#include "perf_precomp.hpp" using namespace std; using namespace cv; using namespace perf; using std::tr1::make_tuple; using std::tr1::get; CV_ENUM(NormType, NORM_L1, NORM_L2, NORM_L2SQR, NORM_HAMMING, NORM_HAMMING2) typedef std::tr1::tuple<NormType, MatType, 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<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 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 = (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; } } }