opencv/modules/features2d/test/test_descriptors_regression.cpp

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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 DESCRIPTOR_DIR = FEATURES2D_DIR + "/descriptor_extractors";

/****************************************************************************************\
*                     Regression tests for descriptor extractors.                        *
\****************************************************************************************/
static void writeMatInBin( const Mat& mat, const string& filename )
{
    FILE* f = fopen( filename.c_str(), "wb");
    if( f )
    {
        int type = mat.type();
        fwrite( (void*)&mat.rows, sizeof(int), 1, f );
        fwrite( (void*)&mat.cols, sizeof(int), 1, f );
        fwrite( (void*)&type, sizeof(int), 1, f );
        int dataSize = (int)(mat.step * mat.rows * mat.channels());
        fwrite( (void*)&dataSize, sizeof(int), 1, f );
        fwrite( (void*)mat.data, 1, dataSize, f );
        fclose(f);
    }
}

static Mat readMatFromBin( const string& filename )
{
    FILE* f = fopen( filename.c_str(), "rb" );
    if( f )
    {
        int rows, cols, type, dataSize;
        size_t elements_read1 = fread( (void*)&rows, sizeof(int), 1, f );
        size_t elements_read2 = fread( (void*)&cols, sizeof(int), 1, f );
        size_t elements_read3 = fread( (void*)&type, sizeof(int), 1, f );
        size_t elements_read4 = fread( (void*)&dataSize, sizeof(int), 1, f );
        CV_Assert(elements_read1 == 1 && elements_read2 == 1 && elements_read3 == 1 && elements_read4 == 1);

        size_t step = dataSize / rows / CV_ELEM_SIZE(type);
        CV_Assert(step >= (size_t)cols);

        Mat m = Mat( rows, step, type).colRange(0, cols);

        size_t elements_read = fread( m.ptr(), 1, dataSize, f );
        CV_Assert(elements_read == (size_t)(dataSize));
        fclose(f);

        return m;
    }
    return Mat();
}

template<class Distance>
class CV_DescriptorExtractorTest : public cvtest::BaseTest
{
public:
    typedef typename Distance::ValueType ValueType;
    typedef typename Distance::ResultType DistanceType;

    CV_DescriptorExtractorTest( const string _name, DistanceType _maxDist, const Ptr<DescriptorExtractor>& _dextractor,
                                Distance d = Distance() ):
            name(_name), maxDist(_maxDist), dextractor(_dextractor), distance(d) {}
protected:
    virtual void createDescriptorExtractor() {}

    void compareDescriptors( const Mat& validDescriptors, const Mat& calcDescriptors )
    {
        if( validDescriptors.size != calcDescriptors.size || validDescriptors.type() != calcDescriptors.type() )
        {
            ts->printf(cvtest::TS::LOG, "Valid and computed descriptors matrices must have the same size and type.\n");
            ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
            return;
        }

        CV_Assert( DataType<ValueType>::type == validDescriptors.type() );

        int dimension = validDescriptors.cols;
        DistanceType curMaxDist = std::numeric_limits<DistanceType>::min();
        for( int y = 0; y < validDescriptors.rows; y++ )
        {
            DistanceType dist = distance( validDescriptors.ptr<ValueType>(y), calcDescriptors.ptr<ValueType>(y), dimension );
            if( dist > curMaxDist )
                curMaxDist = dist;
        }

        stringstream ss;
        ss << "Max distance between valid and computed descriptors " << curMaxDist;
        if( curMaxDist < maxDist )
            ss << "." << endl;
        else
        {
            ss << ">" << maxDist  << " - bad accuracy!"<< endl;
            ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
        }
        ts->printf(cvtest::TS::LOG,  ss.str().c_str() );
    }

    void emptyDataTest()
    {
        assert( !dextractor.empty() );

        // One image.
        Mat image;
        vector<KeyPoint> keypoints;
        Mat descriptors;

        try
        {
            dextractor->compute( image, keypoints, descriptors );
        }
        catch(...)
        {
            ts->printf( cvtest::TS::LOG, "compute() on empty image and empty keypoints must not generate exception (1).\n");
            ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
        }

        image.create( 50, 50, CV_8UC3 );
        try
        {
            dextractor->compute( image, keypoints, descriptors );
        }
        catch(...)
        {
            ts->printf( cvtest::TS::LOG, "compute() on nonempty image and empty keypoints must not generate exception (1).\n");
            ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
        }

        // Several images.
        vector<Mat> images;
        vector<vector<KeyPoint> > keypointsCollection;
        vector<Mat> descriptorsCollection;
        try
        {
            dextractor->compute( images, keypointsCollection, descriptorsCollection );
        }
        catch(...)
        {
            ts->printf( cvtest::TS::LOG, "compute() on empty images and empty keypoints collection must not generate exception (2).\n");
            ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
        }
    }

    void regressionTest()
    {
        assert( !dextractor.empty() );

        // Read the test image.
        string imgFilename =  string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;

        Mat img = imread( imgFilename );
        if( img.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;
        }

        vector<KeyPoint> keypoints;
        FileStorage fs( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::READ );
        if( fs.isOpened() )
        {
            read( fs.getFirstTopLevelNode(), keypoints );

            Mat calcDescriptors;
            double t = (double)getTickCount();
            dextractor->compute( img, keypoints, calcDescriptors );
            t = getTickCount() - t;
            ts->printf(cvtest::TS::LOG, "\nAverage time of computing one descriptor = %g ms.\n", t/((double)getTickFrequency()*1000.)/calcDescriptors.rows);

            if( calcDescriptors.rows != (int)keypoints.size() )
            {
                ts->printf( cvtest::TS::LOG, "Count of computed descriptors and keypoints count must be equal.\n" );
                ts->printf( cvtest::TS::LOG, "Count of keypoints is            %d.\n", (int)keypoints.size() );
                ts->printf( cvtest::TS::LOG, "Count of computed descriptors is %d.\n", calcDescriptors.rows );
                ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
                return;
            }

            if( calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType() )
            {
                ts->printf( cvtest::TS::LOG, "Incorrect descriptor size or descriptor type.\n" );
                ts->printf( cvtest::TS::LOG, "Expected size is   %d.\n", dextractor->descriptorSize() );
                ts->printf( cvtest::TS::LOG, "Calculated size is %d.\n", calcDescriptors.cols );
                ts->printf( cvtest::TS::LOG, "Expected type is   %d.\n", dextractor->descriptorType() );
                ts->printf( cvtest::TS::LOG, "Calculated type is %d.\n", calcDescriptors.type() );
                ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
                return;
            }

            // TODO read and write descriptor extractor parameters and check them
            Mat validDescriptors = readDescriptors();
            if( !validDescriptors.empty() )
                compareDescriptors( validDescriptors, calcDescriptors );
            else
            {
                if( !writeDescriptors( calcDescriptors ) )
                {
                    ts->printf( cvtest::TS::LOG, "Descriptors can not be written.\n" );
                    ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
                    return;
                }
            }
        }
        else
        {
            ts->printf( cvtest::TS::LOG, "Compute and write keypoints.\n" );
            fs.open( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::WRITE );
            if( fs.isOpened() )
            {
                ORB fd;
                fd.detect(img, keypoints);
                write( fs, "keypoints", keypoints );
            }
            else
            {
                ts->printf(cvtest::TS::LOG, "File for writting keypoints can not be opened.\n");
                ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
                return;
            }
        }
    }

    void run(int)
    {
        createDescriptorExtractor();
        if( dextractor.empty() )
        {
            ts->printf(cvtest::TS::LOG, "Descriptor extractor 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 );
    }

    virtual Mat readDescriptors()
    {
        Mat res = readMatFromBin( string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) );
        return res;
    }

    virtual bool writeDescriptors( Mat& descs )
    {
        writeMatInBin( descs,  string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) );
        return true;
    }

    string name;
    const DistanceType maxDist;
    Ptr<DescriptorExtractor> dextractor;
    Distance distance;

private:
    CV_DescriptorExtractorTest& operator=(const CV_DescriptorExtractorTest&) { return *this; }
};

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

TEST( Features2d_DescriptorExtractor_BRISK, regression )
{
    CV_DescriptorExtractorTest<Hamming> test( "descriptor-brisk",  (CV_DescriptorExtractorTest<Hamming>::DistanceType)2.f,
                                                 DescriptorExtractor::create("BRISK") );
    test.safe_run();
}

TEST( Features2d_DescriptorExtractor_ORB, regression )
{
    // TODO adjust the parameters below
    CV_DescriptorExtractorTest<Hamming> test( "descriptor-orb",  (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
                                                 DescriptorExtractor::create("ORB") );
    test.safe_run();
}

TEST( Features2d_DescriptorExtractor_FREAK, regression )
{
    // TODO adjust the parameters below
    CV_DescriptorExtractorTest<Hamming> test( "descriptor-freak",  (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
                                                 DescriptorExtractor::create("FREAK") );
    test.safe_run();
}

TEST( Features2d_DescriptorExtractor_BRIEF, regression )
{
    CV_DescriptorExtractorTest<Hamming> test( "descriptor-brief",  1,
                                               DescriptorExtractor::create("BRIEF") );
    test.safe_run();
}

TEST( Features2d_DescriptorExtractor_OpponentBRIEF, regression )
{
    CV_DescriptorExtractorTest<Hamming> test( "descriptor-opponent-brief",  1,
                                               DescriptorExtractor::create("OpponentBRIEF") );
    test.safe_run();
}