2a6fb2867e
Made all STL usages explicit to be able automatically find all usages of particular class or function.
238 lines
9.6 KiB
C++
238 lines
9.6 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other GpuMaterials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or bpied warranties, including, but not limited to, the bpied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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using namespace cv;
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using namespace cv::gpu;
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#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
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void cv::gpu::BroxOpticalFlow::operator ()(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
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void cv::gpu::interpolateFrames(const GpuMat&, const GpuMat&, const GpuMat&, const GpuMat&, const GpuMat&, const GpuMat&, float, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
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void cv::gpu::createOpticalFlowNeedleMap(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&) { throw_nogpu(); }
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#else
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namespace
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{
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size_t getBufSize(const NCVBroxOpticalFlowDescriptor& desc, const NCVMatrix<Ncv32f>& frame0, const NCVMatrix<Ncv32f>& frame1,
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NCVMatrix<Ncv32f>& u, NCVMatrix<Ncv32f>& v, const cudaDeviceProp& devProp)
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{
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NCVMemStackAllocator gpuCounter(static_cast<Ncv32u>(devProp.textureAlignment));
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ncvSafeCall( NCVBroxOpticalFlow(desc, gpuCounter, frame0, frame1, u, v, 0) );
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return gpuCounter.maxSize();
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}
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}
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namespace
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{
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static void outputHandler(const std::string &msg) { CV_Error(CV_GpuApiCallError, msg.c_str()); }
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}
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void cv::gpu::BroxOpticalFlow::operator ()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& u, GpuMat& v, Stream& s)
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{
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ncvSetDebugOutputHandler(outputHandler);
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CV_Assert(frame0.type() == CV_32FC1);
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CV_Assert(frame1.size() == frame0.size() && frame1.type() == frame0.type());
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u.create(frame0.size(), CV_32FC1);
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v.create(frame0.size(), CV_32FC1);
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cudaDeviceProp devProp;
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cudaSafeCall( cudaGetDeviceProperties(&devProp, getDevice()) );
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NCVBroxOpticalFlowDescriptor desc;
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desc.alpha = alpha;
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desc.gamma = gamma;
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desc.scale_factor = scale_factor;
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desc.number_of_inner_iterations = inner_iterations;
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desc.number_of_outer_iterations = outer_iterations;
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desc.number_of_solver_iterations = solver_iterations;
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NCVMemSegment frame0MemSeg;
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frame0MemSeg.begin.memtype = NCVMemoryTypeDevice;
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frame0MemSeg.begin.ptr = const_cast<uchar*>(frame0.data);
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frame0MemSeg.size = frame0.step * frame0.rows;
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NCVMemSegment frame1MemSeg;
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frame1MemSeg.begin.memtype = NCVMemoryTypeDevice;
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frame1MemSeg.begin.ptr = const_cast<uchar*>(frame1.data);
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frame1MemSeg.size = frame1.step * frame1.rows;
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NCVMemSegment uMemSeg;
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uMemSeg.begin.memtype = NCVMemoryTypeDevice;
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uMemSeg.begin.ptr = u.ptr();
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uMemSeg.size = u.step * u.rows;
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NCVMemSegment vMemSeg;
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vMemSeg.begin.memtype = NCVMemoryTypeDevice;
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vMemSeg.begin.ptr = v.ptr();
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vMemSeg.size = v.step * v.rows;
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NCVMatrixReuse<Ncv32f> frame0Mat(frame0MemSeg, static_cast<Ncv32u>(devProp.textureAlignment), frame0.cols, frame0.rows, static_cast<Ncv32u>(frame0.step));
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NCVMatrixReuse<Ncv32f> frame1Mat(frame1MemSeg, static_cast<Ncv32u>(devProp.textureAlignment), frame1.cols, frame1.rows, static_cast<Ncv32u>(frame1.step));
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NCVMatrixReuse<Ncv32f> uMat(uMemSeg, static_cast<Ncv32u>(devProp.textureAlignment), u.cols, u.rows, static_cast<Ncv32u>(u.step));
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NCVMatrixReuse<Ncv32f> vMat(vMemSeg, static_cast<Ncv32u>(devProp.textureAlignment), v.cols, v.rows, static_cast<Ncv32u>(v.step));
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cudaStream_t stream = StreamAccessor::getStream(s);
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size_t bufSize = getBufSize(desc, frame0Mat, frame1Mat, uMat, vMat, devProp);
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ensureSizeIsEnough(1, static_cast<int>(bufSize), CV_8UC1, buf);
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NCVMemStackAllocator gpuAllocator(NCVMemoryTypeDevice, bufSize, static_cast<Ncv32u>(devProp.textureAlignment), buf.ptr());
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ncvSafeCall( NCVBroxOpticalFlow(desc, gpuAllocator, frame0Mat, frame1Mat, uMat, vMat, stream) );
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}
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void cv::gpu::interpolateFrames(const GpuMat& frame0, const GpuMat& frame1, const GpuMat& fu, const GpuMat& fv, const GpuMat& bu, const GpuMat& bv,
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float pos, GpuMat& newFrame, GpuMat& buf, Stream& s)
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{
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CV_Assert(frame0.type() == CV_32FC1);
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CV_Assert(frame1.size() == frame0.size() && frame1.type() == frame0.type());
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CV_Assert(fu.size() == frame0.size() && fu.type() == frame0.type());
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CV_Assert(fv.size() == frame0.size() && fv.type() == frame0.type());
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CV_Assert(bu.size() == frame0.size() && bu.type() == frame0.type());
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CV_Assert(bv.size() == frame0.size() && bv.type() == frame0.type());
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newFrame.create(frame0.size(), frame0.type());
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buf.create(6 * frame0.rows, frame0.cols, CV_32FC1);
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buf.setTo(Scalar::all(0));
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// occlusion masks
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GpuMat occ0 = buf.rowRange(0 * frame0.rows, 1 * frame0.rows);
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GpuMat occ1 = buf.rowRange(1 * frame0.rows, 2 * frame0.rows);
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// interpolated forward flow
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GpuMat fui = buf.rowRange(2 * frame0.rows, 3 * frame0.rows);
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GpuMat fvi = buf.rowRange(3 * frame0.rows, 4 * frame0.rows);
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// interpolated backward flow
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GpuMat bui = buf.rowRange(4 * frame0.rows, 5 * frame0.rows);
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GpuMat bvi = buf.rowRange(5 * frame0.rows, 6 * frame0.rows);
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size_t step = frame0.step;
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CV_Assert(frame1.step == step && fu.step == step && fv.step == step && bu.step == step && bv.step == step && newFrame.step == step && buf.step == step);
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cudaStream_t stream = StreamAccessor::getStream(s);
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NppStStreamHandler h(stream);
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NppStInterpolationState state;
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state.size = NcvSize32u(frame0.cols, frame0.rows);
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state.nStep = static_cast<Ncv32u>(step);
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state.pSrcFrame0 = const_cast<Ncv32f*>(frame0.ptr<Ncv32f>());
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state.pSrcFrame1 = const_cast<Ncv32f*>(frame1.ptr<Ncv32f>());
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state.pFU = const_cast<Ncv32f*>(fu.ptr<Ncv32f>());
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state.pFV = const_cast<Ncv32f*>(fv.ptr<Ncv32f>());
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state.pBU = const_cast<Ncv32f*>(bu.ptr<Ncv32f>());
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state.pBV = const_cast<Ncv32f*>(bv.ptr<Ncv32f>());
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state.pos = pos;
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state.pNewFrame = newFrame.ptr<Ncv32f>();
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state.ppBuffers[0] = occ0.ptr<Ncv32f>();
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state.ppBuffers[1] = occ1.ptr<Ncv32f>();
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state.ppBuffers[2] = fui.ptr<Ncv32f>();
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state.ppBuffers[3] = fvi.ptr<Ncv32f>();
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state.ppBuffers[4] = bui.ptr<Ncv32f>();
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state.ppBuffers[5] = bvi.ptr<Ncv32f>();
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ncvSafeCall( nppiStInterpolateFrames(&state) );
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if (stream == 0)
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cudaSafeCall( cudaDeviceSynchronize() );
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}
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namespace cv { namespace gpu { namespace device
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{
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namespace optical_flow
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{
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void NeedleMapAverage_gpu(PtrStepSzf u, PtrStepSzf v, PtrStepSzf u_avg, PtrStepSzf v_avg);
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void CreateOpticalFlowNeedleMap_gpu(PtrStepSzf u_avg, PtrStepSzf v_avg, float* vertex_buffer, float* color_data, float max_flow, float xscale, float yscale);
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}
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}}}
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void cv::gpu::createOpticalFlowNeedleMap(const GpuMat& u, const GpuMat& v, GpuMat& vertex, GpuMat& colors)
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{
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using namespace cv::gpu::device::optical_flow;
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CV_Assert(u.type() == CV_32FC1);
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CV_Assert(v.type() == u.type() && v.size() == u.size());
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const int NEEDLE_MAP_SCALE = 16;
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const int x_needles = u.cols / NEEDLE_MAP_SCALE;
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const int y_needles = u.rows / NEEDLE_MAP_SCALE;
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GpuMat u_avg(y_needles, x_needles, CV_32FC1);
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GpuMat v_avg(y_needles, x_needles, CV_32FC1);
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NeedleMapAverage_gpu(u, v, u_avg, v_avg);
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const int NUM_VERTS_PER_ARROW = 6;
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const int num_arrows = x_needles * y_needles * NUM_VERTS_PER_ARROW;
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vertex.create(1, num_arrows, CV_32FC3);
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colors.create(1, num_arrows, CV_32FC3);
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colors.setTo(Scalar::all(1.0));
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double uMax, vMax;
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minMax(u_avg, 0, &uMax);
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minMax(v_avg, 0, &vMax);
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float max_flow = static_cast<float>(std::sqrt(uMax * uMax + vMax * vMax));
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CreateOpticalFlowNeedleMap_gpu(u_avg, v_avg, vertex.ptr<float>(), colors.ptr<float>(), max_flow, 1.0f / u.cols, 1.0f / u.rows);
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cvtColor(colors, colors, COLOR_HSV2RGB);
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}
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#endif /* HAVE_CUDA */
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