minor modification of gpu video tests

disabled NVidia Visualization test, it's functionality (draw rectangles) doesn't used in gpu module
This commit is contained in:
Vladislav Vinogradov 2012-03-27 12:15:52 +00:00
parent 7a62413c94
commit b2a6a257b7
4 changed files with 192 additions and 298 deletions

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@ -67,7 +67,9 @@ void cv::gpu::GoodFeaturesToTrackDetector_GPU::operator ()(const GpuMat& image,
CV_Assert(qualityLevel > 0 && minDistance >= 0 && maxCorners >= 0); CV_Assert(qualityLevel > 0 && minDistance >= 0 && maxCorners >= 0);
CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size())); CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size()));
CV_Assert(TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS));
if (!TargetArchs::builtWith(GLOBAL_ATOMICS) || !DeviceInfo().supports(GLOBAL_ATOMICS))
CV_Error(CV_StsNotImplemented, "The device doesn't support global atomics");
ensureSizeIsEnough(image.size(), CV_32F, eig_); ensureSizeIsEnough(image.size(), CV_32F, eig_);

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@ -156,14 +156,15 @@ TEST_P(NCV, HypothesesFiltration)
ASSERT_TRUE(res); ASSERT_TRUE(res);
} }
TEST_P(NCV, Visualization) TEST_P(NCV, DISABLED_Visualization)
{ {
// this functionality doesn't used in gpu module
bool res = nvidia_NCV_Visualization(path, nvidiaTestOutputLevel); bool res = nvidia_NCV_Visualization(path, nvidiaTestOutputLevel);
ASSERT_TRUE(res); ASSERT_TRUE(res);
} }
INSTANTIATE_TEST_CASE_P(NVidia, NPPST, ALL_DEVICES); INSTANTIATE_TEST_CASE_P(GPU_NVidia, NPPST, ALL_DEVICES);
INSTANTIATE_TEST_CASE_P(NVidia, NCV, ALL_DEVICES); INSTANTIATE_TEST_CASE_P(GPU_NVidia, NCV, ALL_DEVICES);
#endif // HAVE_CUDA #endif // HAVE_CUDA

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@ -41,52 +41,49 @@
#include "precomp.hpp" #include "precomp.hpp"
#ifdef HAVE_CUDA namespace {
using namespace cvtest;
using namespace testing;
//#define DUMP //#define DUMP
#define OPTICAL_FLOW_DUMP_FILE "opticalflow/opticalflow_gold.bin"
#define OPTICAL_FLOW_DUMP_FILE_CC20 "opticalflow/opticalflow_gold_cc20.bin"
#define INTERPOLATE_FRAMES_DUMP_FILE "opticalflow/interpolate_frames_gold.bin"
#define INTERPOLATE_FRAMES_DUMP_FILE_CC20 "opticalflow/interpolate_frames_gold_cc20.bin"
///////////////////////////////////////////////////////////////////////////////////////////////// /////////////////////////////////////////////////////////////////////////////////////////////////
// BroxOpticalFlow // BroxOpticalFlow
struct BroxOpticalFlow : TestWithParam<cv::gpu::DeviceInfo> #define BROX_OPTICAL_FLOW_DUMP_FILE "opticalflow/brox_optical_flow.bin"
#define BROX_OPTICAL_FLOW_DUMP_FILE_CC20 "opticalflow/brox_optical_flow_cc20.bin"
struct BroxOpticalFlow : testing::TestWithParam<cv::gpu::DeviceInfo>
{ {
cv::gpu::DeviceInfo devInfo; cv::gpu::DeviceInfo devInfo;
cv::Mat frame0;
cv::Mat frame1;
cv::Mat u_gold;
cv::Mat v_gold;
virtual void SetUp() virtual void SetUp()
{ {
devInfo = GetParam(); devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID()); cv::gpu::setDevice(devInfo.deviceID());
}
};
frame0 = readImage("opticalflow/frame0.png", cv::IMREAD_GRAYSCALE); TEST_P(BroxOpticalFlow, Regression)
{
cv::Mat frame0 = readImageType("opticalflow/frame0.png", CV_32FC1);
ASSERT_FALSE(frame0.empty()); ASSERT_FALSE(frame0.empty());
frame0.convertTo(frame0, CV_32F, 1.0 / 255.0);
frame1 = readImage("opticalflow/frame1.png", cv::IMREAD_GRAYSCALE); cv::Mat frame1 = readImageType("opticalflow/frame1.png", CV_32FC1);
ASSERT_FALSE(frame1.empty()); ASSERT_FALSE(frame1.empty());
frame1.convertTo(frame1, CV_32F, 1.0 / 255.0);
cv::gpu::BroxOpticalFlow brox(0.197f /*alpha*/, 50.0f /*gamma*/, 0.8f /*scale_factor*/,
10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/);
cv::gpu::GpuMat u;
cv::gpu::GpuMat v;
brox(loadMat(frame0), loadMat(frame1), u, v);
#ifndef DUMP #ifndef DUMP
std::string fname(cvtest::TS::ptr()->get_data_path()); std::string fname(cvtest::TS::ptr()->get_data_path());
if (devInfo.majorVersion() >= 2) if (devInfo.majorVersion() >= 2)
fname += OPTICAL_FLOW_DUMP_FILE_CC20; fname += BROX_OPTICAL_FLOW_DUMP_FILE_CC20;
else else
fname += OPTICAL_FLOW_DUMP_FILE; fname += BROX_OPTICAL_FLOW_DUMP_FILE;
std::ifstream f(fname.c_str(), std::ios_base::binary); std::ifstream f(fname.c_str(), std::ios_base::binary);
@ -95,213 +92,100 @@ struct BroxOpticalFlow : TestWithParam<cv::gpu::DeviceInfo>
f.read((char*)&rows, sizeof(rows)); f.read((char*)&rows, sizeof(rows));
f.read((char*)&cols, sizeof(cols)); f.read((char*)&cols, sizeof(cols));
u_gold.create(rows, cols, CV_32FC1); cv::Mat u_gold(rows, cols, CV_32FC1);
for (int i = 0; i < u_gold.rows; ++i) for (int i = 0; i < u_gold.rows; ++i)
f.read((char*)u_gold.ptr(i), u_gold.cols * sizeof(float)); f.read(u_gold.ptr<char>(i), u_gold.cols * sizeof(float));
v_gold.create(rows, cols, CV_32FC1); cv::Mat v_gold(rows, cols, CV_32FC1);
for (int i = 0; i < v_gold.rows; ++i) for (int i = 0; i < v_gold.rows; ++i)
f.read((char*)v_gold.ptr(i), v_gold.cols * sizeof(float)); f.read(v_gold.ptr<char>(i), v_gold.cols * sizeof(float));
#endif
}
};
TEST_P(BroxOpticalFlow, Regression)
{
cv::Mat u;
cv::Mat v;
cv::gpu::BroxOpticalFlow d_flow(0.197f /*alpha*/, 50.0f /*gamma*/, 0.8f /*scale_factor*/,
10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/);
cv::gpu::GpuMat d_u;
cv::gpu::GpuMat d_v;
d_flow(cv::gpu::GpuMat(frame0), cv::gpu::GpuMat(frame1), d_u, d_v);
d_u.download(u);
d_v.download(v);
#ifndef DUMP
EXPECT_MAT_NEAR(u_gold, u, 0); EXPECT_MAT_NEAR(u_gold, u, 0);
EXPECT_MAT_NEAR(v_gold, v, 0); EXPECT_MAT_NEAR(v_gold, v, 0);
#else #else
std::string fname(cvtest::TS::ptr()->get_data_path()); std::string fname(cvtest::TS::ptr()->get_data_path());
if (devInfo.majorVersion() >= 2) if (devInfo.majorVersion() >= 2)
fname += OPTICAL_FLOW_DUMP_FILE_CC20; fname += BROX_OPTICAL_FLOW_DUMP_FILE_CC20;
else else
fname += OPTICAL_FLOW_DUMP_FILE; fname += BROX_OPTICAL_FLOW_DUMP_FILE;
std::ofstream f(fname.c_str(), std::ios_base::binary); std::ofstream f(fname.c_str(), std::ios_base::binary);
f.write((char*)&u.rows, sizeof(u.rows)); f.write((char*)&u.rows, sizeof(u.rows));
f.write((char*)&u.cols, sizeof(u.cols)); 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) for (int i = 0; i < u.rows; ++i)
f.write((char*)u.ptr(i), u.cols * sizeof(float)); f.write(h_u.ptr<char>(i), u.cols * sizeof(float));
for (int i = 0; i < v.rows; ++i) for (int i = 0; i < v.rows; ++i)
f.write((char*)v.ptr(i), v.cols * sizeof(float)); f.write(h_v.ptr<char>(i), v.cols * sizeof(float));
#endif #endif
} }
INSTANTIATE_TEST_CASE_P(Video, BroxOpticalFlow, ALL_DEVICES); INSTANTIATE_TEST_CASE_P(GPU_Video, BroxOpticalFlow, ALL_DEVICES);
/////////////////////////////////////////////////////////////////////////////////////////////////
// InterpolateFrames
struct InterpolateFrames : TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
cv::Mat frame0;
cv::Mat frame1;
cv::Mat newFrame_gold;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
frame0 = readImage("opticalflow/frame0.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame0.empty());
frame0.convertTo(frame0, CV_32F, 1.0 / 255.0);
frame1 = readImage("opticalflow/frame1.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty());
frame1.convertTo(frame1, CV_32F, 1.0 / 255.0);
#ifndef DUMP
std::string fname(cvtest::TS::ptr()->get_data_path());
if (devInfo.majorVersion() >= 2)
fname += INTERPOLATE_FRAMES_DUMP_FILE_CC20;
else
fname += INTERPOLATE_FRAMES_DUMP_FILE;
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));
newFrame_gold.create(rows, cols, CV_32FC1);
for (int i = 0; i < newFrame_gold.rows; ++i)
f.read((char*)newFrame_gold.ptr(i), newFrame_gold.cols * sizeof(float));
#endif
}
};
TEST_P(InterpolateFrames, Regression)
{
cv::Mat newFrame;
cv::gpu::BroxOpticalFlow d_flow(0.197f /*alpha*/, 50.0f /*gamma*/, 0.8f /*scale_factor*/,
10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/);
cv::gpu::GpuMat d_frame0(frame0);
cv::gpu::GpuMat d_frame1(frame1);
cv::gpu::GpuMat d_fu;
cv::gpu::GpuMat d_fv;
cv::gpu::GpuMat d_bu;
cv::gpu::GpuMat d_bv;
d_flow(d_frame0, d_frame1, d_fu, d_fv);
d_flow(d_frame1, d_frame0, d_bu, d_bv);
cv::gpu::GpuMat d_newFrame;
cv::gpu::GpuMat d_buf;
cv::gpu::interpolateFrames(d_frame0, d_frame1, d_fu, d_fv, d_bu, d_bv, 0.5f, d_newFrame, d_buf);
d_newFrame.download(newFrame);
#ifndef DUMP
EXPECT_MAT_NEAR(newFrame_gold, newFrame, 1e-3);
#else
std::string fname(cvtest::TS::ptr()->get_data_path());
if (devInfo.majorVersion() >= 2)
fname += INTERPOLATE_FRAMES_DUMP_FILE_CC20;
else
fname += INTERPOLATE_FRAMES_DUMP_FILE;
std::ofstream f(fname.c_str(), std::ios_base::binary);
f.write((char*)&newFrame.rows, sizeof(newFrame.rows));
f.write((char*)&newFrame.cols, sizeof(newFrame.cols));
for (int i = 0; i < newFrame.rows; ++i)
f.write((char*)newFrame.ptr(i), newFrame.cols * sizeof(float));
#endif
}
INSTANTIATE_TEST_CASE_P(Video, InterpolateFrames, ALL_DEVICES);
///////////////////////////////////////////////////////////////////////////////////////////////// /////////////////////////////////////////////////////////////////////////////////////////////////
// GoodFeaturesToTrack // GoodFeaturesToTrack
PARAM_TEST_CASE(GoodFeaturesToTrack, cv::gpu::DeviceInfo, double) IMPLEMENT_PARAM_CLASS(MinDistance, double)
PARAM_TEST_CASE(GoodFeaturesToTrack, cv::gpu::DeviceInfo, MinDistance)
{ {
cv::gpu::DeviceInfo devInfo; cv::gpu::DeviceInfo devInfo;
cv::Mat image;
int maxCorners;
double qualityLevel;
double minDistance; double minDistance;
std::vector<cv::Point2f> pts_gold;
virtual void SetUp() virtual void SetUp()
{ {
devInfo = GET_PARAM(0); devInfo = GET_PARAM(0);
minDistance = GET_PARAM(1); minDistance = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID()); cv::gpu::setDevice(devInfo.deviceID());
image = readImage("opticalflow/frame0.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(image.empty());
maxCorners = 1000;
qualityLevel= 0.01;
cv::goodFeaturesToTrack(image, pts_gold, maxCorners, qualityLevel, minDistance);
} }
}; };
TEST_P(GoodFeaturesToTrack, Accuracy) TEST_P(GoodFeaturesToTrack, Accuracy)
{ {
cv::Mat image = readImage("opticalflow/frame0.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(image.empty());
int maxCorners = 1000;
double qualityLevel = 0.01;
cv::gpu::GoodFeaturesToTrackDetector_GPU detector(maxCorners, qualityLevel, minDistance); cv::gpu::GoodFeaturesToTrackDetector_GPU detector(maxCorners, qualityLevel, minDistance);
if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS))
{
try
{
cv::gpu::GpuMat d_pts;
detector(loadMat(image), d_pts);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsNotImplemented, e.code);
}
}
else
{
cv::gpu::GpuMat d_pts; cv::gpu::GpuMat d_pts;
detector(loadMat(image), d_pts); detector(loadMat(image), d_pts);
std::vector<cv::Point2f> pts(d_pts.cols); std::vector<cv::Point2f> pts(d_pts.cols);
cv::Mat pts_mat(1, d_pts.cols, CV_32FC2, (void*)&pts[0]); cv::Mat pts_mat(1, d_pts.cols, CV_32FC2, (void*)&pts[0]);
d_pts.download(pts_mat); d_pts.download(pts_mat);
std::vector<cv::Point2f> pts_gold;
cv::goodFeaturesToTrack(image, pts_gold, maxCorners, qualityLevel, minDistance);
ASSERT_EQ(pts_gold.size(), pts.size()); ASSERT_EQ(pts_gold.size(), pts.size());
size_t mistmatch = 0; size_t mistmatch = 0;
for (size_t i = 0; i < pts.size(); ++i) for (size_t i = 0; i < pts.size(); ++i)
{ {
cv::Point2i a = pts_gold[i]; cv::Point2i a = pts_gold[i];
@ -316,37 +200,38 @@ TEST_P(GoodFeaturesToTrack, Accuracy)
double bad_ratio = static_cast<double>(mistmatch) / pts.size(); double bad_ratio = static_cast<double>(mistmatch) / pts.size();
ASSERT_LE(bad_ratio, 0.01); ASSERT_LE(bad_ratio, 0.01);
}
} }
INSTANTIATE_TEST_CASE_P(Video, GoodFeaturesToTrack, Combine(ALL_DEVICES, Values(0.0, 3.0))); INSTANTIATE_TEST_CASE_P(GPU_Video, GoodFeaturesToTrack, testing::Combine(
ALL_DEVICES,
testing::Values(MinDistance(0.0), MinDistance(3.0))));
///////////////////////////////////////////////////////////////////////////////////////////////// /////////////////////////////////////////////////////////////////////////////////////////////////
// PyrLKOpticalFlow // PyrLKOpticalFlow
PARAM_TEST_CASE(PyrLKOpticalFlowSparse, cv::gpu::DeviceInfo, bool) IMPLEMENT_PARAM_CLASS(UseGray, bool)
PARAM_TEST_CASE(PyrLKOpticalFlow, cv::gpu::DeviceInfo, UseGray)
{ {
cv::gpu::DeviceInfo devInfo; cv::gpu::DeviceInfo devInfo;
bool useGray;
cv::Mat frame0;
cv::Mat frame1;
std::vector<cv::Point2f> pts;
std::vector<cv::Point2f> nextPts_gold;
std::vector<unsigned char> status_gold;
std::vector<float> err_gold;
virtual void SetUp() virtual void SetUp()
{ {
devInfo = GET_PARAM(0); devInfo = GET_PARAM(0);
bool useGray = GET_PARAM(1); useGray = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID()); cv::gpu::setDevice(devInfo.deviceID());
}
};
frame0 = readImage("opticalflow/frame0.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR); TEST_P(PyrLKOpticalFlow, Sparse)
{
cv::Mat frame0 = readImage("opticalflow/frame0.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
ASSERT_FALSE(frame0.empty()); ASSERT_FALSE(frame0.empty());
frame1 = readImage("opticalflow/frame1.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR); cv::Mat frame1 = readImage("opticalflow/frame1.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
ASSERT_FALSE(frame1.empty()); ASSERT_FALSE(frame1.empty());
cv::Mat gray_frame; cv::Mat gray_frame;
@ -355,26 +240,19 @@ PARAM_TEST_CASE(PyrLKOpticalFlowSparse, cv::gpu::DeviceInfo, bool)
else else
cv::cvtColor(frame0, gray_frame, cv::COLOR_BGR2GRAY); cv::cvtColor(frame0, gray_frame, cv::COLOR_BGR2GRAY);
std::vector<cv::Point2f> pts;
cv::goodFeaturesToTrack(gray_frame, pts, 1000, 0.01, 0.0); cv::goodFeaturesToTrack(gray_frame, pts, 1000, 0.01, 0.0);
cv::calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts_gold, status_gold, err_gold, cv::Size(21, 21), 3,
cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 30, 0.01), 0.5);
}
};
TEST_P(PyrLKOpticalFlowSparse, Accuracy)
{
cv::gpu::PyrLKOpticalFlow d_pyrLK;
cv::gpu::GpuMat d_pts; cv::gpu::GpuMat d_pts;
cv::Mat pts_mat(1, pts.size(), CV_32FC2, (void*)&pts[0]); cv::Mat pts_mat(1, pts.size(), CV_32FC2, (void*)&pts[0]);
d_pts.upload(pts_mat); d_pts.upload(pts_mat);
cv::gpu::PyrLKOpticalFlow pyrLK;
cv::gpu::GpuMat d_nextPts; cv::gpu::GpuMat d_nextPts;
cv::gpu::GpuMat d_status; cv::gpu::GpuMat d_status;
cv::gpu::GpuMat d_err; cv::gpu::GpuMat d_err;
pyrLK.sparse(loadMat(frame0), loadMat(frame1), d_pts, d_nextPts, d_status, &d_err);
d_pyrLK.sparse(loadMat(frame0), loadMat(frame1), d_pts, d_nextPts, d_status, &d_err);
std::vector<cv::Point2f> nextPts(d_nextPts.cols); std::vector<cv::Point2f> nextPts(d_nextPts.cols);
cv::Mat nextPts_mat(1, d_nextPts.cols, CV_32FC2, (void*)&nextPts[0]); cv::Mat nextPts_mat(1, d_nextPts.cols, CV_32FC2, (void*)&nextPts[0]);
@ -388,12 +266,16 @@ TEST_P(PyrLKOpticalFlowSparse, Accuracy)
cv::Mat err_mat(1, d_err.cols, CV_32FC1, (void*)&err[0]); cv::Mat err_mat(1, d_err.cols, CV_32FC1, (void*)&err[0]);
d_err.download(err_mat); d_err.download(err_mat);
std::vector<cv::Point2f> nextPts_gold;
std::vector<unsigned char> status_gold;
std::vector<float> err_gold;
cv::calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts_gold, status_gold, err_gold);
ASSERT_EQ(nextPts_gold.size(), nextPts.size()); ASSERT_EQ(nextPts_gold.size(), nextPts.size());
ASSERT_EQ(status_gold.size(), status.size()); ASSERT_EQ(status_gold.size(), status.size());
ASSERT_EQ(err_gold.size(), err.size()); ASSERT_EQ(err_gold.size(), err.size());
size_t mistmatch = 0; size_t mistmatch = 0;
for (size_t i = 0; i < nextPts.size(); ++i) for (size_t i = 0; i < nextPts.size(); ++i)
{ {
if (status[i] != status_gold[i]) if (status[i] != status_gold[i])
@ -420,77 +302,86 @@ TEST_P(PyrLKOpticalFlowSparse, Accuracy)
ASSERT_LE(bad_ratio, 0.01); ASSERT_LE(bad_ratio, 0.01);
} }
INSTANTIATE_TEST_CASE_P(Video, PyrLKOpticalFlowSparse, Combine(ALL_DEVICES, Bool())); INSTANTIATE_TEST_CASE_P(GPU_Video, PyrLKOpticalFlow, testing::Combine(
ALL_DEVICES,
testing::Values(UseGray(true), UseGray(false))));
/////////////////////////////////////////////////////////////////////////////////////////////////
// FarnebackOpticalFlow
PARAM_TEST_CASE(FarnebackOpticalFlowTest, cv::gpu::DeviceInfo, double, int, int, bool) IMPLEMENT_PARAM_CLASS(PyrScale, double)
IMPLEMENT_PARAM_CLASS(PolyN, int)
CV_FLAGS(FarnebackOptFlowFlags, 0, cv::OPTFLOW_FARNEBACK_GAUSSIAN)
IMPLEMENT_PARAM_CLASS(UseInitFlow, bool)
PARAM_TEST_CASE(FarnebackOpticalFlow, cv::gpu::DeviceInfo, PyrScale, PolyN, FarnebackOptFlowFlags, UseInitFlow)
{ {
cv::Mat frame0, frame1; cv::gpu::DeviceInfo devInfo;
double pyrScale; double pyrScale;
int polyN; int polyN;
double polySigma;
int flags; int flags;
bool useInitFlow; bool useInitFlow;
virtual void SetUp() virtual void SetUp()
{ {
frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE); devInfo = GET_PARAM(0);
frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame0.empty()); ASSERT_FALSE(frame1.empty());
cv::gpu::setDevice(GET_PARAM(0).deviceID());
pyrScale = GET_PARAM(1); pyrScale = GET_PARAM(1);
polyN = GET_PARAM(2); polyN = GET_PARAM(2);
polySigma = polyN <= 5 ? 1.1 : 1.5;
flags = GET_PARAM(3); flags = GET_PARAM(3);
useInitFlow = GET_PARAM(4); useInitFlow = GET_PARAM(4);
cv::gpu::setDevice(devInfo.deviceID());
} }
}; };
TEST_P(FarnebackOpticalFlowTest, Accuracy) TEST_P(FarnebackOpticalFlow, Accuracy)
{ {
using namespace cv; cv::Mat frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame0.empty());
gpu::FarnebackOpticalFlow calc; cv::Mat frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty());
double polySigma = polyN <= 5 ? 1.1 : 1.5;
cv::gpu::FarnebackOpticalFlow calc;
calc.pyrScale = pyrScale; calc.pyrScale = pyrScale;
calc.polyN = polyN; calc.polyN = polyN;
calc.polySigma = polySigma; calc.polySigma = polySigma;
calc.flags = flags; calc.flags = flags;
gpu::GpuMat d_flowx, d_flowy; cv::gpu::GpuMat d_flowx, d_flowy;
calc(gpu::GpuMat(frame0), gpu::GpuMat(frame1), d_flowx, d_flowy); calc(loadMat(frame0), loadMat(frame1), d_flowx, d_flowy);
Mat flow; cv::Mat flow;
if (useInitFlow) if (useInitFlow)
{ {
Mat flowxy[] = {(Mat)d_flowx, (Mat)d_flowy}; cv::Mat flowxy[] = {cv::Mat(d_flowx), cv::Mat(d_flowy)};
merge(flowxy, 2, flow); cv::merge(flowxy, 2, flow);
} }
if (useInitFlow) if (useInitFlow)
{ {
calc.flags |= OPTFLOW_USE_INITIAL_FLOW; calc.flags |= cv::OPTFLOW_USE_INITIAL_FLOW;
calc(gpu::GpuMat(frame0), gpu::GpuMat(frame1), d_flowx, d_flowy); calc(loadMat(frame0), loadMat(frame1), d_flowx, d_flowy);
} }
calcOpticalFlowFarneback( cv::calcOpticalFlowFarneback(
frame0, frame1, flow, calc.pyrScale, calc.numLevels, calc.winSize, frame0, frame1, flow, calc.pyrScale, calc.numLevels, calc.winSize,
calc.numIters, calc.polyN, calc.polySigma, calc.flags); calc.numIters, calc.polyN, calc.polySigma, calc.flags);
std::vector<Mat> flowxy; split(flow, flowxy); std::vector<cv::Mat> flowxy;
/*std::cout << checkSimilarity(flowxy[0], (Mat)d_flowx) << " " cv::split(flow, flowxy);
<< checkSimilarity(flowxy[1], (Mat)d_flowy) << std::endl;*/
EXPECT_LT(checkSimilarity(flowxy[0], (Mat)d_flowx), 0.1); EXPECT_MAT_SIMILAR(flowxy[0], d_flowx, 0.1);
EXPECT_LT(checkSimilarity(flowxy[1], (Mat)d_flowy), 0.1); EXPECT_MAT_SIMILAR(flowxy[1], d_flowy, 0.1);
} }
INSTANTIATE_TEST_CASE_P(Video, FarnebackOpticalFlowTest, INSTANTIATE_TEST_CASE_P(GPU_Video, FarnebackOpticalFlow, testing::Combine(
Combine(ALL_DEVICES, ALL_DEVICES,
Values(0.3, 0.5, 0.8), testing::Values(PyrScale(0.3), PyrScale(0.5), PyrScale(0.8)),
Values(5, 7), testing::Values(PolyN(5), PolyN(7)),
Values(0, (int)cv::OPTFLOW_FARNEBACK_GAUSSIAN), testing::Values(FarnebackOptFlowFlags(0), FarnebackOptFlowFlags(cv::OPTFLOW_FARNEBACK_GAUSSIAN)),
Values(false, true))); testing::Values(UseInitFlow(false), UseInitFlow(true))));
#endif // HAVE_CUDA } // namespace

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@ -122,7 +122,7 @@ Mat readImageType(const string& fname, int type)
cvtColor(src, temp, cv::COLOR_BGR2BGRA); cvtColor(src, temp, cv::COLOR_BGR2BGRA);
swap(src, temp); swap(src, temp);
} }
src.convertTo(src, CV_MAT_DEPTH(type)); src.convertTo(src, CV_MAT_DEPTH(type), CV_MAT_DEPTH(type) == CV_32F ? 1.0 / 255.0 : 1.0);
return src; return src;
} }