generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

fixed tests (call resetDevice, if there was a gpu failure)

Browse Source
This commit is contained in:
Vladislav Vinogradov
2013-01-23 15:31:10 +04:00
parent 0773ab4d07
commit b7e6b5af1b
34 changed files with 2673 additions and 2556 deletions
Download Patch File Download Diff File Expand all files Collapse all files

780
modules/gpu/test/test_video.cpp
View File

@@ -41,739 +41,47 @@
#include "test_precomp.hpp"
#ifdef HAVE_CUDA
//#define DUMP
#if defined(HAVE_CUDA) && defined(HAVE_NVCUVID)
//////////////////////////////////////////////////////
// BroxOpticalFlow
// VideoReader
#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;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
};
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::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
std::string fname(cvtest::TS::ptr()->get_data_path());
if (devInfo.majorVersion() >= 2)
fname += BROX_OPTICAL_FLOW_DUMP_FILE_CC20;
else
fname += BROX_OPTICAL_FLOW_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));
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_NEAR(u_gold, u, 0);
EXPECT_MAT_NEAR(v_gold, v, 0);
#else
std::string fname(cvtest::TS::ptr()->get_data_path());
if (devInfo.majorVersion() >= 2)
fname += BROX_OPTICAL_FLOW_DUMP_FILE_CC20;
else
fname += BROX_OPTICAL_FLOW_DUMP_FILE;
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
}
INSTANTIATE_TEST_CASE_P(GPU_Video, BroxOpticalFlow, ALL_DEVICES);
//////////////////////////////////////////////////////
// GoodFeaturesToTrack
IMPLEMENT_PARAM_CLASS(MinDistance, double)
PARAM_TEST_CASE(GoodFeaturesToTrack, cv::gpu::DeviceInfo, MinDistance)
{
cv::gpu::DeviceInfo devInfo;
double minDistance;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
minDistance = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
}
};
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);
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;
detector(loadMat(image), d_pts);
std::vector<cv::Point2f> pts(d_pts.cols);
cv::Mat pts_mat(1, d_pts.cols, CV_32FC2, (void*)&pts[0]);
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());
size_t mistmatch = 0;
for (size_t i = 0; i < pts.size(); ++i)
{
cv::Point2i a = pts_gold[i];
cv::Point2i b = pts[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) / pts.size();
ASSERT_LE(bad_ratio, 0.01);
}
}
TEST_P(GoodFeaturesToTrack, EmptyCorners)
{
int maxCorners = 1000;
double qualityLevel = 0.01;
cv::gpu::GoodFeaturesToTrackDetector_GPU detector(maxCorners, qualityLevel, minDistance);
cv::gpu::GpuMat src(100, 100, CV_8UC1, cv::Scalar::all(0));
cv::gpu::GpuMat corners(1, maxCorners, CV_32FC2);
detector(src, corners);
ASSERT_TRUE( corners.empty() );
}
INSTANTIATE_TEST_CASE_P(GPU_Video, GoodFeaturesToTrack, testing::Combine(
ALL_DEVICES,
testing::Values(MinDistance(0.0), MinDistance(3.0))));
//////////////////////////////////////////////////////
// PyrLKOpticalFlow
IMPLEMENT_PARAM_CLASS(UseGray, bool)
PARAM_TEST_CASE(PyrLKOpticalFlow, cv::gpu::DeviceInfo, UseGray)
{
cv::gpu::DeviceInfo devInfo;
bool useGray;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
useGray = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(PyrLKOpticalFlow, Sparse)
{
cv::Mat frame0 = readImage("opticalflow/frame0.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
ASSERT_FALSE(frame0.empty());
cv::Mat frame1 = readImage("opticalflow/frame1.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
ASSERT_FALSE(frame1.empty());
cv::Mat gray_frame;
if (useGray)
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::gpu::GpuMat d_pts;
cv::Mat pts_mat(1, (int)pts.size(), CV_32FC2, (void*)&pts[0]);
d_pts.upload(pts_mat);
cv::gpu::PyrLKOpticalFlow pyrLK;
cv::gpu::GpuMat d_nextPts;
cv::gpu::GpuMat d_status;
pyrLK.sparse(loadMat(frame0), loadMat(frame1), 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);
std::vector<cv::Point2f> nextPts_gold;
std::vector<unsigned char> status_gold;
cv::calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts_gold, status_gold, cv::noArray());
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(GPU_Video, PyrLKOpticalFlow, testing::Combine(
ALL_DEVICES,
testing::Values(UseGray(true), UseGray(false))));
//////////////////////////////////////////////////////
// FarnebackOpticalFlow
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::gpu::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::gpu::setDevice(devInfo.deviceID());
}
};
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::gpu::FarnebackOpticalFlow calc;
calc.pyrScale = pyrScale;
calc.polyN = polyN;
calc.polySigma = polySigma;
calc.flags = flags;
cv::gpu::GpuMat d_flowx, d_flowy;
calc(loadMat(frame0), loadMat(frame1), d_flowx, d_flowy);
cv::Mat flow;
if (useInitFlow)
{
cv::Mat flowxy[] = {cv::Mat(d_flowx), cv::Mat(d_flowy)};
cv::merge(flowxy, 2, flow);
}
if (useInitFlow)
{
calc.flags |= cv::OPTFLOW_USE_INITIAL_FLOW;
calc(loadMat(frame0), loadMat(frame1), d_flowx, d_flowy);
}
cv::calcOpticalFlowFarneback(
frame0, frame1, flow, calc.pyrScale, calc.numLevels, calc.winSize,
calc.numIters, calc.polyN, calc.polySigma, calc.flags);
std::vector<cv::Mat> flowxy;
cv::split(flow, flowxy);
EXPECT_MAT_SIMILAR(flowxy[0], d_flowx, 0.1);
EXPECT_MAT_SIMILAR(flowxy[1], d_flowy, 0.1);
}
INSTANTIATE_TEST_CASE_P(GPU_Video, 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))));
struct OpticalFlowNan : public BroxOpticalFlow {};
TEST_P(OpticalFlowNan, Regression)
{
cv::Mat frame0 = readImageType("opticalflow/frame0.png", CV_32FC1);
ASSERT_FALSE(frame0.empty());
cv::Mat r_frame0, r_frame1;
cv::resize(frame0, r_frame0, cv::Size(1380,1000));
cv::Mat frame1 = readImageType("opticalflow/frame1.png", CV_32FC1);
ASSERT_FALSE(frame1.empty());
cv::resize(frame1, r_frame1, cv::Size(1380,1000));
cv::gpu::BroxOpticalFlow brox(0.197f /*alpha*/, 50.0f /*gamma*/, 0.8f /*scale_factor*/,
5 /*inner_iterations*/, 150 /*outer_iterations*/, 10 /*solver_iterations*/);
cv::gpu::GpuMat u;
cv::gpu::GpuMat v;
brox(loadMat(r_frame0), loadMat(r_frame1), u, v);
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(GPU_Video, OpticalFlowNan, ALL_DEVICES);
//////////////////////////////////////////////////////
// FGDStatModel
namespace cv
{
template<> void Ptr<CvBGStatModel>::delete_obj()
{
cvReleaseBGStatModel(&obj);
}
}
PARAM_TEST_CASE(FGDStatModel, cv::gpu::DeviceInfo, std::string, Channels)
PARAM_TEST_CASE(VideoReader, cv::gpu::DeviceInfo, std::string)
{
cv::gpu::DeviceInfo devInfo;
std::string inputFile;
int out_cn;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
inputFile = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + GET_PARAM(1);
out_cn = GET_PARAM(2);
inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + inputFile;
}
};
TEST_P(FGDStatModel, Update)
GPU_TEST_P(VideoReader, Regression)
{
cv::VideoCapture cap(inputFile);
ASSERT_TRUE(cap.isOpened());
cv::gpu::VideoReader_GPU reader(inputFile);
ASSERT_TRUE(reader.isOpened());
cv::Mat frame;
cap >> frame;
ASSERT_FALSE(frame.empty());
IplImage ipl_frame = frame;
cv::Ptr<CvBGStatModel> model(cvCreateFGDStatModel(&ipl_frame));
cv::gpu::GpuMat d_frame(frame);
cv::gpu::FGDStatModel d_model(out_cn);
d_model.create(d_frame);
cv::Mat h_background;
cv::Mat h_foreground;
cv::Mat h_background3;
cv::Mat backgroundDiff;
cv::Mat foregroundDiff;
for (int i = 0; i < 5; ++i)
{
cap >> frame;
ASSERT_FALSE(frame.empty());
ipl_frame = frame;
int gold_count = cvUpdateBGStatModel(&ipl_frame, model);
d_frame.upload(frame);
int count = d_model.update(d_frame);
ASSERT_EQ(gold_count, count);
cv::Mat gold_background(model->background);
cv::Mat gold_foreground(model->foreground);
if (out_cn == 3)
d_model.background.download(h_background3);
else
{
d_model.background.download(h_background);
cv::cvtColor(h_background, h_background3, cv::COLOR_BGRA2BGR);
}
d_model.foreground.download(h_foreground);
ASSERT_MAT_NEAR(gold_background, h_background3, 1.0);
ASSERT_MAT_NEAR(gold_foreground, h_foreground, 0.0);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Video, FGDStatModel, testing::Combine(
ALL_DEVICES,
testing::Values(std::string("768x576.avi")),
testing::Values(Channels(3), Channels(4))));
//////////////////////////////////////////////////////
// MOG
IMPLEMENT_PARAM_CLASS(LearningRate, double)
PARAM_TEST_CASE(MOG, cv::gpu::DeviceInfo, std::string, UseGray, LearningRate, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
std::string inputFile;
bool useGray;
double learningRate;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + GET_PARAM(1);
useGray = GET_PARAM(2);
learningRate = GET_PARAM(3);
useRoi = GET_PARAM(4);
}
};
TEST_P(MOG, Update)
{
cv::VideoCapture cap(inputFile);
ASSERT_TRUE(cap.isOpened());
cv::Mat frame;
cap >> frame;
ASSERT_FALSE(frame.empty());
cv::gpu::MOG_GPU mog;
cv::gpu::GpuMat foreground = createMat(frame.size(), CV_8UC1, useRoi);
cv::BackgroundSubtractorMOG mog_gold;
cv::Mat foreground_gold;
cv::gpu::GpuMat frame;
for (int i = 0; i < 10; ++i)
{
cap >> frame;
ASSERT_TRUE(reader.read(frame));
ASSERT_FALSE(frame.empty());
if (useGray)
{
cv::Mat temp;
cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
cv::swap(temp, frame);
}
mog(loadMat(frame, useRoi), foreground, (float)learningRate);
mog_gold(frame, foreground_gold, learningRate);
ASSERT_MAT_NEAR(foreground_gold, foreground, 0.0);
}
reader.close();
ASSERT_FALSE(reader.isOpened());
}
INSTANTIATE_TEST_CASE_P(GPU_Video, MOG, testing::Combine(
INSTANTIATE_TEST_CASE_P(GPU_Video, VideoReader, testing::Combine(
ALL_DEVICES,
testing::Values(std::string("768x576.avi")),
testing::Values(UseGray(true), UseGray(false)),
testing::Values(LearningRate(0.0), LearningRate(0.01)),
WHOLE_SUBMAT));
//////////////////////////////////////////////////////
// MOG2
PARAM_TEST_CASE(MOG2, cv::gpu::DeviceInfo, std::string, UseGray, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
std::string inputFile;
bool useGray;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + GET_PARAM(1);
useGray = GET_PARAM(2);
useRoi = GET_PARAM(3);
}
};
TEST_P(MOG2, Update)
{
cv::VideoCapture cap(inputFile);
ASSERT_TRUE(cap.isOpened());
cv::Mat frame;
cap >> frame;
ASSERT_FALSE(frame.empty());
cv::gpu::MOG2_GPU mog2;
cv::gpu::GpuMat foreground = createMat(frame.size(), CV_8UC1, useRoi);
cv::BackgroundSubtractorMOG2 mog2_gold;
cv::Mat foreground_gold;
for (int i = 0; i < 10; ++i)
{
cap >> frame;
ASSERT_FALSE(frame.empty());
if (useGray)
{
cv::Mat temp;
cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
cv::swap(temp, frame);
}
mog2(loadMat(frame, useRoi), foreground);
mog2_gold(frame, foreground_gold);
double norm = cv::norm(foreground_gold, cv::Mat(foreground), cv::NORM_L1);
norm /= foreground_gold.size().area();
ASSERT_LE(norm, 0.09);
}
}
TEST_P(MOG2, getBackgroundImage)
{
if (useGray)
return;
cv::VideoCapture cap(inputFile);
ASSERT_TRUE(cap.isOpened());
cv::Mat frame;
cv::gpu::MOG2_GPU mog2;
cv::gpu::GpuMat foreground;
cv::BackgroundSubtractorMOG2 mog2_gold;
cv::Mat foreground_gold;
for (int i = 0; i < 10; ++i)
{
cap >> frame;
ASSERT_FALSE(frame.empty());
mog2(loadMat(frame, useRoi), foreground);
mog2_gold(frame, foreground_gold);
}
cv::gpu::GpuMat background = createMat(frame.size(), frame.type(), useRoi);
mog2.getBackgroundImage(background);
cv::Mat background_gold;
mog2_gold.getBackgroundImage(background_gold);
ASSERT_MAT_NEAR(background_gold, background, 0);
}
INSTANTIATE_TEST_CASE_P(GPU_Video, MOG2, testing::Combine(
ALL_DEVICES,
testing::Values(std::string("768x576.avi")),
testing::Values(UseGray(true), UseGray(false)),
WHOLE_SUBMAT));
//////////////////////////////////////////////////////
// VIBE
PARAM_TEST_CASE(VIBE, cv::gpu::DeviceInfo, cv::Size, MatType, UseRoi)
{
};
TEST_P(VIBE, Accuracy)
{
const cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
const cv::Size size = GET_PARAM(1);
const int type = GET_PARAM(2);
const bool useRoi = GET_PARAM(3);
const cv::Mat fullfg(size, CV_8UC1, cv::Scalar::all(255));
cv::Mat frame = randomMat(size, type, 0.0, 100);
cv::gpu::GpuMat d_frame = loadMat(frame, useRoi);
cv::gpu::VIBE_GPU vibe;
cv::gpu::GpuMat d_fgmask = createMat(size, CV_8UC1, useRoi);
vibe.initialize(d_frame);
for (int i = 0; i < 20; ++i)
vibe(d_frame, d_fgmask);
frame = randomMat(size, type, 160, 255);
d_frame = loadMat(frame, useRoi);
vibe(d_frame, d_fgmask);
// now fgmask should be entirely foreground
ASSERT_MAT_NEAR(fullfg, d_fgmask, 0);
}
INSTANTIATE_TEST_CASE_P(GPU_Video, VIBE, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4)),
WHOLE_SUBMAT));
//////////////////////////////////////////////////////
// GMG
PARAM_TEST_CASE(GMG, cv::gpu::DeviceInfo, cv::Size, MatDepth, Channels, UseRoi)
{
};
TEST_P(GMG, Accuracy)
{
const cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
const cv::Size size = GET_PARAM(1);
const int depth = GET_PARAM(2);
const int channels = GET_PARAM(3);
const bool useRoi = GET_PARAM(4);
const int type = CV_MAKE_TYPE(depth, channels);
const cv::Mat zeros(size, CV_8UC1, cv::Scalar::all(0));
const cv::Mat fullfg(size, CV_8UC1, cv::Scalar::all(255));
cv::Mat frame = randomMat(size, type, 0, 100);
cv::gpu::GpuMat d_frame = loadMat(frame, useRoi);
cv::gpu::GMG_GPU gmg;
gmg.numInitializationFrames = 5;
gmg.smoothingRadius = 0;
gmg.initialize(d_frame.size(), 0, 255);
cv::gpu::GpuMat d_fgmask = createMat(size, CV_8UC1, useRoi);
for (int i = 0; i < gmg.numInitializationFrames; ++i)
{
gmg(d_frame, d_fgmask);
// fgmask should be entirely background during training
ASSERT_MAT_NEAR(zeros, d_fgmask, 0);
}
frame = randomMat(size, type, 160, 255);
d_frame = loadMat(frame, useRoi);
gmg(d_frame, d_fgmask);
// now fgmask should be entirely foreground
ASSERT_MAT_NEAR(fullfg, d_fgmask, 0);
}
INSTANTIATE_TEST_CASE_P(GPU_Video, GMG, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8U), MatType(CV_16U), MatType(CV_32F)),
testing::Values(Channels(1), Channels(3), Channels(4)),
WHOLE_SUBMAT));
testing::Values(std::string("768x576.avi"), std::string("1920x1080.avi"))));
//////////////////////////////////////////////////////
// VideoWriter
@@ -785,8 +93,6 @@ PARAM_TEST_CASE(VideoWriter, cv::gpu::DeviceInfo, std::string)
cv::gpu::DeviceInfo devInfo;
std::string inputFile;
std::string outputFile;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
@@ -794,17 +100,17 @@ PARAM_TEST_CASE(VideoWriter, cv::gpu::DeviceInfo, std::string)
cv::gpu::setDevice(devInfo.deviceID());
inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + inputFile;
outputFile = cv::tempfile(".avi");
inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + std::string("video/") + inputFile;
}
};
TEST_P(VideoWriter, Regression)
GPU_TEST_P(VideoWriter, Regression)
{
std::string outputFile = cv::tempfile(".avi");
const double FPS = 25.0;
cv::VideoCapture reader(inputFile);
ASSERT_TRUE( reader.isOpened() );
ASSERT_TRUE(reader.isOpened());
cv::gpu::VideoWriter_GPU d_writer;
@@ -828,12 +134,12 @@ TEST_P(VideoWriter, Regression)
d_writer.close();
reader.open(outputFile);
ASSERT_TRUE( reader.isOpened() );
ASSERT_TRUE(reader.isOpened());
for (int i = 0; i < 5; ++i)
{
reader >> frame;
ASSERT_FALSE( frame.empty() );
ASSERT_FALSE(frame.empty());
}
}
@@ -843,44 +149,4 @@ INSTANTIATE_TEST_CASE_P(GPU_Video, VideoWriter, testing::Combine(
#endif // WIN32
//////////////////////////////////////////////////////
// VideoReader
PARAM_TEST_CASE(VideoReader, cv::gpu::DeviceInfo, std::string)
{
cv::gpu::DeviceInfo devInfo;
std::string inputFile;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
inputFile = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + inputFile;
}
};
TEST_P(VideoReader, Regression)
{
cv::gpu::VideoReader_GPU reader(inputFile);
ASSERT_TRUE( reader.isOpened() );
cv::gpu::GpuMat frame;
for (int i = 0; i < 10; ++i)
{
ASSERT_TRUE( reader.read(frame) );
ASSERT_FALSE( frame.empty() );
}
reader.close();
ASSERT_FALSE( reader.isOpened() );
}
INSTANTIATE_TEST_CASE_P(GPU_Video, VideoReader, testing::Combine(
ALL_DEVICES,
testing::Values(std::string("768x576.avi"), std::string("1920x1080.avi"))));
#endif // HAVE_CUDA
#endif // defined(HAVE_CUDA) && defined(HAVE_NVCUVID)