Added perf test

modules/calib3d/perf/perf_pnp.cpp

@@ -11,6 +11,8 @@ CV_ENUM(pnpAlgo, CV_ITERATIVE, CV_EPNP /*, CV_P3P*/)
 typedef std::tr1::tuple<int, pnpAlgo> PointsNum_Algo_t;
 typedef perf::TestBaseWithParam<PointsNum_Algo_t> PointsNum_Algo;
 
+typedef perf::TestBaseWithParam<int> PointsNum;
+
 PERF_TEST_P(PointsNum_Algo, solvePnP,
             testing::Combine(
                 testing::Values(4, 3*9, 7*13),
@@ -86,3 +88,41 @@ PERF_TEST(PointsNum_Algo, solveP3P)
     SANITY_CHECK(rvec, 1e-6);
     SANITY_CHECK(tvec, 1e-6);
 }
+
+PERF_TEST_P(PointsNum, SolvePnPRansac, testing::Values(4, 3*9, 7*13))
+{
+    int count = GetParam();
+
+    Mat object(1, count, CV_32FC3);
+    randu(object, -100, 100);
+
+    Mat camera_mat(3, 3, CV_32FC1);
+    randu(camera_mat, 0.5, 1);
+    camera_mat.at<float>(0, 1) = 0.f;
+    camera_mat.at<float>(1, 0) = 0.f;
+    camera_mat.at<float>(2, 0) = 0.f;
+    camera_mat.at<float>(2, 1) = 0.f;
+
+    Mat dist_coef(1, 8, CV_32F, cv::Scalar::all(0));
+
+    vector<cv::Point2f> image_vec;
+    Mat rvec_gold(1, 3, CV_32FC1);
+    randu(rvec_gold, 0, 1);
+    Mat tvec_gold(1, 3, CV_32FC1);
+    randu(tvec_gold, 0, 1);
+    projectPoints(object, rvec_gold, tvec_gold, camera_mat, dist_coef, image_vec);
+
+    Mat image(1, count, CV_32FC2, &image_vec[0]);
+
+    Mat rvec;
+    Mat tvec;
+
+    solvePnPRansac(object, image, camera_mat, dist_coef, rvec, tvec);
+
+    declare.time(3.0);
+
+    TEST_CYCLE()
+    {
+        solvePnPRansac(object, image, camera_mat, dist_coef, rvec, tvec);
+    }
+}

modules/core/perf/perf_mat.cpp

@@ -0,0 +1,96 @@
+#include "perf_precomp.hpp"
+
+using namespace std;
+using namespace cv;
+using namespace perf;
+using std::tr1::make_tuple;
+using std::tr1::get;
+
+PERF_TEST_P(Size_MatType, Mat_Eye,
+            testing::Combine(testing::Values(TYPICAL_MAT_SIZES),
+                             testing::Values(TYPICAL_MAT_TYPES))
+
+             )
+{
+    Size size = get<0>(GetParam());
+    int type = get<1>(GetParam());
+    Mat diagonalMatrix(size.height, size.width, type);
+
+    declare.out(diagonalMatrix);
+
+    TEST_CYCLE()
+    {
+        diagonalMatrix = Mat::eye(size, type);
+    }
+
+    SANITY_CHECK(diagonalMatrix, 1);
+}
+
+PERF_TEST_P(Size_MatType, Mat_Zeros,
+            testing::Combine(testing::Values(TYPICAL_MAT_SIZES),
+                             testing::Values(TYPICAL_MAT_TYPES, CV_32FC3))
+
+             )
+{
+    Size size = get<0>(GetParam());
+    int type = get<1>(GetParam());
+    Mat zeroMatrix(size.height, size.width, type);
+
+    declare.out(zeroMatrix);
+
+    TEST_CYCLE()
+    {
+        zeroMatrix = Mat::zeros(size, type);
+    }
+
+    SANITY_CHECK(zeroMatrix, 1);
+}
+
+PERF_TEST_P(Size_MatType, Mat_Clone,
+            testing::Combine(testing::Values(TYPICAL_MAT_SIZES),
+                             testing::Values(TYPICAL_MAT_TYPES))
+
+             )
+{
+    Size size = get<0>(GetParam());
+    int type = get<1>(GetParam());
+    Mat source(size.height, size.width, type);
+    Mat destination(size.height, size.width, type);;
+
+    declare.in(source, WARMUP_RNG).out(destination);
+
+    TEST_CYCLE()
+    {
+        source.clone();
+    }
+    destination = source.clone();
+
+    SANITY_CHECK(destination, 1);
+}
+
+PERF_TEST_P(Size_MatType, Mat_Clone_Roi,
+            testing::Combine(testing::Values(TYPICAL_MAT_SIZES),
+                             testing::Values(TYPICAL_MAT_TYPES))
+
+             )
+{
+    Size size = get<0>(GetParam());
+    int type = get<1>(GetParam());
+
+    unsigned int width = size.width;
+    unsigned int height = size.height;
+    Mat source(height, width, type);
+    Mat destination(size.height/2, size.width/2, type);
+
+    declare.in(source, WARMUP_RNG).out(destination);
+
+    Mat roi(source, Rect(width/4, height/4, 3*width/4, 3*height/4));
+
+    TEST_CYCLE()
+    {
+        roi.clone();
+    }
+    destination = roi.clone();
+
+    SANITY_CHECK(destination, 1);
+}

modules/features2d/perf/perf_batchDistance.cpp

@@ -0,0 +1,175 @@
+#include "perf_precomp.hpp"
+
+using namespace std;
+using namespace cv;
+using namespace perf;
+using std::tr1::make_tuple;
+using std::tr1::get;
+
+CV_FLAGS(NormType, NORM_L1, NORM_L2, NORM_L2SQR, NORM_HAMMING, NORM_HAMMING2)
+CV_ENUM(SourceType, CV_32F, CV_8U)
+CV_ENUM(DestinationType, CV_32F, CV_32S)
+
+typedef std::tr1::tuple<NormType, DestinationType, bool> Norm_Destination_CrossCheck_t;
+typedef perf::TestBaseWithParam<Norm_Destination_CrossCheck_t> Norm_Destination_CrossCheck;
+
+typedef std::tr1::tuple<NormType, bool> Norm_CrossCheck_t;
+typedef perf::TestBaseWithParam<Norm_CrossCheck_t> Norm_CrossCheck;
+
+typedef std::tr1::tuple<SourceType, bool> Source_CrossCheck_t;
+typedef perf::TestBaseWithParam<Source_CrossCheck_t> Source_CrossCheck;
+
+void generateData( Mat& query, Mat& train, const int sourceType );
+
+PERF_TEST_P(Norm_Destination_CrossCheck, batchDistance_8U,
+            testing::Combine(testing::Values((int)NORM_L1, (int)NORM_L2SQR),
+                             testing::Values(CV_32S, CV_32F),
+                             testing::Bool()
+                             )
+            )
+{
+    NormType normType = get<0>(GetParam());
+    DestinationType destinationType = get<1>(GetParam());
+    bool isCrossCheck = get<2>(GetParam());
+
+    Mat queryDescriptors;
+    Mat trainDescriptors;
+    Mat dist;
+    Mat ndix;
+    int knn = 1;
+
+    generateData(queryDescriptors, trainDescriptors, CV_8U);
+    if(!isCrossCheck)
+    {
+        knn = 0;
+    }
+
+    declare.time(30);
+    TEST_CYCLE()
+    {
+        batchDistance(queryDescriptors, trainDescriptors, dist, destinationType, (isCrossCheck) ? ndix : noArray(),
+                      normType, knn, Mat(), 0, isCrossCheck);
+    }
+}
+
+PERF_TEST_P(Norm_CrossCheck, batchDistance_Dest_32S,
+            testing::Combine(testing::Values((int)NORM_HAMMING, (int)NORM_HAMMING2),
+                             testing::Bool()
+                             )
+            )
+{
+    NormType normType = get<0>(GetParam());
+    bool isCrossCheck = get<1>(GetParam());
+
+    Mat queryDescriptors;
+    Mat trainDescriptors;
+    Mat dist;
+    Mat ndix;
+    int knn = 1;
+
+    generateData(queryDescriptors, trainDescriptors, CV_8U);
+    if(!isCrossCheck)
+    {
+        knn = 0;
+    }
+
+    declare.time(30);
+    TEST_CYCLE()
+    {
+        batchDistance(queryDescriptors, trainDescriptors, dist, CV_32S, (isCrossCheck) ? ndix : noArray(),
+                      normType, knn, Mat(), 0, isCrossCheck);
+    }
+}
+
+PERF_TEST_P(Source_CrossCheck, batchDistance_L2,
+            testing::Combine(testing::Values(CV_8U, CV_32F),
+                             testing::Bool()
+                             )
+            )
+{
+    SourceType sourceType = get<0>(GetParam());
+    bool isCrossCheck = get<1>(GetParam());
+
+    Mat queryDescriptors;
+    Mat trainDescriptors;
+    Mat dist;
+    Mat ndix;
+    int knn = 1;
+
+    generateData(queryDescriptors, trainDescriptors, sourceType);
+    if(!isCrossCheck)
+    {
+        knn = 0;
+    }
+
+    declare.time(30);
+    TEST_CYCLE()
+    {
+        batchDistance(queryDescriptors, trainDescriptors, dist, CV_32F, (isCrossCheck) ? ndix : noArray(),
+                      NORM_L2, knn, Mat(), 0, isCrossCheck);
+    }
+}
+
+PERF_TEST_P(Norm_CrossCheck, batchDistance_32F,
+            testing::Combine(testing::Values((int)NORM_L1, (int)NORM_L2SQR),
+                             testing::Bool()
+                             )
+            )
+{
+    NormType normType = get<0>(GetParam());
+    bool isCrossCheck = get<1>(GetParam());
+
+    Mat queryDescriptors;
+    Mat trainDescriptors;
+    Mat dist;
+    Mat ndix;
+    int knn = 1;
+
+    generateData(queryDescriptors, trainDescriptors, CV_32F);
+    if(!isCrossCheck)
+    {
+        knn = 0;
+    }
+
+    declare.time(30);
+    TEST_CYCLE()
+    {
+        batchDistance(queryDescriptors, trainDescriptors, dist, CV_32F, (isCrossCheck) ? ndix : noArray(),
+                      normType, knn, Mat(), 0, isCrossCheck);
+    }
+}
+
+void generateData( Mat& query, Mat& train, const int sourceType )
+{
+    const int dim = 500;
+    const int queryDescCount = 300; // must be even number because we split train data in some cases in two
+    const int countFactor = 4; // do not change it
+    RNG& rng = theRNG();
+
+    // Generate query descriptors randomly.
+    // Descriptor vector elements are integer values.
+    Mat buf( queryDescCount, dim, CV_32SC1 );
+    rng.fill( buf, RNG::UNIFORM, Scalar::all(0), Scalar(3) );
+    buf.convertTo( query, sourceType );
+
+    // Generate train decriptors as follows:
+    // copy each query descriptor to train set countFactor times
+    // and perturb some one element of the copied descriptors in
+    // in ascending order. General boundaries of the perturbation
+    // are (0.f, 1.f).
+    train.create( query.rows*countFactor, query.cols, sourceType );
+    float step = 1.f / countFactor;
+    for( int qIdx = 0; qIdx < query.rows; qIdx++ )
+    {
+        Mat queryDescriptor = query.row(qIdx);
+        for( int c = 0; c < countFactor; c++ )
+        {
+            int tIdx = qIdx * countFactor + c;
+            Mat trainDescriptor = train.row(tIdx);
+            queryDescriptor.copyTo( trainDescriptor );
+            int elem = rng(dim);
+            float diff = rng.uniform( step*c, step*(c+1) );
+            trainDescriptor.at<float>(0, elem) += diff;
+        }
+    }
+}

modules/stitching/perf/perf_stich.cpp

@@ -16,6 +16,8 @@ using std::tr1::get;
 
 typedef TestBaseWithParam<String> stitch;
 typedef TestBaseWithParam<String> match;
+typedef std::tr1::tuple<String, int> matchVector_t;
+typedef TestBaseWithParam<matchVector_t> matchVector;
 
 #ifdef HAVE_OPENCV_NONFREE
 #define TEST_DETECTORS testing::Values("surf", "orb")
@@ -132,3 +134,56 @@ PERF_TEST_P( match, bestOf2Nearest, TEST_DETECTORS)
         matcher->collectGarbage();
     }
 }
+
+PERF_TEST_P( matchVector, bestOf2NearestVectorFeatures, testing::Combine(
+                 TEST_DETECTORS,
+                 testing::Values(2, 4, 6, 8))
+             )
+{
+    Mat img1, img1_full = imread( getDataPath("stitching/b1.jpg") );
+    Mat img2, img2_full = imread( getDataPath("stitching/b2.jpg") );
+    float scale1 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img1_full.total()));
+    float scale2 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img2_full.total()));
+    resize(img1_full, img1, Size(), scale1, scale1);
+    resize(img2_full, img2, Size(), scale2, scale2);
+
+    Ptr<detail::FeaturesFinder> finder;
+    Ptr<detail::FeaturesMatcher> matcher;
+    String detectorName = get<0>(GetParam());
+    int featuresVectorSize = get<1>(GetParam());
+    if (detectorName == "surf")
+    {
+        finder = new detail::SurfFeaturesFinder();
+        matcher = new detail::BestOf2NearestMatcher(false, SURF_MATCH_CONFIDENCE);
+    }
+    else if (detectorName == "orb")
+    {
+        finder = new detail::OrbFeaturesFinder();
+        matcher = new detail::BestOf2NearestMatcher(false, ORB_MATCH_CONFIDENCE);
+    }
+    else
+    {
+        FAIL() << "Unknown 2D features type: " << get<0>(GetParam());
+    }
+
+    detail::ImageFeatures features1, features2;
+    (*finder)(img1, features1);
+    (*finder)(img2, features2);
+    vector<detail::ImageFeatures> features;
+    vector<detail::MatchesInfo> pairwise_matches;
+    for(int i = 0; i < featuresVectorSize/2; i++)
+    {
+        features.push_back(features1);
+        features.push_back(features2);
+    }
+
+    declare.time(200);
+    while(next())
+    {
+        cvflann::seed_random(42);//for predictive FlannBasedMatcher
+        startTimer();
+        (*matcher)(features, pairwise_matches);
+        stopTimer();
+        matcher->collectGarbage();
+    }
+}