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"
using namespace cv;
using namespace cv::gpu;
////////////////////////////////////////////////////////////////////////
//////////////////////////////// GpuMat ////////////////////////////////
////////////////////////////////////////////////////////////////////////
cv::gpu::GpuMat::GpuMat(Size size_, int type_) :
flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0)
{
if (size_.height > 0 && size_.width > 0)
create(size_.height, size_.width, type_);
}
cv::gpu::GpuMat::GpuMat(int rows_, int cols_, int type_, const Scalar& s_) :
flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0)
{
if (rows_ > 0 && cols_ > 0)
{
create(rows_, cols_, type_);
*this = s_;
}
}
cv::gpu::GpuMat::GpuMat(Size size_, int type_, const Scalar& s_) :
flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0)
{
if (size_.height > 0 && size_.width > 0)
{
create(size_.height, size_.width, type_);
*this = s_;
}
}
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_ & 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_ & 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, const Range& rowRange, const 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, const 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 )
{
if( m.refcount )
CV_XADD(m.refcount, 1);
release();
flags = m.flags;
rows = m.rows; cols = m.cols;
step = m.step; data = m.data;
datastart = m.datastart; dataend = m.dataend;
refcount = m.refcount;
}
return *this;
}
GpuMat& cv::gpu::GpuMat::operator = (const Mat& m)
{
upload(m); return *this;
}
cv::gpu::GpuMat::operator Mat() const
{
Mat m;
download(m);
return m;
}
GpuMat cv::gpu::GpuMat::row(int y) const
{
return GpuMat(*this, Range(y, y+1), Range::all());
}
GpuMat cv::gpu::GpuMat::col(int x) const
{
return GpuMat(*this, Range::all(), Range(x, x+1));
}
GpuMat cv::gpu::GpuMat::rowRange(int startrow, int endrow) const
{
return GpuMat(*this, Range(startrow, endrow), Range::all());
}
GpuMat cv::gpu::GpuMat::rowRange(const Range& r) const
{
return GpuMat(*this, r, Range::all());
}
GpuMat cv::gpu::GpuMat::colRange(int startcol, int endcol) const
{
return GpuMat(*this, Range::all(), Range(startcol, endcol));
}
GpuMat cv::gpu::GpuMat::colRange(const Range& r) const
{
return GpuMat(*this, Range::all(), r);
}
void cv::gpu::GpuMat::create(Size size_, int type_)
{
create(size_.height, size_.width, type_);
}
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(), minstep;
ptrdiff_t delta1 = data - datastart, delta2 = dataend - datastart;
CV_DbgAssert( step > 0 );
if( delta1 == 0 )
ofs.x = ofs.y = 0;
else
{
ofs.y = (int)(delta1/step);
ofs.x = (int)((delta1 - step*ofs.y)/esz);
CV_DbgAssert( data == datastart + ofs.y*step + ofs.x*esz );
}
minstep = (ofs.x + cols)*esz;
wholeSize.height = (int)((delta2 - minstep)/step + 1);
wholeSize.height = std::max(wholeSize.height, ofs.y + rows);
wholeSize.width = (int)((delta2 - step*(wholeSize.height-1))/esz);
wholeSize.width = std::max(wholeSize.width, ofs.x + cols);
}
GpuMat& cv::gpu::GpuMat::adjustROI(int dtop, int dbottom, int dleft, int dright)
{
Size wholeSize; Point ofs;
size_t esz = elemSize();
locateROI( wholeSize, ofs );
int row1 = std::max(ofs.y - dtop, 0), row2 = std::min(ofs.y + rows + dbottom, wholeSize.height);
int col1 = std::max(ofs.x - dleft, 0), 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;
}
cv::gpu::GpuMat GpuMat::operator()(Range rowRange, Range colRange) const
{
return GpuMat(*this, rowRange, colRange);
}
cv::gpu::GpuMat GpuMat::operator()(const Rect& roi) const
{
return GpuMat(*this, roi);
}
bool cv::gpu::GpuMat::isContinuous() const
{
return (flags & Mat::CONTINUOUS_FLAG) != 0;
}
size_t cv::gpu::GpuMat::elemSize() const
{
return CV_ELEM_SIZE(flags);
}
size_t cv::gpu::GpuMat::elemSize1() const
{
return CV_ELEM_SIZE1(flags);
}
int cv::gpu::GpuMat::type() const
{
return CV_MAT_TYPE(flags);
}
int cv::gpu::GpuMat::depth() const
{
return CV_MAT_DEPTH(flags);
}
int cv::gpu::GpuMat::channels() const
{
return CV_MAT_CN(flags);
}
Size cv::gpu::GpuMat::size() const
{
return Size(cols, rows);
}
unsigned char* cv::gpu::GpuMat::ptr(int y)
{
CV_DbgAssert( (unsigned)y < (unsigned)rows );
return data + step*y;
}
const unsigned char* cv::gpu::GpuMat::ptr(int y) const
{
CV_DbgAssert( (unsigned)y < (unsigned)rows );
return data + step*y;
}
GpuMat cv::gpu::GpuMat::t() const
{
GpuMat tmp;
transpose(*this, tmp);
return tmp;
}
GpuMat cv::gpu::createContinuous(int rows, int cols, int type)
{
GpuMat m;
createContinuous(rows, cols, type, m);
return m;
}
void cv::gpu::createContinuous(Size size, int type, GpuMat& m)
{
createContinuous(size.height, size.width, type, m);
}
GpuMat cv::gpu::createContinuous(Size size, int type)
{
GpuMat m;
createContinuous(size, type, m);
return m;
}
void cv::gpu::ensureSizeIsEnough(Size size, int type, GpuMat& m)
{
ensureSizeIsEnough(size.height, size.width, type, m);
}
#if !defined (HAVE_CUDA)
void cv::gpu::GpuMat::upload(const Mat&) { throw_nogpu(); }
void cv::gpu::GpuMat::download(cv::Mat&) const { throw_nogpu(); }
void cv::gpu::GpuMat::copyTo(GpuMat&) const { throw_nogpu(); }
void cv::gpu::GpuMat::copyTo(GpuMat&, const GpuMat&) const { throw_nogpu(); }
void cv::gpu::GpuMat::convertTo(GpuMat&, int, double, double) const { throw_nogpu(); }
GpuMat& cv::gpu::GpuMat::operator = (const Scalar&) { throw_nogpu(); return *this; }
GpuMat& cv::gpu::GpuMat::setTo(const Scalar&, const GpuMat&) { throw_nogpu(); return *this; }
GpuMat cv::gpu::GpuMat::reshape(int, int) const { throw_nogpu(); return GpuMat(); }
void cv::gpu::GpuMat::create(int, int, int) { throw_nogpu(); }
void cv::gpu::GpuMat::release() {}
void cv::gpu::createContinuous(int, int, int, GpuMat&) { throw_nogpu(); }
#else /* !defined (HAVE_CUDA) */
namespace cv { namespace gpu { namespace matrix_operations
{
void copy_to_with_mask(const DevMem2D& src, DevMem2D dst, int depth, const DevMem2D& mask, int channels, const cudaStream_t & stream = 0);
template <typename T>
void set_to_gpu(const DevMem2D& mat, const T* scalar, int channels, cudaStream_t stream);
template <typename T>
void set_to_gpu(const DevMem2D& mat, const T* scalar, const DevMem2D& mask, int channels, cudaStream_t stream);
void convert_gpu(const DevMem2D& src, int sdepth, const DevMem2D& dst, int ddepth, double alpha, double beta, cudaStream_t stream = 0);
}}}
void cv::gpu::GpuMat::upload(const Mat& m)
{
CV_DbgAssert(!m.empty());
create(m.size(), m.type());
cudaSafeCall( cudaMemcpy2D(data, step, m.data, m.step, cols * elemSize(), rows, cudaMemcpyHostToDevice) );
}
void cv::gpu::GpuMat::upload(const CudaMem& m, Stream& stream)
{
CV_DbgAssert(!m.empty());
stream.enqueueUpload(m, *this);
}
void cv::gpu::GpuMat::download(cv::Mat& m) const
{
CV_DbgAssert(!this->empty());
m.create(size(), type());
cudaSafeCall( cudaMemcpy2D(m.data, m.step, data, step, cols * elemSize(), rows, cudaMemcpyDeviceToHost) );
}
void cv::gpu::GpuMat::download(CudaMem& m, Stream& stream) const
{
CV_DbgAssert(!m.empty());
stream.enqueueDownload(*this, m);
}
void cv::gpu::GpuMat::copyTo(GpuMat& m) const
{
CV_DbgAssert(!this->empty());
m.create(size(), type());
cudaSafeCall( cudaMemcpy2D(m.data, m.step, data, step, cols * elemSize(), rows, cudaMemcpyDeviceToDevice) );
cudaSafeCall( cudaDeviceSynchronize() );
}
void cv::gpu::GpuMat::copyTo(GpuMat& mat, const GpuMat& mask) const
{
if (mask.empty())
{
copyTo(mat);
}
else
{
mat.create(size(), type());
cv::gpu::matrix_operations::copy_to_with_mask(*this, mat, depth(), mask, channels());
}
}
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_32S> { typedef Npp32s npp_type; };
template<> struct NPPTypeTraits<CV_32F> { typedef Npp32f npp_type; };
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 cvt(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 cvt(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() );
}
};
void convertToKernelCaller(const GpuMat& src, GpuMat& dst)
{
matrix_operations::convert_gpu(src.reshape(1), src.depth(), dst.reshape(1), dst.depth(), 1.0, 0.0);
}
}
void cv::gpu::GpuMat::convertTo( GpuMat& dst, int rtype, double alpha, double beta ) const
{
CV_Assert((depth() != CV_64F && CV_MAT_DEPTH(rtype) != CV_64F) ||
(TargetArchs::builtWith(NATIVE_DOUBLE) && DeviceInfo().supports(NATIVE_DOUBLE)));
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 scn = channels();
int sdepth = depth(), ddepth = CV_MAT_DEPTH(rtype);
if( sdepth == ddepth && noScale )
{
copyTo(dst);
return;
}
GpuMat temp;
const GpuMat* psrc = this;
if( sdepth != ddepth && psrc == &dst )
psrc = &(temp = *this);
dst.create( size(), rtype );
if (!noScale)
matrix_operations::convert_gpu(psrc->reshape(1), sdepth, dst.reshape(1), ddepth, alpha, beta);
else
{
typedef void (*convert_caller_t)(const GpuMat& src, GpuMat& dst);
static const convert_caller_t convert_callers[8][8][4] =
{
{
{0,0,0,0},
{convertToKernelCaller, convertToKernelCaller, convertToKernelCaller, convertToKernelCaller},
{NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C4R>::cvt},
{NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C4R>::cvt},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{NppCvt<CV_8U, CV_32F, nppiConvert_8u32f_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0}
},
{
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0}
},
{
{NppCvt<CV_16U, CV_8U, nppiConvert_16u8u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,NppCvt<CV_16U, CV_8U, nppiConvert_16u8u_C4R>::cvt},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{NppCvt<CV_16U, CV_32S, nppiConvert_16u32s_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{NppCvt<CV_16U, CV_32F, nppiConvert_16u32f_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0}
},
{
{NppCvt<CV_16S, CV_8U, nppiConvert_16s8u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,NppCvt<CV_16S, CV_8U, nppiConvert_16s8u_C4R>::cvt},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0},
{NppCvt<CV_16S, CV_32S, nppiConvert_16s32s_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{NppCvt<CV_16S, CV_32F, nppiConvert_16s32f_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0}
},
{
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0}
},
{
{NppCvt<CV_32F, CV_8U, nppiConvert_32f8u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{NppCvt<CV_32F, CV_16U, nppiConvert_32f16u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{NppCvt<CV_32F, CV_16S, nppiConvert_32f16s_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0}
},
{
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0},
{0,0,0,0}
},
{
{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0}
}
};
convert_callers[sdepth][ddepth][scn-1](*psrc, dst);
}
}
GpuMat& GpuMat::operator = (const Scalar& s)
{
setTo(s);
return *this;
}
namespace
{
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 SDEPTH, int SCN, typename NppSetFunc<SDEPTH, SCN>::func_ptr func> struct NppSet
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
static void set(GpuMat& src, const 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 set(GpuMat& src, const 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 <typename T>
void kernelSet(GpuMat& src, const Scalar& s)
{
Scalar_<T> sf = s;
matrix_operations::set_to_gpu(src, sf.val, src.channels(), 0);
}
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 set(GpuMat& src, const 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 set(GpuMat& src, const 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() );
}
};
template <typename T>
void kernelSetMask(GpuMat& src, const Scalar& s, const GpuMat& mask)
{
Scalar_<T> sf = s;
matrix_operations::set_to_gpu(src, sf.val, mask, src.channels(), 0);
}
}
GpuMat& GpuMat::setTo(const Scalar& s, const GpuMat& mask)
{
CV_Assert(mask.type() == CV_8UC1);
CV_Assert((depth() != CV_64F) ||
(TargetArchs::builtWith(NATIVE_DOUBLE) && DeviceInfo().supports(NATIVE_DOUBLE)));
CV_DbgAssert(!this->empty());
NppiSize sz;
sz.width = cols;
sz.height = rows;
if (mask.empty())
{
if (s[0] == 0.0 && s[1] == 0.0 && s[2] == 0.0 && s[3] == 0.0)
{
cudaSafeCall( cudaMemset2D(data, step, 0, cols * elemSize(), rows) );
return *this;
}
if (depth() == CV_8U)
{
int cn = 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(data, step, val, cols * elemSize(), rows) );
return *this;
}
}
typedef void (*set_caller_t)(GpuMat& src, const Scalar& s);
static const set_caller_t set_callers[8][4] =
{
{NppSet<CV_8U, 1, nppiSet_8u_C1R>::set,kernelSet<uchar>,kernelSet<uchar>,NppSet<CV_8U, 4, nppiSet_8u_C4R>::set},
{kernelSet<schar>,kernelSet<schar>,kernelSet<schar>,kernelSet<schar>},
{NppSet<CV_16U, 1, nppiSet_16u_C1R>::set,NppSet<CV_16U, 2, nppiSet_16u_C2R>::set,kernelSet<ushort>,NppSet<CV_16U, 4, nppiSet_16u_C4R>::set},
{NppSet<CV_16S, 1, nppiSet_16s_C1R>::set,NppSet<CV_16S, 2, nppiSet_16s_C2R>::set,kernelSet<short>,NppSet<CV_16S, 4, nppiSet_16s_C4R>::set},
{NppSet<CV_32S, 1, nppiSet_32s_C1R>::set,kernelSet<int>,kernelSet<int>,NppSet<CV_32S, 4, nppiSet_32s_C4R>::set},
{NppSet<CV_32F, 1, nppiSet_32f_C1R>::set,kernelSet<float>,kernelSet<float>,NppSet<CV_32F, 4, nppiSet_32f_C4R>::set},
{kernelSet<double>,kernelSet<double>,kernelSet<double>,kernelSet<double>},
{0,0,0,0}
};
set_callers[depth()][channels()-1](*this, s);
}
else
{
typedef void (*set_caller_t)(GpuMat& src, const Scalar& s, const GpuMat& mask);
static const set_caller_t set_callers[8][4] =
{
{NppSetMask<CV_8U, 1, nppiSet_8u_C1MR>::set,kernelSetMask<uchar>,kernelSetMask<uchar>,NppSetMask<CV_8U, 4, nppiSet_8u_C4MR>::set},
{kernelSetMask<schar>,kernelSetMask<schar>,kernelSetMask<schar>,kernelSetMask<schar>},
{NppSetMask<CV_16U, 1, nppiSet_16u_C1MR>::set,kernelSetMask<ushort>,kernelSetMask<ushort>,NppSetMask<CV_16U, 4, nppiSet_16u_C4MR>::set},
{NppSetMask<CV_16S, 1, nppiSet_16s_C1MR>::set,kernelSetMask<short>,kernelSetMask<short>,NppSetMask<CV_16S, 4, nppiSet_16s_C4MR>::set},
{NppSetMask<CV_32S, 1, nppiSet_32s_C1MR>::set,kernelSetMask<int>,kernelSetMask<int>,NppSetMask<CV_32S, 4, nppiSet_32s_C4MR>::set},
{NppSetMask<CV_32F, 1, nppiSet_32f_C1MR>::set,kernelSetMask<float>,kernelSetMask<float>,NppSetMask<CV_32F, 4, nppiSet_32f_C4MR>::set},
{kernelSetMask<double>,kernelSetMask<double>,kernelSetMask<double>,kernelSetMask<double>},
{0,0,0,0}
};
set_callers[depth()][channels()-1](*this, s, mask);
}
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;
}
void cv::gpu::GpuMat::create(int _rows, int _cols, int _type)
{
_type &= 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 *dev_ptr;
cudaSafeCall( cudaMallocPitch(&dev_ptr, &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 = (int64)step*rows;
size_t nettosize = (size_t)_nettosize;
datastart = data = (uchar*)dev_ptr;
dataend = data + nettosize;
refcount = (int*)fastMalloc(sizeof(*refcount));
*refcount = 1;
}
}
void cv::gpu::GpuMat::release()
{
if( refcount && CV_XADD(refcount, -1) == 1 )
{
fastFree(refcount);
cudaSafeCall( cudaFree(datastart) );
}
data = datastart = dataend = 0;
step = rows = cols = 0;
refcount = 0;
}
void cv::gpu::createContinuous(int rows, int cols, int type, GpuMat& m)
{
int area = rows * cols;
if (!m.isContinuous() || m.type() != type || m.size().area() != area)
m.create(1, area, type);
m = m.reshape(0, rows);
}
void cv::gpu::ensureSizeIsEnough(int rows, int cols, int type, GpuMat& m)
{
if (m.type() == type && m.rows >= rows && m.cols >= cols)
m = m(Rect(0, 0, cols, rows));
else
m.create(rows, cols, type);
}
#endif /* !defined (HAVE_CUDA) */
modules/gpu/src/matrix_operations.cpp
View file @ b2b1d41d
......@@ -52,554 +52,13 @@ using namespace cv::gpu;
#if !defined (HAVE_CUDA)
namespace cv
{
namespace gpu
{
void GpuMat::upload(const Mat& /*m*/) { throw_nogpu(); }
void GpuMat::download(cv::Mat& /*m*/) const { throw_nogpu(); }
void GpuMat::copyTo( GpuMat& /*m*/ ) const { throw_nogpu(); }
void GpuMat::copyTo( GpuMat& /*m*/, const GpuMat&/* mask */) const { throw_nogpu(); }
void GpuMat::convertTo( GpuMat& /*m*/, int /*rtype*/, double /*alpha*/, double /*beta*/ ) const { throw_nogpu(); }
GpuMat& GpuMat::operator = (const Scalar& /*s*/) { throw_nogpu(); return *this; }
GpuMat& GpuMat::setTo(const Scalar& /*s*/, const GpuMat& /*mask*/) { throw_nogpu(); return *this; }
GpuMat GpuMat::reshape(int /*new_cn*/, int /*new_rows*/) const { throw_nogpu(); return GpuMat(); }
void GpuMat::create(int /*_rows*/, int /*_cols*/, int /*_type*/) { throw_nogpu(); }
void GpuMat::release() {}
void createContinuous(int /*rows*/, int /*cols*/, int /*type*/, GpuMat& /*m*/) { throw_nogpu(); }
void CudaMem::create(int /*_rows*/, int /*_cols*/, int /*_type*/, int /*type_alloc*/) { throw_nogpu(); }
bool CudaMem::canMapHostMemory() { throw_nogpu(); return false; }
void CudaMem::release() { throw_nogpu(); }
GpuMat CudaMem::createGpuMatHeader () const { throw_nogpu(); return GpuMat(); }
}
}
void cv::gpu::CudaMem::create(int /*_rows*/, int /*_cols*/, int /*_type*/, int /*type_alloc*/) { throw_nogpu(); }
bool cv::gpu::CudaMem::canMapHostMemory() { throw_nogpu(); return false; }
void cv::gpu::CudaMem::release() { throw_nogpu(); }
GpuMat cv::gpu::CudaMem::createGpuMatHeader () const { throw_nogpu(); return GpuMat(); }
#else /* !defined (HAVE_CUDA) */
namespace cv
{
namespace gpu
{
namespace matrix_operations
{
void copy_to_with_mask(const DevMem2D& src, DevMem2D dst, int depth, const DevMem2D& mask, int channels, const cudaStream_t & stream = 0);
template <typename T>
void set_to_gpu(const DevMem2D& mat, const T* scalar, int channels, cudaStream_t stream);
template <typename T>
void set_to_gpu(const DevMem2D& mat, const T* scalar, const DevMem2D& mask, int channels, cudaStream_t stream);
void convert_gpu(const DevMem2D& src, int sdepth, const DevMem2D& dst, int ddepth, double alpha, double beta, cudaStream_t stream = 0);
}
}
}
void cv::gpu::GpuMat::upload(const Mat& m)
{
CV_DbgAssert(!m.empty());
create(m.size(), m.type());
cudaSafeCall( cudaMemcpy2D(data, step, m.data, m.step, cols * elemSize(), rows, cudaMemcpyHostToDevice) );
}
void cv::gpu::GpuMat::upload(const CudaMem& m, Stream& stream)
{
CV_DbgAssert(!m.empty());
stream.enqueueUpload(m, *this);
}
void cv::gpu::GpuMat::download(cv::Mat& m) const
{
CV_DbgAssert(!this->empty());
m.create(size(), type());
cudaSafeCall( cudaMemcpy2D(m.data, m.step, data, step, cols * elemSize(), rows, cudaMemcpyDeviceToHost) );
}
void cv::gpu::GpuMat::download(CudaMem& m, Stream& stream) const
{
CV_DbgAssert(!m.empty());
stream.enqueueDownload(*this, m);
}
void cv::gpu::GpuMat::copyTo( GpuMat& m ) const
{
CV_DbgAssert(!this->empty());
m.create(size(), type());
cudaSafeCall( cudaMemcpy2D(m.data, m.step, data, step, cols * elemSize(), rows, cudaMemcpyDeviceToDevice) );
cudaSafeCall( cudaDeviceSynchronize() );
}
void cv::gpu::GpuMat::copyTo( GpuMat& mat, const GpuMat& mask ) const
{
if (mask.empty())
{
copyTo(mat);
}
else
{
mat.create(size(), type());
cv::gpu::matrix_operations::copy_to_with_mask(*this, mat, depth(), mask, channels());
}
}
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_32S> { typedef Npp32s npp_type; };
template<> struct NPPTypeTraits<CV_32F> { typedef Npp32f npp_type; };
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 cvt(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 cvt(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() );
}
};
void convertToKernelCaller(const GpuMat& src, GpuMat& dst)
{
matrix_operations::convert_gpu(src.reshape(1), src.depth(), dst.reshape(1), dst.depth(), 1.0, 0.0);
}
}
void cv::gpu::GpuMat::convertTo( GpuMat& dst, int rtype, double alpha, double beta ) const
{
CV_Assert((depth() != CV_64F && CV_MAT_DEPTH(rtype) != CV_64F) ||
(TargetArchs::builtWith(NATIVE_DOUBLE) && DeviceInfo().supports(NATIVE_DOUBLE)));
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 scn = channels();
int sdepth = depth(), ddepth = CV_MAT_DEPTH(rtype);
if( sdepth == ddepth && noScale )
{
copyTo(dst);
return;
}
GpuMat temp;
const GpuMat* psrc = this;
if( sdepth != ddepth && psrc == &dst )
psrc = &(temp = *this);
dst.create( size(), rtype );
if (!noScale)
matrix_operations::convert_gpu(psrc->reshape(1), sdepth, dst.reshape(1), ddepth, alpha, beta);
else
{
typedef void (*convert_caller_t)(const GpuMat& src, GpuMat& dst);
static const convert_caller_t convert_callers[8][8][4] =
{
{
{0,0,0,0},
{convertToKernelCaller, convertToKernelCaller, convertToKernelCaller, convertToKernelCaller},
{NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C4R>::cvt},
{NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C4R>::cvt},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{NppCvt<CV_8U, CV_32F, nppiConvert_8u32f_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0}
},
{
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0}
},
{
{NppCvt<CV_16U, CV_8U, nppiConvert_16u8u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,NppCvt<CV_16U, CV_8U, nppiConvert_16u8u_C4R>::cvt},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{NppCvt<CV_16U, CV_32S, nppiConvert_16u32s_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{NppCvt<CV_16U, CV_32F, nppiConvert_16u32f_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0}
},
{
{NppCvt<CV_16S, CV_8U, nppiConvert_16s8u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,NppCvt<CV_16S, CV_8U, nppiConvert_16s8u_C4R>::cvt},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0},
{NppCvt<CV_16S, CV_32S, nppiConvert_16s32s_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{NppCvt<CV_16S, CV_32F, nppiConvert_16s32f_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0}
},
{
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0}
},
{
{NppCvt<CV_32F, CV_8U, nppiConvert_32f8u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{NppCvt<CV_32F, CV_16U, nppiConvert_32f16u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{NppCvt<CV_32F, CV_16S, nppiConvert_32f16s_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0}
},
{
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
{0,0,0,0},
{0,0,0,0}
},
{
{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0}
}
};
convert_callers[sdepth][ddepth][scn-1](*psrc, dst);
}
}
GpuMat& GpuMat::operator = (const Scalar& s)
{
setTo(s);
return *this;
}
namespace
{
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 SDEPTH, int SCN, typename NppSetFunc<SDEPTH, SCN>::func_ptr func> struct NppSet
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
static void set(GpuMat& src, const 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 set(GpuMat& src, const 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 <typename T>
void kernelSet(GpuMat& src, const Scalar& s)
{
Scalar_<T> sf = s;
matrix_operations::set_to_gpu(src, sf.val, src.channels(), 0);
}
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 set(GpuMat& src, const 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 set(GpuMat& src, const 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() );
}
};
template <typename T>
void kernelSetMask(GpuMat& src, const Scalar& s, const GpuMat& mask)
{
Scalar_<T> sf = s;
matrix_operations::set_to_gpu(src, sf.val, mask, src.channels(), 0);
}
}
GpuMat& GpuMat::setTo(const Scalar& s, const GpuMat& mask)
{
CV_Assert(mask.type() == CV_8UC1);
CV_Assert((depth() != CV_64F) ||
(TargetArchs::builtWith(NATIVE_DOUBLE) && DeviceInfo().supports(NATIVE_DOUBLE)));
CV_DbgAssert(!this->empty());
NppiSize sz;
sz.width = cols;
sz.height = rows;
if (mask.empty())
{
if (s[0] == 0.0 && s[1] == 0.0 && s[2] == 0.0 && s[3] == 0.0)
{
cudaSafeCall( cudaMemset2D(data, step, 0, cols * elemSize(), rows) );
return *this;
}
if (depth() == CV_8U)
{
int cn = 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(data, step, val, cols * elemSize(), rows) );
return *this;
}
}
typedef void (*set_caller_t)(GpuMat& src, const Scalar& s);
static const set_caller_t set_callers[8][4] =
{
{NppSet<CV_8U, 1, nppiSet_8u_C1R>::set,kernelSet<uchar>,kernelSet<uchar>,NppSet<CV_8U, 4, nppiSet_8u_C4R>::set},
{kernelSet<schar>,kernelSet<schar>,kernelSet<schar>,kernelSet<schar>},
{NppSet<CV_16U, 1, nppiSet_16u_C1R>::set,NppSet<CV_16U, 2, nppiSet_16u_C2R>::set,kernelSet<ushort>,NppSet<CV_16U, 4, nppiSet_16u_C4R>::set},
{NppSet<CV_16S, 1, nppiSet_16s_C1R>::set,NppSet<CV_16S, 2, nppiSet_16s_C2R>::set,kernelSet<short>,NppSet<CV_16S, 4, nppiSet_16s_C4R>::set},
{NppSet<CV_32S, 1, nppiSet_32s_C1R>::set,kernelSet<int>,kernelSet<int>,NppSet<CV_32S, 4, nppiSet_32s_C4R>::set},
{NppSet<CV_32F, 1, nppiSet_32f_C1R>::set,kernelSet<float>,kernelSet<float>,NppSet<CV_32F, 4, nppiSet_32f_C4R>::set},
{kernelSet<double>,kernelSet<double>,kernelSet<double>,kernelSet<double>},
{0,0,0,0}
};
set_callers[depth()][channels()-1](*this, s);
}
else
{
typedef void (*set_caller_t)(GpuMat& src, const Scalar& s, const GpuMat& mask);
static const set_caller_t set_callers[8][4] =
{
{NppSetMask<CV_8U, 1, nppiSet_8u_C1MR>::set,kernelSetMask<uchar>,kernelSetMask<uchar>,NppSetMask<CV_8U, 4, nppiSet_8u_C4MR>::set},
{kernelSetMask<schar>,kernelSetMask<schar>,kernelSetMask<schar>,kernelSetMask<schar>},
{NppSetMask<CV_16U, 1, nppiSet_16u_C1MR>::set,kernelSetMask<ushort>,kernelSetMask<ushort>,NppSetMask<CV_16U, 4, nppiSet_16u_C4MR>::set},
{NppSetMask<CV_16S, 1, nppiSet_16s_C1MR>::set,kernelSetMask<short>,kernelSetMask<short>,NppSetMask<CV_16S, 4, nppiSet_16s_C4MR>::set},
{NppSetMask<CV_32S, 1, nppiSet_32s_C1MR>::set,kernelSetMask<int>,kernelSetMask<int>,NppSetMask<CV_32S, 4, nppiSet_32s_C4MR>::set},
{NppSetMask<CV_32F, 1, nppiSet_32f_C1MR>::set,kernelSetMask<float>,kernelSetMask<float>,NppSetMask<CV_32F, 4, nppiSet_32f_C4MR>::set},
{kernelSetMask<double>,kernelSetMask<double>,kernelSetMask<double>,kernelSetMask<double>},
{0,0,0,0}
};
set_callers[depth()][channels()-1](*this, s, mask);
}
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;
}
void cv::gpu::GpuMat::create(int _rows, int _cols, int _type)
{
_type &= 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 *dev_ptr;
cudaSafeCall( cudaMallocPitch(&dev_ptr, &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 = (int64)step*rows;
size_t nettosize = (size_t)_nettosize;
datastart = data = (uchar*)dev_ptr;
dataend = data + nettosize;
refcount = (int*)fastMalloc(sizeof(*refcount));
*refcount = 1;
}
}
void cv::gpu::GpuMat::release()
{
if( refcount && CV_XADD(refcount, -1) == 1 )
{
fastFree(refcount);
cudaSafeCall( cudaFree(datastart) );
}
data = datastart = dataend = 0;
step = rows = cols = 0;
refcount = 0;
}
void cv::gpu::createContinuous(int rows, int cols, int type, GpuMat& m)
{
int area = rows * cols;
if (!m.isContinuous() || m.type() != type || m.size().area() != area)
m.create(1, area, type);
m = m.reshape(0, rows);
}
void cv::gpu::ensureSizeIsEnough(int rows, int cols, int type, GpuMat& m)
{
if (m.type() == type && m.rows >= rows && m.cols >= cols)
m = m(Rect(0, 0, cols, rows));
else
m.create(rows, cols, type);
}
///////////////////////////////////////////////////////////////////////
//////////////////////////////// CudaMem //////////////////////////////
///////////////////////////////////////////////////////////////////////
......
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