modules/features2d/test/test_nearestneighbors.cpp

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This source file includes following definitions.
checkGetPoins
checkFindBoxed
checkFind
run
createIndex
knnSearch
radiusSearch
releaseModel
createModel
findNeighbors
createModel
findNeighbors
createModel
findNeighbors
createModel
findNeighbors
createModel
findNeighbors
findNeighbors
createModel
TEST
TEST
TEST
TEST
TEST
TEST
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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(); }
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root/modules/features2d/test/test_nearestneighbors.cpp

DEFINITIONS
