modules/ocl/test/test_optflow.cpp

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  45
  46 #include "test_precomp.hpp"
  47 #include <iomanip>
  48
  49 #ifdef HAVE_OPENCL
  50
  51 using namespace cv;
  52 using namespace cv::ocl;
  53 using namespace cvtest;
  54 using namespace testing;
  55 using namespace std;
  56
  57 //////////////////////////////////////////////////////
  58 // GoodFeaturesToTrack
  59 namespace
  60 {
  61     IMPLEMENT_PARAM_CLASS(MinDistance, double)
  62 }
  63 PARAM_TEST_CASE(GoodFeaturesToTrack, MinDistance)
  64 {
  65     double minDistance;
  66
  67     virtual void SetUp()
  68     {
  69         minDistance = GET_PARAM(0);
  70     }
  71 };
  72
  73 OCL_TEST_P(GoodFeaturesToTrack, Accuracy)
  74 {
  75     cv::Mat frame = readImage("gpu/opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
  76     ASSERT_FALSE(frame.empty());
  77
  78     int maxCorners = 1000;
  79     double qualityLevel = 0.01;
  80
  81     cv::ocl::GoodFeaturesToTrackDetector_OCL detector(maxCorners, qualityLevel, minDistance);
  82
  83     cv::ocl::oclMat d_pts;
  84     detector(oclMat(frame), d_pts);
  85
  86     ASSERT_FALSE(d_pts.empty());
  87
  88     std::vector<cv::Point2f> pts(d_pts.cols);
  89
  90     detector.downloadPoints(d_pts, pts);
  91
  92     std::vector<cv::Point2f> pts_gold;
  93     cv::goodFeaturesToTrack(frame, pts_gold, maxCorners, qualityLevel, minDistance);
  94
  95     ASSERT_EQ(pts_gold.size(), pts.size());
  96
  97     size_t mistmatch = 0;
  98     for (size_t i = 0; i < pts.size(); ++i)
  99     {
 100         cv::Point2i a = pts_gold[i];
 101         cv::Point2i b = pts[i];
 102
 103         bool eq = std::abs(a.x - b.x) < 1 && std::abs(a.y - b.y) < 1;
 104
 105         if (!eq)
 106             ++mistmatch;
 107     }
 108
 109     double bad_ratio = static_cast<double>(mistmatch) / pts.size();
 110
 111     ASSERT_LE(bad_ratio, 0.01);
 112 }
 113
 114 OCL_TEST_P(GoodFeaturesToTrack, EmptyCorners)
 115 {
 116     int maxCorners = 1000;
 117     double qualityLevel = 0.01;
 118
 119     cv::ocl::GoodFeaturesToTrackDetector_OCL detector(maxCorners, qualityLevel, minDistance);
 120
 121     cv::ocl::oclMat src(100, 100, CV_8UC1, cv::Scalar::all(0));
 122     cv::ocl::oclMat corners(1, maxCorners, CV_32FC2);
 123
 124     detector(src, corners);
 125
 126     ASSERT_TRUE(corners.empty());
 127 }
 128
 129 INSTANTIATE_TEST_CASE_P(OCL_Video, GoodFeaturesToTrack,
 130     testing::Values(MinDistance(0.0), MinDistance(3.0)));
 131
 132 //////////////////////////////////////////////////////////////////////////
 133 PARAM_TEST_CASE(TVL1, bool)
 134 {
 135     bool useRoi;
 136
 137     virtual void SetUp()
 138     {
 139         useRoi = GET_PARAM(0);
 140     }
 141
 142 };
 143
 144 OCL_TEST_P(TVL1, Accuracy)
 145 {
 146     cv::Mat frame0 = readImage("gpu/opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
 147     ASSERT_FALSE(frame0.empty());
 148
 149     cv::Mat frame1 = readImage("gpu/opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
 150     ASSERT_FALSE(frame1.empty());
 151
 152     cv::ocl::OpticalFlowDual_TVL1_OCL d_alg;
 153     cv::Mat flowx = randomMat(frame0.size(), CV_32FC1, 0, 0, useRoi);
 154     cv::Mat flowy = randomMat(frame0.size(), CV_32FC1, 0, 0, useRoi);
 155     cv::ocl::oclMat d_flowx(flowx), d_flowy(flowy);
 156     d_alg(oclMat(frame0), oclMat(frame1), d_flowx, d_flowy);
 157
 158     cv::Ptr<cv::DenseOpticalFlow> alg = cv::createOptFlow_DualTVL1();
 159     cv::Mat flow;
 160     alg->calc(frame0, frame1, flow);
 161     cv::Mat gold[2];
 162     cv::split(flow, gold);
 163
 164     EXPECT_MAT_SIMILAR(gold[0], d_flowx, 3e-3);
 165     EXPECT_MAT_SIMILAR(gold[1], d_flowy, 3e-3);
 166 }
 167 INSTANTIATE_TEST_CASE_P(OCL_Video, TVL1, Values(false, true));
 168
 169
 170 /////////////////////////////////////////////////////////////////////////////////////////////////
 171 // PyrLKOpticalFlow
 172
 173 PARAM_TEST_CASE(Sparse, bool, bool)
 174 {
 175     bool useGray;
 176     bool UseSmart;
 177
 178     virtual void SetUp()
 179     {
 180         UseSmart = GET_PARAM(0);
 181         useGray = GET_PARAM(1);
 182     }
 183 };
 184
 185 OCL_TEST_P(Sparse, Mat)
 186 {
 187     cv::Mat frame0 = readImage("gpu/opticalflow/rubberwhale1.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
 188     ASSERT_FALSE(frame0.empty());
 189
 190     cv::Mat frame1 = readImage("gpu/opticalflow/rubberwhale2.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
 191     ASSERT_FALSE(frame1.empty());
 192
 193     cv::Mat gray_frame;
 194     if (useGray)
 195         gray_frame = frame0;
 196     else
 197         cv::cvtColor(frame0, gray_frame, cv::COLOR_BGR2GRAY);
 198
 199     std::vector<cv::Point2f> pts;
 200     cv::goodFeaturesToTrack(gray_frame, pts, 1000, 0.01, 0.0);
 201
 202     cv::ocl::oclMat d_pts;
 203     cv::Mat pts_mat(1, (int)pts.size(), CV_32FC2, (void *)&pts[0]);
 204     d_pts.upload(pts_mat);
 205
 206     cv::ocl::PyrLKOpticalFlow pyrLK;
 207
 208     cv::ocl::oclMat oclFrame0;
 209     cv::ocl::oclMat oclFrame1;
 210     cv::ocl::oclMat d_nextPts;
 211     cv::ocl::oclMat d_status;
 212     cv::ocl::oclMat d_err;
 213
 214     oclFrame0 = frame0;
 215     oclFrame1 = frame1;
 216
 217     pyrLK.sparse(oclFrame0, oclFrame1, d_pts, d_nextPts, d_status, &d_err);
 218
 219     std::vector<cv::Point2f> nextPts(d_nextPts.cols);
 220     cv::Mat nextPts_mat(1, d_nextPts.cols, CV_32FC2, (void *)&nextPts[0]);
 221     d_nextPts.download(nextPts_mat);
 222
 223     std::vector<unsigned char> status(d_status.cols);
 224     cv::Mat status_mat(1, d_status.cols, CV_8UC1, (void *)&status[0]);
 225     d_status.download(status_mat);
 226
 227     std::vector<float> err(d_err.cols);
 228     cv::Mat err_mat(1, d_err.cols, CV_32FC1, (void*)&err[0]);
 229     d_err.download(err_mat);
 230
 231     std::vector<cv::Point2f> nextPts_gold;
 232     std::vector<unsigned char> status_gold;
 233     std::vector<float> err_gold;
 234     cv::calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts_gold, status_gold, err_gold);
 235
 236     ASSERT_EQ(nextPts_gold.size(), nextPts.size());
 237     ASSERT_EQ(status_gold.size(), status.size());
 238
 239     size_t mistmatch = 0;
 240     for (size_t i = 0; i < nextPts.size(); ++i)
 241     {
 242         if (status[i] != status_gold[i])
 243         {
 244             ++mistmatch;
 245             continue;
 246         }
 247
 248         if (status[i])
 249         {
 250             cv::Point2i a = nextPts[i];
 251             cv::Point2i b = nextPts_gold[i];
 252
 253             bool eq = std::abs(a.x - b.x) < 1 && std::abs(a.y - b.y) < 1;
 254             //float errdiff = std::abs(err[i] - err_gold[i]);
 255             float errdiff = 0.0f;
 256
 257             if (!eq || errdiff > 1e-1)
 258                 ++mistmatch;
 259         }
 260     }
 261
 262     double bad_ratio = static_cast<double>(mistmatch) / (nextPts.size());
 263
 264     ASSERT_LE(bad_ratio, 0.02f);
 265
 266 }
 267
 268 INSTANTIATE_TEST_CASE_P(OCL_Video, Sparse, Combine(
 269     Values(false, true),
 270     Values(false, true)));
 271 //////////////////////////////////////////////////////
 272 // FarnebackOpticalFlow
 273
 274 namespace
 275 {
 276     IMPLEMENT_PARAM_CLASS(PyrScale, double)
 277         IMPLEMENT_PARAM_CLASS(PolyN, int)
 278         CV_FLAGS(FarnebackOptFlowFlags, 0, OPTFLOW_FARNEBACK_GAUSSIAN)
 279         IMPLEMENT_PARAM_CLASS(UseInitFlow, bool)
 280 }
 281
 282 PARAM_TEST_CASE(Farneback, PyrScale, PolyN, FarnebackOptFlowFlags, UseInitFlow)
 283 {
 284     double pyrScale;
 285     int polyN;
 286     int flags;
 287     bool useInitFlow;
 288
 289     virtual void SetUp()
 290     {
 291         pyrScale = GET_PARAM(0);
 292         polyN = GET_PARAM(1);
 293         flags = GET_PARAM(2);
 294         useInitFlow = GET_PARAM(3);
 295     }
 296 };
 297
 298 OCL_TEST_P(Farneback, Accuracy)
 299 {
 300     cv::Mat frame0 = readImage("gpu/opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
 301     ASSERT_FALSE(frame0.empty());
 302
 303     cv::Mat frame1 = readImage("gpu/opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
 304     ASSERT_FALSE(frame1.empty());
 305
 306     double polySigma = polyN <= 5 ? 1.1 : 1.5;
 307
 308     cv::ocl::FarnebackOpticalFlow farn;
 309     farn.pyrScale = pyrScale;
 310     farn.polyN = polyN;
 311     farn.polySigma = polySigma;
 312     farn.flags = flags;
 313
 314     cv::ocl::oclMat d_flowx, d_flowy;
 315     farn(oclMat(frame0), oclMat(frame1), d_flowx, d_flowy);
 316
 317     cv::Mat flow;
 318     if (useInitFlow)
 319     {
 320         cv::Mat flowxy[] = {cv::Mat(d_flowx), cv::Mat(d_flowy)};
 321         cv::merge(flowxy, 2, flow);
 322
 323         farn.flags |= cv::OPTFLOW_USE_INITIAL_FLOW;
 324         farn(oclMat(frame0), oclMat(frame1), d_flowx, d_flowy);
 325     }
 326
 327     cv::calcOpticalFlowFarneback(
 328         frame0, frame1, flow, farn.pyrScale, farn.numLevels, farn.winSize,
 329         farn.numIters, farn.polyN, farn.polySigma, farn.flags);
 330
 331     std::vector<cv::Mat> flowxy;
 332     cv::split(flow, flowxy);
 333
 334     EXPECT_MAT_SIMILAR(flowxy[0], d_flowx, 0.1);
 335     EXPECT_MAT_SIMILAR(flowxy[1], d_flowy, 0.1);
 336 }
 337
 338 INSTANTIATE_TEST_CASE_P(OCL_Video, Farneback, testing::Combine(
 339     testing::Values(PyrScale(0.3), PyrScale(0.5), PyrScale(0.8)),
 340     testing::Values(PolyN(5), PolyN(7)),
 341     testing::Values(FarnebackOptFlowFlags(0), FarnebackOptFlowFlags(cv::OPTFLOW_FARNEBACK_GAUSSIAN)),
 342     testing::Values(UseInitFlow(false), UseInitFlow(true))));
 343
 344 #endif // HAVE_OPENCL