Open Source Computer Vision Library https://opencv.org/
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///////////////////////////////////////////////////////////////////////////////////////
//
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// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
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//
//
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#include "test_precomp.hpp"
#include <iomanip>
#ifdef HAVE_OPENCL
using namespace cv;
using namespace cv::ocl;
using namespace cvtest;
using namespace testing;
using namespace std;
//////////////////////////////////////////////////////
// GoodFeaturesToTrack
namespace
{
IMPLEMENT_PARAM_CLASS(MinDistance, double)
}
PARAM_TEST_CASE(GoodFeaturesToTrack, MinDistance)
{
double minDistance;
virtual void SetUp()
{
minDistance = GET_PARAM(0);
}
};
OCL_TEST_P(GoodFeaturesToTrack, Accuracy)
{
cv::Mat frame = readImage("gpu/opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame.empty());
int maxCorners = 1000;
double qualityLevel = 0.01;
cv::ocl::GoodFeaturesToTrackDetector_OCL detector(maxCorners, qualityLevel, minDistance);
cv::ocl::oclMat d_pts;
detector(oclMat(frame), d_pts);
ASSERT_FALSE(d_pts.empty());
std::vector<cv::Point2f> pts(d_pts.cols);
detector.downloadPoints(d_pts, pts);
std::vector<cv::Point2f> pts_gold;
cv::goodFeaturesToTrack(frame, 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);
}
OCL_TEST_P(GoodFeaturesToTrack, EmptyCorners)
{
int maxCorners = 1000;
double qualityLevel = 0.01;
cv::ocl::GoodFeaturesToTrackDetector_OCL detector(maxCorners, qualityLevel, minDistance);
cv::ocl::oclMat src(100, 100, CV_8UC1, cv::Scalar::all(0));
cv::ocl::oclMat corners(1, maxCorners, CV_32FC2);
detector(src, corners);
ASSERT_TRUE(corners.empty());
}
INSTANTIATE_TEST_CASE_P(OCL_Video, GoodFeaturesToTrack,
testing::Values(MinDistance(0.0), MinDistance(3.0)));
//////////////////////////////////////////////////////////////////////////
PARAM_TEST_CASE(TVL1, bool)
{
bool useRoi;
virtual void SetUp()
{
useRoi = GET_PARAM(0);
}
};
OCL_TEST_P(TVL1, Accuracy)
{
cv::Mat frame0 = readImage("gpu/opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame0.empty());
cv::Mat frame1 = readImage("gpu/opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty());
cv::ocl::OpticalFlowDual_TVL1_OCL d_alg;
cv::Mat flowx = randomMat(frame0.size(), CV_32FC1, 0, 0, useRoi);
cv::Mat flowy = randomMat(frame0.size(), CV_32FC1, 0, 0, useRoi);
cv::ocl::oclMat d_flowx(flowx), d_flowy(flowy);
d_alg(oclMat(frame0), oclMat(frame1), d_flowx, d_flowy);
cv::Ptr<cv::DenseOpticalFlow> alg = cv::createOptFlow_DualTVL1();
cv::Mat flow;
alg->calc(frame0, frame1, flow);
cv::Mat gold[2];
cv::split(flow, gold);
EXPECT_MAT_SIMILAR(gold[0], d_flowx, 3e-3);
EXPECT_MAT_SIMILAR(gold[1], d_flowy, 3e-3);
}
INSTANTIATE_TEST_CASE_P(OCL_Video, TVL1, Values(false, true));
/////////////////////////////////////////////////////////////////////////////////////////////////
// PyrLKOpticalFlow
PARAM_TEST_CASE(Sparse, bool, bool)
{
bool useGray;
bool UseSmart;
virtual void SetUp()
{
UseSmart = GET_PARAM(0);
useGray = GET_PARAM(1);
}
};
OCL_TEST_P(Sparse, Mat)
{
cv::Mat frame0 = readImage("gpu/opticalflow/rubberwhale1.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
ASSERT_FALSE(frame0.empty());
cv::Mat frame1 = readImage("gpu/opticalflow/rubberwhale2.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::ocl::oclMat d_pts;
cv::Mat pts_mat(1, (int)pts.size(), CV_32FC2, (void *)&pts[0]);
d_pts.upload(pts_mat);
cv::ocl::PyrLKOpticalFlow pyrLK;
cv::ocl::oclMat oclFrame0;
cv::ocl::oclMat oclFrame1;
cv::ocl::oclMat d_nextPts;
cv::ocl::oclMat d_status;
cv::ocl::oclMat d_err;
oclFrame0 = frame0;
oclFrame1 = frame1;
pyrLK.sparse(oclFrame0, oclFrame1, d_pts, d_nextPts, d_status, &d_err);
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<float> err(d_err.cols);
cv::Mat err_mat(1, d_err.cols, CV_32FC1, (void*)&err[0]);
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(status_gold.size(), status.size());
size_t mistmatch = 0;
for (size_t i = 0; i < nextPts.size(); ++i)
{
if (status[i] != status_gold[i])
{
++mistmatch;
continue;
}
if (status[i])
{
cv::Point2i a = nextPts[i];
cv::Point2i b = nextPts_gold[i];
bool eq = std::abs(a.x - b.x) < 1 && std::abs(a.y - b.y) < 1;
//float errdiff = std::abs(err[i] - err_gold[i]);
float errdiff = 0.0f;
if (!eq || errdiff > 1e-1)
++mistmatch;
}
}
double bad_ratio = static_cast<double>(mistmatch) / (nextPts.size());
ASSERT_LE(bad_ratio, 0.02f);
}
INSTANTIATE_TEST_CASE_P(OCL_Video, Sparse, Combine(
Values(false, true),
Values(false, true)));
//////////////////////////////////////////////////////
// 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(Farneback, PyrScale, PolyN, FarnebackOptFlowFlags, UseInitFlow)
{
double pyrScale;
int polyN;
int flags;
bool useInitFlow;
virtual void SetUp()
{
pyrScale = GET_PARAM(0);
polyN = GET_PARAM(1);
flags = GET_PARAM(2);
useInitFlow = GET_PARAM(3);
}
};
OCL_TEST_P(Farneback, Accuracy)
{
cv::Mat frame0 = readImage("gpu/opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame0.empty());
cv::Mat frame1 = readImage("gpu/opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty());
double polySigma = polyN <= 5 ? 1.1 : 1.5;
cv::ocl::FarnebackOpticalFlow farn;
farn.pyrScale = pyrScale;
farn.polyN = polyN;
farn.polySigma = polySigma;
farn.flags = flags;
cv::ocl::oclMat d_flowx, d_flowy;
farn(oclMat(frame0), oclMat(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);
farn.flags |= cv::OPTFLOW_USE_INITIAL_FLOW;
farn(oclMat(frame0), oclMat(frame1), d_flowx, d_flowy);
}
cv::calcOpticalFlowFarneback(
frame0, frame1, flow, farn.pyrScale, farn.numLevels, farn.winSize,
farn.numIters, farn.polyN, farn.polySigma, farn.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(OCL_Video, Farneback, testing::Combine(
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))));
#endif // HAVE_OPENCL