mirror of
https://github.com/opencv/opencv.git
synced 2024-12-06 02:29:14 +08:00
d05714995c
Found via `codespell`
168 lines
5.1 KiB
C++
168 lines
5.1 KiB
C++
#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
|