Skip to content

O
opencv
Commit db1178b5 authored by Vladislav Vinogradov's avatar Vladislav Vinogradov
Browse files
Options

moved GpuMat implementation to separate file

parent 2153a148
Show whitespace changes
Inline Side-by-side
Showing with 1377 additions and 1303 deletions
modules/core/include/opencv2/core/gpu.hpp
View file @ db1178b5
......@@ -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"
......
modules/core/include/opencv2/core/gpu.inl.hpp
View file @ db1178b5
......@@ -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_)
{
create(size_.height, size_.width, type_);
}
inline
GpuMat GpuMat::operator()(Range _rowRange, Range _colRange) const
GpuMat GpuMat::operator ()(Range rowRange_, Range colRange_) const
{
return GpuMat(*this, _rowRange, _colRange);
return GpuMat(*this, rowRange_, colRange_);
}
inline
GpuMat GpuMat::operator()(Rect roi) const
GpuMat GpuMat::operator ()(Rect roi) const
{
return GpuMat(*this, roi);
}
......@@ -232,48 +286,21 @@ int GpuMat::channels() const
}
inline
Size GpuMat::size() const
{
return Size(cols, rows);
}
inline
uchar* GpuMat::ptr(int y)
size_t GpuMat::step1() const
{
CV_DbgAssert((unsigned)y < (unsigned)rows);
return data + step * y;
return step / elemSize1();
}
inline
const uchar* GpuMat::ptr(int y) const
Size GpuMat::size() const
{
CV_DbgAssert((unsigned)y < (unsigned)rows);
return data + step * y;
return Size(cols, rows);
}
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)
bool GpuMat::empty() const
{
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__
modules/core/src/cuda/matrix_operations.cu
View file @ db1178b5
......@@ -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;
......@@ -164,64 +154,65 @@ namespace cv { namespace gpu { namespace cudev
}
}
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)
{
size_t x = blockIdx.x * blockDim.x + threadIdx.x;
size_t y = blockIdx.y * blockDim.y + threadIdx.y;
if ((x < cols * channels ) && (y < rows))
if (mask[y * step_mask + x / channels] != 0)
{
size_t idx = y * ( step >> shift_and_sizeof<T>::shift ) + x;
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, 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, 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, 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 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(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;
if ((x < cols * channels ) && (y < rows))
if (mask[y * step_mask + x / channels] != 0)
{
size_t idx = y * ( step >> shift_and_sizeof<T>::shift ) + x;
mat[idx] = readScalar<T>(x % channels);
}
}
template <typename T>
void set_to_gpu(PtrStepSzb mat, const T* scalar, 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_without_mask<T><<<numBlocks, threadsPerBlock, 0, stream>>>((T*)mat.data, mat.cols, mat.rows, mat.step, channels);
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, 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
modules/core/src/cuda/matrix_operations.hpp 0 → 100644
View file @ db1178b5
/*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);
}}}
modules/core/src/gpu.cpp
View file @ db1178b5
......@@ -509,1020 +509,6 @@ void cv::gpu::printShortCudaDeviceInfo(int device)
#endif // HAVE_CUDA
//////////////////////////////// GpuMat ///////////////////////////////
cv::gpu::GpuMat::GpuMat(const GpuMat& m)
: flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend)
{
if (refcount)
CV_XADD(refcount, 1);
}
cv::gpu::GpuMat::GpuMat(int rows_, int cols_, int type_, void* data_, size_t step_) :
flags(Mat::MAGIC_VAL + (type_ & Mat::TYPE_MASK)), rows(rows_), cols(cols_),
step(step_), data((uchar*)data_), refcount(0),
datastart((uchar*)data_), dataend((uchar*)data_)
{
size_t minstep = cols * elemSize();
if (step == Mat::AUTO_STEP)
{
step = minstep;
flags |= Mat::CONTINUOUS_FLAG;
}
else
{
if (rows == 1)
step = minstep;
CV_DbgAssert(step >= minstep);
flags |= step == minstep ? Mat::CONTINUOUS_FLAG : 0;
}
dataend += step * (rows - 1) + minstep;
}
cv::gpu::GpuMat::GpuMat(Size size_, int type_, void* data_, size_t step_) :
flags(Mat::MAGIC_VAL + (type_ & Mat::TYPE_MASK)), rows(size_.height), cols(size_.width),
step(step_), data((uchar*)data_), refcount(0),
datastart((uchar*)data_), dataend((uchar*)data_)
{
size_t minstep = cols * elemSize();
if (step == Mat::AUTO_STEP)
{
step = minstep;
flags |= Mat::CONTINUOUS_FLAG;
}
else
{
if (rows == 1)
step = minstep;
CV_DbgAssert(step >= minstep);
flags |= step == minstep ? Mat::CONTINUOUS_FLAG : 0;
}
dataend += step * (rows - 1) + minstep;
}
cv::gpu::GpuMat::GpuMat(const GpuMat& m, Range _rowRange, Range _colRange)
{
flags = m.flags;
step = m.step; refcount = m.refcount;
data = m.data; datastart = m.datastart; dataend = m.dataend;
if (_rowRange == Range::all())
rows = m.rows;
else
{
CV_Assert(0 <= _rowRange.start && _rowRange.start <= _rowRange.end && _rowRange.end <= m.rows);
rows = _rowRange.size();
data += step*_rowRange.start;
}
if (_colRange == Range::all())
cols = m.cols;
else
{
CV_Assert(0 <= _colRange.start && _colRange.start <= _colRange.end && _colRange.end <= m.cols);
cols = _colRange.size();
data += _colRange.start*elemSize();
flags &= cols < m.cols ? ~Mat::CONTINUOUS_FLAG : -1;
}
if (rows == 1)
flags |= Mat::CONTINUOUS_FLAG;
if (refcount)
CV_XADD(refcount, 1);
if (rows <= 0 || cols <= 0)
rows = cols = 0;
}
cv::gpu::GpuMat::GpuMat(const GpuMat& m, Rect roi) :
flags(m.flags), rows(roi.height), cols(roi.width),
step(m.step), data(m.data + roi.y*step), refcount(m.refcount),
datastart(m.datastart), dataend(m.dataend)
{
flags &= roi.width < m.cols ? ~Mat::CONTINUOUS_FLAG : -1;
data += roi.x * elemSize();
CV_Assert(0 <= roi.x && 0 <= roi.width && roi.x + roi.width <= m.cols && 0 <= roi.y && 0 <= roi.height && roi.y + roi.height <= m.rows);
if (refcount)
CV_XADD(refcount, 1);
if (rows <= 0 || cols <= 0)
rows = cols = 0;
}
cv::gpu::GpuMat::GpuMat(const Mat& m) :
flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0)
{
upload(m);
}
GpuMat& cv::gpu::GpuMat::operator = (const GpuMat& m)
{
if (this != &m)
{
GpuMat temp(m);
swap(temp);
}
return *this;
}
void cv::gpu::GpuMat::swap(GpuMat& b)
{
std::swap(flags, b.flags);
std::swap(rows, b.rows);
std::swap(cols, b.cols);
std::swap(step, b.step);
std::swap(data, b.data);
std::swap(datastart, b.datastart);
std::swap(dataend, b.dataend);
std::swap(refcount, b.refcount);
}
void cv::gpu::GpuMat::locateROI(Size& wholeSize, Point& ofs) const
{
size_t esz = elemSize();
ptrdiff_t delta1 = data - datastart;
ptrdiff_t delta2 = dataend - datastart;
CV_DbgAssert(step > 0);
if (delta1 == 0)
ofs.x = ofs.y = 0;
else
{
ofs.y = static_cast<int>(delta1 / step);
ofs.x = static_cast<int>((delta1 - step * ofs.y) / esz);
CV_DbgAssert(data == datastart + ofs.y * step + ofs.x * esz);
}
size_t minstep = (ofs.x + cols) * esz;
wholeSize.height = std::max(static_cast<int>((delta2 - minstep) / step + 1), ofs.y + rows);
wholeSize.width = std::max(static_cast<int>((delta2 - step * (wholeSize.height - 1)) / esz), ofs.x + cols);
}
GpuMat& cv::gpu::GpuMat::adjustROI(int dtop, int dbottom, int dleft, int dright)
{
Size wholeSize;
Point ofs;
locateROI(wholeSize, ofs);
size_t esz = elemSize();
int row1 = std::max(ofs.y - dtop, 0);
int row2 = std::min(ofs.y + rows + dbottom, wholeSize.height);
int col1 = std::max(ofs.x - dleft, 0);
int col2 = std::min(ofs.x + cols + dright, wholeSize.width);
data += (row1 - ofs.y) * step + (col1 - ofs.x) * esz;
rows = row2 - row1;
cols = col2 - col1;
if (esz * cols == step || rows == 1)
flags |= Mat::CONTINUOUS_FLAG;
else
flags &= ~Mat::CONTINUOUS_FLAG;
return *this;
}
GpuMat cv::gpu::GpuMat::reshape(int new_cn, int new_rows) const
{
GpuMat hdr = *this;
int cn = channels();
if (new_cn == 0)
new_cn = cn;
int total_width = cols * cn;
if ((new_cn > total_width || total_width % new_cn != 0) && new_rows == 0)
new_rows = rows * total_width / new_cn;
if (new_rows != 0 && new_rows != rows)
{
int total_size = total_width * rows;
if (!isContinuous())
CV_Error(CV_BadStep, "The matrix is not continuous, thus its number of rows can not be changed");
if ((unsigned)new_rows > (unsigned)total_size)
CV_Error(CV_StsOutOfRange, "Bad new number of rows");
total_width = total_size / new_rows;
if (total_width * new_rows != total_size)
CV_Error(CV_StsBadArg, "The total number of matrix elements is not divisible by the new number of rows");
hdr.rows = new_rows;
hdr.step = total_width * elemSize1();
}
int new_width = total_width / new_cn;
if (new_width * new_cn != total_width)
CV_Error(CV_BadNumChannels, "The total width is not divisible by the new number of channels");
hdr.cols = new_width;
hdr.flags = (hdr.flags & ~CV_MAT_CN_MASK) | ((new_cn - 1) << CV_CN_SHIFT);
return hdr;
}
cv::Mat::Mat(const GpuMat& m) : flags(0), dims(0), rows(0), cols(0), data(0), refcount(0), datastart(0), dataend(0), datalimit(0), allocator(0), size(&rows)
{
m.download(*this);
}
void cv::gpu::createContinuous(int rows, int cols, int type, GpuMat& m)
{
int area = rows * cols;
if (m.empty() || m.type() != type || !m.isContinuous() || m.size().area() < area)
m.create(1, area, type);
m.cols = cols;
m.rows = rows;
m.step = m.elemSize() * cols;
m.flags |= Mat::CONTINUOUS_FLAG;
}
void cv::gpu::ensureSizeIsEnough(int rows, int cols, int type, GpuMat& m)
{
if (m.empty() || m.type() != type || m.data != m.datastart)
m.create(rows, cols, type);
else
{
const size_t esz = m.elemSize();
const ptrdiff_t delta2 = m.dataend - m.datastart;
const size_t minstep = m.cols * esz;
Size wholeSize;
wholeSize.height = std::max(static_cast<int>((delta2 - minstep) / m.step + 1), m.rows);
wholeSize.width = std::max(static_cast<int>((delta2 - m.step * (wholeSize.height - 1)) / esz), m.cols);
if (wholeSize.height < rows || wholeSize.width < cols)
m.create(rows, cols, type);
else
{
m.cols = cols;
m.rows = rows;
}
}
}
GpuMat cv::gpu::allocMatFromBuf(int rows, int cols, int type, GpuMat &mat)
{
if (!mat.empty() && mat.type() == type && mat.rows >= rows && mat.cols >= cols)
return mat(Rect(0, 0, cols, rows));
return mat = GpuMat(rows, cols, type);
}
namespace
{
class GpuFuncTable
{
public:
virtual ~GpuFuncTable() {}
virtual void copy(const Mat& src, GpuMat& dst) const = 0;
virtual void copy(const GpuMat& src, Mat& dst) const = 0;
virtual void copy(const GpuMat& src, GpuMat& dst) const = 0;
virtual void copyWithMask(const GpuMat& src, GpuMat& dst, const GpuMat& mask) const = 0;
virtual void convert(const GpuMat& src, GpuMat& dst) const = 0;
virtual void convert(const GpuMat& src, GpuMat& dst, double alpha, double beta) const = 0;
virtual void setTo(GpuMat& m, Scalar s, const GpuMat& mask) const = 0;
virtual void mallocPitch(void** devPtr, size_t* step, size_t width, size_t height) const = 0;
virtual void free(void* devPtr) const = 0;
};
}
#ifndef HAVE_CUDA
namespace
{
class EmptyFuncTable : public GpuFuncTable
{
public:
void copy(const Mat&, GpuMat&) const { throw_no_cuda(); }
void copy(const GpuMat&, Mat&) const { throw_no_cuda(); }
void copy(const GpuMat&, GpuMat&) const { throw_no_cuda(); }
void copyWithMask(const GpuMat&, GpuMat&, const GpuMat&) const { throw_no_cuda(); }
void convert(const GpuMat&, GpuMat&) const { throw_no_cuda(); }
void convert(const GpuMat&, GpuMat&, double, double) const { throw_no_cuda(); }
void setTo(GpuMat&, Scalar, const GpuMat&) const { throw_no_cuda(); }
void mallocPitch(void**, size_t*, size_t, size_t) const { throw_no_cuda(); }
void free(void*) const {}
};
const GpuFuncTable* gpuFuncTable()
{
static EmptyFuncTable empty;
return &empty;
}
}
#else // HAVE_CUDA
namespace cv { namespace gpu { namespace cudev
{
void copyToWithMask_gpu(PtrStepSzb src, PtrStepSzb dst, size_t elemSize1, int cn, PtrStepSzb mask, bool colorMask, cudaStream_t stream);
template <typename T>
void set_to_gpu(PtrStepSzb mat, const T* scalar, int channels, cudaStream_t stream);
template <typename T>
void set_to_gpu(PtrStepSzb mat, const T* scalar, PtrStepSzb mask, int channels, cudaStream_t stream);
void convert_gpu(PtrStepSzb src, int sdepth, PtrStepSzb dst, int ddepth, double alpha, double beta, cudaStream_t stream);
}}}
namespace
{
template <typename T> void kernelSetCaller(GpuMat& src, Scalar s, cudaStream_t stream)
{
Scalar_<T> sf = s;
cv::gpu::cudev::set_to_gpu(src, sf.val, src.channels(), stream);
}
template <typename T> void kernelSetCaller(GpuMat& src, Scalar s, const GpuMat& mask, cudaStream_t stream)
{
Scalar_<T> sf = s;
cv::gpu::cudev::set_to_gpu(src, sf.val, mask, src.channels(), stream);
}
}
namespace cv { namespace gpu
{
CV_EXPORTS void copyWithMask(const cv::gpu::GpuMat&, cv::gpu::GpuMat&, const cv::gpu::GpuMat&, CUstream_st*);
CV_EXPORTS void convertTo(const cv::gpu::GpuMat&, cv::gpu::GpuMat&);
CV_EXPORTS void convertTo(const cv::gpu::GpuMat&, cv::gpu::GpuMat&, double, double, CUstream_st*);
CV_EXPORTS void setTo(cv::gpu::GpuMat&, cv::Scalar, CUstream_st*);
CV_EXPORTS void setTo(cv::gpu::GpuMat&, cv::Scalar, const cv::gpu::GpuMat&, CUstream_st*);
CV_EXPORTS void setTo(cv::gpu::GpuMat&, cv::Scalar);
CV_EXPORTS void setTo(cv::gpu::GpuMat&, cv::Scalar, const cv::gpu::GpuMat&);
}}
namespace cv { namespace gpu
{
void copyWithMask(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream = 0)
{
CV_Assert(src.size() == dst.size() && src.type() == dst.type());
CV_Assert(src.size() == mask.size() && mask.depth() == CV_8U && (mask.channels() == 1 || mask.channels() == src.channels()));
cv::gpu::cudev::copyToWithMask_gpu(src.reshape(1), dst.reshape(1), src.elemSize1(), src.channels(), mask.reshape(1), mask.channels() != 1, stream);
}
void convertTo(const GpuMat& src, GpuMat& dst)
{
cv::gpu::cudev::convert_gpu(src.reshape(1), src.depth(), dst.reshape(1), dst.depth(), 1.0, 0.0, 0);
}
void convertTo(const GpuMat& src, GpuMat& dst, double alpha, double beta, cudaStream_t stream = 0)
{
cv::gpu::cudev::convert_gpu(src.reshape(1), src.depth(), dst.reshape(1), dst.depth(), alpha, beta, stream);
}
void setTo(GpuMat& src, Scalar s, cudaStream_t stream)
{
typedef void (*caller_t)(GpuMat& src, Scalar s, cudaStream_t stream);
static const caller_t callers[] =
{
kernelSetCaller<uchar>, kernelSetCaller<schar>, kernelSetCaller<ushort>, kernelSetCaller<short>, kernelSetCaller<int>,
kernelSetCaller<float>, kernelSetCaller<double>
};
callers[src.depth()](src, s, stream);
}
void setTo(GpuMat& src, Scalar s, const GpuMat& mask, cudaStream_t stream)
{
typedef void (*caller_t)(GpuMat& src, Scalar s, const GpuMat& mask, cudaStream_t stream);
static const caller_t callers[] =
{
kernelSetCaller<uchar>, kernelSetCaller<schar>, kernelSetCaller<ushort>, kernelSetCaller<short>, kernelSetCaller<int>,
kernelSetCaller<float>, kernelSetCaller<double>
};
callers[src.depth()](src, s, mask, stream);
}
void setTo(GpuMat& src, Scalar s)
{
setTo(src, s, 0);
}
void setTo(GpuMat& src, Scalar s, const GpuMat& mask)
{
setTo(src, s, mask, 0);
}
}}
namespace
{
template<int n> struct NPPTypeTraits;
template<> struct NPPTypeTraits<CV_8U> { typedef Npp8u npp_type; };
template<> struct NPPTypeTraits<CV_8S> { typedef Npp8s npp_type; };
template<> struct NPPTypeTraits<CV_16U> { typedef Npp16u npp_type; };
template<> struct NPPTypeTraits<CV_16S> { typedef Npp16s npp_type; };
template<> struct NPPTypeTraits<CV_32S> { typedef Npp32s npp_type; };
template<> struct NPPTypeTraits<CV_32F> { typedef Npp32f npp_type; };
template<> struct NPPTypeTraits<CV_64F> { typedef Npp64f npp_type; };
//////////////////////////////////////////////////////////////////////////
// Convert
template<int SDEPTH, int DDEPTH> struct NppConvertFunc
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t;
typedef NppStatus (*func_ptr)(const src_t* pSrc, int nSrcStep, dst_t* pDst, int nDstStep, NppiSize oSizeROI);
};
template<int DDEPTH> struct NppConvertFunc<CV_32F, DDEPTH>
{
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t;
typedef NppStatus (*func_ptr)(const Npp32f* pSrc, int nSrcStep, dst_t* pDst, int nDstStep, NppiSize oSizeROI, NppRoundMode eRoundMode);
};
template<int SDEPTH, int DDEPTH, typename NppConvertFunc<SDEPTH, DDEPTH>::func_ptr func> struct NppCvt
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t;
static void call(const GpuMat& src, GpuMat& dst)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), dst.ptr<dst_t>(), static_cast<int>(dst.step), sz) );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template<int DDEPTH, typename NppConvertFunc<CV_32F, DDEPTH>::func_ptr func> struct NppCvt<CV_32F, DDEPTH, func>
{
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t;
static void call(const GpuMat& src, GpuMat& dst)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( func(src.ptr<Npp32f>(), static_cast<int>(src.step), dst.ptr<dst_t>(), static_cast<int>(dst.step), sz, NPP_RND_NEAR) );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
//////////////////////////////////////////////////////////////////////////
// Set
template<int SDEPTH, int SCN> struct NppSetFunc
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef NppStatus (*func_ptr)(const src_t values[], src_t* pSrc, int nSrcStep, NppiSize oSizeROI);
};
template<int SDEPTH> struct NppSetFunc<SDEPTH, 1>
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef NppStatus (*func_ptr)(src_t val, src_t* pSrc, int nSrcStep, NppiSize oSizeROI);
};
template<int SCN> struct NppSetFunc<CV_8S, SCN>
{
typedef NppStatus (*func_ptr)(Npp8s values[], Npp8s* pSrc, int nSrcStep, NppiSize oSizeROI);
};
template<> struct NppSetFunc<CV_8S, 1>
{
typedef NppStatus (*func_ptr)(Npp8s val, Npp8s* pSrc, int nSrcStep, NppiSize oSizeROI);
};
template<int SDEPTH, int SCN, typename NppSetFunc<SDEPTH, SCN>::func_ptr func> struct NppSet
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
static void call(GpuMat& src, Scalar s)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Scalar_<src_t> nppS = s;
nppSafeCall( func(nppS.val, src.ptr<src_t>(), static_cast<int>(src.step), sz) );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template<int SDEPTH, typename NppSetFunc<SDEPTH, 1>::func_ptr func> struct NppSet<SDEPTH, 1, func>
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
static void call(GpuMat& src, Scalar s)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Scalar_<src_t> nppS = s;
nppSafeCall( func(nppS[0], src.ptr<src_t>(), static_cast<int>(src.step), sz) );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template<int SDEPTH, int SCN> struct NppSetMaskFunc
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef NppStatus (*func_ptr)(const src_t values[], src_t* pSrc, int nSrcStep, NppiSize oSizeROI, const Npp8u* pMask, int nMaskStep);
};
template<int SDEPTH> struct NppSetMaskFunc<SDEPTH, 1>
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef NppStatus (*func_ptr)(src_t val, src_t* pSrc, int nSrcStep, NppiSize oSizeROI, const Npp8u* pMask, int nMaskStep);
};
template<int SDEPTH, int SCN, typename NppSetMaskFunc<SDEPTH, SCN>::func_ptr func> struct NppSetMask
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
static void call(GpuMat& src, Scalar s, const GpuMat& mask)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Scalar_<src_t> nppS = s;
nppSafeCall( func(nppS.val, src.ptr<src_t>(), static_cast<int>(src.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template<int SDEPTH, typename NppSetMaskFunc<SDEPTH, 1>::func_ptr func> struct NppSetMask<SDEPTH, 1, func>
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
static void call(GpuMat& src, Scalar s, const GpuMat& mask)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Scalar_<src_t> nppS = s;
nppSafeCall( func(nppS[0], src.ptr<src_t>(), static_cast<int>(src.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
//////////////////////////////////////////////////////////////////////////
// CopyMasked
template<int SDEPTH> struct NppCopyMaskedFunc
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef NppStatus (*func_ptr)(const src_t* pSrc, int nSrcStep, src_t* pDst, int nDstStep, NppiSize oSizeROI, const Npp8u* pMask, int nMaskStep);
};
template<int SDEPTH, typename NppCopyMaskedFunc<SDEPTH>::func_ptr func> struct NppCopyMasked
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
static void call(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t /*stream*/)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), dst.ptr<src_t>(), static_cast<int>(dst.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template <typename T> static inline bool isAligned(const T* ptr, size_t size)
{
return reinterpret_cast<size_t>(ptr) % size == 0;
}
//////////////////////////////////////////////////////////////////////////
// CudaFuncTable
class CudaFuncTable : public GpuFuncTable
{
public:
void copy(const Mat& src, GpuMat& dst) const
{
cudaSafeCall( cudaMemcpy2D(dst.data, dst.step, src.data, src.step, src.cols * src.elemSize(), src.rows, cudaMemcpyHostToDevice) );
}
void copy(const GpuMat& src, Mat& dst) const
{
cudaSafeCall( cudaMemcpy2D(dst.data, dst.step, src.data, src.step, src.cols * src.elemSize(), src.rows, cudaMemcpyDeviceToHost) );
}
void copy(const GpuMat& src, GpuMat& dst) const
{
cudaSafeCall( cudaMemcpy2D(dst.data, dst.step, src.data, src.step, src.cols * src.elemSize(), src.rows, cudaMemcpyDeviceToDevice) );
}
void copyWithMask(const GpuMat& src, GpuMat& dst, const GpuMat& mask) const
{
CV_Assert(src.depth() <= CV_64F && src.channels() <= 4);
CV_Assert(src.size() == dst.size() && src.type() == dst.type());
CV_Assert(src.size() == mask.size() && mask.depth() == CV_8U && (mask.channels() == 1 || mask.channels() == src.channels()));
if (src.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
typedef void (*func_t)(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream);
static const func_t funcs[7][4] =
{
/* 8U */ {NppCopyMasked<CV_8U , nppiCopy_8u_C1MR >::call, cv::gpu::copyWithMask, NppCopyMasked<CV_8U , nppiCopy_8u_C3MR >::call, NppCopyMasked<CV_8U , nppiCopy_8u_C4MR >::call},
/* 8S */ {cv::gpu::copyWithMask , cv::gpu::copyWithMask, cv::gpu::copyWithMask , cv::gpu::copyWithMask },
/* 16U */ {NppCopyMasked<CV_16U, nppiCopy_16u_C1MR>::call, cv::gpu::copyWithMask, NppCopyMasked<CV_16U, nppiCopy_16u_C3MR>::call, NppCopyMasked<CV_16U, nppiCopy_16u_C4MR>::call},
/* 16S */ {NppCopyMasked<CV_16S, nppiCopy_16s_C1MR>::call, cv::gpu::copyWithMask, NppCopyMasked<CV_16S, nppiCopy_16s_C3MR>::call, NppCopyMasked<CV_16S, nppiCopy_16s_C4MR>::call},
/* 32S */ {NppCopyMasked<CV_32S, nppiCopy_32s_C1MR>::call, cv::gpu::copyWithMask, NppCopyMasked<CV_32S, nppiCopy_32s_C3MR>::call, NppCopyMasked<CV_32S, nppiCopy_32s_C4MR>::call},
/* 32F */ {NppCopyMasked<CV_32F, nppiCopy_32f_C1MR>::call, cv::gpu::copyWithMask, NppCopyMasked<CV_32F, nppiCopy_32f_C3MR>::call, NppCopyMasked<CV_32F, nppiCopy_32f_C4MR>::call},
/* 64F */ {cv::gpu::copyWithMask , cv::gpu::copyWithMask, cv::gpu::copyWithMask , cv::gpu::copyWithMask }
};
const func_t func = mask.channels() == src.channels() ? funcs[src.depth()][src.channels() - 1] : cv::gpu::copyWithMask;
func(src, dst, mask, 0);
}
void convert(const GpuMat& src, GpuMat& dst) const
{
typedef void (*func_t)(const GpuMat& src, GpuMat& dst);
static const func_t funcs[7][7][4] =
{
{
/* 8U -> 8U */ {0, 0, 0, 0},
/* 8U -> 8S */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
/* 8U -> 16U */ {NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C1R>::call, cv::gpu::convertTo, cv::gpu::convertTo, NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C4R>::call},
/* 8U -> 16S */ {NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C1R>::call, cv::gpu::convertTo, cv::gpu::convertTo, NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C4R>::call},
/* 8U -> 32S */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
/* 8U -> 32F */ {NppCvt<CV_8U, CV_32F, nppiConvert_8u32f_C1R>::call, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
/* 8U -> 64F */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo }
},
{
/* 8S -> 8U */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 8S -> 8S */ {0,0,0,0},
/* 8S -> 16U */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 8S -> 16S */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 8S -> 32S */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 8S -> 32F */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 8S -> 64F */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo}
},
{
/* 16U -> 8U */ {NppCvt<CV_16U, CV_8U , nppiConvert_16u8u_C1R >::call, cv::gpu::convertTo, cv::gpu::convertTo, NppCvt<CV_16U, CV_8U, nppiConvert_16u8u_C4R>::call},
/* 16U -> 8S */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
/* 16U -> 16U */ {0,0,0,0},
/* 16U -> 16S */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
/* 16U -> 32S */ {NppCvt<CV_16U, CV_32S, nppiConvert_16u32s_C1R>::call, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
/* 16U -> 32F */ {NppCvt<CV_16U, CV_32F, nppiConvert_16u32f_C1R>::call, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
/* 16U -> 64F */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo }
},
{
/* 16S -> 8U */ {NppCvt<CV_16S, CV_8U , nppiConvert_16s8u_C1R >::call, cv::gpu::convertTo, cv::gpu::convertTo, NppCvt<CV_16S, CV_8U, nppiConvert_16s8u_C4R>::call},
/* 16S -> 8S */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
/* 16S -> 16U */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
/* 16S -> 16S */ {0,0,0,0},
/* 16S -> 32S */ {NppCvt<CV_16S, CV_32S, nppiConvert_16s32s_C1R>::call, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
/* 16S -> 32F */ {NppCvt<CV_16S, CV_32F, nppiConvert_16s32f_C1R>::call, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
/* 16S -> 64F */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo }
},
{
/* 32S -> 8U */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 32S -> 8S */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 32S -> 16U */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 32S -> 16S */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 32S -> 32S */ {0,0,0,0},
/* 32S -> 32F */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 32S -> 64F */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo}
},
{
/* 32F -> 8U */ {NppCvt<CV_32F, CV_8U , nppiConvert_32f8u_C1R >::call, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 32F -> 8S */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 32F -> 16U */ {NppCvt<CV_32F, CV_16U, nppiConvert_32f16u_C1R>::call, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 32F -> 16S */ {NppCvt<CV_32F, CV_16S, nppiConvert_32f16s_C1R>::call, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 32F -> 32S */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 32F -> 32F */ {0,0,0,0},
/* 32F -> 64F */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo}
},
{
/* 64F -> 8U */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 64F -> 8S */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 64F -> 16U */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 64F -> 16S */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 64F -> 32S */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 64F -> 32F */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
/* 64F -> 64F */ {0,0,0,0}
}
};
CV_Assert(src.depth() <= CV_64F && src.channels() <= 4);
CV_Assert(dst.depth() <= CV_64F);
CV_Assert(src.size() == dst.size() && src.channels() == dst.channels());
if (src.depth() == CV_64F || dst.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
bool aligned = isAligned(src.data, 16) && isAligned(dst.data, 16);
if (!aligned)
{
cv::gpu::convertTo(src, dst);
return;
}
const func_t func = funcs[src.depth()][dst.depth()][src.channels() - 1];
CV_DbgAssert(func != 0);
func(src, dst);
}
void convert(const GpuMat& src, GpuMat& dst, double alpha, double beta) const
{
CV_Assert(src.depth() <= CV_64F && src.channels() <= 4);
CV_Assert(dst.depth() <= CV_64F);
if (src.depth() == CV_64F || dst.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
cv::gpu::convertTo(src, dst, alpha, beta);
}
void setTo(GpuMat& m, Scalar s, const GpuMat& mask) const
{
if (mask.empty())
{
if (s[0] == 0.0 && s[1] == 0.0 && s[2] == 0.0 && s[3] == 0.0)
{
cudaSafeCall( cudaMemset2D(m.data, m.step, 0, m.cols * m.elemSize(), m.rows) );
return;
}
if (m.depth() == CV_8U)
{
int cn = m.channels();
if (cn == 1 || (cn == 2 && s[0] == s[1]) || (cn == 3 && s[0] == s[1] && s[0] == s[2]) || (cn == 4 && s[0] == s[1] && s[0] == s[2] && s[0] == s[3]))
{
int val = saturate_cast<uchar>(s[0]);
cudaSafeCall( cudaMemset2D(m.data, m.step, val, m.cols * m.elemSize(), m.rows) );
return;
}
}
typedef void (*func_t)(GpuMat& src, Scalar s);
static const func_t funcs[7][4] =
{
{NppSet<CV_8U , 1, nppiSet_8u_C1R >::call, cv::gpu::setTo , cv::gpu::setTo , NppSet<CV_8U , 4, nppiSet_8u_C4R >::call},
{NppSet<CV_8S , 1, nppiSet_8s_C1R >::call, NppSet<CV_8S , 2, nppiSet_8s_C2R >::call, NppSet<CV_8S, 3, nppiSet_8s_C3R>::call, NppSet<CV_8S , 4, nppiSet_8s_C4R >::call},
{NppSet<CV_16U, 1, nppiSet_16u_C1R>::call, NppSet<CV_16U, 2, nppiSet_16u_C2R>::call, cv::gpu::setTo , NppSet<CV_16U, 4, nppiSet_16u_C4R>::call},
{NppSet<CV_16S, 1, nppiSet_16s_C1R>::call, NppSet<CV_16S, 2, nppiSet_16s_C2R>::call, cv::gpu::setTo , NppSet<CV_16S, 4, nppiSet_16s_C4R>::call},
{NppSet<CV_32S, 1, nppiSet_32s_C1R>::call, cv::gpu::setTo , cv::gpu::setTo , NppSet<CV_32S, 4, nppiSet_32s_C4R>::call},
{NppSet<CV_32F, 1, nppiSet_32f_C1R>::call, cv::gpu::setTo , cv::gpu::setTo , NppSet<CV_32F, 4, nppiSet_32f_C4R>::call},
{cv::gpu::setTo , cv::gpu::setTo , cv::gpu::setTo , cv::gpu::setTo }
};
CV_Assert(m.depth() <= CV_64F && m.channels() <= 4);
if (m.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
funcs[m.depth()][m.channels() - 1](m, s);
}
else
{
typedef void (*func_t)(GpuMat& src, Scalar s, const GpuMat& mask);
static const func_t funcs[7][4] =
{
{NppSetMask<CV_8U , 1, nppiSet_8u_C1MR >::call, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_8U , 4, nppiSet_8u_C4MR >::call},
{cv::gpu::setTo , cv::gpu::setTo, cv::gpu::setTo, cv::gpu::setTo },
{NppSetMask<CV_16U, 1, nppiSet_16u_C1MR>::call, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_16U, 4, nppiSet_16u_C4MR>::call},
{NppSetMask<CV_16S, 1, nppiSet_16s_C1MR>::call, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_16S, 4, nppiSet_16s_C4MR>::call},
{NppSetMask<CV_32S, 1, nppiSet_32s_C1MR>::call, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_32S, 4, nppiSet_32s_C4MR>::call},
{NppSetMask<CV_32F, 1, nppiSet_32f_C1MR>::call, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_32F, 4, nppiSet_32f_C4MR>::call},
{cv::gpu::setTo , cv::gpu::setTo, cv::gpu::setTo, cv::gpu::setTo }
};
CV_Assert(m.depth() <= CV_64F && m.channels() <= 4);
if (m.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
funcs[m.depth()][m.channels() - 1](m, s, mask);
}
}
void mallocPitch(void** devPtr, size_t* step, size_t width, size_t height) const
{
cudaSafeCall( cudaMallocPitch(devPtr, step, width, height) );
}
void free(void* devPtr) const
{
cudaFree(devPtr);
}
};
const GpuFuncTable* gpuFuncTable()
{
static CudaFuncTable funcTable;
return &funcTable;
}
}
#endif // HAVE_CUDA
void cv::gpu::GpuMat::upload(const Mat& m)
{
CV_DbgAssert(!m.empty());
create(m.size(), m.type());
gpuFuncTable()->copy(m, *this);
}
void cv::gpu::GpuMat::download(Mat& m) const
{
CV_DbgAssert(!empty());
m.create(size(), type());
gpuFuncTable()->copy(*this, m);
}
void cv::gpu::GpuMat::copyTo(GpuMat& m) const
{
CV_DbgAssert(!empty());
m.create(size(), type());
gpuFuncTable()->copy(*this, m);
}
void cv::gpu::GpuMat::copyTo(GpuMat& mat, const GpuMat& mask) const
{
if (mask.empty())
copyTo(mat);
else
{
mat.create(size(), type());
gpuFuncTable()->copyWithMask(*this, mat, mask);
}
}
void cv::gpu::GpuMat::convertTo(GpuMat& dst, int rtype, double alpha, double beta) const
{
bool noScale = fabs(alpha - 1) < std::numeric_limits<double>::epsilon() && fabs(beta) < std::numeric_limits<double>::epsilon();
if (rtype < 0)
rtype = type();
else
rtype = CV_MAKETYPE(CV_MAT_DEPTH(rtype), channels());
int sdepth = depth();
int ddepth = CV_MAT_DEPTH(rtype);
if (sdepth == ddepth && noScale)
{
copyTo(dst);
return;
}
GpuMat temp;
const GpuMat* psrc = this;
if (sdepth != ddepth && psrc == &dst)
{
temp = *this;
psrc = &temp;
}
dst.create(size(), rtype);
if (noScale)
gpuFuncTable()->convert(*psrc, dst);
else
gpuFuncTable()->convert(*psrc, dst, alpha, beta);
}
GpuMat& cv::gpu::GpuMat::setTo(Scalar s, const GpuMat& mask)
{
CV_Assert(mask.empty() || mask.type() == CV_8UC1);
CV_DbgAssert(!empty());
gpuFuncTable()->setTo(*this, s, mask);
return *this;
}
void cv::gpu::GpuMat::create(int _rows, int _cols, int _type)
{
_type &= Mat::TYPE_MASK;
if (rows == _rows && cols == _cols && type() == _type && data)
return;
if (data)
release();
CV_DbgAssert(_rows >= 0 && _cols >= 0);
if (_rows > 0 && _cols > 0)
{
flags = Mat::MAGIC_VAL + _type;
rows = _rows;
cols = _cols;
size_t esz = elemSize();
void* devPtr;
gpuFuncTable()->mallocPitch(&devPtr, &step, esz * cols, rows);
// Single row must be continuous
if (rows == 1)
step = esz * cols;
if (esz * cols == step)
flags |= Mat::CONTINUOUS_FLAG;
int64 _nettosize = static_cast<int64>(step) * rows;
size_t nettosize = static_cast<size_t>(_nettosize);
datastart = data = static_cast<uchar*>(devPtr);
dataend = data + nettosize;
refcount = static_cast<int*>(fastMalloc(sizeof(*refcount)));
*refcount = 1;
}
}
void cv::gpu::GpuMat::release()
{
if (refcount && CV_XADD(refcount, -1) == 1)
{
fastFree(refcount);
gpuFuncTable()->free(datastart);
}
data = datastart = dataend = 0;
step = rows = cols = 0;
refcount = 0;
}
////////////////////////////////////////////////////////////////////////
// Error handling
......
modules/core/src/gpu_mat.cpp 0 → 100644
View file @ db1178b5
/*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);
}
modules/core/src/gpu_stream.cpp
View file @ db1178b5
......@@ -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
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment