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added gpu::FGDStatModel (Background/foreground segmentation)

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This commit is contained in:
Vladislav Vinogradov
2012-06-05 13:32:04 +00:00
parent 3c16c9c92d
commit bfb390e82a
13 changed files with 2200 additions and 28 deletions
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110
modules/gpu/test/test_video.cpp
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@@ -41,11 +41,9 @@
#include "precomp.hpp"
namespace {
//#define DUMP
/////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////
// BroxOpticalFlow
#define BROX_OPTICAL_FLOW_DUMP_FILE "opticalflow/brox_optical_flow.bin"
@@ -130,7 +128,7 @@ TEST_P(BroxOpticalFlow, Regression)
INSTANTIATE_TEST_CASE_P(GPU_Video, BroxOpticalFlow, ALL_DEVICES);
/////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////
// GoodFeaturesToTrack
IMPLEMENT_PARAM_CLASS(MinDistance, double)
@@ -207,7 +205,7 @@ 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)
@@ -306,7 +304,7 @@ INSTANTIATE_TEST_CASE_P(GPU_Video, PyrLKOpticalFlow, testing::Combine(
ALL_DEVICES,
testing::Values(UseGray(true), UseGray(false))));
/////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////
// FarnebackOpticalFlow
IMPLEMENT_PARAM_CLASS(PyrScale, double)
@@ -413,7 +411,87 @@ TEST_P(OpticalFlowNan, Regression)
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)
{
};
TEST_P(FGDStatModel, Accuracy)
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
std::string inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + GET_PARAM(1);
int out_cn = GET_PARAM(2);
cv::VideoCapture cap(inputFile);
ASSERT_TRUE(cap.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);
EXPECT_MAT_NEAR(gold_background, h_background3, 1.0);
EXPECT_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))));
//////////////////////////////////////////////////////
// VideoWriter
#ifdef WIN32
@@ -447,17 +525,13 @@ TEST_P(VideoWriter, Regression)
cv::gpu::VideoWriter_GPU d_writer;
cv::Mat frame;
std::vector<cv::Mat> frames;
cv::gpu::GpuMat d_frame;
for (int i = 1; i < 10; ++i)
for (int i = 0; i < 10; ++i)
{
reader >> frame;
ASSERT_FALSE(frame.empty());
if (frame.empty())
break;
frames.push_back(frame.clone());
d_frame.upload(frame);
if (!d_writer.isOpened())
@@ -481,11 +555,11 @@ TEST_P(VideoWriter, Regression)
INSTANTIATE_TEST_CASE_P(GPU_Video, VideoWriter, testing::Combine(
ALL_DEVICES,
testing::Values(std::string("VID00003-20100701-2204.mpg"), std::string("big_buck_bunny.mpg"))));
testing::Values(std::string("768x576.avi"), std::string("1920x1080.avi"))));
#endif // WIN32
/////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////
// VideoReader
PARAM_TEST_CASE(VideoReader, cv::gpu::DeviceInfo, std::string)
@@ -511,7 +585,7 @@ TEST_P(VideoReader, Regression)
cv::gpu::GpuMat frame;
for (int i = 0; i < 5; ++i)
for (int i = 0; i < 10; ++i)
{
ASSERT_TRUE( reader.read(frame) );
ASSERT_FALSE( frame.empty() );
@@ -523,6 +597,4 @@ TEST_P(VideoReader, Regression)
INSTANTIATE_TEST_CASE_P(GPU_Video, VideoReader, testing::Combine(
ALL_DEVICES,
testing::Values(std::string("VID00003-20100701-2204.mpg"))));
} // namespace
testing::Values(std::string("768x576.avi"), std::string("1920x1080.avi"))));