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opencv/modules/cudaoptflow/test/test_optflow.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
// By downloading, copying, installing or using the software you agree to this license.
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// copy or use the software.
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//
// License Agreement
// For Open Source Computer Vision Library
//
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#include "test_precomp.hpp"
#ifdef HAVE_CUDA
namespace opencv_test { namespace {
//////////////////////////////////////////////////////
// BroxOpticalFlow
//#define BROX_DUMP
struct BroxOpticalFlow : testing::TestWithParam<cv::cuda::DeviceInfo>
{
cv::cuda::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(BroxOpticalFlow, Regression)
{
cv::Mat frame0 = readImageType("opticalflow/frame0.png", CV_32FC1);
ASSERT_FALSE(frame0.empty());
cv::Mat frame1 = readImageType("opticalflow/frame1.png", CV_32FC1);
ASSERT_FALSE(frame1.empty());
cv::Ptr<cv::cuda::BroxOpticalFlow> brox =
cv::cuda::BroxOpticalFlow::create(0.197 /*alpha*/, 50.0 /*gamma*/, 0.8 /*scale_factor*/,
10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/);
cv::cuda::GpuMat flow;
brox->calc(loadMat(frame0), loadMat(frame1), flow);
cv::cuda::GpuMat flows[2];
cv::cuda::split(flow, flows);
cv::cuda::GpuMat u = flows[0];
cv::cuda::GpuMat v = flows[1];
std::string fname(cvtest::TS::ptr()->get_data_path());
if (devInfo.majorVersion() >= 2)
fname += "opticalflow/brox_optical_flow_cc20.bin";
else
fname += "opticalflow/brox_optical_flow.bin";
#ifndef BROX_DUMP
std::ifstream f(fname.c_str(), std::ios_base::binary);
int rows, cols;
f.read((char*) &rows, sizeof(rows));
f.read((char*) &cols, sizeof(cols));
cv::Mat u_gold(rows, cols, CV_32FC1);
for (int i = 0; i < u_gold.rows; ++i)
f.read(u_gold.ptr<char>(i), u_gold.cols * sizeof(float));
cv::Mat v_gold(rows, cols, CV_32FC1);
for (int i = 0; i < v_gold.rows; ++i)
f.read(v_gold.ptr<char>(i), v_gold.cols * sizeof(float));
EXPECT_MAT_SIMILAR(u_gold, u, 1e-3);
EXPECT_MAT_SIMILAR(v_gold, v, 1e-3);
#else
std::ofstream f(fname.c_str(), std::ios_base::binary);
f.write((char*) &u.rows, sizeof(u.rows));
f.write((char*) &u.cols, sizeof(u.cols));
cv::Mat h_u(u);
cv::Mat h_v(v);
for (int i = 0; i < u.rows; ++i)
f.write(h_u.ptr<char>(i), u.cols * sizeof(float));
for (int i = 0; i < v.rows; ++i)
f.write(h_v.ptr<char>(i), v.cols * sizeof(float));
#endif
}
CUDA_TEST_P(BroxOpticalFlow, OpticalFlowNan)
{
cv::Mat frame0 = readImageType("opticalflow/frame0.png", CV_32FC1);
ASSERT_FALSE(frame0.empty());
cv::Mat frame1 = readImageType("opticalflow/frame1.png", CV_32FC1);
ASSERT_FALSE(frame1.empty());
cv::Mat r_frame0, r_frame1;
cv::resize(frame0, r_frame0, cv::Size(1380,1000));
cv::resize(frame1, r_frame1, cv::Size(1380,1000));
cv::Ptr<cv::cuda::BroxOpticalFlow> brox =
cv::cuda::BroxOpticalFlow::create(0.197 /*alpha*/, 50.0 /*gamma*/, 0.8 /*scale_factor*/,
10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/);
cv::cuda::GpuMat flow;
brox->calc(loadMat(frame0), loadMat(frame1), flow);
cv::cuda::GpuMat flows[2];
cv::cuda::split(flow, flows);
cv::cuda::GpuMat u = flows[0];
cv::cuda::GpuMat v = flows[1];
cv::Mat h_u, h_v;
u.download(h_u);
v.download(h_v);
EXPECT_TRUE(cv::checkRange(h_u));
EXPECT_TRUE(cv::checkRange(h_v));
};
INSTANTIATE_TEST_CASE_P(CUDA_OptFlow, BroxOpticalFlow, ALL_DEVICES);
//////////////////////////////////////////////////////
// PyrLKOpticalFlow
namespace
{
IMPLEMENT_PARAM_CLASS(Chan, int)
IMPLEMENT_PARAM_CLASS(DataType, int)
}
PARAM_TEST_CASE(PyrLKOpticalFlow, cv::cuda::DeviceInfo, Chan, DataType)
{
cv::cuda::DeviceInfo devInfo;
int channels;
int dataType;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
channels = GET_PARAM(1);
dataType = GET_PARAM(2);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(PyrLKOpticalFlow, Sparse)
{
cv::Mat frame0 = readImage("opticalflow/frame0.png", channels == 1 ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
ASSERT_FALSE(frame0.empty());
cv::Mat frame1 = readImage("opticalflow/frame1.png", channels == 1 ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
ASSERT_FALSE(frame1.empty());
cv::Mat gray_frame;
if (channels == 1)
gray_frame = frame0;
else
cv::cvtColor(frame0, gray_frame, cv::COLOR_BGR2GRAY);
std::vector<cv::Point2f> pts;
cv::goodFeaturesToTrack(gray_frame, pts, 1000, 0.01, 0.0);
cv::cuda::GpuMat d_pts;
cv::Mat pts_mat(1, (int) pts.size(), CV_32FC2, (void*) &pts[0]);
d_pts.upload(pts_mat);
cv::Ptr<cv::cuda::SparsePyrLKOpticalFlow> pyrLK =
cv::cuda::SparsePyrLKOpticalFlow::create();
std::vector<cv::Point2f> nextPts_gold;
std::vector<unsigned char> status_gold;
cv::calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts_gold, status_gold, cv::noArray());
cv::cuda::GpuMat d_nextPts;
cv::cuda::GpuMat d_status;
cv::Mat converted0, converted1;
if(channels == 4)
{
cv::cvtColor(frame0, frame0, cv::COLOR_BGR2BGRA);
cv::cvtColor(frame1, frame1, cv::COLOR_BGR2BGRA);
}
frame0.convertTo(converted0, dataType);
frame1.convertTo(converted1, dataType);
pyrLK->calc(loadMat(converted0), loadMat(converted1), d_pts, d_nextPts, d_status);
std::vector<cv::Point2f> nextPts(d_nextPts.cols);
cv::Mat nextPts_mat(1, d_nextPts.cols, CV_32FC2, (void*)&nextPts[0]);
d_nextPts.download(nextPts_mat);
std::vector<unsigned char> status(d_status.cols);
cv::Mat status_mat(1, d_status.cols, CV_8UC1, (void*)&status[0]);
d_status.download(status_mat);
ASSERT_EQ(nextPts_gold.size(), nextPts.size());
ASSERT_EQ(status_gold.size(), status.size());
size_t mistmatch = 0;
for (size_t i = 0; i < nextPts.size(); ++i)
{
cv::Point2i a = nextPts[i];
cv::Point2i b = nextPts_gold[i];
if (status[i] != status_gold[i])
{
++mistmatch;
continue;
}
if (status[i])
{
bool eq = std::abs(a.x - b.x) <= 1 && std::abs(a.y - b.y) <= 1;
if (!eq)
++mistmatch;
}
}
double bad_ratio = static_cast<double>(mistmatch) / nextPts.size();
ASSERT_LE(bad_ratio, 0.01);
}
INSTANTIATE_TEST_CASE_P(CUDA_OptFlow, PyrLKOpticalFlow, testing::Combine(
ALL_DEVICES,
testing::Values(Chan(1), Chan(3), Chan(4)),
testing::Values(DataType(CV_8U), DataType(CV_16U), DataType(CV_32S), DataType(CV_32F))));
//////////////////////////////////////////////////////
// FarnebackOpticalFlow
namespace
{
IMPLEMENT_PARAM_CLASS(PyrScale, double)
IMPLEMENT_PARAM_CLASS(PolyN, int)
CV_FLAGS(FarnebackOptFlowFlags, 0, OPTFLOW_FARNEBACK_GAUSSIAN)
IMPLEMENT_PARAM_CLASS(UseInitFlow, bool)
}
PARAM_TEST_CASE(FarnebackOpticalFlow, cv::cuda::DeviceInfo, PyrScale, PolyN, FarnebackOptFlowFlags, UseInitFlow)
{
cv::cuda::DeviceInfo devInfo;
double pyrScale;
int polyN;
int flags;
bool useInitFlow;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
pyrScale = GET_PARAM(1);
polyN = GET_PARAM(2);
flags = GET_PARAM(3);
useInitFlow = GET_PARAM(4);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(FarnebackOpticalFlow, Accuracy)
{
cv::Mat frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame0.empty());
cv::Mat frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty());
double polySigma = polyN <= 5 ? 1.1 : 1.5;
cv::Ptr<cv::cuda::FarnebackOpticalFlow> farn =
cv::cuda::FarnebackOpticalFlow::create();
farn->setPyrScale(pyrScale);
farn->setPolyN(polyN);
farn->setPolySigma(polySigma);
farn->setFlags(flags);
cv::cuda::GpuMat d_flow;
farn->calc(loadMat(frame0), loadMat(frame1), d_flow);
cv::Mat flow;
if (useInitFlow)
{
d_flow.download(flow);
farn->setFlags(farn->getFlags() | cv::OPTFLOW_USE_INITIAL_FLOW);
farn->calc(loadMat(frame0), loadMat(frame1), d_flow);
}
cv::calcOpticalFlowFarneback(
frame0, frame1, flow, farn->getPyrScale(), farn->getNumLevels(), farn->getWinSize(),
farn->getNumIters(), farn->getPolyN(), farn->getPolySigma(), farn->getFlags());
// Relax test limit when the flag is set
if (farn->getFlags() & cv::OPTFLOW_FARNEBACK_GAUSSIAN)
{
EXPECT_MAT_SIMILAR(flow, d_flow, 2e-2);
}
else
{
EXPECT_MAT_SIMILAR(flow, d_flow, 1e-4);
}
}
INSTANTIATE_TEST_CASE_P(CUDA_OptFlow, FarnebackOpticalFlow, testing::Combine(
ALL_DEVICES,
testing::Values(PyrScale(0.3), PyrScale(0.5), PyrScale(0.8)),
testing::Values(PolyN(5), PolyN(7)),
testing::Values(FarnebackOptFlowFlags(0), FarnebackOptFlowFlags(cv::OPTFLOW_FARNEBACK_GAUSSIAN)),
testing::Values(UseInitFlow(false), UseInitFlow(true))));
//////////////////////////////////////////////////////
// OpticalFlowDual_TVL1
namespace
{
IMPLEMENT_PARAM_CLASS(Gamma, double)
}
PARAM_TEST_CASE(OpticalFlowDual_TVL1, cv::cuda::DeviceInfo, Gamma)
{
cv::cuda::DeviceInfo devInfo;
double gamma;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
gamma = GET_PARAM(1);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(OpticalFlowDual_TVL1, Accuracy)
{
cv::Mat frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame0.empty());
cv::Mat frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty());
cv::Ptr<cv::cuda::OpticalFlowDual_TVL1> d_alg =
cv::cuda::OpticalFlowDual_TVL1::create();
d_alg->setNumIterations(10);
d_alg->setGamma(gamma);
cv::cuda::GpuMat d_flow;
d_alg->calc(loadMat(frame0), loadMat(frame1), d_flow);
cv::Ptr<cv::DualTVL1OpticalFlow> alg = cv::createOptFlow_DualTVL1();
alg->setMedianFiltering(1);
alg->setInnerIterations(1);
alg->setOuterIterations(d_alg->getNumIterations());
alg->setGamma(gamma);
cv::Mat flow;
alg->calc(frame0, frame1, flow);
EXPECT_MAT_SIMILAR(flow, d_flow, 4e-3);
}
class TVL1AsyncParallelLoopBody : public cv::ParallelLoopBody
{
public:
TVL1AsyncParallelLoopBody(const cv::cuda::GpuMat& d_img1_, const cv::cuda::GpuMat& d_img2_, cv::cuda::GpuMat* d_flow_, int iterations_, double gamma_)
: d_img1(d_img1_), d_img2(d_img2_), d_flow(d_flow_), iterations(iterations_), gamma(gamma_) {}
~TVL1AsyncParallelLoopBody() {}
void operator()(const cv::Range& r) const
{
for (int i = r.start; i < r.end; i++) {
cv::cuda::Stream stream;
cv::Ptr<cv::cuda::OpticalFlowDual_TVL1> d_alg = cv::cuda::OpticalFlowDual_TVL1::create();
d_alg->setNumIterations(iterations);
d_alg->setGamma(gamma);
d_alg->calc(d_img1, d_img2, d_flow[i], stream);
stream.waitForCompletion();
}
}
protected:
const cv::cuda::GpuMat& d_img1;
const cv::cuda::GpuMat& d_img2;
cv::cuda::GpuMat* d_flow;
int iterations;
double gamma;
};
#define NUM_STREAMS 16
CUDA_TEST_P(OpticalFlowDual_TVL1, Async)
{
if (!supportFeature(devInfo, cv::cuda::FEATURE_SET_COMPUTE_30))
{
throw SkipTestException("CUDA device doesn't support texture objects");
}
else
{
cv::Mat frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame0.empty());
cv::Mat frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty());
const int iterations = 10;
// Synchronous call
cv::Ptr<cv::cuda::OpticalFlowDual_TVL1> d_alg =
cv::cuda::OpticalFlowDual_TVL1::create();
d_alg->setNumIterations(iterations);
d_alg->setGamma(gamma);
cv::cuda::GpuMat d_flow_gold;
d_alg->calc(loadMat(frame0), loadMat(frame1), d_flow_gold);
// Asynchronous call
cv::cuda::GpuMat d_flow[NUM_STREAMS];
cv::parallel_for_(cv::Range(0, NUM_STREAMS), TVL1AsyncParallelLoopBody(loadMat(frame0), loadMat(frame1), d_flow, iterations, gamma));
// Compare the results of synchronous call and asynchronous call
for (int i = 0; i < NUM_STREAMS; i++)
EXPECT_MAT_NEAR(d_flow_gold, d_flow[i], 0.0);
}
}
INSTANTIATE_TEST_CASE_P(CUDA_OptFlow, OpticalFlowDual_TVL1, testing::Combine(
ALL_DEVICES,
testing::Values(Gamma(0.0), Gamma(1.0))));
}} // namespace
#endif // HAVE_CUDA
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