generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

optimized gpu pyrDown and pyrUp

Browse Source
This commit is contained in:
Vladislav Vinogradov
2011-08-24 11:16:42 +00:00
parent 3a3bc18381
commit 173ac5a64b
8 changed files with 714 additions and 160 deletions
Download Patch File Download Diff File Expand all files Collapse all files

86
modules/gpu/src/imgproc.cpp
View File

@@ -93,11 +93,7 @@ void cv::gpu::convolve(const GpuMat&, const GpuMat&, GpuMat&, bool, ConvolveBuf&
void cv::gpu::downsample(const GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::upsample(const GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::pyrDown(const GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::PyrDownBuf::create(Size, int) { throw_nogpu(); }
void cv::gpu::pyrDown(const GpuMat&, GpuMat&, PyrDownBuf&, Stream&) { throw_nogpu(); }
void cv::gpu::pyrUp(const GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::PyrUpBuf::create(Size, int) { throw_nogpu(); }
void cv::gpu::pyrUp(const GpuMat&, GpuMat&, PyrUpBuf&, Stream&) { throw_nogpu(); }
void cv::gpu::Canny(const GpuMat&, GpuMat&, double, double, int, bool) { throw_nogpu(); }
void cv::gpu::Canny(const GpuMat&, CannyBuf&, GpuMat&, double, double, int, bool) { throw_nogpu(); }
void cv::gpu::Canny(const GpuMat&, const GpuMat&, GpuMat&, double, double, bool) { throw_nogpu(); }
@@ -1598,66 +1594,64 @@ void cv::gpu::upsample(const GpuMat& src, GpuMat& dst, Stream& stream)
//////////////////////////////////////////////////////////////////////////////
// pyrDown
namespace cv { namespace gpu { namespace imgproc
{
template <typename T, int cn> void pyrDown_gpu(const DevMem2D& src, const DevMem2D& dst, cudaStream_t stream);
}}}
void cv::gpu::pyrDown(const GpuMat& src, GpuMat& dst, Stream& stream)
{
PyrDownBuf buf;
pyrDown(src, dst, buf, stream);
}
using namespace cv::gpu::imgproc;
cv::Mat cv::gpu::PyrDownBuf::ker;
typedef void (*func_t)(const DevMem2D& src, const DevMem2D& dst, cudaStream_t stream);
void cv::gpu::PyrDownBuf::create(Size image_size, int image_type_)
{
if (ker.empty() || image_type_ != image_type)
ker = getGaussianKernel(5, 0, std::max(CV_32F, CV_MAT_DEPTH(image_type_)));
ensureSizeIsEnough(image_size.height, image_size.width, image_type_, buf);
if (filter.empty() || image_type_ != image_type)
static const func_t funcs[6][4] =
{
image_type = image_type_;
filter = createSeparableLinearFilter_GPU(image_type, image_type, ker, ker);
}
}
{pyrDown_gpu<uchar, 1>, pyrDown_gpu<uchar, 2>, pyrDown_gpu<uchar, 3>, pyrDown_gpu<uchar, 4>},
{pyrDown_gpu<schar, 1>, pyrDown_gpu<schar, 2>, pyrDown_gpu<schar, 3>, pyrDown_gpu<schar, 4>},
{pyrDown_gpu<ushort, 1>, pyrDown_gpu<ushort, 2>, pyrDown_gpu<ushort, 3>, pyrDown_gpu<ushort, 4>},
{pyrDown_gpu<short, 1>, pyrDown_gpu<short, 2>, pyrDown_gpu<short, 3>, pyrDown_gpu<short, 4>},
{pyrDown_gpu<int, 1>, pyrDown_gpu<int, 2>, pyrDown_gpu<int, 3>, pyrDown_gpu<int, 4>},
{pyrDown_gpu<float, 1>, pyrDown_gpu<float, 2>, pyrDown_gpu<float, 3>, pyrDown_gpu<float, 4>},
};
void cv::gpu::pyrDown(const GpuMat& src, GpuMat& dst, PyrDownBuf& buf, Stream& stream)
{
buf.create(src.size(), src.type());
buf.filter->apply(src, buf.buf, Rect(0, 0, src.cols, src.rows), stream);
downsample(buf.buf, dst, stream);
CV_Assert(src.depth() <= CV_32F && src.channels() <= 4);
dst.create((src.rows + 1) / 2, (src.cols + 1) / 2, src.type());
funcs[src.depth()][src.channels() - 1](src, dst, StreamAccessor::getStream(stream));
}
//////////////////////////////////////////////////////////////////////////////
// pyrUp
namespace cv { namespace gpu { namespace imgproc
{
template <typename T, int cn> void pyrUp_gpu(const DevMem2D& src, const DevMem2D& dst, cudaStream_t stream);
}}}
void cv::gpu::pyrUp(const GpuMat& src, GpuMat& dst, Stream& stream)
{
PyrUpBuf buf;
pyrUp(src, dst, buf, stream);
}
using namespace cv::gpu::imgproc;
cv::Mat cv::gpu::PyrUpBuf::ker;
typedef void (*func_t)(const DevMem2D& src, const DevMem2D& dst, cudaStream_t stream);
void cv::gpu::PyrUpBuf::create(Size image_size, int image_type_)
{
if (ker.empty() || image_type_ != image_type)
ker = getGaussianKernel(5, 0, std::max(CV_32F, CV_MAT_DEPTH(image_type_))) * 2;
ensureSizeIsEnough(image_size.height * 2, image_size.width * 2, image_type_, buf);
if (filter.empty() || image_type_ != image_type)
static const func_t funcs[6][4] =
{
image_type = image_type_;
filter = createSeparableLinearFilter_GPU(image_type, image_type, ker, ker);
}
}
{pyrUp_gpu<uchar, 1>, pyrUp_gpu<uchar, 2>, pyrUp_gpu<uchar, 3>, pyrUp_gpu<uchar, 4>},
{pyrUp_gpu<schar, 1>, pyrUp_gpu<schar, 2>, pyrUp_gpu<schar, 3>, pyrUp_gpu<schar, 4>},
{pyrUp_gpu<ushort, 1>, pyrUp_gpu<ushort, 2>, pyrUp_gpu<ushort, 3>, pyrUp_gpu<ushort, 4>},
{pyrUp_gpu<short, 1>, pyrUp_gpu<short, 2>, pyrUp_gpu<short, 3>, pyrUp_gpu<short, 4>},
{pyrUp_gpu<int, 1>, pyrUp_gpu<int, 2>, pyrUp_gpu<int, 3>, pyrUp_gpu<int, 4>},
{pyrUp_gpu<float, 1>, pyrUp_gpu<float, 2>, pyrUp_gpu<float, 3>, pyrUp_gpu<float, 4>},
};
void cv::gpu::pyrUp(const GpuMat& src, GpuMat& dst, PyrUpBuf& buf, Stream& stream)
{
buf.create(src.size(), src.type());
upsample(src, buf.buf, stream);
buf.filter->apply(buf.buf, dst, Rect(0, 0, buf.buf.cols, buf.buf.rows), stream);
CV_Assert(src.depth() <= CV_32F && src.channels() <= 4);
dst.create(src.rows*2, src.cols*2, src.type());
funcs[src.depth()][src.channels() - 1](src, dst, StreamAccessor::getStream(stream));
}