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Complete DIS optical flow implementation

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Added variational refinement as a separate class (based on implementation
inside DeepFlow, but significantly accelerated, about 4-6 times faster),
accelerated the main dense inverse search algorithm. Added several new
features including patch mean normalization for increased robustness to
illumination changes and spatial propagation, which often helps to recover
from errors introduced by the coarse-to-fine scheme. Expanded the
documentation, added new accuracy and perf tests. Refactored some of
the already existing optical flow accuracy tests.
This commit is contained in:
Alexander Bokov
2016-06-02 02:50:29 +03:00
parent d4c3765e4f
commit b96a5f5963
14 changed files with 2702 additions and 535 deletions
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77
modules/optflow/perf/perf_variational_refinement.cpp Normal file
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/*
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#include "perf_precomp.hpp"
using std::tr1::tuple;
using std::tr1::get;
using namespace perf;
using namespace testing;
using namespace cv;
using namespace cv::optflow;
typedef tuple<Size, int, int> VarRefParams;
typedef TestBaseWithParam<VarRefParams> DenseOpticalFlow_VariationalRefinement;
PERF_TEST_P(DenseOpticalFlow_VariationalRefinement, perf, Combine(Values(szQVGA, szVGA), Values(5, 10), Values(5, 10)))
{
VarRefParams params = GetParam();
Size sz = get<0>(params);
int sorIter = get<1>(params);
int fixedPointIter = get<2>(params);
Mat frame1(sz, CV_8U);
Mat frame2(sz, CV_8U);
Mat flow(sz, CV_32FC2);
randu(frame1, 0, 255);
randu(frame2, 0, 255);
flow.setTo(0.0f);
cv::setNumThreads(cv::getNumberOfCPUs());
TEST_CYCLE_N(10)
{
Ptr<VariationalRefinement> var = createVariationalFlowRefinement();
var->setAlpha(20.0f);
var->setGamma(10.0f);
var->setDelta(5.0f);
var->setSorIterations(sorIter);
var->setFixedPointIterations(fixedPointIter);
var->calc(frame1, frame2, flow);
}
SANITY_CHECK_NOTHING();
}