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/*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 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::cuda;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
Ptr<cv::cuda::SparsePyrLKOpticalFlow> cv::cuda::SparsePyrLKOpticalFlow::create(Size, int, int, bool) { throw_no_cuda(); return Ptr<SparsePyrLKOpticalFlow>(); }
Ptr<cv::cuda::DensePyrLKOpticalFlow> cv::cuda::DensePyrLKOpticalFlow::create(Size, int, int, bool) { throw_no_cuda(); return Ptr<DensePyrLKOpticalFlow>(); }
#else /* !defined (HAVE_CUDA) */
namespace pyrlk
{
void loadConstants(int* winSize, int iters, cudaStream_t stream);
void loadWinSize(int* winSize, int* halfWinSize, cudaStream_t stream);
void loadIters(int* iters, cudaStream_t stream);
template<typename T, int cn> struct pyrLK_caller
{
static void sparse(PtrStepSz<typename device::TypeVec<T, cn>::vec_type> I, PtrStepSz<typename device::TypeVec<T, cn>::vec_type> J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, dim3 patch, cudaStream_t stream);
static void dense(PtrStepSzf I, PtrStepSzf J, PtrStepSzf u, PtrStepSzf v, PtrStepSzf prevU, PtrStepSzf prevV,
PtrStepSzf err, int2 winSize, cudaStream_t stream);
};
template<typename T, int cn> void dispatcher(GpuMat I, GpuMat J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, dim3 patch, cudaStream_t stream)
{
pyrLK_caller<T, cn>::sparse(I, J, prevPts, nextPts, status, err, ptcount, level, block, patch, stream);
}
}
namespace
{
class PyrLKOpticalFlowBase
{
public:
PyrLKOpticalFlowBase(Size winSize, int maxLevel, int iters, bool useInitialFlow);
void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts,
GpuMat& status, GpuMat* err, Stream& stream);
void sparse(std::vector<GpuMat>& prevPyr, std::vector<GpuMat>& nextPyr, const GpuMat& prevPts, GpuMat& nextPts,
GpuMat& status, GpuMat* err, Stream& stream);
void dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, Stream& stream);
protected:
int winSize_[2];
int halfWinSize_[2];
int maxLevel_;
int iters_;
bool useInitialFlow_;
void buildImagePyramid(const GpuMat& prevImg, std::vector<GpuMat>& prevPyr, const GpuMat& nextImg, std::vector<GpuMat>& nextPyr, Stream stream);
private:
friend class SparsePyrLKOpticalFlowImpl;
std::vector<GpuMat> prevPyr_;
std::vector<GpuMat> nextPyr_;
};
PyrLKOpticalFlowBase::PyrLKOpticalFlowBase(Size winSize, int maxLevel, int iters, bool useInitialFlow) :
maxLevel_(maxLevel), iters_(iters), useInitialFlow_(useInitialFlow)
{
winSize_[0] = winSize.width;
winSize_[1] = winSize.height;
halfWinSize_[0] = (winSize.width - 1) / 2;
halfWinSize_[1] = (winSize.height - 1) / 2;
pyrlk::loadWinSize(winSize_, halfWinSize_, 0);
pyrlk::loadIters(&iters_, 0);
}
void calcPatchSize(Size winSize, dim3& block, dim3& patch)
{
if (winSize.width > 32 && winSize.width > 2 * winSize.height)
{
block.x = deviceSupports(FEATURE_SET_COMPUTE_12) ? 32 : 16;
block.y = 8;
}
else
{
block.x = 16;
block.y = deviceSupports(FEATURE_SET_COMPUTE_12) ? 16 : 8;
}
patch.x = (winSize.width + block.x - 1) / block.x;
patch.y = (winSize.height + block.y - 1) / block.y;
block.z = patch.z = 1;
}
void PyrLKOpticalFlowBase::buildImagePyramid(const GpuMat& prevImg, std::vector<GpuMat>& prevPyr, const GpuMat& nextImg, std::vector<GpuMat>& nextPyr, Stream stream)
{
prevPyr.resize(maxLevel_ + 1);
nextPyr.resize(maxLevel_ + 1);
int cn = prevImg.channels();
CV_Assert(cn == 1 || cn == 3 || cn == 4);
prevPyr[0] = prevImg;
nextPyr[0] = nextImg;
for (int level = 1; level <= maxLevel_; ++level)
{
cuda::pyrDown(prevPyr[level - 1], prevPyr[level], stream);
cuda::pyrDown(nextPyr[level - 1], nextPyr[level], stream);
}
}
void PyrLKOpticalFlowBase::sparse(std::vector<GpuMat>& prevPyr, std::vector<GpuMat>& nextPyr, const GpuMat& prevPts, GpuMat& nextPts,
GpuMat& status, GpuMat* err, Stream& stream)
{
CV_Assert(prevPyr.size() && nextPyr.size() && "Pyramid needs to at least contain the original matrix as the first element");
CV_Assert(prevPyr[0].size() == nextPyr[0].size());
CV_Assert(prevPts.rows == 1 && prevPts.type() == CV_32FC2);
CV_Assert(maxLevel_ >= 0);
CV_Assert(winSize_[0] > 2 && winSize_[1] > 2);
if (useInitialFlow_)
CV_Assert(nextPts.size() == prevPts.size() && nextPts.type() == prevPts.type());
else
ensureSizeIsEnough(1, prevPts.cols, prevPts.type(), nextPts);
GpuMat temp1 = (useInitialFlow_ ? nextPts : prevPts).reshape(1);
GpuMat temp2 = nextPts.reshape(1);
cuda::multiply(temp1, Scalar::all(1.0 / (1 << maxLevel_) / 2.0), temp2, 1, -1, stream);
ensureSizeIsEnough(1, prevPts.cols, CV_8UC1, status);
status.setTo(Scalar::all(1), stream);
if (err)
ensureSizeIsEnough(1, prevPts.cols, CV_32FC1, *err);
if (prevPyr.size() != size_t(maxLevel_ + 1) || nextPyr.size() != size_t(maxLevel_ + 1))
{
buildImagePyramid(prevPyr[0], prevPyr, nextPyr[0], nextPyr, stream);
}
dim3 block, patch;
calcPatchSize(Size(winSize_[0], winSize_[1]), block, patch);
CV_Assert(patch.x > 0 && patch.x < 6 && patch.y > 0 && patch.y < 6);
cudaStream_t stream_ = StreamAccessor::getStream(stream);
pyrlk::loadWinSize(winSize_, halfWinSize_, stream_);
pyrlk::loadIters(&iters_, stream_);
const int cn = prevPyr[0].channels();
const int type = prevPyr[0].depth();
typedef void(*func_t)(GpuMat I, GpuMat J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, dim3 patch, cudaStream_t stream);
// Current int datatype is disabled due to pyrDown not implementing it
// while ushort does work, it has significantly worse performance, and thus doesn't pass accuracy tests.
static const func_t funcs[6][4] =
{
{ pyrlk::dispatcher<uchar, 1> , /*pyrlk::dispatcher<uchar, 2>*/ 0, pyrlk::dispatcher<uchar, 3> , pyrlk::dispatcher<uchar, 4> },
{ /*pyrlk::dispatcher<char, 1>*/ 0, /*pyrlk::dispatcher<char, 2>*/ 0, /*pyrlk::dispatcher<char, 3>*/ 0 , /*pyrlk::dispatcher<char, 4>*/ 0 },
{ pyrlk::dispatcher<ushort, 1> , /*pyrlk::dispatcher<ushort, 2>*/0, pyrlk::dispatcher<ushort, 3> , pyrlk::dispatcher<ushort, 4> },
{ /*pyrlk::dispatcher<short, 1>*/ 0, /*pyrlk::dispatcher<short, 2>*/ 0, /*pyrlk::dispatcher<short, 3>*/ 0 , /*pyrlk::dispatcher<short, 4>*/0 },
{ pyrlk::dispatcher<int, 1> , /*pyrlk::dispatcher<int, 2>*/ 0, pyrlk::dispatcher<int, 3> , pyrlk::dispatcher<int, 4> },
{ pyrlk::dispatcher<float, 1> , /*pyrlk::dispatcher<float, 2>*/ 0, pyrlk::dispatcher<float, 3> , pyrlk::dispatcher<float, 4> }
};
func_t func = funcs[type][cn-1];
CV_Assert(func != NULL && "Datatype not implemented");
for (int level = maxLevel_; level >= 0; level--)
{
func(prevPyr[level], nextPyr[level],
prevPts.ptr<float2>(), nextPts.ptr<float2>(),
status.ptr(), level == 0 && err ? err->ptr<float>() : 0,
prevPts.cols, level, block, patch,
stream_);
}
}
void PyrLKOpticalFlowBase::sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts, GpuMat& status, GpuMat* err, Stream& stream)
{
if (prevPts.empty())
{
nextPts.release();
status.release();
if (err) err->release();
return;
}
CV_Assert( prevImg.channels() == 1 || prevImg.channels() == 3 || prevImg.channels() == 4 );
CV_Assert( prevImg.size() == nextImg.size() && prevImg.type() == nextImg.type() );
// build the image pyramids.
buildImagePyramid(prevImg, prevPyr_, nextImg, nextPyr_, stream);
sparse(prevPyr_, nextPyr_, prevPts, nextPts, status, err, stream);
}
void PyrLKOpticalFlowBase::dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, Stream& stream)
{
CV_Assert( prevImg.type() == CV_8UC1 );
CV_Assert( prevImg.size() == nextImg.size() && prevImg.type() == nextImg.type() );
CV_Assert( maxLevel_ >= 0 );
CV_Assert( winSize_[0] > 2 && winSize_[1] > 2 );
// build the image pyramids.
prevPyr_.resize(maxLevel_ + 1);
nextPyr_.resize(maxLevel_ + 1);
//prevPyr_[0] = prevImg;
prevImg.convertTo(prevPyr_[0], CV_32F, stream);
nextImg.convertTo(nextPyr_[0], CV_32F, stream);
for (int level = 1; level <= maxLevel_; ++level)
{
cuda::pyrDown(prevPyr_[level - 1], prevPyr_[level], stream);
cuda::pyrDown(nextPyr_[level - 1], nextPyr_[level], stream);
}
BufferPool pool(stream);
GpuMat uPyr[] = {
pool.getBuffer(prevImg.size(), CV_32FC1),
pool.getBuffer(prevImg.size(), CV_32FC1),
};
GpuMat vPyr[] = {
pool.getBuffer(prevImg.size(), CV_32FC1),
pool.getBuffer(prevImg.size(), CV_32FC1),
};
uPyr[0].setTo(Scalar::all(0), stream);
vPyr[0].setTo(Scalar::all(0), stream);
uPyr[1].setTo(Scalar::all(0), stream);
vPyr[1].setTo(Scalar::all(0), stream);
cudaStream_t stream_ = StreamAccessor::getStream(stream);
pyrlk::loadWinSize(winSize_, halfWinSize_, stream_);
pyrlk::loadIters(&iters_, stream_);
int2 winSize2i = make_int2(winSize_[0], winSize_[1]);
//pyrlk::loadConstants(winSize2i, iters_, StreamAccessor::getStream(stream));
int idx = 0;
for (int level = maxLevel_; level >= 0; level--)
{
int idx2 = (idx + 1) & 1;
pyrlk::pyrLK_caller<float,1>::dense(prevPyr_[level], nextPyr_[level],
uPyr[idx], vPyr[idx], uPyr[idx2], vPyr[idx2],
PtrStepSzf(), winSize2i,
stream_);
if (level > 0)
idx = idx2;
}
uPyr[idx].copyTo(u, stream);
vPyr[idx].copyTo(v, stream);
}
class SparsePyrLKOpticalFlowImpl : public cv::cuda::SparsePyrLKOpticalFlow, private PyrLKOpticalFlowBase
{
public:
SparsePyrLKOpticalFlowImpl(Size winSize, int maxLevel, int iters, bool useInitialFlow) :
PyrLKOpticalFlowBase(winSize, maxLevel, iters, useInitialFlow)
{
}
virtual Size getWinSize() const { return cv::Size(winSize_[0], winSize_[1]); }
virtual void setWinSize(Size winSize) {
winSize_[0] = winSize.width;
winSize_[1] = winSize.height;
halfWinSize_[0] = (winSize.width - 1) / 2;
halfWinSize_[1] = (winSize.height -1) / 2;
}
virtual int getMaxLevel() const { return maxLevel_; }
virtual void setMaxLevel(int maxLevel) { maxLevel_ = maxLevel; }
virtual int getNumIters() const { return iters_; }
virtual void setNumIters(int iters) { iters_ = iters; }
virtual bool getUseInitialFlow() const { return useInitialFlow_; }
virtual void setUseInitialFlow(bool useInitialFlow) { useInitialFlow_ = useInitialFlow; }
virtual void calc(InputArray _prevImg, InputArray _nextImg,
InputArray _prevPts, InputOutputArray _nextPts,
OutputArray _status,
OutputArray _err,
Stream& stream)
{
const GpuMat prevPts = _prevPts.getGpuMat();
GpuMat& nextPts = _nextPts.getGpuMatRef();
GpuMat& status = _status.getGpuMatRef();
GpuMat* err = _err.needed() ? &(_err.getGpuMatRef()) : NULL;
if (_prevImg.kind() == _InputArray::STD_VECTOR_CUDA_GPU_MAT && _prevImg.kind() == _InputArray::STD_VECTOR_CUDA_GPU_MAT)
{
std::vector<GpuMat> prevPyr, nextPyr;
_prevImg.getGpuMatVector(prevPyr);
_nextImg.getGpuMatVector(nextPyr);
sparse(prevPyr, nextPyr, prevPts, nextPts, status, err, stream);
}
else
{
const GpuMat prevImg = _prevImg.getGpuMat();
const GpuMat nextImg = _nextImg.getGpuMat();
sparse(prevImg, nextImg, prevPts, nextPts, status, err, stream);
}
}
};
class DensePyrLKOpticalFlowImpl : public DensePyrLKOpticalFlow, private PyrLKOpticalFlowBase
{
public:
DensePyrLKOpticalFlowImpl(Size winSize, int maxLevel, int iters, bool useInitialFlow) :
PyrLKOpticalFlowBase(winSize, maxLevel, iters, useInitialFlow)
{
}
virtual Size getWinSize() const { return cv::Size(winSize_[0], winSize_[1]); }
virtual void setWinSize(Size winSize) {
winSize_[0] = winSize.width;
winSize_[1] = winSize.height;
halfWinSize_[0] = (winSize.width - 1) / 2;
halfWinSize_[1] = (winSize.height -1) / 2;
}
virtual int getMaxLevel() const { return maxLevel_; }
virtual void setMaxLevel(int maxLevel) { maxLevel_ = maxLevel; }
virtual int getNumIters() const { return iters_; }
virtual void setNumIters(int iters) { iters_ = iters; }
virtual bool getUseInitialFlow() const { return useInitialFlow_; }
virtual void setUseInitialFlow(bool useInitialFlow) { useInitialFlow_ = useInitialFlow; }
virtual void calc(InputArray _prevImg, InputArray _nextImg, InputOutputArray _flow, Stream& stream)
{
const GpuMat prevImg = _prevImg.getGpuMat();
const GpuMat nextImg = _nextImg.getGpuMat();
BufferPool pool(stream);
GpuMat u = pool.getBuffer(prevImg.size(), CV_32FC1);
GpuMat v = pool.getBuffer(prevImg.size(), CV_32FC1);
dense(prevImg, nextImg, u, v, stream);
GpuMat flows[] = {u, v};
cuda::merge(flows, 2, _flow, stream);
}
};
}
Ptr<cv::cuda::SparsePyrLKOpticalFlow> cv::cuda::SparsePyrLKOpticalFlow::create(Size winSize, int maxLevel, int iters, bool useInitialFlow)
{
return makePtr<SparsePyrLKOpticalFlowImpl>(winSize, maxLevel, iters, useInitialFlow);
}
Ptr<cv::cuda::DensePyrLKOpticalFlow> cv::cuda::DensePyrLKOpticalFlow::create(Size winSize, int maxLevel, int iters, bool useInitialFlow)
{
return makePtr<DensePyrLKOpticalFlowImpl>(winSize, maxLevel, iters, useInitialFlow);
}
#endif /* !defined (HAVE_CUDA) */