mirror of
https://github.com/opencv/opencv.git
synced 2024-11-25 11:40:44 +08:00
moved GpuMat implementation to separate file
This commit is contained in:
Vladislav Vinogradov
parent
2153a14872
commit
db1178b5df
@ -53,6 +53,181 @@
|
||||
|
||||
namespace cv { namespace gpu
|
||||
{
|
||||
|
||||
//////////////////////////////// GpuMat ///////////////////////////////
|
||||
|
||||
// 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 GpuMat of the specified size and type
|
||||
GpuMat(int rows, int cols, int type);
|
||||
GpuMat(Size size, int type);
|
||||
|
||||
//! constucts GpuMat and fills it with the specified value _s
|
||||
GpuMat(int rows, int cols, int type, Scalar s);
|
||||
GpuMat(Size size, int type, Scalar s);
|
||||
|
||||
//! copy constructor
|
||||
GpuMat(const GpuMat& m);
|
||||
|
||||
//! constructor for GpuMat 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 GpuMat header for a part of the bigger matrix
|
||||
GpuMat(const GpuMat& m, Range rowRange, Range colRange);
|
||||
GpuMat(const GpuMat& m, 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);
|
||||
|
||||
//! allocates new GpuMat data unless the GpuMat already has specified size and type
|
||||
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);
|
||||
|
||||
//! pefroms blocking upload data to GpuMat
|
||||
void upload(const Mat& m);
|
||||
|
||||
//! downloads data from device to host memory (Blocking calls)
|
||||
void download(Mat& m) const;
|
||||
|
||||
//! returns deep copy of the GpuMat, i.e. the data is copied
|
||||
GpuMat clone() const;
|
||||
|
||||
//! copies the GpuMat content to "m"
|
||||
void copyTo(GpuMat& m) const;
|
||||
|
||||
//! copies those GpuMat elements to "m" that are marked with non-zero mask elements
|
||||
void copyTo(GpuMat& m, const GpuMat& mask) const;
|
||||
|
||||
//! sets some of the GpuMat elements to s, according to the mask
|
||||
GpuMat& setTo(Scalar s, const GpuMat& mask = GpuMat());
|
||||
|
||||
//! converts GpuMat to another datatype with optional scaling
|
||||
void convertTo(GpuMat& m, int rtype, double alpha = 1, double beta = 0) const;
|
||||
|
||||
void assignTo(GpuMat& m, int type=-1) 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;
|
||||
|
||||
template <typename _Tp> operator PtrStepSz<_Tp>() const;
|
||||
template <typename _Tp> operator PtrStep<_Tp>() const;
|
||||
|
||||
//! returns a new GpuMat header for the specified row
|
||||
GpuMat row(int y) const;
|
||||
|
||||
//! returns a new GpuMat header for the specified column
|
||||
GpuMat col(int x) const;
|
||||
|
||||
//! ... for the specified row span
|
||||
GpuMat rowRange(int startrow, int endrow) const;
|
||||
GpuMat rowRange(Range r) const;
|
||||
|
||||
//! ... for the specified column span
|
||||
GpuMat colRange(int startcol, int endcol) const;
|
||||
GpuMat colRange(Range r) const;
|
||||
|
||||
//! extracts a rectangular sub-GpuMat (this is a generalized form of row, rowRange etc.)
|
||||
GpuMat operator ()(Range rowRange, Range colRange) const;
|
||||
GpuMat operator ()(Rect roi) const;
|
||||
|
||||
//! creates alternative GpuMat header for the same data, with different
|
||||
//! number of channels and/or different number of rows
|
||||
GpuMat reshape(int cn, int rows = 0) const;
|
||||
|
||||
//! locates GpuMat header within a parent GpuMat
|
||||
void locateROI(Size& wholeSize, Point& ofs) const;
|
||||
|
||||
//! moves/resizes the current GpuMat ROI inside the parent GpuMat
|
||||
GpuMat& adjustROI(int dtop, int dbottom, int dleft, int dright);
|
||||
|
||||
//! returns true iff the GpuMat data is continuous
|
||||
//! (i.e. when there are no gaps between successive rows)
|
||||
bool isContinuous() const;
|
||||
|
||||
//! returns element size in bytes
|
||||
size_t elemSize() const;
|
||||
|
||||
//! returns the size of element channel in bytes
|
||||
size_t elemSize1() const;
|
||||
|
||||
//! returns element type
|
||||
int type() const;
|
||||
|
||||
//! returns element type
|
||||
int depth() const;
|
||||
|
||||
//! returns number of channels
|
||||
int channels() const;
|
||||
|
||||
//! returns step/elemSize1()
|
||||
size_t step1() const;
|
||||
|
||||
//! returns GpuMat size : width == number of columns, height == number of rows
|
||||
Size size() const;
|
||||
|
||||
//! returns true if GpuMat data is NULL
|
||||
bool empty() 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 GpuMat 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);
|
||||
|
||||
//! 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 GpuMat allocMatFromBuf(int rows, int cols, int type, GpuMat& mat);
|
||||
|
||||
//////////////////////////////// CudaMem ////////////////////////////////
|
||||
// CudaMem is limited cv::Mat with page locked memory allocation.
|
||||
// Page locked memory is only needed for async and faster coping to GPU.
|
||||
@ -289,169 +464,6 @@ CV_EXPORTS void printCudaDeviceInfo(int device);
|
||||
|
||||
CV_EXPORTS void printShortCudaDeviceInfo(int device);
|
||||
|
||||
//////////////////////////////// GpuMat ///////////////////////////////
|
||||
|
||||
//! 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, Scalar s);
|
||||
GpuMat(Size size, int type, 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, Range rowRange, Range colRange);
|
||||
GpuMat(const GpuMat& m, 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);
|
||||
|
||||
//! pefroms blocking upload data to GpuMat.
|
||||
void upload(const Mat& m);
|
||||
|
||||
//! downloads data from device to host memory. Blocking calls.
|
||||
void download(Mat& m) 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(Range r) const;
|
||||
//! ... for the specified column span
|
||||
GpuMat colRange(int startcol, int endcol) const;
|
||||
GpuMat colRange(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 = (Scalar s);
|
||||
//! sets some of the GpuMatrix elements to s, according to the mask
|
||||
GpuMat& setTo(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()(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;
|
||||
|
||||
template <typename _Tp> operator PtrStepSz<_Tp>() const;
|
||||
template <typename _Tp> operator PtrStep<_Tp>() 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);
|
||||
|
||||
//! 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 GpuMat allocMatFromBuf(int rows, int cols, int type, GpuMat &mat);
|
||||
|
||||
}} // cv::gpu
|
||||
|
||||
#include "opencv2/core/gpu.inl.hpp"
|
||||
|
||||
@ -94,12 +94,58 @@ GpuMat::GpuMat(Size size_, int type_, Scalar 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(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)
|
||||
{
|
||||
GpuMat temp(m);
|
||||
swap(temp);
|
||||
}
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
inline
|
||||
void GpuMat::create(Size size_, int type_)
|
||||
{
|
||||
create(size_.height, size_.width, type_);
|
||||
}
|
||||
|
||||
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
|
||||
GpuMat GpuMat::clone() const
|
||||
{
|
||||
@ -118,15 +164,17 @@ void GpuMat::assignTo(GpuMat& m, int _type) const
|
||||
}
|
||||
|
||||
inline
|
||||
size_t GpuMat::step1() const
|
||||
uchar* GpuMat::ptr(int y)
|
||||
{
|
||||
return step / elemSize1();
|
||||
CV_DbgAssert( (unsigned)y < (unsigned)rows );
|
||||
return data + step * y;
|
||||
}
|
||||
|
||||
inline
|
||||
bool GpuMat::empty() const
|
||||
const uchar* GpuMat::ptr(int y) const
|
||||
{
|
||||
return data == 0;
|
||||
CV_DbgAssert( (unsigned)y < (unsigned)rows );
|
||||
return data + step * y;
|
||||
}
|
||||
|
||||
template<typename _Tp> inline
|
||||
@ -141,6 +189,18 @@ const _Tp* GpuMat::ptr(int y) const
|
||||
return (const _Tp*)ptr(y);
|
||||
}
|
||||
|
||||
template <class T> inline
|
||||
GpuMat::operator PtrStepSz<T>() const
|
||||
{
|
||||
return PtrStepSz<T>(rows, cols, (T*)data, step);
|
||||
}
|
||||
|
||||
template <class T> inline
|
||||
GpuMat::operator PtrStep<T>() const
|
||||
{
|
||||
return PtrStep<T>((T*)data, step);
|
||||
}
|
||||
|
||||
inline
|
||||
GpuMat GpuMat::row(int y) const
|
||||
{
|
||||
@ -178,19 +238,13 @@ GpuMat GpuMat::colRange(Range r) const
|
||||
}
|
||||
|
||||
inline
|
||||
void GpuMat::create(Size size_, int type_)
|
||||
GpuMat GpuMat::operator ()(Range rowRange_, Range colRange_) const
|
||||
{
|
||||
create(size_.height, size_.width, type_);
|
||||
return GpuMat(*this, rowRange_, colRange_);
|
||||
}
|
||||
|
||||
inline
|
||||
GpuMat GpuMat::operator()(Range _rowRange, Range _colRange) const
|
||||
{
|
||||
return GpuMat(*this, _rowRange, _colRange);
|
||||
}
|
||||
|
||||
inline
|
||||
GpuMat GpuMat::operator()(Rect roi) const
|
||||
GpuMat GpuMat::operator ()(Rect roi) const
|
||||
{
|
||||
return GpuMat(*this, roi);
|
||||
}
|
||||
@ -231,6 +285,12 @@ int GpuMat::channels() const
|
||||
return CV_MAT_CN(flags);
|
||||
}
|
||||
|
||||
inline
|
||||
size_t GpuMat::step1() const
|
||||
{
|
||||
return step / elemSize1();
|
||||
}
|
||||
|
||||
inline
|
||||
Size GpuMat::size() const
|
||||
{
|
||||
@ -238,42 +298,9 @@ Size GpuMat::size() const
|
||||
}
|
||||
|
||||
inline
|
||||
uchar* GpuMat::ptr(int y)
|
||||
bool GpuMat::empty() const
|
||||
{
|
||||
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;
|
||||
}
|
||||
|
||||
inline
|
||||
GpuMat& GpuMat::operator = (Scalar s)
|
||||
{
|
||||
setTo(s);
|
||||
return *this;
|
||||
}
|
||||
|
||||
template <class T> inline
|
||||
GpuMat::operator PtrStepSz<T>() const
|
||||
{
|
||||
return PtrStepSz<T>(rows, cols, (T*)data, step);
|
||||
}
|
||||
|
||||
template <class T> inline
|
||||
GpuMat::operator PtrStep<T>() const
|
||||
{
|
||||
return PtrStep<T>((T*)data, step);
|
||||
}
|
||||
|
||||
static inline
|
||||
void swap(GpuMat& a, GpuMat& b)
|
||||
{
|
||||
a.swap(b);
|
||||
return data == 0;
|
||||
}
|
||||
|
||||
static inline
|
||||
@ -304,6 +331,23 @@ void ensureSizeIsEnough(Size size, int type, GpuMat& m)
|
||||
ensureSizeIsEnough(size.height, size.width, type, m);
|
||||
}
|
||||
|
||||
static inline
|
||||
void swap(GpuMat& a, GpuMat& b)
|
||||
{
|
||||
a.swap(b);
|
||||
}
|
||||
|
||||
}} // namespace cv { namespace gpu
|
||||
|
||||
namespace cv {
|
||||
|
||||
inline
|
||||
Mat::Mat(const gpu::GpuMat& m)
|
||||
: flags(0), dims(0), rows(0), cols(0), data(0), refcount(0), datastart(0), dataend(0), datalimit(0), allocator(0), size(&rows)
|
||||
{
|
||||
m.download(*this);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
#endif // __OPENCV_CORE_GPUINL_HPP__
|
||||
|
||||
@ -45,18 +45,7 @@
|
||||
#include "opencv2/core/cuda/functional.hpp"
|
||||
#include "opencv2/core/cuda/type_traits.hpp"
|
||||
|
||||
namespace cv { namespace gpu { namespace cudev
|
||||
{
|
||||
void writeScalar(const uchar*);
|
||||
void writeScalar(const schar*);
|
||||
void writeScalar(const ushort*);
|
||||
void writeScalar(const short int*);
|
||||
void writeScalar(const int*);
|
||||
void writeScalar(const float*);
|
||||
void writeScalar(const double*);
|
||||
void copyToWithMask_gpu(PtrStepSzb src, PtrStepSzb dst, size_t elemSize1, int cn, PtrStepSzb mask, bool colorMask, cudaStream_t stream);
|
||||
void convert_gpu(PtrStepSzb, int, PtrStepSzb, int, double, double, cudaStream_t);
|
||||
}}}
|
||||
#include "matrix_operations.hpp"
|
||||
|
||||
namespace cv { namespace gpu { namespace cudev
|
||||
{
|
||||
@ -73,32 +62,33 @@ namespace cv { namespace gpu { namespace cudev
|
||||
////////////////////////////////// CopyTo /////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <typename T> void copyToWithMask(PtrStepSzb src, PtrStepSzb dst, int cn, PtrStepSzb mask, bool colorMask, cudaStream_t stream)
|
||||
template <typename T>
|
||||
void copyWithMask(PtrStepSzb src, PtrStepSzb dst, int cn, PtrStepSzb mask, bool multiChannelMask, cudaStream_t stream)
|
||||
{
|
||||
if (colorMask)
|
||||
if (multiChannelMask)
|
||||
cv::gpu::cudev::transform((PtrStepSz<T>)src, (PtrStepSz<T>)dst, identity<T>(), SingleMask(mask), stream);
|
||||
else
|
||||
cv::gpu::cudev::transform((PtrStepSz<T>)src, (PtrStepSz<T>)dst, identity<T>(), SingleMaskChannels(mask, cn), stream);
|
||||
}
|
||||
|
||||
void copyToWithMask_gpu(PtrStepSzb src, PtrStepSzb dst, size_t elemSize1, int cn, PtrStepSzb mask, bool colorMask, cudaStream_t stream)
|
||||
void copyWithMask(PtrStepSzb src, PtrStepSzb dst, size_t elemSize1, int cn, PtrStepSzb mask, bool multiChannelMask, cudaStream_t stream)
|
||||
{
|
||||
typedef void (*func_t)(PtrStepSzb src, PtrStepSzb dst, int cn, PtrStepSzb mask, bool colorMask, cudaStream_t stream);
|
||||
typedef void (*func_t)(PtrStepSzb src, PtrStepSzb dst, int cn, PtrStepSzb mask, bool multiChannelMask, cudaStream_t stream);
|
||||
|
||||
static func_t tab[] =
|
||||
{
|
||||
0,
|
||||
copyToWithMask<unsigned char>,
|
||||
copyToWithMask<unsigned short>,
|
||||
copyWithMask<unsigned char>,
|
||||
copyWithMask<unsigned short>,
|
||||
0,
|
||||
copyToWithMask<int>,
|
||||
copyWithMask<int>,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
copyToWithMask<double>
|
||||
copyWithMask<double>
|
||||
};
|
||||
|
||||
tab[elemSize1](src, dst, cn, mask, colorMask, stream);
|
||||
tab[elemSize1](src, dst, cn, mask, multiChannelMask, stream);
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////
|
||||
@ -122,37 +112,37 @@ namespace cv { namespace gpu { namespace cudev
|
||||
template <> __device__ __forceinline__ float readScalar<float>(int i) {return scalar_32f[i];}
|
||||
template <> __device__ __forceinline__ double readScalar<double>(int i) {return scalar_64f[i];}
|
||||
|
||||
void writeScalar(const uchar* vals)
|
||||
static inline void writeScalar(const uchar* vals)
|
||||
{
|
||||
cudaSafeCall( cudaMemcpyToSymbol(scalar_8u, vals, sizeof(uchar) * 4) );
|
||||
}
|
||||
void writeScalar(const schar* vals)
|
||||
static inline void writeScalar(const schar* vals)
|
||||
{
|
||||
cudaSafeCall( cudaMemcpyToSymbol(scalar_8s, vals, sizeof(schar) * 4) );
|
||||
}
|
||||
void writeScalar(const ushort* vals)
|
||||
static inline void writeScalar(const ushort* vals)
|
||||
{
|
||||
cudaSafeCall( cudaMemcpyToSymbol(scalar_16u, vals, sizeof(ushort) * 4) );
|
||||
}
|
||||
void writeScalar(const short* vals)
|
||||
static inline void writeScalar(const short* vals)
|
||||
{
|
||||
cudaSafeCall( cudaMemcpyToSymbol(scalar_16s, vals, sizeof(short) * 4) );
|
||||
}
|
||||
void writeScalar(const int* vals)
|
||||
static inline void writeScalar(const int* vals)
|
||||
{
|
||||
cudaSafeCall( cudaMemcpyToSymbol(scalar_32s, vals, sizeof(int) * 4) );
|
||||
}
|
||||
void writeScalar(const float* vals)
|
||||
static inline void writeScalar(const float* vals)
|
||||
{
|
||||
cudaSafeCall( cudaMemcpyToSymbol(scalar_32f, vals, sizeof(float) * 4) );
|
||||
}
|
||||
void writeScalar(const double* vals)
|
||||
static inline void writeScalar(const double* vals)
|
||||
{
|
||||
cudaSafeCall( cudaMemcpyToSymbol(scalar_64f, vals, sizeof(double) * 4) );
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
__global__ void set_to_without_mask(T* mat, int cols, int rows, size_t step, int channels)
|
||||
__global__ void set(T* mat, int cols, int rows, size_t step, int channels)
|
||||
{
|
||||
size_t x = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
size_t y = blockIdx.y * blockDim.y + threadIdx.y;
|
||||
@ -164,8 +154,31 @@ namespace cv { namespace gpu { namespace cudev
|
||||
}
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void set(PtrStepSz<T> mat, const T* scalar, int channels, cudaStream_t stream)
|
||||
{
|
||||
writeScalar(scalar);
|
||||
|
||||
dim3 threadsPerBlock(32, 8, 1);
|
||||
dim3 numBlocks(mat.cols * channels / threadsPerBlock.x + 1, mat.rows / threadsPerBlock.y + 1, 1);
|
||||
|
||||
set<T><<<numBlocks, threadsPerBlock, 0, stream>>>(mat.data, mat.cols, mat.rows, mat.step, channels);
|
||||
cudaSafeCall( cudaGetLastError() );
|
||||
|
||||
if (stream == 0)
|
||||
cudaSafeCall ( cudaDeviceSynchronize() );
|
||||
}
|
||||
|
||||
template void set<uchar >(PtrStepSz<uchar > mat, const uchar* scalar, int channels, cudaStream_t stream);
|
||||
template void set<schar >(PtrStepSz<schar > mat, const schar* scalar, int channels, cudaStream_t stream);
|
||||
template void set<ushort>(PtrStepSz<ushort> mat, const ushort* scalar, int channels, cudaStream_t stream);
|
||||
template void set<short >(PtrStepSz<short > mat, const short* scalar, int channels, cudaStream_t stream);
|
||||
template void set<int >(PtrStepSz<int > mat, const int* scalar, int channels, cudaStream_t stream);
|
||||
template void set<float >(PtrStepSz<float > mat, const float* scalar, int channels, cudaStream_t stream);
|
||||
template void set<double>(PtrStepSz<double> mat, const double* scalar, int channels, cudaStream_t stream);
|
||||
|
||||
template<typename T>
|
||||
__global__ void set_to_with_mask(T* mat, const uchar* mask, int cols, int rows, size_t step, int channels, size_t step_mask)
|
||||
__global__ void set(T* mat, const uchar* mask, int cols, int rows, size_t step, int channels, size_t step_mask)
|
||||
{
|
||||
size_t x = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
size_t y = blockIdx.y * blockDim.y + threadIdx.y;
|
||||
@ -177,51 +190,29 @@ namespace cv { namespace gpu { namespace cudev
|
||||
mat[idx] = readScalar<T>(x % channels);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void set_to_gpu(PtrStepSzb mat, const T* scalar, PtrStepSzb mask, int channels, cudaStream_t stream)
|
||||
void set(PtrStepSz<T> mat, const T* scalar, PtrStepSzb mask, int channels, cudaStream_t stream)
|
||||
{
|
||||
writeScalar(scalar);
|
||||
|
||||
dim3 threadsPerBlock(32, 8, 1);
|
||||
dim3 numBlocks (mat.cols * channels / threadsPerBlock.x + 1, mat.rows / threadsPerBlock.y + 1, 1);
|
||||
dim3 numBlocks(mat.cols * channels / threadsPerBlock.x + 1, mat.rows / threadsPerBlock.y + 1, 1);
|
||||
|
||||
set_to_with_mask<T><<<numBlocks, threadsPerBlock, 0, stream>>>((T*)mat.data, (uchar*)mask.data, mat.cols, mat.rows, mat.step, channels, mask.step);
|
||||
set<T><<<numBlocks, threadsPerBlock, 0, stream>>>(mat.data, mask.data, mat.cols, mat.rows, mat.step, channels, mask.step);
|
||||
cudaSafeCall( cudaGetLastError() );
|
||||
|
||||
if (stream == 0)
|
||||
cudaSafeCall ( cudaDeviceSynchronize() );
|
||||
}
|
||||
|
||||
template void set_to_gpu<uchar >(PtrStepSzb mat, const uchar* scalar, PtrStepSzb mask, int channels, cudaStream_t stream);
|
||||
template void set_to_gpu<schar >(PtrStepSzb mat, const schar* scalar, PtrStepSzb mask, int channels, cudaStream_t stream);
|
||||
template void set_to_gpu<ushort>(PtrStepSzb mat, const ushort* scalar, PtrStepSzb mask, int channels, cudaStream_t stream);
|
||||
template void set_to_gpu<short >(PtrStepSzb mat, const short* scalar, PtrStepSzb mask, int channels, cudaStream_t stream);
|
||||
template void set_to_gpu<int >(PtrStepSzb mat, const int* scalar, PtrStepSzb mask, int channels, cudaStream_t stream);
|
||||
template void set_to_gpu<float >(PtrStepSzb mat, const float* scalar, PtrStepSzb mask, int channels, cudaStream_t stream);
|
||||
template void set_to_gpu<double>(PtrStepSzb mat, const double* scalar, PtrStepSzb mask, int channels, cudaStream_t stream);
|
||||
|
||||
template <typename T>
|
||||
void set_to_gpu(PtrStepSzb mat, const T* scalar, int channels, cudaStream_t stream)
|
||||
{
|
||||
writeScalar(scalar);
|
||||
|
||||
dim3 threadsPerBlock(32, 8, 1);
|
||||
dim3 numBlocks (mat.cols * channels / threadsPerBlock.x + 1, mat.rows / threadsPerBlock.y + 1, 1);
|
||||
|
||||
set_to_without_mask<T><<<numBlocks, threadsPerBlock, 0, stream>>>((T*)mat.data, mat.cols, mat.rows, mat.step, channels);
|
||||
cudaSafeCall( cudaGetLastError() );
|
||||
|
||||
if (stream == 0)
|
||||
cudaSafeCall ( cudaDeviceSynchronize() );
|
||||
}
|
||||
|
||||
template void set_to_gpu<uchar >(PtrStepSzb mat, const uchar* scalar, int channels, cudaStream_t stream);
|
||||
template void set_to_gpu<schar >(PtrStepSzb mat, const schar* scalar, int channels, cudaStream_t stream);
|
||||
template void set_to_gpu<ushort>(PtrStepSzb mat, const ushort* scalar, int channels, cudaStream_t stream);
|
||||
template void set_to_gpu<short >(PtrStepSzb mat, const short* scalar, int channels, cudaStream_t stream);
|
||||
template void set_to_gpu<int >(PtrStepSzb mat, const int* scalar, int channels, cudaStream_t stream);
|
||||
template void set_to_gpu<float >(PtrStepSzb mat, const float* scalar, int channels, cudaStream_t stream);
|
||||
template void set_to_gpu<double>(PtrStepSzb mat, const double* scalar, int channels, cudaStream_t stream);
|
||||
template void set<uchar >(PtrStepSz<uchar > mat, const uchar* scalar, PtrStepSzb mask, int channels, cudaStream_t stream);
|
||||
template void set<schar >(PtrStepSz<schar > mat, const schar* scalar, PtrStepSzb mask, int channels, cudaStream_t stream);
|
||||
template void set<ushort>(PtrStepSz<ushort> mat, const ushort* scalar, PtrStepSzb mask, int channels, cudaStream_t stream);
|
||||
template void set<short >(PtrStepSz<short > mat, const short* scalar, PtrStepSzb mask, int channels, cudaStream_t stream);
|
||||
template void set<int >(PtrStepSz<int > mat, const int* scalar, PtrStepSzb mask, int channels, cudaStream_t stream);
|
||||
template void set<float >(PtrStepSz<float > mat, const float* scalar, PtrStepSzb mask, int channels, cudaStream_t stream);
|
||||
template void set<double>(PtrStepSz<double> mat, const double* scalar, PtrStepSzb mask, int channels, cudaStream_t stream);
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////
|
||||
//////////////////////////////// ConvertTo ////////////////////////////////
|
||||
@ -296,12 +287,7 @@ namespace cv { namespace gpu { namespace cudev
|
||||
cv::gpu::cudev::transform((PtrStepSz<T>)src, (PtrStepSz<D>)dst, op, WithOutMask(), stream);
|
||||
}
|
||||
|
||||
#if defined __clang__
|
||||
# pragma clang diagnostic push
|
||||
# pragma clang diagnostic ignored "-Wmissing-declarations"
|
||||
#endif
|
||||
|
||||
void convert_gpu(PtrStepSzb src, int sdepth, PtrStepSzb dst, int ddepth, double alpha, double beta, cudaStream_t stream)
|
||||
void convert(PtrStepSzb src, int sdepth, PtrStepSzb dst, int ddepth, double alpha, double beta, cudaStream_t stream)
|
||||
{
|
||||
typedef void (*caller_t)(PtrStepSzb src, PtrStepSzb dst, double alpha, double beta, cudaStream_t stream);
|
||||
|
||||
@ -372,11 +358,7 @@ namespace cv { namespace gpu { namespace cudev
|
||||
}
|
||||
};
|
||||
|
||||
caller_t func = tab[sdepth][ddepth];
|
||||
const caller_t func = tab[sdepth][ddepth];
|
||||
func(src, dst, alpha, beta, stream);
|
||||
}
|
||||
|
||||
#if defined __clang__
|
||||
# pragma clang diagnostic pop
|
||||
#endif
|
||||
}}} // namespace cv { namespace gpu { namespace cudev
|
||||
|
||||
57
modules/core/src/cuda/matrix_operations.hpp
Normal file
57
modules/core/src/cuda/matrix_operations.hpp
Normal file
@ -0,0 +1,57 @@
|
||||
/*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.
|
||||
// Copyright (C) 2013, OpenCV Foundation, 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 "opencv2/core/cuda/common.hpp"
|
||||
|
||||
namespace cv { namespace gpu { namespace cudev
|
||||
{
|
||||
void copyWithMask(PtrStepSzb src, PtrStepSzb dst, size_t elemSize1, int cn, PtrStepSzb mask, bool multiChannelMask, cudaStream_t stream);
|
||||
|
||||
template <typename T>
|
||||
void set(PtrStepSz<T> mat, const T* scalar, int channels, cudaStream_t stream);
|
||||
|
||||
template <typename T>
|
||||
void set(PtrStepSz<T> mat, const T* scalar, PtrStepSzb mask, int channels, cudaStream_t stream);
|
||||
|
||||
void convert(PtrStepSzb src, int sdepth, PtrStepSzb dst, int ddepth, double alpha, double beta, cudaStream_t stream);
|
||||
}}}
|
||||
File diff suppressed because it is too large
Load Diff
993
modules/core/src/gpu_mat.cpp
Normal file
993
modules/core/src/gpu_mat.cpp
Normal file
@ -0,0 +1,993 @@
|
||||
/*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.
|
||||
// Copyright (C) 2013, OpenCV Foundation, 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;
|
||||
|
||||
/////////////////////////// matrix operations /////////////////////////
|
||||
|
||||
#ifdef HAVE_CUDA
|
||||
|
||||
// CUDA implementation
|
||||
|
||||
#include "cuda/matrix_operations.hpp"
|
||||
|
||||
namespace
|
||||
{
|
||||
template <typename T> void cudaSet_(GpuMat& src, Scalar s, cudaStream_t stream)
|
||||
{
|
||||
Scalar_<T> sf = s;
|
||||
cudev::set<T>(PtrStepSz<T>(src), sf.val, src.channels(), stream);
|
||||
}
|
||||
|
||||
void cudaSet(GpuMat& src, Scalar s, cudaStream_t stream)
|
||||
{
|
||||
typedef void (*func_t)(GpuMat& src, Scalar s, cudaStream_t stream);
|
||||
static const func_t funcs[] =
|
||||
{
|
||||
cudaSet_<uchar>,
|
||||
cudaSet_<schar>,
|
||||
cudaSet_<ushort>,
|
||||
cudaSet_<short>,
|
||||
cudaSet_<int>,
|
||||
cudaSet_<float>,
|
||||
cudaSet_<double>
|
||||
};
|
||||
|
||||
funcs[src.depth()](src, s, stream);
|
||||
}
|
||||
|
||||
template <typename T> void cudaSet_(GpuMat& src, Scalar s, PtrStepSzb mask, cudaStream_t stream)
|
||||
{
|
||||
Scalar_<T> sf = s;
|
||||
cudev::set<T>(PtrStepSz<T>(src), sf.val, mask, src.channels(), stream);
|
||||
}
|
||||
|
||||
void cudaSet(GpuMat& src, Scalar s, const GpuMat& mask, cudaStream_t stream)
|
||||
{
|
||||
typedef void (*func_t)(GpuMat& src, Scalar s, PtrStepSzb mask, cudaStream_t stream);
|
||||
static const func_t funcs[] =
|
||||
{
|
||||
cudaSet_<uchar>,
|
||||
cudaSet_<schar>,
|
||||
cudaSet_<ushort>,
|
||||
cudaSet_<short>,
|
||||
cudaSet_<int>,
|
||||
cudaSet_<float>,
|
||||
cudaSet_<double>
|
||||
};
|
||||
|
||||
funcs[src.depth()](src, s, mask, stream);
|
||||
}
|
||||
|
||||
void cudaCopyWithMask(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
|
||||
{
|
||||
cudev::copyWithMask(src.reshape(1), dst.reshape(1), src.elemSize1(), src.channels(), mask.reshape(1), mask.channels() != 1, stream);
|
||||
}
|
||||
|
||||
void cudaConvert(const GpuMat& src, GpuMat& dst, cudaStream_t stream)
|
||||
{
|
||||
cudev::convert(src.reshape(1), src.depth(), dst.reshape(1), dst.depth(), 1.0, 0.0, stream);
|
||||
}
|
||||
|
||||
void cudaConvert(const GpuMat& src, GpuMat& dst, double alpha, double beta, cudaStream_t stream)
|
||||
{
|
||||
cudev::convert(src.reshape(1), src.depth(), dst.reshape(1), dst.depth(), alpha, beta, stream);
|
||||
}
|
||||
}
|
||||
|
||||
// NPP implementation
|
||||
|
||||
namespace
|
||||
{
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
// Convert
|
||||
|
||||
template<int SDEPTH, int DDEPTH> struct NppConvertFunc
|
||||
{
|
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
|
||||
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t;
|
||||
|
||||
typedef NppStatus (*func_ptr)(const src_t* pSrc, int nSrcStep, dst_t* pDst, int nDstStep, NppiSize oSizeROI);
|
||||
};
|
||||
template<int DDEPTH> struct NppConvertFunc<CV_32F, DDEPTH>
|
||||
{
|
||||
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t;
|
||||
|
||||
typedef NppStatus (*func_ptr)(const Npp32f* pSrc, int nSrcStep, dst_t* pDst, int nDstStep, NppiSize oSizeROI, NppRoundMode eRoundMode);
|
||||
};
|
||||
|
||||
template<int SDEPTH, int DDEPTH, typename NppConvertFunc<SDEPTH, DDEPTH>::func_ptr func> struct NppCvt
|
||||
{
|
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
|
||||
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t;
|
||||
|
||||
static void call(const GpuMat& src, GpuMat& dst, cudaStream_t stream)
|
||||
{
|
||||
NppiSize sz;
|
||||
sz.width = src.cols;
|
||||
sz.height = src.rows;
|
||||
|
||||
NppStreamHandler h(stream);
|
||||
|
||||
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), dst.ptr<dst_t>(), static_cast<int>(dst.step), sz) );
|
||||
|
||||
if (stream == 0)
|
||||
cudaSafeCall( cudaDeviceSynchronize() );
|
||||
}
|
||||
};
|
||||
template<int DDEPTH, typename NppConvertFunc<CV_32F, DDEPTH>::func_ptr func> struct NppCvt<CV_32F, DDEPTH, func>
|
||||
{
|
||||
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t;
|
||||
|
||||
static void call(const GpuMat& src, GpuMat& dst, cudaStream_t stream)
|
||||
{
|
||||
NppiSize sz;
|
||||
sz.width = src.cols;
|
||||
sz.height = src.rows;
|
||||
|
||||
NppStreamHandler h(stream);
|
||||
|
||||
nppSafeCall( func(src.ptr<Npp32f>(), static_cast<int>(src.step), dst.ptr<dst_t>(), static_cast<int>(dst.step), sz, NPP_RND_NEAR) );
|
||||
|
||||
if (stream == 0)
|
||||
cudaSafeCall( cudaDeviceSynchronize() );
|
||||
}
|
||||
};
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
// Set
|
||||
|
||||
template<int SDEPTH, int SCN> struct NppSetFunc
|
||||
{
|
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
|
||||
|
||||
typedef NppStatus (*func_ptr)(const src_t values[], src_t* pSrc, int nSrcStep, NppiSize oSizeROI);
|
||||
};
|
||||
template<int SDEPTH> struct NppSetFunc<SDEPTH, 1>
|
||||
{
|
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
|
||||
|
||||
typedef NppStatus (*func_ptr)(src_t val, src_t* pSrc, int nSrcStep, NppiSize oSizeROI);
|
||||
};
|
||||
template<int SCN> struct NppSetFunc<CV_8S, SCN>
|
||||
{
|
||||
typedef NppStatus (*func_ptr)(Npp8s values[], Npp8s* pSrc, int nSrcStep, NppiSize oSizeROI);
|
||||
};
|
||||
template<> struct NppSetFunc<CV_8S, 1>
|
||||
{
|
||||
typedef NppStatus (*func_ptr)(Npp8s val, Npp8s* pSrc, int nSrcStep, NppiSize oSizeROI);
|
||||
};
|
||||
|
||||
template<int SDEPTH, int SCN, typename NppSetFunc<SDEPTH, SCN>::func_ptr func> struct NppSet
|
||||
{
|
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
|
||||
|
||||
static void call(GpuMat& src, Scalar s, cudaStream_t stream)
|
||||
{
|
||||
NppiSize sz;
|
||||
sz.width = src.cols;
|
||||
sz.height = src.rows;
|
||||
|
||||
Scalar_<src_t> nppS = s;
|
||||
|
||||
NppStreamHandler h(stream);
|
||||
|
||||
nppSafeCall( func(nppS.val, src.ptr<src_t>(), static_cast<int>(src.step), sz) );
|
||||
|
||||
if (stream == 0)
|
||||
cudaSafeCall( cudaDeviceSynchronize() );
|
||||
}
|
||||
};
|
||||
template<int SDEPTH, typename NppSetFunc<SDEPTH, 1>::func_ptr func> struct NppSet<SDEPTH, 1, func>
|
||||
{
|
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
|
||||
|
||||
static void call(GpuMat& src, Scalar s, cudaStream_t stream)
|
||||
{
|
||||
NppiSize sz;
|
||||
sz.width = src.cols;
|
||||
sz.height = src.rows;
|
||||
|
||||
Scalar_<src_t> nppS = s;
|
||||
|
||||
NppStreamHandler h(stream);
|
||||
|
||||
nppSafeCall( func(nppS[0], src.ptr<src_t>(), static_cast<int>(src.step), sz) );
|
||||
|
||||
if (stream == 0)
|
||||
cudaSafeCall( cudaDeviceSynchronize() );
|
||||
}
|
||||
};
|
||||
|
||||
template<int SDEPTH, int SCN> struct NppSetMaskFunc
|
||||
{
|
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
|
||||
|
||||
typedef NppStatus (*func_ptr)(const src_t values[], src_t* pSrc, int nSrcStep, NppiSize oSizeROI, const Npp8u* pMask, int nMaskStep);
|
||||
};
|
||||
template<int SDEPTH> struct NppSetMaskFunc<SDEPTH, 1>
|
||||
{
|
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
|
||||
|
||||
typedef NppStatus (*func_ptr)(src_t val, src_t* pSrc, int nSrcStep, NppiSize oSizeROI, const Npp8u* pMask, int nMaskStep);
|
||||
};
|
||||
|
||||
template<int SDEPTH, int SCN, typename NppSetMaskFunc<SDEPTH, SCN>::func_ptr func> struct NppSetMask
|
||||
{
|
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
|
||||
|
||||
static void call(GpuMat& src, Scalar s, const GpuMat& mask, cudaStream_t stream)
|
||||
{
|
||||
NppiSize sz;
|
||||
sz.width = src.cols;
|
||||
sz.height = src.rows;
|
||||
|
||||
Scalar_<src_t> nppS = s;
|
||||
|
||||
NppStreamHandler h(stream);
|
||||
|
||||
nppSafeCall( func(nppS.val, src.ptr<src_t>(), static_cast<int>(src.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) );
|
||||
|
||||
if (stream == 0)
|
||||
cudaSafeCall( cudaDeviceSynchronize() );
|
||||
}
|
||||
};
|
||||
template<int SDEPTH, typename NppSetMaskFunc<SDEPTH, 1>::func_ptr func> struct NppSetMask<SDEPTH, 1, func>
|
||||
{
|
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
|
||||
|
||||
static void call(GpuMat& src, Scalar s, const GpuMat& mask, cudaStream_t stream)
|
||||
{
|
||||
NppiSize sz;
|
||||
sz.width = src.cols;
|
||||
sz.height = src.rows;
|
||||
|
||||
Scalar_<src_t> nppS = s;
|
||||
|
||||
NppStreamHandler h(stream);
|
||||
|
||||
nppSafeCall( func(nppS[0], src.ptr<src_t>(), static_cast<int>(src.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) );
|
||||
|
||||
if (stream == 0)
|
||||
cudaSafeCall( cudaDeviceSynchronize() );
|
||||
}
|
||||
};
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
// CopyMasked
|
||||
|
||||
template<int SDEPTH> struct NppCopyWithMaskFunc
|
||||
{
|
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
|
||||
|
||||
typedef NppStatus (*func_ptr)(const src_t* pSrc, int nSrcStep, src_t* pDst, int nDstStep, NppiSize oSizeROI, const Npp8u* pMask, int nMaskStep);
|
||||
};
|
||||
|
||||
template<int SDEPTH, typename NppCopyWithMaskFunc<SDEPTH>::func_ptr func> struct NppCopyWithMask
|
||||
{
|
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
|
||||
|
||||
static void call(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
|
||||
{
|
||||
NppiSize sz;
|
||||
sz.width = src.cols;
|
||||
sz.height = src.rows;
|
||||
|
||||
NppStreamHandler h(stream);
|
||||
|
||||
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), dst.ptr<src_t>(), static_cast<int>(dst.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) );
|
||||
|
||||
if (stream == 0)
|
||||
cudaSafeCall( cudaDeviceSynchronize() );
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
// Dispatcher
|
||||
|
||||
namespace cv { namespace gpu
|
||||
{
|
||||
void copyWithMask(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream = 0);
|
||||
void convert(const GpuMat& src, GpuMat& dst, cudaStream_t stream = 0);
|
||||
void convert(const GpuMat& src, GpuMat& dst, double alpha, double beta, cudaStream_t stream = 0);
|
||||
void set(GpuMat& m, Scalar s, cudaStream_t stream = 0);
|
||||
void set(GpuMat& m, Scalar s, const GpuMat& mask, cudaStream_t stream = 0);
|
||||
}}
|
||||
|
||||
namespace cv { namespace gpu
|
||||
{
|
||||
void copyWithMask(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
|
||||
{
|
||||
CV_DbgAssert( src.size() == dst.size() && src.type() == dst.type() );
|
||||
|
||||
CV_Assert( src.depth() <= CV_64F && src.channels() <= 4 );
|
||||
CV_Assert( src.size() == mask.size() && mask.depth() == CV_8U && (mask.channels() == 1 || mask.channels() == src.channels()) );
|
||||
|
||||
if (src.depth() == CV_64F)
|
||||
{
|
||||
CV_Assert( deviceSupports(NATIVE_DOUBLE) );
|
||||
}
|
||||
|
||||
typedef void (*func_t)(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream);
|
||||
static const func_t funcs[7][4] =
|
||||
{
|
||||
/* 8U */ {NppCopyWithMask<CV_8U , nppiCopy_8u_C1MR >::call, cudaCopyWithMask, NppCopyWithMask<CV_8U , nppiCopy_8u_C3MR >::call, NppCopyWithMask<CV_8U , nppiCopy_8u_C4MR >::call},
|
||||
/* 8S */ {cudaCopyWithMask , cudaCopyWithMask, cudaCopyWithMask , cudaCopyWithMask },
|
||||
/* 16U */ {NppCopyWithMask<CV_16U, nppiCopy_16u_C1MR>::call, cudaCopyWithMask, NppCopyWithMask<CV_16U, nppiCopy_16u_C3MR>::call, NppCopyWithMask<CV_16U, nppiCopy_16u_C4MR>::call},
|
||||
/* 16S */ {NppCopyWithMask<CV_16S, nppiCopy_16s_C1MR>::call, cudaCopyWithMask, NppCopyWithMask<CV_16S, nppiCopy_16s_C3MR>::call, NppCopyWithMask<CV_16S, nppiCopy_16s_C4MR>::call},
|
||||
/* 32S */ {NppCopyWithMask<CV_32S, nppiCopy_32s_C1MR>::call, cudaCopyWithMask, NppCopyWithMask<CV_32S, nppiCopy_32s_C3MR>::call, NppCopyWithMask<CV_32S, nppiCopy_32s_C4MR>::call},
|
||||
/* 32F */ {NppCopyWithMask<CV_32F, nppiCopy_32f_C1MR>::call, cudaCopyWithMask, NppCopyWithMask<CV_32F, nppiCopy_32f_C3MR>::call, NppCopyWithMask<CV_32F, nppiCopy_32f_C4MR>::call},
|
||||
/* 64F */ {cudaCopyWithMask , cudaCopyWithMask, cudaCopyWithMask , cudaCopyWithMask }
|
||||
};
|
||||
|
||||
const func_t func = mask.channels() == src.channels() ? funcs[src.depth()][src.channels() - 1] : cudaCopyWithMask;
|
||||
|
||||
func(src, dst, mask, stream);
|
||||
}
|
||||
|
||||
void convert(const GpuMat& src, GpuMat& dst, cudaStream_t stream)
|
||||
{
|
||||
CV_DbgAssert( src.size() == dst.size() && src.channels() == dst.channels() );
|
||||
|
||||
CV_Assert( src.depth() <= CV_64F && src.channels() <= 4 );
|
||||
CV_Assert( dst.depth() <= CV_64F );
|
||||
|
||||
if (src.depth() == CV_64F || dst.depth() == CV_64F)
|
||||
{
|
||||
CV_Assert( deviceSupports(NATIVE_DOUBLE) );
|
||||
}
|
||||
|
||||
typedef void (*func_t)(const GpuMat& src, GpuMat& dst, cudaStream_t stream);
|
||||
static const func_t funcs[7][7][4] =
|
||||
{
|
||||
{
|
||||
/* 8U -> 8U */ {0, 0, 0, 0},
|
||||
/* 8U -> 8S */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert },
|
||||
/* 8U -> 16U */ {NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C1R>::call, cudaConvert, cudaConvert, NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C4R>::call},
|
||||
/* 8U -> 16S */ {NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C1R>::call, cudaConvert, cudaConvert, NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C4R>::call},
|
||||
/* 8U -> 32S */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert },
|
||||
/* 8U -> 32F */ {NppCvt<CV_8U, CV_32F, nppiConvert_8u32f_C1R>::call, cudaConvert, cudaConvert, cudaConvert },
|
||||
/* 8U -> 64F */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert }
|
||||
},
|
||||
{
|
||||
/* 8S -> 8U */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 8S -> 8S */ {0,0,0,0},
|
||||
/* 8S -> 16U */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 8S -> 16S */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 8S -> 32S */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 8S -> 32F */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 8S -> 64F */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert}
|
||||
},
|
||||
{
|
||||
/* 16U -> 8U */ {NppCvt<CV_16U, CV_8U , nppiConvert_16u8u_C1R >::call, cudaConvert, cudaConvert, NppCvt<CV_16U, CV_8U, nppiConvert_16u8u_C4R>::call},
|
||||
/* 16U -> 8S */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert },
|
||||
/* 16U -> 16U */ {0,0,0,0},
|
||||
/* 16U -> 16S */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert },
|
||||
/* 16U -> 32S */ {NppCvt<CV_16U, CV_32S, nppiConvert_16u32s_C1R>::call, cudaConvert, cudaConvert, cudaConvert },
|
||||
/* 16U -> 32F */ {NppCvt<CV_16U, CV_32F, nppiConvert_16u32f_C1R>::call, cudaConvert, cudaConvert, cudaConvert },
|
||||
/* 16U -> 64F */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert }
|
||||
},
|
||||
{
|
||||
/* 16S -> 8U */ {NppCvt<CV_16S, CV_8U , nppiConvert_16s8u_C1R >::call, cudaConvert, cudaConvert, NppCvt<CV_16S, CV_8U, nppiConvert_16s8u_C4R>::call},
|
||||
/* 16S -> 8S */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert },
|
||||
/* 16S -> 16U */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert },
|
||||
/* 16S -> 16S */ {0,0,0,0},
|
||||
/* 16S -> 32S */ {NppCvt<CV_16S, CV_32S, nppiConvert_16s32s_C1R>::call, cudaConvert, cudaConvert, cudaConvert },
|
||||
/* 16S -> 32F */ {NppCvt<CV_16S, CV_32F, nppiConvert_16s32f_C1R>::call, cudaConvert, cudaConvert, cudaConvert },
|
||||
/* 16S -> 64F */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert }
|
||||
},
|
||||
{
|
||||
/* 32S -> 8U */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 32S -> 8S */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 32S -> 16U */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 32S -> 16S */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 32S -> 32S */ {0,0,0,0},
|
||||
/* 32S -> 32F */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 32S -> 64F */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert}
|
||||
},
|
||||
{
|
||||
/* 32F -> 8U */ {NppCvt<CV_32F, CV_8U , nppiConvert_32f8u_C1R >::call, cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 32F -> 8S */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 32F -> 16U */ {NppCvt<CV_32F, CV_16U, nppiConvert_32f16u_C1R>::call, cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 32F -> 16S */ {NppCvt<CV_32F, CV_16S, nppiConvert_32f16s_C1R>::call, cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 32F -> 32S */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 32F -> 32F */ {0,0,0,0},
|
||||
/* 32F -> 64F */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert}
|
||||
},
|
||||
{
|
||||
/* 64F -> 8U */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 64F -> 8S */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 64F -> 16U */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 64F -> 16S */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 64F -> 32S */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 64F -> 32F */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert},
|
||||
/* 64F -> 64F */ {0,0,0,0}
|
||||
}
|
||||
};
|
||||
|
||||
const bool aligned = isAligned(src.data, 16) && isAligned(dst.data, 16);
|
||||
if (!aligned)
|
||||
{
|
||||
cudaConvert(src, dst, stream);
|
||||
return;
|
||||
}
|
||||
|
||||
const func_t func = funcs[src.depth()][dst.depth()][src.channels() - 1];
|
||||
CV_DbgAssert( func != 0 );
|
||||
|
||||
func(src, dst, stream);
|
||||
}
|
||||
|
||||
void convert(const GpuMat& src, GpuMat& dst, double alpha, double beta, cudaStream_t stream)
|
||||
{
|
||||
CV_DbgAssert( src.size() == dst.size() && src.channels() == dst.channels() );
|
||||
|
||||
CV_Assert( src.depth() <= CV_64F && src.channels() <= 4 );
|
||||
CV_Assert( dst.depth() <= CV_64F );
|
||||
|
||||
if (src.depth() == CV_64F || dst.depth() == CV_64F)
|
||||
{
|
||||
CV_Assert( deviceSupports(NATIVE_DOUBLE) );
|
||||
}
|
||||
|
||||
cudaConvert(src, dst, alpha, beta, stream);
|
||||
}
|
||||
|
||||
void set(GpuMat& m, Scalar s, cudaStream_t stream)
|
||||
{
|
||||
if (s[0] == 0.0 && s[1] == 0.0 && s[2] == 0.0 && s[3] == 0.0)
|
||||
{
|
||||
if (stream)
|
||||
cudaSafeCall( cudaMemset2DAsync(m.data, m.step, 0, m.cols * m.elemSize(), m.rows, stream) );
|
||||
else
|
||||
cudaSafeCall( cudaMemset2D(m.data, m.step, 0, m.cols * m.elemSize(), m.rows) );
|
||||
return;
|
||||
}
|
||||
|
||||
if (m.depth() == CV_8U)
|
||||
{
|
||||
int cn = m.channels();
|
||||
|
||||
if (cn == 1 || (cn == 2 && s[0] == s[1]) || (cn == 3 && s[0] == s[1] && s[0] == s[2]) || (cn == 4 && s[0] == s[1] && s[0] == s[2] && s[0] == s[3]))
|
||||
{
|
||||
int val = saturate_cast<uchar>(s[0]);
|
||||
if (stream)
|
||||
cudaSafeCall( cudaMemset2DAsync(m.data, m.step, val, m.cols * m.elemSize(), m.rows, stream) );
|
||||
else
|
||||
cudaSafeCall( cudaMemset2D(m.data, m.step, val, m.cols * m.elemSize(), m.rows) );
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
typedef void (*func_t)(GpuMat& src, Scalar s, cudaStream_t stream);
|
||||
static const func_t funcs[7][4] =
|
||||
{
|
||||
{NppSet<CV_8U , 1, nppiSet_8u_C1R >::call, cudaSet , cudaSet , NppSet<CV_8U , 4, nppiSet_8u_C4R >::call},
|
||||
{NppSet<CV_8S , 1, nppiSet_8s_C1R >::call, NppSet<CV_8S , 2, nppiSet_8s_C2R >::call, NppSet<CV_8S, 3, nppiSet_8s_C3R>::call, NppSet<CV_8S , 4, nppiSet_8s_C4R >::call},
|
||||
{NppSet<CV_16U, 1, nppiSet_16u_C1R>::call, NppSet<CV_16U, 2, nppiSet_16u_C2R>::call, cudaSet , NppSet<CV_16U, 4, nppiSet_16u_C4R>::call},
|
||||
{NppSet<CV_16S, 1, nppiSet_16s_C1R>::call, NppSet<CV_16S, 2, nppiSet_16s_C2R>::call, cudaSet , NppSet<CV_16S, 4, nppiSet_16s_C4R>::call},
|
||||
{NppSet<CV_32S, 1, nppiSet_32s_C1R>::call, cudaSet , cudaSet , NppSet<CV_32S, 4, nppiSet_32s_C4R>::call},
|
||||
{NppSet<CV_32F, 1, nppiSet_32f_C1R>::call, cudaSet , cudaSet , NppSet<CV_32F, 4, nppiSet_32f_C4R>::call},
|
||||
{cudaSet , cudaSet , cudaSet , cudaSet }
|
||||
};
|
||||
|
||||
CV_Assert( m.depth() <= CV_64F && m.channels() <= 4 );
|
||||
|
||||
if (m.depth() == CV_64F)
|
||||
{
|
||||
CV_Assert( deviceSupports(NATIVE_DOUBLE) );
|
||||
}
|
||||
|
||||
funcs[m.depth()][m.channels() - 1](m, s, stream);
|
||||
}
|
||||
|
||||
void set(GpuMat& m, Scalar s, const GpuMat& mask, cudaStream_t stream)
|
||||
{
|
||||
CV_DbgAssert( !mask.empty() );
|
||||
|
||||
CV_Assert( m.depth() <= CV_64F && m.channels() <= 4 );
|
||||
|
||||
if (m.depth() == CV_64F)
|
||||
{
|
||||
CV_Assert( deviceSupports(NATIVE_DOUBLE) );
|
||||
}
|
||||
|
||||
typedef void (*func_t)(GpuMat& src, Scalar s, const GpuMat& mask, cudaStream_t stream);
|
||||
static const func_t funcs[7][4] =
|
||||
{
|
||||
{NppSetMask<CV_8U , 1, nppiSet_8u_C1MR >::call, cudaSet, cudaSet, NppSetMask<CV_8U , 4, nppiSet_8u_C4MR >::call},
|
||||
{cudaSet , cudaSet, cudaSet, cudaSet },
|
||||
{NppSetMask<CV_16U, 1, nppiSet_16u_C1MR>::call, cudaSet, cudaSet, NppSetMask<CV_16U, 4, nppiSet_16u_C4MR>::call},
|
||||
{NppSetMask<CV_16S, 1, nppiSet_16s_C1MR>::call, cudaSet, cudaSet, NppSetMask<CV_16S, 4, nppiSet_16s_C4MR>::call},
|
||||
{NppSetMask<CV_32S, 1, nppiSet_32s_C1MR>::call, cudaSet, cudaSet, NppSetMask<CV_32S, 4, nppiSet_32s_C4MR>::call},
|
||||
{NppSetMask<CV_32F, 1, nppiSet_32f_C1MR>::call, cudaSet, cudaSet, NppSetMask<CV_32F, 4, nppiSet_32f_C4MR>::call},
|
||||
{cudaSet , cudaSet, cudaSet, cudaSet }
|
||||
};
|
||||
|
||||
funcs[m.depth()][m.channels() - 1](m, s, mask, stream);
|
||||
}
|
||||
}}
|
||||
|
||||
#endif // HAVE_CUDA
|
||||
|
||||
cv::gpu::GpuMat::GpuMat(int rows_, int cols_, int type_, void* data_, size_t step_) :
|
||||
flags(Mat::MAGIC_VAL + (type_ & Mat::TYPE_MASK)), rows(rows_), cols(cols_),
|
||||
step(step_), data((uchar*)data_), refcount(0),
|
||||
datastart((uchar*)data_), dataend((uchar*)data_)
|
||||
{
|
||||
size_t minstep = cols * elemSize();
|
||||
|
||||
if (step == Mat::AUTO_STEP)
|
||||
{
|
||||
step = minstep;
|
||||
flags |= Mat::CONTINUOUS_FLAG;
|
||||
}
|
||||
else
|
||||
{
|
||||
if (rows == 1)
|
||||
step = minstep;
|
||||
|
||||
CV_DbgAssert( step >= minstep );
|
||||
|
||||
flags |= step == minstep ? Mat::CONTINUOUS_FLAG : 0;
|
||||
}
|
||||
|
||||
dataend += step * (rows - 1) + minstep;
|
||||
}
|
||||
|
||||
cv::gpu::GpuMat::GpuMat(Size size_, int type_, void* data_, size_t step_) :
|
||||
flags(Mat::MAGIC_VAL + (type_ & Mat::TYPE_MASK)), rows(size_.height), cols(size_.width),
|
||||
step(step_), data((uchar*)data_), refcount(0),
|
||||
datastart((uchar*)data_), dataend((uchar*)data_)
|
||||
{
|
||||
size_t minstep = cols * elemSize();
|
||||
|
||||
if (step == Mat::AUTO_STEP)
|
||||
{
|
||||
step = minstep;
|
||||
flags |= Mat::CONTINUOUS_FLAG;
|
||||
}
|
||||
else
|
||||
{
|
||||
if (rows == 1)
|
||||
step = minstep;
|
||||
|
||||
CV_DbgAssert( step >= minstep );
|
||||
|
||||
flags |= step == minstep ? Mat::CONTINUOUS_FLAG : 0;
|
||||
}
|
||||
dataend += step * (rows - 1) + minstep;
|
||||
}
|
||||
|
||||
cv::gpu::GpuMat::GpuMat(const GpuMat& m, Range rowRange_, Range colRange_)
|
||||
{
|
||||
flags = m.flags;
|
||||
step = m.step; refcount = m.refcount;
|
||||
data = m.data; datastart = m.datastart; dataend = m.dataend;
|
||||
|
||||
if (rowRange_ == Range::all())
|
||||
{
|
||||
rows = m.rows;
|
||||
}
|
||||
else
|
||||
{
|
||||
CV_Assert( 0 <= rowRange_.start && rowRange_.start <= rowRange_.end && rowRange_.end <= m.rows );
|
||||
|
||||
rows = rowRange_.size();
|
||||
data += step*rowRange_.start;
|
||||
}
|
||||
|
||||
if (colRange_ == Range::all())
|
||||
{
|
||||
cols = m.cols;
|
||||
}
|
||||
else
|
||||
{
|
||||
CV_Assert( 0 <= colRange_.start && colRange_.start <= colRange_.end && colRange_.end <= m.cols );
|
||||
|
||||
cols = colRange_.size();
|
||||
data += colRange_.start*elemSize();
|
||||
flags &= cols < m.cols ? ~Mat::CONTINUOUS_FLAG : -1;
|
||||
}
|
||||
|
||||
if (rows == 1)
|
||||
flags |= Mat::CONTINUOUS_FLAG;
|
||||
|
||||
if (refcount)
|
||||
CV_XADD(refcount, 1);
|
||||
|
||||
if (rows <= 0 || cols <= 0)
|
||||
rows = cols = 0;
|
||||
}
|
||||
|
||||
cv::gpu::GpuMat::GpuMat(const GpuMat& m, Rect roi) :
|
||||
flags(m.flags), rows(roi.height), cols(roi.width),
|
||||
step(m.step), data(m.data + roi.y*step), refcount(m.refcount),
|
||||
datastart(m.datastart), dataend(m.dataend)
|
||||
{
|
||||
flags &= roi.width < m.cols ? ~Mat::CONTINUOUS_FLAG : -1;
|
||||
data += roi.x * elemSize();
|
||||
|
||||
CV_Assert( 0 <= roi.x && 0 <= roi.width && roi.x + roi.width <= m.cols && 0 <= roi.y && 0 <= roi.height && roi.y + roi.height <= m.rows );
|
||||
|
||||
if (refcount)
|
||||
CV_XADD(refcount, 1);
|
||||
|
||||
if (rows <= 0 || cols <= 0)
|
||||
rows = cols = 0;
|
||||
}
|
||||
|
||||
void cv::gpu::GpuMat::create(int _rows, int _cols, int _type)
|
||||
{
|
||||
#ifndef HAVE_CUDA
|
||||
(void) _rows;
|
||||
(void) _cols;
|
||||
(void) _type;
|
||||
throw_no_cuda();
|
||||
#else
|
||||
_type &= Mat::TYPE_MASK;
|
||||
|
||||
if (rows == _rows && cols == _cols && type() == _type && data)
|
||||
return;
|
||||
|
||||
if (data)
|
||||
release();
|
||||
|
||||
CV_DbgAssert( _rows >= 0 && _cols >= 0 );
|
||||
|
||||
if (_rows > 0 && _cols > 0)
|
||||
{
|
||||
flags = Mat::MAGIC_VAL + _type;
|
||||
rows = _rows;
|
||||
cols = _cols;
|
||||
|
||||
size_t esz = elemSize();
|
||||
|
||||
void* devPtr;
|
||||
cudaSafeCall( cudaMallocPitch(&devPtr, &step, esz * cols, rows) );
|
||||
|
||||
// Single row must be continuous
|
||||
if (rows == 1)
|
||||
step = esz * cols;
|
||||
|
||||
if (esz * cols == step)
|
||||
flags |= Mat::CONTINUOUS_FLAG;
|
||||
|
||||
int64 _nettosize = static_cast<int64>(step) * rows;
|
||||
size_t nettosize = static_cast<size_t>(_nettosize);
|
||||
|
||||
datastart = data = static_cast<uchar*>(devPtr);
|
||||
dataend = data + nettosize;
|
||||
|
||||
refcount = static_cast<int*>(fastMalloc(sizeof(*refcount)));
|
||||
*refcount = 1;
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
void cv::gpu::GpuMat::release()
|
||||
{
|
||||
#ifdef HAVE_CUDA
|
||||
if (refcount && CV_XADD(refcount, -1) == 1)
|
||||
{
|
||||
cudaFree(datastart);
|
||||
fastFree(refcount);
|
||||
}
|
||||
|
||||
data = datastart = dataend = 0;
|
||||
step = rows = cols = 0;
|
||||
refcount = 0;
|
||||
#endif
|
||||
}
|
||||
|
||||
void cv::gpu::GpuMat::upload(const Mat& m)
|
||||
{
|
||||
#ifndef HAVE_CUDA
|
||||
(void) m;
|
||||
throw_no_cuda();
|
||||
#else
|
||||
CV_DbgAssert( !m.empty() );
|
||||
|
||||
create(m.size(), m.type());
|
||||
|
||||
cudaSafeCall( cudaMemcpy2D(data, step, m.data, m.step, cols * elemSize(), rows, cudaMemcpyHostToDevice) );
|
||||
#endif
|
||||
}
|
||||
|
||||
void cv::gpu::GpuMat::download(Mat& m) const
|
||||
{
|
||||
#ifndef HAVE_CUDA
|
||||
(void) m;
|
||||
throw_no_cuda();
|
||||
#else
|
||||
CV_DbgAssert( !empty() );
|
||||
|
||||
m.create(size(), type());
|
||||
|
||||
cudaSafeCall( cudaMemcpy2D(m.data, m.step, data, step, cols * elemSize(), rows, cudaMemcpyDeviceToHost) );
|
||||
#endif
|
||||
}
|
||||
|
||||
void cv::gpu::GpuMat::copyTo(GpuMat& m) const
|
||||
{
|
||||
#ifndef HAVE_CUDA
|
||||
(void) m;
|
||||
throw_no_cuda();
|
||||
#else
|
||||
CV_DbgAssert( !empty() );
|
||||
|
||||
m.create(size(), type());
|
||||
|
||||
cudaSafeCall( cudaMemcpy2D(m.data, m.step, data, step, cols * elemSize(), rows, cudaMemcpyDeviceToDevice) );
|
||||
#endif
|
||||
}
|
||||
|
||||
void cv::gpu::GpuMat::copyTo(GpuMat& mat, const GpuMat& mask) const
|
||||
{
|
||||
#ifndef HAVE_CUDA
|
||||
(void) mat;
|
||||
(void) mask;
|
||||
throw_no_cuda();
|
||||
#else
|
||||
CV_DbgAssert( !empty() );
|
||||
|
||||
if (mask.empty())
|
||||
{
|
||||
copyTo(mat);
|
||||
}
|
||||
else
|
||||
{
|
||||
mat.create(size(), type());
|
||||
|
||||
copyWithMask(*this, mat, mask);
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
GpuMat& cv::gpu::GpuMat::setTo(Scalar s, const GpuMat& mask)
|
||||
{
|
||||
#ifndef HAVE_CUDA
|
||||
(void) s;
|
||||
(void) mask;
|
||||
throw_no_cuda();
|
||||
return *this;
|
||||
#else
|
||||
CV_DbgAssert( !empty() );
|
||||
|
||||
if (mask.empty())
|
||||
set(*this, s);
|
||||
else
|
||||
set(*this, s, mask);
|
||||
|
||||
return *this;
|
||||
#endif
|
||||
}
|
||||
|
||||
void cv::gpu::GpuMat::convertTo(GpuMat& dst, int rtype, double alpha, double beta) const
|
||||
{
|
||||
#ifndef HAVE_CUDA
|
||||
(void) dst;
|
||||
(void) rtype;
|
||||
(void) alpha;
|
||||
(void) beta;
|
||||
throw_no_cuda();
|
||||
#else
|
||||
bool noScale = fabs(alpha - 1) < std::numeric_limits<double>::epsilon() && fabs(beta) < std::numeric_limits<double>::epsilon();
|
||||
|
||||
if (rtype < 0)
|
||||
rtype = type();
|
||||
else
|
||||
rtype = CV_MAKETYPE(CV_MAT_DEPTH(rtype), channels());
|
||||
|
||||
int sdepth = depth();
|
||||
int ddepth = CV_MAT_DEPTH(rtype);
|
||||
if (sdepth == ddepth && noScale)
|
||||
{
|
||||
copyTo(dst);
|
||||
return;
|
||||
}
|
||||
|
||||
GpuMat temp;
|
||||
const GpuMat* psrc = this;
|
||||
if (sdepth != ddepth && psrc == &dst)
|
||||
{
|
||||
temp = *this;
|
||||
psrc = &temp;
|
||||
}
|
||||
|
||||
dst.create(size(), rtype);
|
||||
|
||||
if (noScale)
|
||||
convert(*psrc, dst);
|
||||
else
|
||||
convert(*psrc, dst, alpha, beta);
|
||||
#endif
|
||||
}
|
||||
|
||||
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::Error::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::Error::StsOutOfRange, "Bad new number of rows");
|
||||
|
||||
total_width = total_size / new_rows;
|
||||
|
||||
if (total_width * new_rows != total_size)
|
||||
CV_Error(cv::Error::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::Error::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::locateROI(Size& wholeSize, Point& ofs) const
|
||||
{
|
||||
CV_DbgAssert( step > 0 );
|
||||
|
||||
size_t esz = elemSize();
|
||||
ptrdiff_t delta1 = data - datastart;
|
||||
ptrdiff_t delta2 = dataend - datastart;
|
||||
|
||||
if (delta1 == 0)
|
||||
{
|
||||
ofs.x = ofs.y = 0;
|
||||
}
|
||||
else
|
||||
{
|
||||
ofs.y = static_cast<int>(delta1 / step);
|
||||
ofs.x = static_cast<int>((delta1 - step * ofs.y) / esz);
|
||||
|
||||
CV_DbgAssert( data == datastart + ofs.y * step + ofs.x * esz );
|
||||
}
|
||||
|
||||
size_t minstep = (ofs.x + cols) * esz;
|
||||
|
||||
wholeSize.height = std::max(static_cast<int>((delta2 - minstep) / step + 1), ofs.y + rows);
|
||||
wholeSize.width = std::max(static_cast<int>((delta2 - step * (wholeSize.height - 1)) / esz), ofs.x + cols);
|
||||
}
|
||||
|
||||
GpuMat& cv::gpu::GpuMat::adjustROI(int dtop, int dbottom, int dleft, int dright)
|
||||
{
|
||||
Size wholeSize;
|
||||
Point ofs;
|
||||
locateROI(wholeSize, ofs);
|
||||
|
||||
size_t esz = elemSize();
|
||||
|
||||
int row1 = std::max(ofs.y - dtop, 0);
|
||||
int row2 = std::min(ofs.y + rows + dbottom, wholeSize.height);
|
||||
|
||||
int col1 = std::max(ofs.x - dleft, 0);
|
||||
int col2 = std::min(ofs.x + cols + dright, wholeSize.width);
|
||||
|
||||
data += (row1 - ofs.y) * step + (col1 - ofs.x) * esz;
|
||||
rows = row2 - row1;
|
||||
cols = col2 - col1;
|
||||
|
||||
if (esz * cols == step || rows == 1)
|
||||
flags |= Mat::CONTINUOUS_FLAG;
|
||||
else
|
||||
flags &= ~Mat::CONTINUOUS_FLAG;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
void cv::gpu::createContinuous(int rows, int cols, int type, GpuMat& m)
|
||||
{
|
||||
const int area = rows * cols;
|
||||
|
||||
if (m.empty() || m.type() != type || !m.isContinuous() || m.size().area() < area)
|
||||
m.create(1, area, type);
|
||||
|
||||
m.cols = cols;
|
||||
m.rows = rows;
|
||||
m.step = m.elemSize() * cols;
|
||||
m.flags |= Mat::CONTINUOUS_FLAG;
|
||||
}
|
||||
|
||||
void cv::gpu::ensureSizeIsEnough(int rows, int cols, int type, GpuMat& m)
|
||||
{
|
||||
if (m.empty() || m.type() != type || m.data != m.datastart)
|
||||
{
|
||||
m.create(rows, cols, type);
|
||||
}
|
||||
else
|
||||
{
|
||||
const size_t esz = m.elemSize();
|
||||
const ptrdiff_t delta2 = m.dataend - m.datastart;
|
||||
|
||||
const size_t minstep = m.cols * esz;
|
||||
|
||||
Size wholeSize;
|
||||
wholeSize.height = std::max(static_cast<int>((delta2 - minstep) / m.step + 1), m.rows);
|
||||
wholeSize.width = std::max(static_cast<int>((delta2 - m.step * (wholeSize.height - 1)) / esz), m.cols);
|
||||
|
||||
if (wholeSize.height < rows || wholeSize.width < cols)
|
||||
{
|
||||
m.create(rows, cols, type);
|
||||
}
|
||||
else
|
||||
{
|
||||
m.cols = cols;
|
||||
m.rows = rows;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
GpuMat cv::gpu::allocMatFromBuf(int rows, int cols, int type, GpuMat& mat)
|
||||
{
|
||||
if (!mat.empty() && mat.type() == type && mat.rows >= rows && mat.cols >= cols)
|
||||
return mat(Rect(0, 0, cols, rows));
|
||||
|
||||
return mat = GpuMat(rows, cols, type);
|
||||
}
|
||||
@ -72,10 +72,10 @@ void cv::gpu::Stream::release() { throw_no_cuda(); }
|
||||
|
||||
namespace cv { namespace gpu
|
||||
{
|
||||
void copyWithMask(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream);
|
||||
void convertTo(const GpuMat& src, GpuMat& dst, double alpha, double beta, cudaStream_t stream);
|
||||
void setTo(GpuMat& src, Scalar s, cudaStream_t stream);
|
||||
void setTo(GpuMat& src, Scalar s, const GpuMat& mask, cudaStream_t stream);
|
||||
void copyWithMask(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream = 0);
|
||||
void convert(const GpuMat& src, GpuMat& dst, double alpha, double beta, cudaStream_t stream = 0);
|
||||
void set(GpuMat& m, Scalar s, cudaStream_t stream = 0);
|
||||
void set(GpuMat& m, Scalar s, const GpuMat& mask, cudaStream_t stream = 0);
|
||||
}}
|
||||
|
||||
struct Stream::Impl
|
||||
@ -217,7 +217,7 @@ void cv::gpu::Stream::enqueueMemSet(GpuMat& src, Scalar val)
|
||||
}
|
||||
}
|
||||
|
||||
setTo(src, val, stream);
|
||||
set(src, val, stream);
|
||||
}
|
||||
|
||||
void cv::gpu::Stream::enqueueMemSet(GpuMat& src, Scalar val, const GpuMat& mask)
|
||||
@ -234,7 +234,7 @@ void cv::gpu::Stream::enqueueMemSet(GpuMat& src, Scalar val, const GpuMat& mask)
|
||||
|
||||
cudaStream_t stream = Impl::getStream(impl);
|
||||
|
||||
setTo(src, val, mask, stream);
|
||||
set(src, val, mask, stream);
|
||||
}
|
||||
|
||||
void cv::gpu::Stream::enqueueConvert(const GpuMat& src, GpuMat& dst, int dtype, double alpha, double beta)
|
||||
@ -265,7 +265,7 @@ void cv::gpu::Stream::enqueueConvert(const GpuMat& src, GpuMat& dst, int dtype,
|
||||
dst.create(src.size(), dtype);
|
||||
|
||||
cudaStream_t stream = Impl::getStream(impl);
|
||||
convertTo(src, dst, alpha, beta, stream);
|
||||
convert(src, dst, alpha, beta, stream);
|
||||
}
|
||||
|
||||
#if CUDART_VERSION >= 5000
|
||||
|
||||
Loading…
Reference in New Issue
Block a user