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Commit 09ba133d authored by Vladislav Vinogradov's avatar Vladislav Vinogradov
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prepared GpuMat for moving to core module

parent 0b4e7d60
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Showing with 724 additions and 582 deletions
modules/gpu/include/opencv2/gpu/gpumat.hpp
View file @ 09ba133d
......@@ -48,21 +48,51 @@
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:
//! 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;
template <class T> operator PtrStep<T>() const;
//! builds GpuMat from Mat. Perfom blocking upload to device.
explicit GpuMat(const Mat& m);
//! pefroms blocking upload data to GpuMat.
void upload(const cv::Mat& m);
//! downloads data from device to host memory. Blocking calls.
void download(cv::Mat& m) const;
operator Mat() const
{
Mat m;
download(m);
return m;
}
//! 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);
......@@ -74,35 +104,11 @@ namespace cv { namespace gpu
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;
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;
......@@ -119,13 +125,13 @@ namespace cv { namespace gpu
GpuMat clone() const;
//! copies the GpuMatrix content to "m".
// It calls m.create(this->size(), this->type()).
void copyTo( GpuMat& m ) const;
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;
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 convertTo(GpuMat& m, int rtype, double alpha = 1, double beta = 0) const;
void assignTo( GpuMat& m, int type=-1 ) const;
void assignTo(GpuMat& m, int type=-1) const;
//! sets every GpuMatrix element to s
GpuMat& operator = (const Scalar& s);
......@@ -147,13 +153,13 @@ namespace cv { namespace gpu
void swap(GpuMat& mat);
//! locates GpuMatrix header within a parent GpuMatrix. See below
void locateROI( Size& wholeSize, Point& ofs ) const;
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 );
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;
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).
......@@ -186,9 +192,6 @@ namespace cv { namespace gpu
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
......@@ -196,10 +199,13 @@ namespace cv { namespace gpu
- 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;
......@@ -224,22 +230,59 @@ namespace cv { namespace gpu
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) {}
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)); }
template <class T> inline GpuMat::operator PtrStep<T>() const { return PtrStep<T>((T*)data, step); }
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)
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(); }
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_);
}
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)); }
template <class T> inline GpuMat::operator PtrStep<T>() const { return PtrStep<T>((T*)data, step); }
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_);
setTo(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_);
setTo(s_);
}
}
inline GpuMat::~GpuMat()
{
release();
}
inline GpuMat GpuMat::clone() const
{
......@@ -250,15 +293,21 @@ namespace cv { namespace gpu
inline void GpuMat::assignTo(GpuMat& m, int type) const
{
if( type < 0 )
if (type < 0)
m = *this;
else
convertTo(m, type);
}
inline size_t GpuMat::step1() const { return step/elemSize1(); }
inline size_t GpuMat::step1() const
{
return step / elemSize1();
}
inline bool GpuMat::empty() const { return data == 0; }
inline bool GpuMat::empty() const
{
return data == 0;
}
template<typename _Tp> inline _Tp* GpuMat::ptr(int y)
{
......@@ -270,7 +319,148 @@ namespace cv { namespace gpu
return (const _Tp*)ptr(y);
}
inline void swap(GpuMat& a, GpuMat& b) { a.swap(b); }
inline void swap(GpuMat& a, GpuMat& b)
{
a.swap(b);
}
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 void GpuMat::create(Size size_, int type_)
{
create(size_.height, size_.width, type_);
}
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 GpuMat::size() const
{
return Size(cols, rows);
}
inline unsigned char* GpuMat::ptr(int y)
{
CV_DbgAssert((unsigned)y < (unsigned)rows);
return data + step * y;
}
inline const unsigned char* GpuMat::ptr(int y) const
{
CV_DbgAssert((unsigned)y < (unsigned)rows);
return data + step * y;
}
inline GpuMat& GpuMat::operator = (const Scalar& s)
{
setTo(s);
return *this;
}
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);
}
inline void 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);
}
inline void 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 // __OPENCV_GPUMAT_HPP__
modules/gpu/src/gpumat.cpp
View file @ 09ba133d
......@@ -44,37 +44,7 @@
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_;
}
}
using namespace std;
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)
......@@ -84,10 +54,12 @@ cv::gpu::GpuMat::GpuMat(const GpuMat& m) :
}
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),
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;
......@@ -95,8 +67,11 @@ cv::gpu::GpuMat::GpuMat(int rows_, int cols_, int type_, void* data_, size_t ste
}
else
{
if (rows == 1) step = minstep;
CV_DbgAssert( step >= minstep );
if (rows == 1)
step = minstep;
CV_DbgAssert(step >= minstep);
flags |= step == minstep ? Mat::CONTINUOUS_FLAG : 0;
}
dataend += step * (rows - 1) + minstep;
......@@ -108,6 +83,7 @@ cv::gpu::GpuMat::GpuMat(Size size_, int type_, void* data_, size_t step_) :
datastart((uchar*)data_), dataend((uchar*)data_)
{
size_t minstep = cols * elemSize();
if (step == Mat::AUTO_STEP)
{
step = minstep;
......@@ -115,8 +91,11 @@ cv::gpu::GpuMat::GpuMat(Size size_, int type_, void* data_, size_t step_) :
}
else
{
if (rows == 1) step = minstep;
CV_DbgAssert( step >= minstep );
if (rows == 1)
step = minstep;
CV_DbgAssert(step >= minstep);
flags |= step == minstep ? Mat::CONTINUOUS_FLAG : 0;
}
dataend += step * (rows - 1) + minstep;
......@@ -132,7 +111,8 @@ cv::gpu::GpuMat::GpuMat(const GpuMat& m, const Range& rowRange, const Range& col
rows = m.rows;
else
{
CV_Assert( 0 <= rowRange.start && rowRange.start <= rowRange.end && rowRange.end <= m.rows );
CV_Assert(0 <= rowRange.start && rowRange.start <= rowRange.end && rowRange.end <= m.rows);
rows = rowRange.size();
data += step*rowRange.start;
}
......@@ -141,18 +121,20 @@ cv::gpu::GpuMat::GpuMat(const GpuMat& m, const Range& rowRange, const Range& col
cols = m.cols;
else
{
CV_Assert( 0 <= colRange.start && colRange.start <= colRange.end && colRange.end <= m.cols );
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 )
if (rows == 1)
flags |= Mat::CONTINUOUS_FLAG;
if( refcount )
if (refcount)
CV_XADD(refcount, 1);
if( rows <= 0 || cols <= 0 )
if (rows <= 0 || cols <= 0)
rows = cols = 0;
}
......@@ -162,12 +144,14 @@ cv::gpu::GpuMat::GpuMat(const GpuMat& m, const Rect& roi) :
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 )
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 )
if (rows <= 0 || cols <= 0)
rows = cols = 0;
}
......@@ -179,282 +163,218 @@ cv::gpu::GpuMat::GpuMat(const Mat& m) :
GpuMat& cv::gpu::GpuMat::operator = (const GpuMat& m)
{
if( this != &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;
GpuMat temp(m);
swap(temp);
}
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_);
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 );
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 )
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 = (int)(delta1/step);
ofs.x = (int)((delta1 - step*ofs.y)/esz);
CV_DbgAssert( data == datastart + ofs.y*step + ofs.x*esz );
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);
}
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);
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;
Size wholeSize;
Point ofs;
locateROI(wholeSize, 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 )
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;
}
cv::gpu::GpuMat GpuMat::operator()(Range rowRange, Range colRange) const
{
return GpuMat(*this, rowRange, colRange);
}
GpuMat cv::gpu::GpuMat::reshape(int new_cn, int new_rows) const
{
GpuMat hdr = *this;
cv::gpu::GpuMat GpuMat::operator()(const Rect& roi) const
{
return GpuMat(*this, roi);
}
int cn = channels();
if (new_cn == 0)
new_cn = cn;
bool cv::gpu::GpuMat::isContinuous() const
{
return (flags & Mat::CONTINUOUS_FLAG) != 0;
}
int total_width = cols * cn;
size_t cv::gpu::GpuMat::elemSize() const
{
return CV_ELEM_SIZE(flags);
}
if ((new_cn > total_width || total_width % new_cn != 0) && new_rows == 0)
new_rows = rows * total_width / new_cn;
size_t cv::gpu::GpuMat::elemSize1() const
{
return CV_ELEM_SIZE1(flags);
}
if (new_rows != 0 && new_rows != rows)
{
int total_size = total_width * rows;
int cv::gpu::GpuMat::type() const
{
return CV_MAT_TYPE(flags);
}
if (!isContinuous())
CV_Error(CV_BadStep, "The matrix is not continuous, thus its number of rows can not be changed");
int cv::gpu::GpuMat::depth() const
{
return CV_MAT_DEPTH(flags);
}
if ((unsigned)new_rows > (unsigned)total_size)
CV_Error(CV_StsOutOfRange, "Bad new number of rows");
int cv::gpu::GpuMat::channels() const
{
return CV_MAT_CN(flags);
}
total_width = total_size / new_rows;
Size cv::gpu::GpuMat::size() const
{
return Size(cols, 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");
unsigned char* cv::gpu::GpuMat::ptr(int y)
{
CV_DbgAssert( (unsigned)y < (unsigned)rows );
return data + step*y;
}
hdr.rows = new_rows;
hdr.step = total_width * elemSize1();
}
const unsigned char* cv::gpu::GpuMat::ptr(int y) const
{
CV_DbgAssert( (unsigned)y < (unsigned)rows );
return data + step*y;
}
int new_width = total_width / new_cn;
GpuMat cv::gpu::GpuMat::t() const
{
GpuMat tmp;
transpose(*this, tmp);
return tmp;
}
if (new_width * new_cn != total_width)
CV_Error(CV_BadNumChannels, "The total width is not divisible by the new number of channels");
GpuMat cv::gpu::createContinuous(int rows, int cols, int type)
{
GpuMat m;
createContinuous(rows, cols, type, m);
return m;
}
hdr.cols = new_width;
hdr.flags = (hdr.flags & ~CV_MAT_CN_MASK) | ((new_cn - 1) << CV_CN_SHIFT);
void cv::gpu::createContinuous(Size size, int type, GpuMat& m)
{
createContinuous(size.height, size.width, type, m);
return hdr;
}
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(); }
void cv::gpu::ensureSizeIsEnough(int, int, int, GpuMat&) { throw_nogpu(); }
#else /* !defined (HAVE_CUDA) */
namespace cv { namespace gpu { namespace device
{
void copy_to_with_mask(const DevMem2Db& src, DevMem2Db dst, int depth, const DevMem2Db& mask, int channels, const cudaStream_t & stream = 0);
template <typename T>
void set_to_gpu(const DevMem2Db& mat, const T* scalar, int channels, cudaStream_t stream);
template <typename T>
void set_to_gpu(const DevMem2Db& mat, const T* scalar, const DevMem2Db& mask, int channels, cudaStream_t stream);
void convert_gpu(const DevMem2Db& src, int sdepth, const DevMem2Db& 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
class GpuFuncTable
{
CV_DbgAssert(!this->empty());
m.create(size(), type());
cudaSafeCall( cudaMemcpy2D(m.data, m.step, data, step, cols * elemSize(), rows, cudaMemcpyDeviceToHost) );
}
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;
void cv::gpu::GpuMat::download(CudaMem& m, Stream& stream) const
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, const 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;
};
#if !defined (HAVE_CUDA)
class EmptyFuncTable : public GpuFuncTable
{
CV_DbgAssert(!m.empty());
stream.enqueueDownload(*this, m);
}
public:
void copy(const Mat&, GpuMat&) const { throw_nogpu(); }
void copy(const GpuMat&, Mat&) const { throw_nogpu(); }
void copy(const GpuMat&, GpuMat&) const { throw_nogpu(); }
void cv::gpu::GpuMat::copyTo(GpuMat& m) const
void copyWithMask(const GpuMat&, GpuMat&, const GpuMat&) const { throw_nogpu(); }
void convert(const GpuMat&, GpuMat&) const { throw_nogpu(); }
void convert(const GpuMat&, GpuMat&, double, double) const { throw_nogpu(); }
void setTo(GpuMat&, const Scalar&, const GpuMat&) const { throw_nogpu(); }
void mallocPitch(void**, size_t*, size_t, size_t) const { throw_nogpu(); }
void free(void*) const {}
};
const GpuFuncTable* gpuFuncTable()
{
CV_DbgAssert(!this->empty());
m.create(size(), type());
cudaSafeCall( cudaMemcpy2D(m.data, m.step, data, step, cols * elemSize(), rows, cudaMemcpyDeviceToDevice) );
cudaSafeCall( cudaDeviceSynchronize() );
static EmptyFuncTable empty;
return &empty;
}
void cv::gpu::GpuMat::copyTo(GpuMat& mat, const GpuMat& mask) const
#else
namespace cv { namespace gpu { namespace device
{
if (mask.empty())
{
copyTo(mat);
}
else
{
mat.create(size(), type());
device::copy_to_with_mask(*this, mat, depth(), mask, channels());
}
}
void copy_to_with_mask(const DevMem2Db& src, DevMem2Db dst, int depth, const DevMem2Db& mask, int channels, const cudaStream_t& stream = 0);
template <typename T>
void set_to_gpu(const DevMem2Db& mat, const T* scalar, int channels, cudaStream_t stream);
template <typename T>
void set_to_gpu(const DevMem2Db& mat, const T* scalar, const DevMem2Db& mask, int channels, cudaStream_t stream);
void convert_gpu(const DevMem2Db& src, int sdepth, const DevMem2Db& dst, int ddepth, double alpha, double beta, cudaStream_t stream = 0);
}}}
namespace
{
//////////////////////////////////////////////////////////////////////////
// Convert
template<int n> struct NPPTypeTraits;
template<> struct NPPTypeTraits<CV_8U> { typedef Npp8u npp_type; };
template<> struct NPPTypeTraits<CV_16U> { typedef Npp16u npp_type; };
......@@ -510,129 +430,10 @@ namespace
{
device::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)
device::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
{
//////////////////////////////////////////////////////////////////////////
// Set
template<int SDEPTH, int SCN> struct NppSetFunc
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
......@@ -655,7 +456,9 @@ namespace
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() );
......@@ -670,7 +473,9 @@ namespace
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() );
......@@ -706,7 +511,9 @@ namespace
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() );
......@@ -721,7 +528,9 @@ namespace
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() );
......@@ -736,122 +545,281 @@ namespace
}
}
GpuMat& GpuMat::setTo(const Scalar& s, const GpuMat& mask)
class CudaFuncTable : public GpuFuncTable
{
CV_Assert(mask.type() == CV_8UC1);
public:
virtual 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) );
}
virtual 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) );
}
virtual 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) );
}
CV_Assert((depth() != CV_64F) ||
(TargetArchs::builtWith(NATIVE_DOUBLE) && DeviceInfo().supports(NATIVE_DOUBLE)));
virtual void copyWithMask(const GpuMat& src, GpuMat& dst, const GpuMat& mask) const
{
device::copy_to_with_mask(src, dst, src.depth(), mask, src.channels());
}
CV_DbgAssert(!this->empty());
void convert(const GpuMat& src, GpuMat& dst) const
{
typedef void (*caller_t)(const GpuMat& src, GpuMat& dst);
static const caller_t callers[7][7][7] =
{
{
/* 8U -> 8U */ {0, 0, 0, 0},
/* 8U -> 8S */ {convertToKernelCaller, convertToKernelCaller, convertToKernelCaller, convertToKernelCaller},
/* 8U -> 16U */ {NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C4R>::cvt},
/* 8U -> 16S */ {NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C4R>::cvt},
/* 8U -> 32S */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 8U -> 32F */ {NppCvt<CV_8U, CV_32F, nppiConvert_8u32f_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 8U -> 64F */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}
},
{
/* 8S -> 8U */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 8S -> 8S */ {0,0,0,0},
/* 8S -> 16U */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 8S -> 16S */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 8S -> 32S */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 8S -> 32F */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 8S -> 64F */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}
},
{
/* 16U -> 8U */ {NppCvt<CV_16U, CV_8U, nppiConvert_16u8u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,NppCvt<CV_16U, CV_8U, nppiConvert_16u8u_C4R>::cvt},
/* 16U -> 8S */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 16U -> 16U */ {0,0,0,0},
/* 16U -> 16S */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 16U -> 32S */ {NppCvt<CV_16U, CV_32S, nppiConvert_16u32s_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 16U -> 32F */ {NppCvt<CV_16U, CV_32F, nppiConvert_16u32f_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 16U -> 64F */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}
},
{
/* 16S -> 8U */ {NppCvt<CV_16S, CV_8U, nppiConvert_16s8u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,NppCvt<CV_16S, CV_8U, nppiConvert_16s8u_C4R>::cvt},
/* 16S -> 8S */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 16S -> 16U */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 16S -> 16S */ {0,0,0,0},
/* 16S -> 32S */ {NppCvt<CV_16S, CV_32S, nppiConvert_16s32s_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 16S -> 32F */ {NppCvt<CV_16S, CV_32F, nppiConvert_16s32f_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 16S -> 64F */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}
},
{
/* 32S -> 8U */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 32S -> 8S */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 32S -> 16U */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 32S -> 16S */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 32S -> 32S */ {0,0,0,0},
/* 32S -> 32F */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 32S -> 64F */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}
},
{
/* 32F -> 8U */ {NppCvt<CV_32F, CV_8U, nppiConvert_32f8u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 32F -> 8S */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 32F -> 16U */ {NppCvt<CV_32F, CV_16U, nppiConvert_32f16u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 32F -> 16S */ {NppCvt<CV_32F, CV_16S, nppiConvert_32f16s_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 32F -> 32S */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 32F -> 32F */ {0,0,0,0},
/* 32F -> 64F */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}
},
{
/* 64F -> 8U */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 64F -> 8S */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 64F -> 16U */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 64F -> 16S */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 64F -> 32S */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 64F -> 32F */ {convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller},
/* 64F -> 64F */ {0,0,0,0}
}
};
NppiSize sz;
sz.width = cols;
sz.height = rows;
caller_t func = callers[src.depth()][dst.depth()][src.channels() - 1];
CV_DbgAssert(func != 0);
if (mask.empty())
func(src, dst);
}
void convert(const GpuMat& src, GpuMat& dst, double alpha, double beta) const
{
device::convert_gpu(src.reshape(1), src.depth(), dst.reshape(1), dst.depth(), alpha, beta);
}
void setTo(GpuMat& m, const Scalar& s, const GpuMat& mask) const
{
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)
NppiSize sz;
sz.width = m.cols;
sz.height = m.rows;
if (mask.empty())
{
int cn = channels();
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 (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]))
if (m.depth() == CV_8U)
{
int val = saturate_cast<uchar>(s[0]);
cudaSafeCall( cudaMemset2D(data, step, val, cols * elemSize(), rows) );
return *this;
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<gpu::uchar>(s[0]);
cudaSafeCall( cudaMemset2D(m.data, m.step, val, m.cols * m.elemSize(), m.rows) );
return;
}
}
typedef void (*caller_t)(GpuMat& src, const Scalar& s);
static const caller_t callers[7][4] =
{
{NppSet<CV_8U, 1, nppiSet_8u_C1R>::set,kernelSet<gpu::uchar>,kernelSet<gpu::uchar>,NppSet<CV_8U, 4, nppiSet_8u_C4R>::set},
{kernelSet<gpu::schar>,kernelSet<gpu::schar>,kernelSet<gpu::schar>,kernelSet<gpu::schar>},
{NppSet<CV_16U, 1, nppiSet_16u_C1R>::set,NppSet<CV_16U, 2, nppiSet_16u_C2R>::set,kernelSet<gpu::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>}
};
callers[m.depth()][m.channels() - 1](m, s);
}
typedef void (*set_caller_t)(GpuMat& src, const Scalar& s);
static const set_caller_t set_callers[8][4] =
else
{
{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);
typedef void (*caller_t)(GpuMat& src, const Scalar& s, const GpuMat& mask);
static const caller_t callers[7][4] =
{
{NppSetMask<CV_8U, 1, nppiSet_8u_C1MR>::set,kernelSetMask<gpu::uchar>,kernelSetMask<gpu::uchar>,NppSetMask<CV_8U, 4, nppiSet_8u_C4MR>::set},
{kernelSetMask<gpu::schar>,kernelSetMask<gpu::schar>,kernelSetMask<gpu::schar>,kernelSetMask<gpu::schar>},
{NppSetMask<CV_16U, 1, nppiSet_16u_C1MR>::set,kernelSetMask<gpu::ushort>,kernelSetMask<gpu::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>}
};
callers[m.depth()][m.channels() - 1](m, s, mask);
}
}
else
void mallocPitch(void** devPtr, size_t* step, size_t width, size_t height) const
{
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);
cudaSafeCall( cudaMallocPitch(devPtr, step, width, height) );
}
return *this;
void free(void* devPtr) const
{
cudaFree(devPtr);
}
};
const GpuFuncTable* gpuFuncTable()
{
static CudaFuncTable cuda;
return &cuda;
}
#endif
GpuMat cv::gpu::GpuMat::reshape(int new_cn, int new_rows) const
void cv::gpu::GpuMat::upload(const Mat& m)
{
GpuMat hdr = *this;
CV_DbgAssert(!m.empty());
int cn = channels();
if( new_cn == 0 )
new_cn = cn;
create(m.size(), m.type());
int total_width = cols * cn;
gpuFuncTable()->copy(m, *this);
}
if( (new_cn > total_width || total_width % new_cn != 0) && new_rows == 0 )
new_rows = rows * total_width / new_cn;
void cv::gpu::GpuMat::download(cv::Mat& m) const
{
CV_DbgAssert(!empty());
if( new_rows != 0 && new_rows != rows )
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
{
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" );
mat.create(size(), type());
if( (unsigned)new_rows > (unsigned)total_size )
CV_Error( CV_StsOutOfRange, "Bad new number of rows" );
gpuFuncTable()->copyWithMask(*this, mat, mask);
}
}
total_width = total_size / new_rows;
void cv::gpu::GpuMat::convertTo(GpuMat& dst, int rtype, double alpha, double beta) const
{
bool noScale = fabs(alpha - 1) < numeric_limits<double>::epsilon() && fabs(beta) < numeric_limits<double>::epsilon();
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" );
if (rtype < 0)
rtype = type();
else
rtype = CV_MAKETYPE(CV_MAT_DEPTH(rtype), channels());
hdr.rows = new_rows;
hdr.step = total_width * elemSize1();
int scn = channels();
int sdepth = depth();
int ddepth = CV_MAT_DEPTH(rtype);
if (sdepth == ddepth && noScale)
{
copyTo(dst);
return;
}
int new_width = total_width / new_cn;
GpuMat temp;
const GpuMat* psrc = this;
if (sdepth != ddepth && psrc == &dst)
{
temp = *this;
psrc = &temp;
}
if( new_width * new_cn != total_width )
CV_Error( CV_BadNumChannels, "The total width is not divisible by the new number of channels" );
dst.create(size(), rtype);
hdr.cols = new_width;
hdr.flags = (hdr.flags & ~CV_MAT_CN_MASK) | ((new_cn-1) << CV_CN_SHIFT);
return hdr;
if (noScale)
gpuFuncTable()->convert(*psrc, dst);
else
gpuFuncTable()->convert(*psrc, dst, alpha, beta);
}
GpuMat& cv::gpu::GpuMat::setTo(const 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 &= TYPE_MASK;
if( rows == _rows && cols == _cols && type() == _type && data )
if (rows == _rows && cols == _cols && type() == _type && data)
return;
if( data )
if (data)
release();
CV_DbgAssert( _rows >= 0 && _cols >= 0 );
if( _rows > 0 && _cols > 0 )
CV_DbgAssert(_rows >= 0 && _cols >= 0);
if (_rows > 0 && _cols > 0)
{
flags = Mat::MAGIC_VAL + _type;
rows = _rows;
......@@ -859,8 +827,8 @@ void cv::gpu::GpuMat::create(int _rows, int _cols, int _type)
size_t esz = elemSize();
void *dev_ptr;
cudaSafeCall( cudaMallocPitch(&dev_ptr, &step, esz * cols, rows) );
void* devPtr;
gpuFuncTable()->mallocPitch(&devPtr, &step, esz * cols, rows);
// Single row must be continuous
if (rows == 1)
......@@ -869,43 +837,27 @@ void cv::gpu::GpuMat::create(int _rows, int _cols, int _type)
if (esz * cols == step)
flags |= Mat::CONTINUOUS_FLAG;
int64 _nettosize = (int64)step*rows;
size_t nettosize = (size_t)_nettosize;
int64 _nettosize = static_cast<int64>(step) * rows;
size_t nettosize = static_cast<size_t>(_nettosize);
datastart = data = (uchar*)dev_ptr;
datastart = data = static_cast<uchar*>(devPtr);
dataend = data + nettosize;
refcount = (int*)fastMalloc(sizeof(*refcount));
refcount = static_cast<int*>(fastMalloc(sizeof(*refcount)));
*refcount = 1;
}
}
void cv::gpu::GpuMat::release()
{
if( refcount && CV_XADD(refcount, -1) == 1 )
if (refcount && CV_XADD(refcount, -1) == 1)
{
fastFree(refcount);
cudaFree(datastart);
gpuFuncTable()->free(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/stitching/src/blenders.cpp
View file @ 09ba133d
......@@ -387,7 +387,7 @@ void createLaplacePyrGpu(const Mat &img, int num_levels, vector<Mat> &pyr)
pyr.resize(num_levels + 1);
vector<gpu::GpuMat> gpu_pyr(num_levels + 1);
gpu_pyr[0] = img;
gpu_pyr[0].upload(img);
for (int i = 0; i < num_levels; ++i)
gpu::pyrDown(gpu_pyr[i], gpu_pyr[i + 1]);
......@@ -396,10 +396,10 @@ void createLaplacePyrGpu(const Mat &img, int num_levels, vector<Mat> &pyr)
{
gpu::pyrUp(gpu_pyr[i + 1], tmp);
gpu::subtract(gpu_pyr[i], tmp, gpu_pyr[i]);
pyr[i] = gpu_pyr[i];
gpu_pyr[i].download(pyr[i]);
}
pyr[num_levels] = gpu_pyr[num_levels];
gpu_pyr[num_levels].download(pyr[num_levels]);
#endif
}
......@@ -425,7 +425,7 @@ void restoreImageFromLaplacePyrGpu(vector<Mat> &pyr)
vector<gpu::GpuMat> gpu_pyr(pyr.size());
for (size_t i = 0; i < pyr.size(); ++i)
gpu_pyr[i] = pyr[i];
gpu_pyr[i].upload(pyr[i]);
gpu::GpuMat tmp;
for (size_t i = pyr.size() - 1; i > 0; --i)
......@@ -434,7 +434,7 @@ void restoreImageFromLaplacePyrGpu(vector<Mat> &pyr)
gpu::add(tmp, gpu_pyr[i - 1], gpu_pyr[i - 1]);
}
pyr[0] = gpu_pyr[0];
gpu_pyr[0].download(pyr[0]);
#endif
}
......
samples/gpu/performance/tests.cpp
View file @ 09ba133d
......@@ -37,7 +37,7 @@ TEST(matchTemplate)
matchTemplate(src, templ, dst, CV_TM_CCORR);
CPU_OFF;
d_templ = templ;
d_templ.upload(templ);
d_dst.create(d_src.rows - d_templ.rows + 1, d_src.cols - d_templ.cols + 1, CV_32F);
GPU_ON;
......@@ -65,7 +65,7 @@ TEST(minMaxLoc)
minMaxLoc(src, &min_val, &max_val, &min_loc, &max_loc);
CPU_OFF;
d_src = src;
d_src.upload(src);
GPU_ON;
gpu::minMaxLoc(d_src, &min_val, &max_val, &min_loc, &max_loc);
......@@ -107,9 +107,9 @@ TEST(remap)
remap(src, dst, xmap, ymap, interpolation, borderMode);
CPU_OFF;
d_src = src;
d_xmap = xmap;
d_ymap = ymap;
d_src.upload(src);
d_xmap.upload(xmap);
d_ymap.upload(ymap);
d_dst.create(d_xmap.size(), d_src.type());
GPU_ON;
......@@ -142,9 +142,9 @@ TEST(remap)
remap(src, dst, xmap, ymap, interpolation, borderMode);
CPU_OFF;
d_src = src;
d_xmap = xmap;
d_ymap = ymap;
d_src.upload(src);
d_xmap.upload(xmap);
d_ymap.upload(ymap);
d_dst.create(d_xmap.size(), d_src.type());
GPU_ON;
......@@ -177,9 +177,9 @@ TEST(remap)
remap(src, dst, xmap, ymap, interpolation, borderMode);
CPU_OFF;
d_src = src;
d_xmap = xmap;
d_ymap = ymap;
d_src.upload(src);
d_xmap.upload(xmap);
d_ymap.upload(ymap);
d_dst.create(d_xmap.size(), d_src.type());
GPU_ON;
......@@ -212,9 +212,9 @@ TEST(remap)
remap(src, dst, xmap, ymap, interpolation, borderMode);
CPU_OFF;
d_src = src;
d_xmap = xmap;
d_ymap = ymap;
d_src.upload(src);
d_xmap.upload(xmap);
d_ymap.upload(ymap);
d_dst.create(d_xmap.size(), d_src.type());
GPU_ON;
......@@ -240,7 +240,7 @@ TEST(dft)
dft(src, dst);
CPU_OFF;
d_src = src;
d_src.upload(src);
d_dst.create(d_src.size(), d_src.type());
GPU_ON;
......@@ -266,7 +266,7 @@ TEST(cornerHarris)
cornerHarris(src, dst, 5, 7, 0.1, BORDER_REFLECT101);
CPU_OFF;
d_src = src;
d_src.upload(src);
d_dst.create(src.size(), src.type());
GPU_ON;
......@@ -286,7 +286,7 @@ TEST(integral)
gen(src, size, size, CV_8U, 0, 256);
sum.create(size + 1, size + 1, CV_32S);
d_src = src;
d_src.upload(src);
d_sum.create(size + 1, size + 1, CV_32S);
for (int i = 0; i < 5; ++i)
......@@ -320,7 +320,7 @@ TEST(norm)
norm(src, NORM_INF);
CPU_OFF;
d_src = src;
d_src.upload(src);
GPU_ON;
for (int i = 0; i < 5; ++i)
......@@ -350,7 +350,7 @@ TEST(meanShift)
gen(src, size, size, CV_8UC4, Scalar::all(0), Scalar::all(256));
d_src = src;
d_src.upload(src);
d_dst.create(d_src.size(), d_src.type());
GPU_ON;
......@@ -483,8 +483,8 @@ TEST(magnitude)
magnitude(x, y, mag);
CPU_OFF;
d_x = x;
d_y = y;
d_x.upload(x);
d_y.upload(y);
d_mag.create(size, size, CV_32F);
GPU_ON;
......@@ -511,8 +511,8 @@ TEST(add)
add(src1, src2, dst);
CPU_OFF;
d_src1 = src1;
d_src2 = src2;
d_src1.upload(src1);
d_src2.upload(src2);
d_dst.create(size, size, CV_32F);
GPU_ON;
......@@ -538,7 +538,7 @@ TEST(log)
log(src, dst);
CPU_OFF;
d_src = src;
d_src.upload(src);
d_dst.create(size, size, CV_32F);
GPU_ON;
......@@ -564,7 +564,7 @@ TEST(exp)
exp(src, dst);
CPU_OFF;
d_src = src;
d_src.upload(src);
d_dst.create(size, size, CV_32F);
GPU_ON;
......@@ -591,8 +591,8 @@ TEST(mulSpectrums)
mulSpectrums(src1, src2, dst, 0, true);
CPU_OFF;
d_src1 = src1;
d_src2 = src2;
d_src1.upload(src1);
d_src2.upload(src2);
d_dst.create(size, size, CV_32FC2);
GPU_ON;
......@@ -618,7 +618,7 @@ TEST(resize)
resize(src, dst, dst.size());
CPU_OFF;
d_src = src;
d_src.upload(src);
d_dst.create(size * 2, size * 2, CV_8U);
GPU_ON;
......@@ -636,7 +636,7 @@ TEST(resize)
resize(src, dst, dst.size());
CPU_OFF;
d_src = src;
d_src.upload(src);
d_dst.create(size / 2, size / 2, CV_8U);
GPU_ON;
......@@ -654,7 +654,7 @@ TEST(resize)
resize(src, dst, dst.size());
CPU_OFF;
d_src = src;
d_src.upload(src);
d_dst.create(size * 2, size * 2, CV_8U);
GPU_ON;
......@@ -672,7 +672,7 @@ TEST(resize)
resize(src, dst, dst.size());
CPU_OFF;
d_src = src;
d_src.upload(src);
d_dst.create(size / 2, size / 2, CV_8U);
GPU_ON;
......@@ -690,7 +690,7 @@ TEST(resize)
resize(src, dst, dst.size());
CPU_OFF;
d_src = src;
d_src.upload(src);
d_dst.create(size * 2, size * 2, CV_8U);
GPU_ON;
......@@ -708,7 +708,7 @@ TEST(resize)
resize(src, dst, dst.size());
CPU_OFF;
d_src = src;
d_src.upload(src);
d_dst.create(size / 2, size / 2, CV_8U);
GPU_ON;
......@@ -726,7 +726,7 @@ TEST(resize)
resize(src, dst, dst.size());
CPU_OFF;
d_src = src;
d_src.upload(src);
d_dst.create(size * 2, size * 2, CV_8U);
GPU_ON;
......@@ -744,7 +744,7 @@ TEST(resize)
resize(src, dst, dst.size());
CPU_OFF;
d_src = src;
d_src.upload(src);
d_dst.create(size / 2, size / 2, CV_8U);
GPU_ON;
......@@ -900,7 +900,7 @@ TEST(erode)
erode(src, dst, ker);
CPU_OFF;
d_src = src;
d_src.upload(src);
d_dst.create(d_src.size(), d_src.type());
GPU_ON;
......@@ -925,7 +925,7 @@ TEST(threshold)
threshold(src, dst, 50.0, 0.0, THRESH_BINARY);
CPU_OFF;
d_src = src;
d_src.upload(src);
d_dst.create(size, size, CV_8U);
GPU_ON;
......@@ -944,7 +944,7 @@ TEST(threshold)
threshold(src, dst, 50.0, 0.0, THRESH_BINARY);
CPU_OFF;
d_src = src;
d_src.upload(src);
d_dst.create(size, size, CV_32F);
GPU_ON;
......@@ -969,7 +969,7 @@ TEST(pow)
pow(src, -2.0, dst);
CPU_OFF;
d_src = src;
d_src.upload(src);
d_dst.create(size, size, CV_32F);
GPU_ON;
......@@ -1004,7 +1004,7 @@ TEST(projectPoints)
projectPoints(src, rvec, tvec, camera_mat, Mat::zeros(1, 8, CV_32F), dst);
CPU_OFF;
d_src = src;
d_src.upload(src);
d_dst.create(1, size, CV_32FC2);
GPU_ON;
......@@ -1491,9 +1491,9 @@ TEST(gemm)
gemm(src1, src2, 1.0, src3, 1.0, dst);
CPU_OFF;
d_src1 = src1;
d_src2 = src2;
d_src3 = src3;
d_src1.upload(src1);
d_src2.upload(src2);
d_src3.upload(src3);
d_dst.create(d_src1.size(), d_src1.type());
GPU_ON;
......
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