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d05714995c
Found via `codespell`
168 lines
5.1 KiB
C++
168 lines
5.1 KiB
C++
#include "perf_precomp.hpp"
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namespace opencv_test
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{
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using namespace perf;
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CV_ENUM(NormType, NORM_L1, NORM_L2, NORM_L2SQR, NORM_HAMMING, NORM_HAMMING2)
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typedef tuple<NormType, MatType, bool> Norm_Destination_CrossCheck_t;
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typedef perf::TestBaseWithParam<Norm_Destination_CrossCheck_t> Norm_Destination_CrossCheck;
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typedef tuple<NormType, bool> Norm_CrossCheck_t;
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typedef perf::TestBaseWithParam<Norm_CrossCheck_t> Norm_CrossCheck;
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typedef tuple<MatType, bool> Source_CrossCheck_t;
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typedef perf::TestBaseWithParam<Source_CrossCheck_t> Source_CrossCheck;
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void generateData( Mat& query, Mat& train, const int sourceType );
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PERF_TEST_P(Norm_Destination_CrossCheck, batchDistance_8U,
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testing::Combine(testing::Values((int)NORM_L1, (int)NORM_L2SQR),
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testing::Values(CV_32S, CV_32F),
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testing::Bool()
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)
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)
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{
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NormType normType = get<0>(GetParam());
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int destinationType = get<1>(GetParam());
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bool isCrossCheck = get<2>(GetParam());
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int knn = isCrossCheck ? 1 : 0;
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Mat queryDescriptors;
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Mat trainDescriptors;
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Mat dist;
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Mat ndix;
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generateData(queryDescriptors, trainDescriptors, CV_8U);
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TEST_CYCLE()
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{
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batchDistance(queryDescriptors, trainDescriptors, dist, destinationType, (isCrossCheck) ? ndix : noArray(),
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normType, knn, Mat(), 0, isCrossCheck);
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}
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SANITY_CHECK(dist);
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if (isCrossCheck) SANITY_CHECK(ndix);
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}
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PERF_TEST_P(Norm_CrossCheck, batchDistance_Dest_32S,
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testing::Combine(testing::Values((int)NORM_HAMMING, (int)NORM_HAMMING2),
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testing::Bool()
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)
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)
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{
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NormType normType = get<0>(GetParam());
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bool isCrossCheck = get<1>(GetParam());
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int knn = isCrossCheck ? 1 : 0;
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Mat queryDescriptors;
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Mat trainDescriptors;
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Mat dist;
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Mat ndix;
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generateData(queryDescriptors, trainDescriptors, CV_8U);
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TEST_CYCLE()
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{
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batchDistance(queryDescriptors, trainDescriptors, dist, CV_32S, (isCrossCheck) ? ndix : noArray(),
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normType, knn, Mat(), 0, isCrossCheck);
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}
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SANITY_CHECK(dist);
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if (isCrossCheck) SANITY_CHECK(ndix);
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}
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PERF_TEST_P(Source_CrossCheck, batchDistance_L2,
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testing::Combine(testing::Values(CV_8U, CV_32F),
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testing::Bool()
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)
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)
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{
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int sourceType = get<0>(GetParam());
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bool isCrossCheck = get<1>(GetParam());
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int knn = isCrossCheck ? 1 : 0;
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Mat queryDescriptors;
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Mat trainDescriptors;
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Mat dist;
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Mat ndix;
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generateData(queryDescriptors, trainDescriptors, sourceType);
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declare.time(50);
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TEST_CYCLE()
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{
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batchDistance(queryDescriptors, trainDescriptors, dist, CV_32F, (isCrossCheck) ? ndix : noArray(),
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NORM_L2, knn, Mat(), 0, isCrossCheck);
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}
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SANITY_CHECK(dist);
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if (isCrossCheck) SANITY_CHECK(ndix);
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}
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PERF_TEST_P(Norm_CrossCheck, batchDistance_32F,
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testing::Combine(testing::Values((int)NORM_L1, (int)NORM_L2SQR),
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testing::Bool()
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)
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)
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{
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NormType normType = get<0>(GetParam());
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bool isCrossCheck = get<1>(GetParam());
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int knn = isCrossCheck ? 1 : 0;
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Mat queryDescriptors;
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Mat trainDescriptors;
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Mat dist;
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Mat ndix;
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generateData(queryDescriptors, trainDescriptors, CV_32F);
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declare.time(100);
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TEST_CYCLE()
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{
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batchDistance(queryDescriptors, trainDescriptors, dist, CV_32F, (isCrossCheck) ? ndix : noArray(),
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normType, knn, Mat(), 0, isCrossCheck);
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}
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SANITY_CHECK(dist, 1e-4);
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if (isCrossCheck) SANITY_CHECK(ndix);
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}
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void generateData( Mat& query, Mat& train, const int sourceType )
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{
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const int dim = 500;
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const int queryDescCount = 300; // must be even number because we split train data in some cases in two
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const int countFactor = 4; // do not change it
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RNG& rng = theRNG();
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// Generate query descriptors randomly.
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// Descriptor vector elements are integer values.
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Mat buf( queryDescCount, dim, CV_32SC1 );
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rng.fill( buf, RNG::UNIFORM, Scalar::all(0), Scalar(3) );
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buf.convertTo( query, sourceType );
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// Generate train descriptors as follows:
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// copy each query descriptor to train set countFactor times
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// and perturb some one element of the copied descriptors in
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// in ascending order. General boundaries of the perturbation
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// are (0.f, 1.f).
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train.create( query.rows*countFactor, query.cols, sourceType );
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float step = (sourceType == CV_8U ? 256.f : 1.f) / countFactor;
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for( int qIdx = 0; qIdx < query.rows; qIdx++ )
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{
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Mat queryDescriptor = query.row(qIdx);
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for( int c = 0; c < countFactor; c++ )
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{
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int tIdx = qIdx * countFactor + c;
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Mat trainDescriptor = train.row(tIdx);
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queryDescriptor.copyTo( trainDescriptor );
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int elem = rng(dim);
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float diff = rng.uniform( step*c, step*(c+1) );
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trainDescriptor.col(elem) += diff;
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}
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}
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}
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} // namespace
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