root/modules/features2d/test/test_detectors_regression.cpp

DEFINITIONS

1. fdetector
2. emptyDataTest
3. isSimilarKeypoints
4. compareKeypointSets
5. regressionTest
6. run
7. TEST
8. TEST
9. TEST
10. TEST
11. TEST
12. TEST
13. TEST
14. TEST
15. TEST

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#include "test_precomp.hpp"
#include "opencv2/highgui.hpp"

using namespace std;
using namespace cv;

const string FEATURES2D_DIR = "features2d";
const string IMAGE_FILENAME = "tsukuba.png";
const string DETECTOR_DIR = FEATURES2D_DIR + "/feature_detectors";

/****************************************************************************************\
*            Regression tests for feature detectors comparing keypoints.                 *
\****************************************************************************************/

class CV_FeatureDetectorTest : public cvtest::BaseTest
{
public:
    CV_FeatureDetectorTest( const string& _name, const Ptr<FeatureDetector>& _fdetector ) :
        name(_name), fdetector(_fdetector) {}

protected:
    bool isSimilarKeypoints( const KeyPoint& p1, const KeyPoint& p2 );
    void compareKeypointSets( const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints );

    void emptyDataTest();
    void regressionTest(); // TODO test of detect() with mask

    virtual void run( int );

    string name;
    Ptr<FeatureDetector> fdetector;
};

void CV_FeatureDetectorTest::emptyDataTest()
{
    // One image.
    Mat image;
    vector<KeyPoint> keypoints;
    try
    {
        fdetector->detect( image, keypoints );
    }
    catch(...)
    {
        ts->printf( cvtest::TS::LOG, "detect() on empty image must not generate exception (1).\n" );
        ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
    }

    if( !keypoints.empty() )
    {
        ts->printf( cvtest::TS::LOG, "detect() on empty image must return empty keypoints vector (1).\n" );
        ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
        return;
    }

    // Several images.
    vector<Mat> images;
    vector<vector<KeyPoint> > keypointCollection;
    try
    {
        fdetector->detect( images, keypointCollection );
    }
    catch(...)
    {
        ts->printf( cvtest::TS::LOG, "detect() on empty image vector must not generate exception (2).\n" );
        ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
    }
}

bool CV_FeatureDetectorTest::isSimilarKeypoints( const KeyPoint& p1, const KeyPoint& p2 )
{
    const float maxPtDif = 1.f;
    const float maxSizeDif = 1.f;
    const float maxAngleDif = 2.f;
    const float maxResponseDif = 0.1f;

    float dist = (float)norm( p1.pt - p2.pt );
    return (dist < maxPtDif &&
            fabs(p1.size - p2.size) < maxSizeDif &&
            abs(p1.angle - p2.angle) < maxAngleDif &&
            abs(p1.response - p2.response) < maxResponseDif &&
            p1.octave == p2.octave &&
            p1.class_id == p2.class_id );
}

void CV_FeatureDetectorTest::compareKeypointSets( const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints )
{
    const float maxCountRatioDif = 0.01f;

    // Compare counts of validation and calculated keypoints.
    float countRatio = (float)validKeypoints.size() / (float)calcKeypoints.size();
    if( countRatio < 1 - maxCountRatioDif || countRatio > 1.f + maxCountRatioDif )
    {
        ts->printf( cvtest::TS::LOG, "Bad keypoints count ratio (validCount = %d, calcCount = %d).\n",
                    validKeypoints.size(), calcKeypoints.size() );
        ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
        return;
    }

    int progress = 0, progressCount = (int)(validKeypoints.size() * calcKeypoints.size());
    int badPointCount = 0, commonPointCount = max((int)validKeypoints.size(), (int)calcKeypoints.size());
    for( size_t v = 0; v < validKeypoints.size(); v++ )
    {
        int nearestIdx = -1;
        float minDist = std::numeric_limits<float>::max();

        for( size_t c = 0; c < calcKeypoints.size(); c++ )
        {
            progress = update_progress( progress, (int)(v*calcKeypoints.size() + c), progressCount, 0 );
            float curDist = (float)norm( calcKeypoints[c].pt - validKeypoints[v].pt );
            if( curDist < minDist )
            {
                minDist = curDist;
                nearestIdx = (int)c;
            }
        }

        assert( minDist >= 0 );
        if( !isSimilarKeypoints( validKeypoints[v], calcKeypoints[nearestIdx] ) )
            badPointCount++;
    }
    ts->printf( cvtest::TS::LOG, "badPointCount = %d; validPointCount = %d; calcPointCount = %d\n",
                badPointCount, validKeypoints.size(), calcKeypoints.size() );
    if( badPointCount > 0.9 * commonPointCount )
    {
        ts->printf( cvtest::TS::LOG, " - Bad accuracy!\n" );
        ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
        return;
    }
    ts->printf( cvtest::TS::LOG, " - OK\n" );
}

void CV_FeatureDetectorTest::regressionTest()
{
    assert( !fdetector.empty() );
    string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
    string resFilename = string(ts->get_data_path()) + DETECTOR_DIR + "/" + string(name) + ".xml.gz";

    // Read the test image.
    Mat image = imread( imgFilename );
    if( image.empty() )
    {
        ts->printf( cvtest::TS::LOG, "Image %s can not be read.\n", imgFilename.c_str() );
        ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
        return;
    }

    FileStorage fs( resFilename, FileStorage::READ );

    // Compute keypoints.
    vector<KeyPoint> calcKeypoints;
    fdetector->detect( image, calcKeypoints );

    if( fs.isOpened() ) // Compare computed and valid keypoints.
    {
        // TODO compare saved feature detector params with current ones

        // Read validation keypoints set.
        vector<KeyPoint> validKeypoints;
        read( fs["keypoints"], validKeypoints );
        if( validKeypoints.empty() )
        {
            ts->printf( cvtest::TS::LOG, "Keypoints can not be read.\n" );
            ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
            return;
        }

        compareKeypointSets( validKeypoints, calcKeypoints );
    }
    else // Write detector parameters and computed keypoints as validation data.
    {
        fs.open( resFilename, FileStorage::WRITE );
        if( !fs.isOpened() )
        {
            ts->printf( cvtest::TS::LOG, "File %s can not be opened to write.\n", resFilename.c_str() );
            ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
            return;
        }
        else
        {
            fs << "detector_params" << "{";
            fdetector->write( fs );
            fs << "}";

            write( fs, "keypoints", calcKeypoints );
        }
    }
}

void CV_FeatureDetectorTest::run( int /*start_from*/ )
{
    if( !fdetector )
    {
        ts->printf( cvtest::TS::LOG, "Feature detector is empty.\n" );
        ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
        return;
    }

    emptyDataTest();
    regressionTest();

    ts->set_failed_test_info( cvtest::TS::OK );
}

/****************************************************************************************\
*                                Tests registrations                                     *
\****************************************************************************************/

TEST( Features2d_Detector_BRISK, regression )
{
    CV_FeatureDetectorTest test( "detector-brisk", BRISK::create() );
    test.safe_run();
}

TEST( Features2d_Detector_FAST, regression )
{
    CV_FeatureDetectorTest test( "detector-fast", FastFeatureDetector::create() );
    test.safe_run();
}

TEST( Features2d_Detector_AGAST, regression )
{
    CV_FeatureDetectorTest test( "detector-agast", AgastFeatureDetector::create() );
    test.safe_run();
}

TEST( Features2d_Detector_GFTT, regression )
{
    CV_FeatureDetectorTest test( "detector-gftt", GFTTDetector::create() );
    test.safe_run();
}

TEST( Features2d_Detector_Harris, regression )
{
    Ptr<GFTTDetector> gftt = GFTTDetector::create();
    gftt->setHarrisDetector(true);
    CV_FeatureDetectorTest test( "detector-harris", gftt);
    test.safe_run();
}

TEST( Features2d_Detector_MSER, DISABLED_regression )
{
    CV_FeatureDetectorTest test( "detector-mser", MSER::create() );
    test.safe_run();
}

TEST( Features2d_Detector_ORB, regression )
{
    CV_FeatureDetectorTest test( "detector-orb", ORB::create() );
    test.safe_run();
}

TEST( Features2d_Detector_KAZE, regression )
{
    CV_FeatureDetectorTest test( "detector-kaze", KAZE::create() );
    test.safe_run();
}

TEST( Features2d_Detector_AKAZE, regression )
{
    CV_FeatureDetectorTest test( "detector-akaze", AKAZE::create() );
    test.safe_run();
}