root/modules/features2d/test/test_descriptors_regression.cpp

DEFINITIONS

1. writeMatInBin
2. readMatFromBin
3. detector
4. createDescriptorExtractor
5. compareDescriptors
6. emptyDataTest
7. regressionTest
8. run
9. readDescriptors
10. writeDescriptors
11. TEST
12. TEST
13. TEST
14. TEST
15. TEST
16. TEST

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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 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.ptr(), 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);

        int step = dataSize / rows / CV_ELEM_SIZE(type);
        CV_Assert(step >= 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(), Ptr<FeatureDetector> _detector = Ptr<FeatureDetector>()):
        name(_name), maxDist(_maxDist), dextractor(_dextractor), distance(d) , detector(_detector) {}

    ~CV_DescriptorExtractorTest()
    {
    }
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 );

        // 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 );

        // 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(!detector.empty()) {
            detector->detect(img, keypoints);
        } else {
            read( fs.getFirstTopLevelNode(), keypoints );
        }
        if(!keypoints.empty())
        {
            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;
                }
            }
        }
        if(!fs.isOpened())
        {
            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() )
            {
                Ptr<ORB> fd = ORB::create();
                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 )
        {
            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;
    Ptr<FeatureDetector> detector;

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,
                                            BRISK::create() );
    test.safe_run();
}

TEST( Features2d_DescriptorExtractor_ORB, regression )
{
    // TODO adjust the parameters below
    CV_DescriptorExtractorTest<Hamming> test( "descriptor-orb",
#if CV_NEON
                                              (CV_DescriptorExtractorTest<Hamming>::DistanceType)25.f,
#else
                                              (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
#endif
                                             ORB::create() );
    test.safe_run();
}

TEST( Features2d_DescriptorExtractor_KAZE, regression )
{
    CV_DescriptorExtractorTest< L2<float> > test( "descriptor-kaze",  0.03f,
                                                 KAZE::create(),
                                                 L2<float>(), KAZE::create() );
    test.safe_run();
}

TEST( Features2d_DescriptorExtractor_AKAZE, regression )
{
    CV_DescriptorExtractorTest<Hamming> test( "descriptor-akaze",
                                              (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
                                              AKAZE::create(),
                                              Hamming(), AKAZE::create());
    test.safe_run();
}

TEST( Features2d_DescriptorExtractor, batch )
{
    string path = string(cvtest::TS::ptr()->get_data_path() + "detectors_descriptors_evaluation/images_datasets/graf");
    vector<Mat> imgs, descriptors;
    vector<vector<KeyPoint> > keypoints;
    int i, n = 6;
    Ptr<ORB> orb = ORB::create();

    for( i = 0; i < n; i++ )
    {
        string imgname = format("%s/img%d.png", path.c_str(), i+1);
        Mat img = imread(imgname, 0);
        imgs.push_back(img);
    }

    orb->detect(imgs, keypoints);
    orb->compute(imgs, keypoints, descriptors);

    ASSERT_EQ((int)keypoints.size(), n);
    ASSERT_EQ((int)descriptors.size(), n);

    for( i = 0; i < n; i++ )
    {
        EXPECT_GT((int)keypoints[i].size(), 100);
        EXPECT_GT(descriptors[i].rows, 100);
    }
}

TEST( Features2d_Feature2d, no_crash )
{
    const String& pattern = string(cvtest::TS::ptr()->get_data_path() + "shared/*.png");
    vector<String> fnames;
    glob(pattern, fnames, false);
    sort(fnames.begin(), fnames.end());

    Ptr<AKAZE> akaze = AKAZE::create();
    Ptr<ORB> orb = ORB::create();
    Ptr<KAZE> kaze = KAZE::create();
    Ptr<BRISK> brisk = BRISK::create();
    size_t i, n = fnames.size();
    vector<KeyPoint> keypoints;
    Mat descriptors;
    orb->setMaxFeatures(5000);

    for( i = 0; i < n; i++ )
    {
        printf("%d. image: %s:\n", (int)i, fnames[i].c_str());
        if( strstr(fnames[i].c_str(), "MP.png") != 0 )
            continue;
        bool checkCount = strstr(fnames[i].c_str(), "templ.png") == 0;

        Mat img = imread(fnames[i], -1);
        printf("\tAKAZE ... "); fflush(stdout);
        akaze->detectAndCompute(img, noArray(), keypoints, descriptors);
        printf("(%d keypoints) ", (int)keypoints.size()); fflush(stdout);
        if( checkCount )
        {
            EXPECT_GT((int)keypoints.size(), 0);
        }
        ASSERT_EQ(descriptors.rows, (int)keypoints.size());
        printf("ok\n");

        printf("\tKAZE ... "); fflush(stdout);
        kaze->detectAndCompute(img, noArray(), keypoints, descriptors);
        printf("(%d keypoints) ", (int)keypoints.size()); fflush(stdout);
        if( checkCount )
        {
            EXPECT_GT((int)keypoints.size(), 0);
        }
        ASSERT_EQ(descriptors.rows, (int)keypoints.size());
        printf("ok\n");

        printf("\tORB ... "); fflush(stdout);
        orb->detectAndCompute(img, noArray(), keypoints, descriptors);
        printf("(%d keypoints) ", (int)keypoints.size()); fflush(stdout);
        if( checkCount )
        {
            EXPECT_GT((int)keypoints.size(), 0);
        }
        ASSERT_EQ(descriptors.rows, (int)keypoints.size());
        printf("ok\n");

        printf("\tBRISK ... "); fflush(stdout);
        brisk->detectAndCompute(img, noArray(), keypoints, descriptors);
        printf("(%d keypoints) ", (int)keypoints.size()); fflush(stdout);
        if( checkCount )
        {
            EXPECT_GT((int)keypoints.size(), 0);
        }
        ASSERT_EQ(descriptors.rows, (int)keypoints.size());
        printf("ok\n");
    }
}