mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2025-01-19 15:04:01 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

refactored 1D Sum Filters

Browse Source
This commit is contained in:
Vladislav Vinogradov 2013-04-29 17:09:17 +04:00
parent 4bb297afc2
commit 4317cd1ffa
2 changed files with 106 additions and 207 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

91
modules/gpufilters/include/opencv2/gpufilters.hpp
View File

@ -254,95 +254,16 @@ CV_EXPORTS Ptr<Filter> createBoxMinFilter(int srcType, Size ksize,
Point anchor = Point(-1, -1),
int borderMode = BORDER_DEFAULT, Scalar borderVal = Scalar::all(0));
////////////////////////////////////////////////////////////////////////////////////////////////////
// 1D Sum Filter
/*!
The Base Class for 1D or Row-wise Filters
This is the base class for linear or non-linear filters that process 1D data.
In particular, such filters are used for the "horizontal" filtering parts in separable filters.
*/
class CV_EXPORTS BaseRowFilter_GPU
{
public:
BaseRowFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {}
virtual ~BaseRowFilter_GPU() {}
virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
int ksize, anchor;
};
/*!
The Base Class for Column-wise Filters
This is the base class for linear or non-linear filters that process columns of 2D arrays.
Such filters are used for the "vertical" filtering parts in separable filters.
*/
class CV_EXPORTS BaseColumnFilter_GPU
{
public:
BaseColumnFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {}
virtual ~BaseColumnFilter_GPU() {}
virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
int ksize, anchor;
};
/*!
The Base Class for Non-Separable 2D Filters.
This is the base class for linear or non-linear 2D filters.
*/
class CV_EXPORTS BaseFilter_GPU
{
public:
BaseFilter_GPU(const Size& ksize_, const Point& anchor_) : ksize(ksize_), anchor(anchor_) {}
virtual ~BaseFilter_GPU() {}
virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
Size ksize;
Point anchor;
};
/*!
The Base Class for Filter Engine.
The class can be used to apply an arbitrary filtering operation to an image.
It contains all the necessary intermediate buffers.
*/
class CV_EXPORTS FilterEngine_GPU
{
public:
virtual ~FilterEngine_GPU() {}
virtual void apply(const GpuMat& src, GpuMat& dst, Rect roi = Rect(0,0,-1,-1), Stream& stream = Stream::Null()) = 0;
};
//! returns horizontal 1D box filter
//! creates a horizontal 1D box filter
//! supports only CV_8UC1 source type and CV_32FC1 sum type
CV_EXPORTS Ptr<BaseRowFilter_GPU> getRowSumFilter_GPU(int srcType, int sumType, int ksize, int anchor = -1);
CV_EXPORTS Ptr<Filter> createRowSumFilter(int srcType, int dstType, int ksize, int anchor = -1, int borderMode = BORDER_DEFAULT, Scalar borderVal = Scalar::all(0));
//! returns vertical 1D box filter
//! creates a vertical 1D box filter
//! supports only CV_8UC1 sum type and CV_32FC1 dst type
CV_EXPORTS Ptr<BaseColumnFilter_GPU> getColumnSumFilter_GPU(int sumType, int dstType, int ksize, int anchor = -1);
CV_EXPORTS Ptr<Filter> createColumnSumFilter(int srcType, int dstType, int ksize, int anchor = -1, int borderMode = BORDER_DEFAULT, Scalar borderVal = Scalar::all(0));
}} // namespace cv { namespace gpu {

222
modules/gpufilters/src/filtering.cpp
View File

@ -66,14 +66,8 @@ Ptr<Filter> cv::gpu::createMorphologyFilter(int, int, InputArray, Point, int) {
Ptr<Filter> cv::gpu::createBoxMaxFilter(int, Size, Point, int, Scalar) { throw_no_cuda(); return Ptr<Filter>(); }
Ptr<Filter> cv::gpu::createBoxMinFilter(int, Size, Point, int, Scalar) { throw_no_cuda(); return Ptr<Filter>(); }
Ptr<BaseRowFilter_GPU> cv::gpu::getRowSumFilter_GPU(int, int, int, int) { throw_no_cuda(); return Ptr<BaseRowFilter_GPU>(0); }
Ptr<BaseColumnFilter_GPU> cv::gpu::getColumnSumFilter_GPU(int, int, int, int) { throw_no_cuda(); return Ptr<BaseColumnFilter_GPU>(0); }
Ptr<Filter> cv::gpu::createRowSumFilter(int, int, int, int, int, Scalar) { throw_no_cuda(); return Ptr<Filter>(); }
Ptr<Filter> cv::gpu::createColumnSumFilter(int, int, int, int, int, Scalar) { throw_no_cuda(); return Ptr<Filter>(); }
#else
@ -876,145 +870,129 @@ Ptr<Filter> cv::gpu::createBoxMinFilter(int srcType, Size ksize, Point anchor, i
return new NPPRankFilter(RANK_MIN, srcType, ksize, anchor, borderMode, borderVal);
}
namespace
{
inline void normalizeROI(Rect& roi, const Size& ksize, const Point& anchor, const Size& src_size)
{
if (roi == Rect(0,0,-1,-1))
roi = Rect(anchor.x, anchor.y, src_size.width - ksize.width, src_size.height - ksize.height);
CV_Assert(roi.x >= 0 && roi.y >= 0 && roi.width <= src_size.width && roi.height <= src_size.height);
}
inline void normalizeKernel(const Mat& kernel, GpuMat& gpu_krnl, int type = CV_8U, int* nDivisor = 0, bool reverse = false)
{
int scale = nDivisor && (kernel.depth() == CV_32F || kernel.depth() == CV_64F) ? 256 : 1;
if (nDivisor) *nDivisor = scale;
Mat temp(kernel.size(), type);
kernel.convertTo(temp, type, scale);
Mat cont_krnl = temp.reshape(1, 1);
if (reverse)
{
int count = cont_krnl.cols >> 1;
for (int i = 0; i < count; ++i)
{
std::swap(cont_krnl.at<int>(0, i), cont_krnl.at<int>(0, cont_krnl.cols - 1 - i));
}
}
gpu_krnl.upload(cont_krnl);
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////
// 1D Sum Filter
namespace
{
struct NppRowSumFilter : public BaseRowFilter_GPU
class NppRowSumFilter : public Filter
{
NppRowSumFilter(int ksize_, int anchor_) : BaseRowFilter_GPU(ksize_, anchor_) {}
public:
NppRowSumFilter(int srcType, int dstType, int ksize, int anchor, int borderMode, Scalar borderVal);
virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& s = Stream::Null())
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
void apply(InputArray src, OutputArray dst, Stream& stream = Stream::Null());
cudaStream_t stream = StreamAccessor::getStream(s);
private:
int srcType_, dstType_;
int ksize_;
int anchor_;
int borderMode_;
Scalar borderVal_;
NppStreamHandler h(stream);
nppSafeCall( nppiSumWindowRow_8u32f_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz, ksize, anchor) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
GpuMat srcBorder_;
};
NppRowSumFilter::NppRowSumFilter(int srcType, int dstType, int ksize, int anchor, int borderMode, Scalar borderVal) :
srcType_(srcType), dstType_(dstType), ksize_(ksize), anchor_(anchor), borderMode_(borderMode), borderVal_(borderVal)
{
CV_Assert( srcType_ == CV_8UC1 );
CV_Assert( dstType_ == CV_32FC1 );
normalizeAnchor(anchor_, ksize_);
}
void NppRowSumFilter::apply(InputArray _src, OutputArray _dst, Stream& _stream)
{
GpuMat src = _src.getGpuMat();
CV_Assert( src.type() == srcType_ );
gpu::copyMakeBorder(src, srcBorder_, 0, 0, ksize_, ksize_, borderMode_, borderVal_, _stream);
_dst.create(src.size(), dstType_);
GpuMat dst = _dst.getGpuMat();
GpuMat srcRoi = srcBorder_(Rect(ksize_, 0, src.cols, src.rows));
cudaStream_t stream = StreamAccessor::getStream(_stream);
NppStreamHandler h(stream);
NppiSize oSizeROI;
oSizeROI.width = src.cols;
oSizeROI.height = src.rows;
nppSafeCall( nppiSumWindowRow_8u32f_C1R(srcRoi.ptr<Npp8u>(), static_cast<int>(srcRoi.step),
dst.ptr<Npp32f>(), static_cast<int>(dst.step),
oSizeROI, ksize_, anchor_) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
}
Ptr<BaseRowFilter_GPU> cv::gpu::getRowSumFilter_GPU(int srcType, int sumType, int ksize, int anchor)
Ptr<Filter> cv::gpu::createRowSumFilter(int srcType, int dstType, int ksize, int anchor, int borderMode, Scalar borderVal)
{
CV_Assert(srcType == CV_8UC1 && sumType == CV_32FC1);
normalizeAnchor(anchor, ksize);
return Ptr<BaseRowFilter_GPU>(new NppRowSumFilter(ksize, anchor));
return new NppRowSumFilter(srcType, dstType, ksize, anchor, borderMode, borderVal);
}
namespace
{
struct NppColumnSumFilter : public BaseColumnFilter_GPU
class NppColumnSumFilter : public Filter
{
NppColumnSumFilter(int ksize_, int anchor_) : BaseColumnFilter_GPU(ksize_, anchor_) {}
public:
NppColumnSumFilter(int srcType, int dstType, int ksize, int anchor, int borderMode, Scalar borderVal);
virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& s = Stream::Null())
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
void apply(InputArray src, OutputArray dst, Stream& stream = Stream::Null());
cudaStream_t stream = StreamAccessor::getStream(s);
private:
int srcType_, dstType_;
int ksize_;
int anchor_;
int borderMode_;
Scalar borderVal_;
NppStreamHandler h(stream);
nppSafeCall( nppiSumWindowColumn_8u32f_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz, ksize, anchor) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
GpuMat srcBorder_;
};
NppColumnSumFilter::NppColumnSumFilter(int srcType, int dstType, int ksize, int anchor, int borderMode, Scalar borderVal) :
srcType_(srcType), dstType_(dstType), ksize_(ksize), anchor_(anchor), borderMode_(borderMode), borderVal_(borderVal)
{
CV_Assert( srcType_ == CV_8UC1 );
CV_Assert( dstType_ == CV_32FC1 );
normalizeAnchor(anchor_, ksize_);
}
void NppColumnSumFilter::apply(InputArray _src, OutputArray _dst, Stream& _stream)
{
GpuMat src = _src.getGpuMat();
CV_Assert( src.type() == srcType_ );
gpu::copyMakeBorder(src, srcBorder_, ksize_, ksize_, 0, 0, borderMode_, borderVal_, _stream);
_dst.create(src.size(), dstType_);
GpuMat dst = _dst.getGpuMat();
GpuMat srcRoi = srcBorder_(Rect(0, ksize_, src.cols, src.rows));
cudaStream_t stream = StreamAccessor::getStream(_stream);
NppStreamHandler h(stream);
NppiSize oSizeROI;
oSizeROI.width = src.cols;
oSizeROI.height = src.rows;
nppSafeCall( nppiSumWindowColumn_8u32f_C1R(srcRoi.ptr<Npp8u>(), static_cast<int>(srcRoi.step),
dst.ptr<Npp32f>(), static_cast<int>(dst.step),
oSizeROI, ksize_, anchor_) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
}
Ptr<BaseColumnFilter_GPU> cv::gpu::getColumnSumFilter_GPU(int sumType, int dstType, int ksize, int anchor)
Ptr<Filter> cv::gpu::createColumnSumFilter(int srcType, int dstType, int ksize, int anchor, int borderMode, Scalar borderVal)
{
CV_Assert(sumType == CV_8UC1 && dstType == CV_32FC1);
normalizeAnchor(anchor, ksize);
return Ptr<BaseColumnFilter_GPU>(new NppColumnSumFilter(ksize, anchor));
return new NppColumnSumFilter(srcType, dstType, ksize, anchor, borderMode, borderVal);
}
#endif