'master'-like Haar perf test
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@ -83,3 +83,53 @@ PERF_TEST(HaarFixture, Haar)
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else
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else
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OCL_PERF_ELSE
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OCL_PERF_ELSE
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}
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}
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using namespace std;
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using namespace cv;
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using namespace perf;
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using std::tr1::make_tuple;
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using std::tr1::get;
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typedef std::tr1::tuple<std::string, std::string, int> Cascade_Image_MinSize_t;
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typedef perf::TestBaseWithParam<Cascade_Image_MinSize_t> Cascade_Image_MinSize;
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PERF_TEST_P( Cascade_Image_MinSize, CascadeClassifier_UMat,
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testing::Combine(
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testing::Values( string("cv/cascadeandhog/cascades/haarcascade_frontalface_alt.xml") ),
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testing::Values( string("cv/shared/lena.png"),
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string("cv/cascadeandhog/images/bttf301.png"),
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string("cv/cascadeandhog/images/class57.png") ),
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testing::Values(30, 64, 90) ) )
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{
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const string cascasePath = get<0>(GetParam());
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const string imagePath = get<1>(GetParam());
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const int min_size = get<2>(GetParam());
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Size minSize(min_size, min_size);
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ocl::OclCascadeClassifier cc;
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if (!cc.load( getDataPath(cascasePath) ))
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FAIL() << "Can't load cascade file: " << getDataPath(cascasePath);
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Mat img = imread(getDataPath(imagePath), IMREAD_GRAYSCALE);
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if (img.empty())
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FAIL() << "Can't load source image: " << getDataPath(imagePath);
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vector<Rect> faces;
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equalizeHist(img, img);
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declare.in(img).time(60);
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ocl::oclMat uimg(img);
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while(next())
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{
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faces.clear();
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startTimer();
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cc.detectMultiScale(uimg, faces, 1.1, 3, 0, minSize);
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stopTimer();
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}
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//sort(faces.begin(), faces.end(), comparators::RectLess());
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SANITY_CHECK_NOTHING();//(faces, min_size/5);
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}
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