239 lines
9.7 KiB
C++
239 lines
9.7 KiB
C++
/*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.
|
|
// 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 GpuMaterials 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 bpied warranties, including, but not limited to, the bpied
|
|
// 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;
|
|
using namespace std;
|
|
|
|
#if !defined (HAVE_CUDA)
|
|
|
|
void cv::gpu::BroxOpticalFlow::operator ()(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
|
|
void cv::gpu::interpolateFrames(const GpuMat&, const GpuMat&, const GpuMat&, const GpuMat&, const GpuMat&, const GpuMat&, float, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
|
|
void cv::gpu::createOpticalFlowNeedleMap(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&) { throw_nogpu(); }
|
|
|
|
#else
|
|
|
|
namespace
|
|
{
|
|
size_t getBufSize(const NCVBroxOpticalFlowDescriptor& desc, const NCVMatrix<Ncv32f>& frame0, const NCVMatrix<Ncv32f>& frame1,
|
|
NCVMatrix<Ncv32f>& u, NCVMatrix<Ncv32f>& v, const cudaDeviceProp& devProp)
|
|
{
|
|
NCVMemStackAllocator gpuCounter(static_cast<Ncv32u>(devProp.textureAlignment));
|
|
|
|
ncvSafeCall( NCVBroxOpticalFlow(desc, gpuCounter, frame0, frame1, u, v, 0) );
|
|
|
|
return gpuCounter.maxSize();
|
|
}
|
|
}
|
|
|
|
namespace
|
|
{
|
|
static void outputHandler(const std::string &msg) { CV_Error(CV_GpuApiCallError, msg.c_str()); }
|
|
}
|
|
|
|
void cv::gpu::BroxOpticalFlow::operator ()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& u, GpuMat& v, Stream& s)
|
|
{
|
|
ncvSetDebugOutputHandler(outputHandler);
|
|
|
|
CV_Assert(frame0.type() == CV_32FC1);
|
|
CV_Assert(frame1.size() == frame0.size() && frame1.type() == frame0.type());
|
|
|
|
u.create(frame0.size(), CV_32FC1);
|
|
v.create(frame0.size(), CV_32FC1);
|
|
|
|
cudaDeviceProp devProp;
|
|
cudaSafeCall( cudaGetDeviceProperties(&devProp, getDevice()) );
|
|
|
|
NCVBroxOpticalFlowDescriptor desc;
|
|
|
|
desc.alpha = alpha;
|
|
desc.gamma = gamma;
|
|
desc.scale_factor = scale_factor;
|
|
desc.number_of_inner_iterations = inner_iterations;
|
|
desc.number_of_outer_iterations = outer_iterations;
|
|
desc.number_of_solver_iterations = solver_iterations;
|
|
|
|
NCVMemSegment frame0MemSeg;
|
|
frame0MemSeg.begin.memtype = NCVMemoryTypeDevice;
|
|
frame0MemSeg.begin.ptr = const_cast<uchar*>(frame0.data);
|
|
frame0MemSeg.size = frame0.step * frame0.rows;
|
|
|
|
NCVMemSegment frame1MemSeg;
|
|
frame1MemSeg.begin.memtype = NCVMemoryTypeDevice;
|
|
frame1MemSeg.begin.ptr = const_cast<uchar*>(frame1.data);
|
|
frame1MemSeg.size = frame1.step * frame1.rows;
|
|
|
|
NCVMemSegment uMemSeg;
|
|
uMemSeg.begin.memtype = NCVMemoryTypeDevice;
|
|
uMemSeg.begin.ptr = u.ptr();
|
|
uMemSeg.size = u.step * u.rows;
|
|
|
|
NCVMemSegment vMemSeg;
|
|
vMemSeg.begin.memtype = NCVMemoryTypeDevice;
|
|
vMemSeg.begin.ptr = v.ptr();
|
|
vMemSeg.size = v.step * v.rows;
|
|
|
|
NCVMatrixReuse<Ncv32f> frame0Mat(frame0MemSeg, devProp.textureAlignment, frame0.cols, frame0.rows, frame0.step);
|
|
NCVMatrixReuse<Ncv32f> frame1Mat(frame1MemSeg, devProp.textureAlignment, frame1.cols, frame1.rows, frame1.step);
|
|
NCVMatrixReuse<Ncv32f> uMat(uMemSeg, devProp.textureAlignment, u.cols, u.rows, u.step);
|
|
NCVMatrixReuse<Ncv32f> vMat(vMemSeg, devProp.textureAlignment, v.cols, v.rows, v.step);
|
|
|
|
cudaStream_t stream = StreamAccessor::getStream(s);
|
|
|
|
size_t bufSize = getBufSize(desc, frame0Mat, frame1Mat, uMat, vMat, devProp);
|
|
|
|
ensureSizeIsEnough(1, bufSize, CV_8UC1, buf);
|
|
|
|
NCVMemStackAllocator gpuAllocator(NCVMemoryTypeDevice, bufSize, static_cast<Ncv32u>(devProp.textureAlignment), buf.ptr());
|
|
|
|
ncvSafeCall( NCVBroxOpticalFlow(desc, gpuAllocator, frame0Mat, frame1Mat, uMat, vMat, stream) );
|
|
}
|
|
|
|
void cv::gpu::interpolateFrames(const GpuMat& frame0, const GpuMat& frame1, const GpuMat& fu, const GpuMat& fv, const GpuMat& bu, const GpuMat& bv,
|
|
float pos, GpuMat& newFrame, GpuMat& buf, Stream& s)
|
|
{
|
|
CV_Assert(frame0.type() == CV_32FC1);
|
|
CV_Assert(frame1.size() == frame0.size() && frame1.type() == frame0.type());
|
|
CV_Assert(fu.size() == frame0.size() && fu.type() == frame0.type());
|
|
CV_Assert(fv.size() == frame0.size() && fv.type() == frame0.type());
|
|
CV_Assert(bu.size() == frame0.size() && bu.type() == frame0.type());
|
|
CV_Assert(bv.size() == frame0.size() && bv.type() == frame0.type());
|
|
|
|
newFrame.create(frame0.size(), frame0.type());
|
|
|
|
buf.create(6 * frame0.rows, frame0.cols, CV_32FC1);
|
|
buf.setTo(Scalar::all(0));
|
|
|
|
// occlusion masks
|
|
GpuMat occ0 = buf.rowRange(0 * frame0.rows, 1 * frame0.rows);
|
|
GpuMat occ1 = buf.rowRange(1 * frame0.rows, 2 * frame0.rows);
|
|
|
|
// interpolated forward flow
|
|
GpuMat fui = buf.rowRange(2 * frame0.rows, 3 * frame0.rows);
|
|
GpuMat fvi = buf.rowRange(3 * frame0.rows, 4 * frame0.rows);
|
|
|
|
// interpolated backward flow
|
|
GpuMat bui = buf.rowRange(4 * frame0.rows, 5 * frame0.rows);
|
|
GpuMat bvi = buf.rowRange(5 * frame0.rows, 6 * frame0.rows);
|
|
|
|
size_t step = frame0.step;
|
|
|
|
CV_Assert(frame1.step == step && fu.step == step && fv.step == step && bu.step == step && bv.step == step && newFrame.step == step && buf.step == step);
|
|
|
|
cudaStream_t stream = StreamAccessor::getStream(s);
|
|
NppStStreamHandler h(stream);
|
|
|
|
NppStInterpolationState state;
|
|
|
|
state.size = NcvSize32u(frame0.cols, frame0.rows);
|
|
state.nStep = static_cast<Ncv32u>(step);
|
|
state.pSrcFrame0 = const_cast<Ncv32f*>(frame0.ptr<Ncv32f>());
|
|
state.pSrcFrame1 = const_cast<Ncv32f*>(frame1.ptr<Ncv32f>());
|
|
state.pFU = const_cast<Ncv32f*>(fu.ptr<Ncv32f>());
|
|
state.pFV = const_cast<Ncv32f*>(fv.ptr<Ncv32f>());
|
|
state.pBU = const_cast<Ncv32f*>(bu.ptr<Ncv32f>());
|
|
state.pBV = const_cast<Ncv32f*>(bv.ptr<Ncv32f>());
|
|
state.pos = pos;
|
|
state.pNewFrame = newFrame.ptr<Ncv32f>();
|
|
state.ppBuffers[0] = occ0.ptr<Ncv32f>();
|
|
state.ppBuffers[1] = occ1.ptr<Ncv32f>();
|
|
state.ppBuffers[2] = fui.ptr<Ncv32f>();
|
|
state.ppBuffers[3] = fvi.ptr<Ncv32f>();
|
|
state.ppBuffers[4] = bui.ptr<Ncv32f>();
|
|
state.ppBuffers[5] = bvi.ptr<Ncv32f>();
|
|
|
|
ncvSafeCall( nppiStInterpolateFrames(&state) );
|
|
|
|
if (stream == 0)
|
|
cudaSafeCall( cudaDeviceSynchronize() );
|
|
}
|
|
|
|
namespace cv { namespace gpu { namespace device
|
|
{
|
|
namespace optical_flow
|
|
{
|
|
void NeedleMapAverage_gpu(DevMem2Df u, DevMem2Df v, DevMem2Df u_avg, DevMem2Df v_avg);
|
|
void CreateOpticalFlowNeedleMap_gpu(DevMem2Df u_avg, DevMem2Df v_avg, float* vertex_buffer, float* color_data, float max_flow, float xscale, float yscale);
|
|
}
|
|
}}}
|
|
|
|
void cv::gpu::createOpticalFlowNeedleMap(const GpuMat& u, const GpuMat& v, GpuMat& vertex, GpuMat& colors)
|
|
{
|
|
using namespace cv::gpu::device::optical_flow;
|
|
|
|
CV_Assert(u.type() == CV_32FC1);
|
|
CV_Assert(v.type() == u.type() && v.size() == u.size());
|
|
|
|
const int NEEDLE_MAP_SCALE = 16;
|
|
|
|
const int x_needles = u.cols / NEEDLE_MAP_SCALE;
|
|
const int y_needles = u.rows / NEEDLE_MAP_SCALE;
|
|
|
|
GpuMat u_avg(y_needles, x_needles, CV_32FC1);
|
|
GpuMat v_avg(y_needles, x_needles, CV_32FC1);
|
|
|
|
NeedleMapAverage_gpu(u, v, u_avg, v_avg);
|
|
|
|
const int NUM_VERTS_PER_ARROW = 6;
|
|
|
|
const int num_arrows = x_needles * y_needles * NUM_VERTS_PER_ARROW;
|
|
|
|
vertex.create(1, num_arrows, CV_32FC3);
|
|
colors.create(1, num_arrows, CV_32FC3);
|
|
|
|
colors.setTo(Scalar::all(1.0));
|
|
|
|
double uMax, vMax;
|
|
minMax(u_avg, 0, &uMax);
|
|
minMax(v_avg, 0, &vMax);
|
|
|
|
float max_flow = static_cast<float>(sqrt(uMax * uMax + vMax * vMax));
|
|
|
|
CreateOpticalFlowNeedleMap_gpu(u_avg, v_avg, vertex.ptr<float>(), colors.ptr<float>(), max_flow, 1.0f / u.cols, 1.0f / u.rows);
|
|
|
|
cvtColor(colors, colors, COLOR_HSV2RGB);
|
|
}
|
|
|
|
#endif /* HAVE_CUDA */
|