initial commit

modules/features2d/perf/perf_batchDistance.cpp

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+#include "perf_precomp.hpp"
+
+namespace opencv_test
+{
+using namespace perf;
+
+CV_ENUM(NormType, NORM_L1, NORM_L2, NORM_L2SQR, NORM_HAMMING, NORM_HAMMING2)
+
+typedef tuple<NormType, MatType, bool> Norm_Destination_CrossCheck_t;
+typedef perf::TestBaseWithParam<Norm_Destination_CrossCheck_t> Norm_Destination_CrossCheck;
+
+typedef tuple<NormType, bool> Norm_CrossCheck_t;
+typedef perf::TestBaseWithParam<Norm_CrossCheck_t> Norm_CrossCheck;
+
+typedef tuple<MatType, 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());
+    int destinationType = get<1>(GetParam());
+    bool isCrossCheck = get<2>(GetParam());
+    int knn = isCrossCheck ? 1 : 0;
+
+    Mat queryDescriptors;
+    Mat trainDescriptors;
+    Mat dist;
+    Mat ndix;
+
+    generateData(queryDescriptors, trainDescriptors, CV_8U);
+
+    TEST_CYCLE()
+    {
+        batchDistance(queryDescriptors, trainDescriptors, dist, destinationType, (isCrossCheck) ? ndix : noArray(),
+                      normType, knn, Mat(), 0, isCrossCheck);
+    }
+
+    SANITY_CHECK(dist);
+    if (isCrossCheck) SANITY_CHECK(ndix);
+}
+
+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());
+    int knn = isCrossCheck ? 1 : 0;
+
+    Mat queryDescriptors;
+    Mat trainDescriptors;
+    Mat dist;
+    Mat ndix;
+
+    generateData(queryDescriptors, trainDescriptors, CV_8U);
+
+    TEST_CYCLE()
+    {
+        batchDistance(queryDescriptors, trainDescriptors, dist, CV_32S, (isCrossCheck) ? ndix : noArray(),
+                      normType, knn, Mat(), 0, isCrossCheck);
+    }
+
+    SANITY_CHECK(dist);
+    if (isCrossCheck) SANITY_CHECK(ndix);
+}
+
+PERF_TEST_P(Source_CrossCheck, batchDistance_L2,
+            testing::Combine(testing::Values(CV_8U, CV_32F),
+                             testing::Bool()
+                             )
+            )
+{
+    int sourceType = get<0>(GetParam());
+    bool isCrossCheck = get<1>(GetParam());
+    int knn = isCrossCheck ? 1 : 0;
+
+    Mat queryDescriptors;
+    Mat trainDescriptors;
+    Mat dist;
+    Mat ndix;
+
+    generateData(queryDescriptors, trainDescriptors, sourceType);
+
+    declare.time(50);
+    TEST_CYCLE()
+    {
+        batchDistance(queryDescriptors, trainDescriptors, dist, CV_32F, (isCrossCheck) ? ndix : noArray(),
+                      NORM_L2, knn, Mat(), 0, isCrossCheck);
+    }
+
+    SANITY_CHECK(dist);
+    if (isCrossCheck) SANITY_CHECK(ndix);
+}
+
+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());
+    int knn = isCrossCheck ? 1 : 0;
+
+    Mat queryDescriptors;
+    Mat trainDescriptors;
+    Mat dist;
+    Mat ndix;
+
+    generateData(queryDescriptors, trainDescriptors, CV_32F);
+    declare.time(100);
+
+    TEST_CYCLE()
+    {
+        batchDistance(queryDescriptors, trainDescriptors, dist, CV_32F, (isCrossCheck) ? ndix : noArray(),
+                      normType, knn, Mat(), 0, isCrossCheck);
+    }
+
+    SANITY_CHECK(dist, 1e-4);
+    if (isCrossCheck) SANITY_CHECK(ndix);
+}
+
+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 descriptors 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 = (sourceType == CV_8U ? 256.f : 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.col(elem) += diff;
+        }
+    }
+}
+
+} // namespace