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#include "test_precomp.hpp"
#include <algorithm>
#include <vector>
#include <iostream>
using namespace std;
using namespace cv;
using namespace cv::flann;
//--------------------------------------------------------------------------------
class NearestNeighborTest : public cvtest::BaseTest
{
public:
NearestNeighborTest() {}
protected:
static const int minValue = 0;
static const int maxValue = 1;
static const int dims = 30;
static const int featuresCount = 2000;
static const int K = 1; // * should also test 2nd nn etc.?
virtual void run( int start_from );
virtual void createModel( const Mat& data ) = 0;
virtual int findNeighbors( Mat& points, Mat& neighbors ) = 0;
virtual int checkGetPoins( const Mat& data );
virtual int checkFindBoxed();
virtual int checkFind( const Mat& data );
virtual void releaseModel() = 0;
};
int NearestNeighborTest::checkGetPoins( const Mat& )
{
return cvtest::TS::OK;
}
int NearestNeighborTest::checkFindBoxed()
{
return cvtest::TS::OK;
}
int NearestNeighborTest::checkFind( const Mat& data )
{
int code = cvtest::TS::OK;
int pointsCount = 1000;
float noise = 0.2f;
RNG rng;
Mat points( pointsCount, dims, CV_32FC1 );
Mat results( pointsCount, K, CV_32SC1 );
std::vector<int> fmap( pointsCount );
for( int pi = 0; pi < pointsCount; pi++ )
{
int fi = rng.next() % featuresCount;
fmap[pi] = fi;
for( int d = 0; d < dims; d++ )
points.at<float>(pi, d) = data.at<float>(fi, d) + rng.uniform(0.0f, 1.0f) * noise;
}
code = findNeighbors( points, results );
if( code == cvtest::TS::OK )
{
int correctMatches = 0;
for( int pi = 0; pi < pointsCount; pi++ )
{
if( fmap[pi] == results.at<int>(pi, 0) )
correctMatches++;
}
double correctPerc = correctMatches / (double)pointsCount;
if (correctPerc < .75)
{
ts->printf( cvtest::TS::LOG, "correct_perc = %d\n", correctPerc );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
}
return code;
}
void NearestNeighborTest::run( int /*start_from*/ ) {
int code = cvtest::TS::OK, tempCode;
Mat desc( featuresCount, dims, CV_32FC1 );
randu( desc, Scalar(minValue), Scalar(maxValue) );
createModel( desc );
tempCode = checkGetPoins( desc );
if( tempCode != cvtest::TS::OK )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of GetPoints \n" );
code = tempCode;
}
tempCode = checkFindBoxed();
if( tempCode != cvtest::TS::OK )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of FindBoxed \n" );
code = tempCode;
}
tempCode = checkFind( desc );
if( tempCode != cvtest::TS::OK )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of Find \n" );
code = tempCode;
}
releaseModel();
ts->set_failed_test_info( code );
}
//--------------------------------------------------------------------------------
class CV_FlannTest : public NearestNeighborTest
{
public:
CV_FlannTest() {}
protected:
void createIndex( const Mat& data, const IndexParams& params );
int knnSearch( Mat& points, Mat& neighbors );
int radiusSearch( Mat& points, Mat& neighbors );
virtual void releaseModel();
Index* index;
};
void CV_FlannTest::createIndex( const Mat& data, const IndexParams& params )
{
index = new Index( data, params );
}
int CV_FlannTest::knnSearch( Mat& points, Mat& neighbors )
{
Mat dist( points.rows, neighbors.cols, CV_32FC1);
int knn = 1, j;
// 1st way
index->knnSearch( points, neighbors, dist, knn, SearchParams() );
// 2nd way
Mat neighbors1( neighbors.size(), CV_32SC1 );
for( int i = 0; i < points.rows; i++ )
{
float* fltPtr = points.ptr<float>(i);
vector<float> query( fltPtr, fltPtr + points.cols );
vector<int> indices( neighbors1.cols, 0 );
vector<float> dists( dist.cols, 0 );
index->knnSearch( query, indices, dists, knn, SearchParams() );
vector<int>::const_iterator it = indices.begin();
for( j = 0; it != indices.end(); ++it, j++ )
neighbors1.at<int>(i,j) = *it;
}
// compare results
if( cvtest::norm( neighbors, neighbors1, NORM_L1 ) != 0 )
return cvtest::TS::FAIL_BAD_ACCURACY;
return cvtest::TS::OK;
}
int CV_FlannTest::radiusSearch( Mat& points, Mat& neighbors )
{
Mat dist( 1, neighbors.cols, CV_32FC1);
Mat neighbors1( neighbors.size(), CV_32SC1 );
float radius = 10.0f;
int j;
// radiusSearch can only search one feature at a time for range search
for( int i = 0; i < points.rows; i++ )
{
// 1st way
Mat p( 1, points.cols, CV_32FC1, points.ptr<float>(i) ),
n( 1, neighbors.cols, CV_32SC1, neighbors.ptr<int>(i) );
index->radiusSearch( p, n, dist, radius, neighbors.cols, SearchParams() );
// 2nd way
float* fltPtr = points.ptr<float>(i);
vector<float> query( fltPtr, fltPtr + points.cols );
vector<int> indices( neighbors1.cols, 0 );
vector<float> dists( dist.cols, 0 );
index->radiusSearch( query, indices, dists, radius, neighbors.cols, SearchParams() );
vector<int>::const_iterator it = indices.begin();
for( j = 0; it != indices.end(); ++it, j++ )
neighbors1.at<int>(i,j) = *it;
}
// compare results
if( cvtest::norm( neighbors, neighbors1, NORM_L1 ) != 0 )
return cvtest::TS::FAIL_BAD_ACCURACY;
return cvtest::TS::OK;
}
void CV_FlannTest::releaseModel()
{
delete index;
}
//---------------------------------------
class CV_FlannLinearIndexTest : public CV_FlannTest
{
public:
CV_FlannLinearIndexTest() {}
protected:
virtual void createModel( const Mat& data ) { createIndex( data, LinearIndexParams() ); }
virtual int findNeighbors( Mat& points, Mat& neighbors ) { return knnSearch( points, neighbors ); }
};
//---------------------------------------
class CV_FlannKMeansIndexTest : public CV_FlannTest
{
public:
CV_FlannKMeansIndexTest() {}
protected:
virtual void createModel( const Mat& data ) { createIndex( data, KMeansIndexParams() ); }
virtual int findNeighbors( Mat& points, Mat& neighbors ) { return radiusSearch( points, neighbors ); }
};
//---------------------------------------
class CV_FlannKDTreeIndexTest : public CV_FlannTest
{
public:
CV_FlannKDTreeIndexTest() {}
protected:
virtual void createModel( const Mat& data ) { createIndex( data, KDTreeIndexParams() ); }
virtual int findNeighbors( Mat& points, Mat& neighbors ) { return radiusSearch( points, neighbors ); }
};
//----------------------------------------
class CV_FlannCompositeIndexTest : public CV_FlannTest
{
public:
CV_FlannCompositeIndexTest() {}
protected:
virtual void createModel( const Mat& data ) { createIndex( data, CompositeIndexParams() ); }
virtual int findNeighbors( Mat& points, Mat& neighbors ) { return knnSearch( points, neighbors ); }
};
//----------------------------------------
class CV_FlannAutotunedIndexTest : public CV_FlannTest
{
public:
CV_FlannAutotunedIndexTest() {}
protected:
virtual void createModel( const Mat& data ) { createIndex( data, AutotunedIndexParams() ); }
virtual int findNeighbors( Mat& points, Mat& neighbors ) { return knnSearch( points, neighbors ); }
};
//----------------------------------------
class CV_FlannSavedIndexTest : public CV_FlannTest
{
public:
CV_FlannSavedIndexTest() {}
protected:
virtual void createModel( const Mat& data );
virtual int findNeighbors( Mat& points, Mat& neighbors ) { return knnSearch( points, neighbors ); }
};
void CV_FlannSavedIndexTest::createModel(const cv::Mat &data)
{
switch ( cvtest::randInt(ts->get_rng()) % 2 )
{
//case 0: createIndex( data, LinearIndexParams() ); break; // nothing to save for linear search
case 0: createIndex( data, KMeansIndexParams() ); break;
case 1: createIndex( data, KDTreeIndexParams() ); break;
//case 2: createIndex( data, CompositeIndexParams() ); break; // nothing to save for linear search
//case 2: createIndex( data, AutotunedIndexParams() ); break; // possible linear index !
default: assert(0);
}
string filename = tempfile();
index->save( filename );
createIndex( data, SavedIndexParams(filename.c_str()));
remove( filename.c_str() );
}
TEST(Features2d_FLANN_Linear, regression) { CV_FlannLinearIndexTest test; test.safe_run(); }
TEST(Features2d_FLANN_KMeans, regression) { CV_FlannKMeansIndexTest test; test.safe_run(); }
TEST(Features2d_FLANN_KDTree, regression) { CV_FlannKDTreeIndexTest test; test.safe_run(); }
TEST(Features2d_FLANN_Composite, regression) { CV_FlannCompositeIndexTest test; test.safe_run(); }
TEST(Features2d_FLANN_Auto, regression) { CV_FlannAutotunedIndexTest test; test.safe_run(); }
TEST(Features2d_FLANN_Saved, regression) { CV_FlannSavedIndexTest test; test.safe_run(); }