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Merge pull request #7774 from savuor:openvx_pyrlk

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OpenVX optical flow PyrLK wrappers added (#7774)

* wrappers for vx_pyramid added

* initial version of Optical Flow PyrLK wrappers added

* array downloading code simplified

* disabled due to bad accuracy; fixed bugs, e.g. vendor-specific ones

* rewritten for new macro use
This commit is contained in:
Rostislav Vasilikhin 2016-12-16 14:46:03 +04:00 committed by Alexander Alekhin
parent 9c7088d95f
commit 5372c8b331
1 changed files with 157 additions and 0 deletions
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157
modules/video/src/lkpyramid.cpp
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@ -46,6 +46,8 @@
#include "opencl_kernels_video.hpp" #include "opencl_kernels_video.hpp"
#include "opencv2/core/hal/intrin.hpp" #include "opencv2/core/hal/intrin.hpp"
#include "opencv2/core/openvx/ovx_defs.hpp"
#define CV_DESCALE(x,n) (((x) + (1 << ((n)-1))) >> (n)) #define CV_DESCALE(x,n) (((x) + (1 << ((n)-1))) >> (n))
namespace namespace
@ -1055,8 +1057,159 @@ namespace
return sparse(_prevImg.getUMat(), _nextImg.getUMat(), _prevPts.getUMat(), umatNextPts, umatStatus, umatErr); return sparse(_prevImg.getUMat(), _nextImg.getUMat(), _prevPts.getUMat(), umatNextPts, umatStatus, umatErr);
} }
#endif #endif
#ifdef HAVE_OPENVX
bool openvx_pyrlk(InputArray _prevImg, InputArray _nextImg, InputArray _prevPts, InputOutputArray _nextPts,
OutputArray _status, OutputArray _err)
{
using namespace ivx;
// Pyramids as inputs are not acceptable because there's no (direct or simple) way
// to build vx_pyramid on user data
if(_prevImg.kind() != _InputArray::MAT || _nextImg.kind() != _InputArray::MAT)
return false;
Mat prevImgMat = _prevImg.getMat(), nextImgMat = _nextImg.getMat();
if(prevImgMat.type() != CV_8UC1 || nextImgMat.type() != CV_8UC1)
return false;
CV_Assert(prevImgMat.size() == nextImgMat.size());
Mat prevPtsMat = _prevPts.getMat();
int checkPrev = prevPtsMat.checkVector(2, CV_32F, false);
CV_Assert( checkPrev >= 0 );
size_t npoints = checkPrev;
if( !(flags & OPTFLOW_USE_INITIAL_FLOW) )
_nextPts.create(prevPtsMat.size(), prevPtsMat.type(), -1, true);
Mat nextPtsMat = _nextPts.getMat();
CV_Assert( nextPtsMat.checkVector(2, CV_32F, false) == (int)npoints );
_status.create((int)npoints, 1, CV_8U, -1, true);
Mat statusMat = _status.getMat();
uchar* status = statusMat.ptr();
for(size_t i = 0; i < npoints; i++ )
status[i] = true;
Mat errMat;
if( _err.needed() )
{
_err.create((int)npoints, 1, CV_32F, -1, true);
errMat = _err.getMat();
}
try
{
Context context = Context::create();
if(context.vendorID() == VX_ID_KHRONOS)
{
// PyrLK in OVX 1.0.1 performs vxCommitImagePatch incorrecty and crashes
if(VX_VERSION == VX_VERSION_1_0)
return false;
// Implementation ignores border mode
// So check that minimal size of image in pyramid is big enough
int width = prevImgMat.cols, height = prevImgMat.rows;
for(int i = 0; i < maxLevel+1; i++)
{
if(width < winSize.width + 1 || height < winSize.height + 1)
return false;
else
{
width /= 2; height /= 2;
}
}
}
Image prevImg = Image::createFromHandle(context, Image::matTypeToFormat(prevImgMat.type()),
Image::createAddressing(prevImgMat), (void*)prevImgMat.data);
Image nextImg = Image::createFromHandle(context, Image::matTypeToFormat(nextImgMat.type()),
Image::createAddressing(nextImgMat), (void*)nextImgMat.data);
Graph graph = Graph::create(context);
Pyramid prevPyr = Pyramid::createVirtual(graph, (vx_size)maxLevel+1, VX_SCALE_PYRAMID_HALF,
prevImg.width(), prevImg.height(), prevImg.format());
Pyramid nextPyr = Pyramid::createVirtual(graph, (vx_size)maxLevel+1, VX_SCALE_PYRAMID_HALF,
nextImg.width(), nextImg.height(), nextImg.format());
ivx::Node::create(graph, VX_KERNEL_GAUSSIAN_PYRAMID, prevImg, prevPyr);
ivx::Node::create(graph, VX_KERNEL_GAUSSIAN_PYRAMID, nextImg, nextPyr);
Array prevPts = Array::create(context, VX_TYPE_KEYPOINT, npoints);
Array estimatedPts = Array::create(context, VX_TYPE_KEYPOINT, npoints);
Array nextPts = Array::create(context, VX_TYPE_KEYPOINT, npoints);
std::vector<vx_keypoint_t> vxPrevPts(npoints), vxEstPts(npoints), vxNextPts(npoints);
for(size_t i = 0; i < npoints; i++)
{
vx_keypoint_t& prevPt = vxPrevPts[i]; vx_keypoint_t& estPt = vxEstPts[i];
prevPt.x = prevPtsMat.at<Point2f>(i).x; prevPt.y = prevPtsMat.at<Point2f>(i).y;
estPt.x = nextPtsMat.at<Point2f>(i).x; estPt.y = nextPtsMat.at<Point2f>(i).y;
prevPt.tracking_status = estPt.tracking_status = vx_true_e;
}
prevPts.addItems(vxPrevPts); estimatedPts.addItems(vxEstPts);
if( (criteria.type & TermCriteria::COUNT) == 0 )
criteria.maxCount = 30;
else
criteria.maxCount = std::min(std::max(criteria.maxCount, 0), 100);
if( (criteria.type & TermCriteria::EPS) == 0 )
criteria.epsilon = 0.01;
else
criteria.epsilon = std::min(std::max(criteria.epsilon, 0.), 10.);
criteria.epsilon *= criteria.epsilon;
vx_enum termEnum = (criteria.type == TermCriteria::COUNT) ? VX_TERM_CRITERIA_ITERATIONS :
(criteria.type == TermCriteria::EPS) ? VX_TERM_CRITERIA_EPSILON :
VX_TERM_CRITERIA_BOTH;
//minEigThreshold is fixed to 0.0001f
ivx::Scalar termination = ivx::Scalar::create<VX_TYPE_ENUM>(context, termEnum);
ivx::Scalar epsilon = ivx::Scalar::create<VX_TYPE_FLOAT32>(context, criteria.epsilon);
ivx::Scalar numIterations = ivx::Scalar::create<VX_TYPE_UINT32>(context, criteria.maxCount);
ivx::Scalar useInitial = ivx::Scalar::create<VX_TYPE_BOOL>(context, (vx_bool)(flags & OPTFLOW_USE_INITIAL_FLOW));
//assume winSize is square
ivx::Scalar windowSize = ivx::Scalar::create<VX_TYPE_SIZE>(context, (vx_size)winSize.width);
ivx::Node::create(graph, VX_KERNEL_OPTICAL_FLOW_PYR_LK, prevPyr, nextPyr, prevPts, estimatedPts,
nextPts, termination, epsilon, numIterations, useInitial, windowSize);
graph.verify();
graph.process();
nextPts.copyTo(vxNextPts);
for(size_t i = 0; i < npoints; i++)
{
vx_keypoint_t kp = vxNextPts[i];
nextPtsMat.at<Point2f>(i) = Point2f(kp.x, kp.y);
statusMat.at<uchar>(i) = (bool)kp.tracking_status;
// OpenVX doesn't return detection errors
errMat.at<float>(i) = 0;
}
#ifdef VX_VERSION_1_1
//we should take user memory back before release
//(it's not done automatically according to standard)
prevImg.swapHandle(); nextImg.swapHandle();
#endif
}
catch (RuntimeError & e)
{
VX_DbgThrow(e.what());
}
catch (WrapperError & e)
{
VX_DbgThrow(e.what());
}
return true;
}
#endif
}; };
void SparsePyrLKOpticalFlowImpl::calc( InputArray _prevImg, InputArray _nextImg, void SparsePyrLKOpticalFlowImpl::calc( InputArray _prevImg, InputArray _nextImg,
InputArray _prevPts, InputOutputArray _nextPts, InputArray _prevPts, InputOutputArray _nextPts,
OutputArray _status, OutputArray _err) OutputArray _status, OutputArray _err)
@ -1068,6 +1221,10 @@ void SparsePyrLKOpticalFlowImpl::calc( InputArray _prevImg, InputArray _nextImg,
ocl::Image2D::isFormatSupported(CV_32F, 1, false), ocl::Image2D::isFormatSupported(CV_32F, 1, false),
ocl_calcOpticalFlowPyrLK(_prevImg, _nextImg, _prevPts, _nextPts, _status, _err)) ocl_calcOpticalFlowPyrLK(_prevImg, _nextImg, _prevPts, _nextPts, _status, _err))
// Disabled due to bad accuracy
CV_OVX_RUN(false,
openvx_pyrlk(_prevImg, _nextImg, _prevPts, _nextPts, _status, _err))
Mat prevPtsMat = _prevPts.getMat(); Mat prevPtsMat = _prevPts.getMat();
const int derivDepth = DataType<cv::detail::deriv_type>::depth; const int derivDepth = DataType<cv::detail::deriv_type>::depth;