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#include "test_precomp.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();
}

TEST( Features2d_Detector_AKAZE, detect_and_compute_split )
{
    Mat testImg(100, 100, CV_8U);
    RNG rng(101);
    rng.fill(testImg, RNG::UNIFORM, Scalar(0), Scalar(255), true);

    Ptr<Feature2D> ext = AKAZE::create(AKAZE::DESCRIPTOR_MLDB, 0, 3, 0.001f, 1, 1, KAZE::DIFF_PM_G2);
    vector<KeyPoint> detAndCompKps;
    Mat desc;
    ext->detectAndCompute(testImg, noArray(), detAndCompKps, desc);

    vector<KeyPoint> detKps;
    ext->detect(testImg, detKps);

    ASSERT_EQ(detKps.size(), detAndCompKps.size());

    for(size_t i = 0; i < detKps.size(); i++)
        ASSERT_EQ(detKps[i].hash(), detAndCompKps[i].hash());
}
