test_io.cpp

#include "test_precomp.hpp"

using namespace cv;
using namespace std;

static SparseMat cvTsGetRandomSparseMat(int dims, const int* sz, int type,
                                        int nzcount, double a, double b, RNG& rng)
{
    SparseMat m(dims, sz, type);
    int i, j;
    CV_Assert(CV_MAT_CN(type) == 1);
    for( i = 0; i < nzcount; i++ )
    {
        int idx[CV_MAX_DIM];
        for( j = 0; j < dims; j++ )
            idx[j] = cvtest::randInt(rng) % sz[j];
        double val = cvtest::randReal(rng)*(b - a) + a;
        uchar* ptr = m.ptr(idx, true, 0);
        if( type == CV_8U )
            *(uchar*)ptr = saturate_cast<uchar>(val);
        else if( type == CV_8S )
            *(schar*)ptr = saturate_cast<schar>(val);
        else if( type == CV_16U )
            *(ushort*)ptr = saturate_cast<ushort>(val);
        else if( type == CV_16S )
            *(short*)ptr = saturate_cast<short>(val);
        else if( type == CV_32S )
            *(int*)ptr = saturate_cast<int>(val);
        else if( type == CV_32F )
            *(float*)ptr = saturate_cast<float>(val);
        else
            *(double*)ptr = saturate_cast<double>(val);
    }

    return m;
}

static bool cvTsCheckSparse(const CvSparseMat* m1, const CvSparseMat* m2, double eps)
{
    CvSparseMatIterator it1;
    CvSparseNode* node1;
    int depth = CV_MAT_DEPTH(m1->type);

    if( m1->heap->active_count != m2->heap->active_count ||
       m1->dims != m2->dims || CV_MAT_TYPE(m1->type) != CV_MAT_TYPE(m2->type) )
        return false;

    for( node1 = cvInitSparseMatIterator( m1, &it1 );
        node1 != 0; node1 = cvGetNextSparseNode( &it1 ))
    {
        uchar* v1 = (uchar*)CV_NODE_VAL(m1,node1);
        uchar* v2 = cvPtrND( m2, CV_NODE_IDX(m1,node1), 0, 0, &node1->hashval );
        if( !v2 )
            return false;
        if( depth == CV_8U || depth == CV_8S )
        {
            if( *v1 != *v2 )
                return false;
        }
        else if( depth == CV_16U || depth == CV_16S )
        {
            if( *(ushort*)v1 != *(ushort*)v2 )
                return false;
        }
        else if( depth == CV_32S )
        {
            if( *(int*)v1 != *(int*)v2 )
                return false;
        }
        else if( depth == CV_32F )
        {
            if( fabs(*(float*)v1 - *(float*)v2) > eps*(fabs(*(float*)v2) + 1) )
                return false;
        }
        else if( fabs(*(double*)v1 - *(double*)v2) > eps*(fabs(*(double*)v2) + 1) )
            return false;
    }

    return true;
}


class Core_IOTest : public cvtest::BaseTest
{
public:
    Core_IOTest() { }
protected:
    void run(int)
    {
        double ranges[][2] = {{0, 256}, {-128, 128}, {0, 65536}, {-32768, 32768},
            {-1000000, 1000000}, {-10, 10}, {-10, 10}};
        RNG& rng = ts->get_rng();
        RNG rng0;
        test_case_count = 4;
        int progress = 0;
        MemStorage storage(cvCreateMemStorage(0));

        for( int idx = 0; idx < test_case_count; idx++ )
        {
            ts->update_context( this, idx, false );
            progress = update_progress( progress, idx, test_case_count, 0 );

            cvClearMemStorage(storage);

            bool mem = (idx % 4) >= 2;
            string filename = tempfile(idx % 2 ? ".yml" : ".xml");

            FileStorage fs(filename, FileStorage::WRITE + (mem ? FileStorage::MEMORY : 0));

            int test_int = (int)cvtest::randInt(rng);
            double test_real = (cvtest::randInt(rng)%2?1:-1)*exp(cvtest::randReal(rng)*18-9);
            string test_string = "vw wv23424rt\"&amp;&lt;&gt;&amp;&apos;@#$@$%$%&%IJUKYILFD@#$@%$&*&() ";

            int depth = cvtest::randInt(rng) % (CV_64F+1);
            int cn = cvtest::randInt(rng) % 4 + 1;
            Mat test_mat(cvtest::randInt(rng)%30+1, cvtest::randInt(rng)%30+1, CV_MAKETYPE(depth, cn));

            rng0.fill(test_mat, CV_RAND_UNI, Scalar::all(ranges[depth][0]), Scalar::all(ranges[depth][1]));
            if( depth >= CV_32F )
            {
                exp(test_mat, test_mat);
                Mat test_mat_scale(test_mat.size(), test_mat.type());
                rng0.fill(test_mat_scale, CV_RAND_UNI, Scalar::all(-1), Scalar::all(1));
                multiply(test_mat, test_mat_scale, test_mat);
            }

            CvSeq* seq = cvCreateSeq(test_mat.type(), (int)sizeof(CvSeq),
                                     (int)test_mat.elemSize(), storage);
            cvSeqPushMulti(seq, test_mat.ptr(), test_mat.cols*test_mat.rows);

            CvGraph* graph = cvCreateGraph( CV_ORIENTED_GRAPH,
                                           sizeof(CvGraph), sizeof(CvGraphVtx),
                                           sizeof(CvGraphEdge), storage );
            int edges[][2] = {{0,1},{1,2},{2,0},{0,3},{3,4},{4,1}};
            int i, vcount = 5, ecount = 6;
            for( i = 0; i < vcount; i++ )
                cvGraphAddVtx(graph);
            for( i = 0; i < ecount; i++ )
            {
                CvGraphEdge* edge;
                cvGraphAddEdge(graph, edges[i][0], edges[i][1], 0, &edge);
                edge->weight = (float)(i+1);
            }

            depth = cvtest::randInt(rng) % (CV_64F+1);
            cn = cvtest::randInt(rng) % 4 + 1;
            int sz[] = {
                static_cast<int>(cvtest::randInt(rng)%10+1),
                static_cast<int>(cvtest::randInt(rng)%10+1),
                static_cast<int>(cvtest::randInt(rng)%10+1),
            };
            MatND test_mat_nd(3, sz, CV_MAKETYPE(depth, cn));

            rng0.fill(test_mat_nd, CV_RAND_UNI, Scalar::all(ranges[depth][0]), Scalar::all(ranges[depth][1]));
            if( depth >= CV_32F )
            {
                exp(test_mat_nd, test_mat_nd);
                MatND test_mat_scale(test_mat_nd.dims, test_mat_nd.size, test_mat_nd.type());
                rng0.fill(test_mat_scale, CV_RAND_UNI, Scalar::all(-1), Scalar::all(1));
                multiply(test_mat_nd, test_mat_scale, test_mat_nd);
            }

            int ssz[] = {
                static_cast<int>(cvtest::randInt(rng)%10+1),
                static_cast<int>(cvtest::randInt(rng)%10+1),
                static_cast<int>(cvtest::randInt(rng)%10+1),
                static_cast<int>(cvtest::randInt(rng)%10+1),
            };
            SparseMat test_sparse_mat = cvTsGetRandomSparseMat(4, ssz, cvtest::randInt(rng)%(CV_64F+1),
                                                               cvtest::randInt(rng) % 10000, 0, 100, rng);

            fs << "test_int" << test_int << "test_real" << test_real << "test_string" << test_string;
            fs << "test_mat" << test_mat;
            fs << "test_mat_nd" << test_mat_nd;
            fs << "test_sparse_mat" << test_sparse_mat;

            fs << "test_list" << "[" << 0.0000000000001 << 2 << CV_PI << -3435345 << "2-502 2-029 3egegeg" <<
            "{:" << "month" << 12 << "day" << 31 << "year" << 1969 << "}" << "]";
            fs << "test_map" << "{" << "x" << 1 << "y" << 2 << "width" << 100 << "height" << 200 << "lbp" << "[:";

            const uchar arr[] = {0, 1, 1, 0, 1, 1, 0, 1};
            fs.writeRaw("u", arr, (int)(sizeof(arr)/sizeof(arr[0])));

            fs << "]" << "}";
            cvWriteComment(*fs, "test comment", 0);

            fs.writeObj("test_seq", seq);
            fs.writeObj("test_graph",graph);
            CvGraph* graph2 = (CvGraph*)cvClone(graph);

            string content = fs.releaseAndGetString();

            if(!fs.open(mem ? content : filename, FileStorage::READ + (mem ? FileStorage::MEMORY : 0)))
            {
                ts->printf( cvtest::TS::LOG, "filename %s can not be read\n", !mem ? filename.c_str() : content.c_str());
                ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
                return;
            }

            int real_int = (int)fs["test_int"];
            double real_real = (double)fs["test_real"];
            String real_string = (String)fs["test_string"];

            if( real_int != test_int ||
               fabs(real_real - test_real) > DBL_EPSILON*(fabs(test_real)+1) ||
               real_string != test_string )
            {
                ts->printf( cvtest::TS::LOG, "the read scalars are not correct\n" );
                ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
                return;
            }

            CvMat* m = (CvMat*)fs["test_mat"].readObj();
            CvMat _test_mat = test_mat;
            double max_diff = 0;
            CvMat stub1, _test_stub1;
            cvReshape(m, &stub1, 1, 0);
            cvReshape(&_test_mat, &_test_stub1, 1, 0);
            vector<int> pt;

            if( !m || !CV_IS_MAT(m) || m->rows != test_mat.rows || m->cols != test_mat.cols ||
               cvtest::cmpEps( cv::cvarrToMat(&stub1), cv::cvarrToMat(&_test_stub1), &max_diff, 0, &pt, true) < 0 )
            {
                ts->printf( cvtest::TS::LOG, "the read matrix is not correct: (%.20g vs %.20g) at (%d,%d)\n",
                            cvGetReal2D(&stub1, pt[0], pt[1]), cvGetReal2D(&_test_stub1, pt[0], pt[1]),
                            pt[0], pt[1] );
                ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
                return;
            }
            if( m && CV_IS_MAT(m))
                cvReleaseMat(&m);

            CvMatND* m_nd = (CvMatND*)fs["test_mat_nd"].readObj();
            CvMatND _test_mat_nd = test_mat_nd;

            if( !m_nd || !CV_IS_MATND(m_nd) )
            {
                ts->printf( cvtest::TS::LOG, "the read nd-matrix is not correct\n" );
                ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
                return;
            }

            CvMat stub, _test_stub;
            cvGetMat(m_nd, &stub, 0, 1);
            cvGetMat(&_test_mat_nd, &_test_stub, 0, 1);
            cvReshape(&stub, &stub1, 1, 0);
            cvReshape(&_test_stub, &_test_stub1, 1, 0);

            if( !CV_ARE_TYPES_EQ(&stub, &_test_stub) ||
               !CV_ARE_SIZES_EQ(&stub, &_test_stub) ||
               //cvNorm(&stub, &_test_stub, CV_L2) != 0 )
               cvtest::cmpEps( cv::cvarrToMat(&stub1), cv::cvarrToMat(&_test_stub1), &max_diff, 0, &pt, true) < 0 )
            {
                ts->printf( cvtest::TS::LOG, "readObj method: the read nd matrix is not correct: (%.20g vs %.20g) vs at (%d,%d)\n",
                           cvGetReal2D(&stub1, pt[0], pt[1]), cvGetReal2D(&_test_stub1, pt[0], pt[1]),
                           pt[0], pt[1] );
                ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
                return;
            }

            MatND mat_nd2;
            fs["test_mat_nd"] >> mat_nd2;
            CvMatND m_nd2 = mat_nd2;
            cvGetMat(&m_nd2, &stub, 0, 1);
            cvReshape(&stub, &stub1, 1, 0);

            if( !CV_ARE_TYPES_EQ(&stub, &_test_stub) ||
               !CV_ARE_SIZES_EQ(&stub, &_test_stub) ||
               //cvNorm(&stub, &_test_stub, CV_L2) != 0 )
               cvtest::cmpEps( cv::cvarrToMat(&stub1), cv::cvarrToMat(&_test_stub1), &max_diff, 0, &pt, true) < 0 )
            {
                ts->printf( cvtest::TS::LOG, "C++ method: the read nd matrix is not correct: (%.20g vs %.20g) vs at (%d,%d)\n",
                           cvGetReal2D(&stub1, pt[0], pt[1]), cvGetReal2D(&_test_stub1, pt[1], pt[0]),
                           pt[0], pt[1] );
                ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
                return;
            }

            cvRelease((void**)&m_nd);

            Ptr<CvSparseMat> m_s((CvSparseMat*)fs["test_sparse_mat"].readObj());
            Ptr<CvSparseMat> _test_sparse_(cvCreateSparseMat(test_sparse_mat));
            Ptr<CvSparseMat> _test_sparse((CvSparseMat*)cvClone(_test_sparse_));
            SparseMat m_s2;
            fs["test_sparse_mat"] >> m_s2;
            Ptr<CvSparseMat> _m_s2(cvCreateSparseMat(m_s2));

            if( !m_s || !CV_IS_SPARSE_MAT(m_s) ||
               !cvTsCheckSparse(m_s, _test_sparse, 0) ||
               !cvTsCheckSparse(_m_s2, _test_sparse, 0))
            {
                ts->printf( cvtest::TS::LOG, "the read sparse matrix is not correct\n" );
                ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
                return;
            }

            FileNode tl = fs["test_list"];
            if( tl.type() != FileNode::SEQ || tl.size() != 6 ||
               fabs((double)tl[0] - 0.0000000000001) >= DBL_EPSILON ||
               (int)tl[1] != 2 ||
               fabs((double)tl[2] - CV_PI) >= DBL_EPSILON ||
               (int)tl[3] != -3435345 ||
               (String)tl[4] != "2-502 2-029 3egegeg" ||
               tl[5].type() != FileNode::MAP || tl[5].size() != 3 ||
               (int)tl[5]["month"] != 12 ||
               (int)tl[5]["day"] != 31 ||
               (int)tl[5]["year"] != 1969 )
            {
                ts->printf( cvtest::TS::LOG, "the test list is incorrect\n" );
                ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
                return;
            }

            FileNode tm = fs["test_map"];
            FileNode tm_lbp = tm["lbp"];

            int real_x = (int)tm["x"];
            int real_y = (int)tm["y"];
            int real_width = (int)tm["width"];
            int real_height = (int)tm["height"];

            int real_lbp_val = 0;
            FileNodeIterator it;
            it = tm_lbp.begin();
            real_lbp_val |= (int)*it << 0;
            ++it;
            real_lbp_val |= (int)*it << 1;
            it++;
            real_lbp_val |= (int)*it << 2;
            it += 1;
            real_lbp_val |= (int)*it << 3;
            FileNodeIterator it2(it);
            it2 += 4;
            real_lbp_val |= (int)*it2 << 7;
            --it2;
            real_lbp_val |= (int)*it2 << 6;
            it2--;
            real_lbp_val |= (int)*it2 << 5;
            it2 -= 1;
            real_lbp_val |= (int)*it2 << 4;
            it2 += -1;
            CV_Assert( it == it2 );

            if( tm.type() != FileNode::MAP || tm.size() != 5 ||
               real_x != 1 ||
               real_y != 2 ||
               real_width != 100 ||
               real_height != 200 ||
               tm_lbp.type() != FileNode::SEQ ||
               tm_lbp.size() != 8 ||
               real_lbp_val != 0xb6 )
            {
                ts->printf( cvtest::TS::LOG, "the test map is incorrect\n" );
                ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
                return;
            }

            CvGraph* graph3 = (CvGraph*)fs["test_graph"].readObj();
            if(graph2->active_count != vcount || graph3->active_count != vcount ||
               graph2->edges->active_count != ecount || graph3->edges->active_count != ecount)
            {
                ts->printf( cvtest::TS::LOG, "the cloned or read graph have wrong number of vertices or edges\n" );
                ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
                return;
            }

            for( i = 0; i < ecount; i++ )
            {
                CvGraphEdge* edge2 = cvFindGraphEdge(graph2, edges[i][0], edges[i][1]);
                CvGraphEdge* edge3 = cvFindGraphEdge(graph3, edges[i][0], edges[i][1]);
                if( !edge2 || edge2->weight != (float)(i+1) ||
                   !edge3 || edge3->weight != (float)(i+1) )
                {
                    ts->printf( cvtest::TS::LOG, "the cloned or read graph do not have the edge (%d, %d)\n", edges[i][0], edges[i][1] );
                    ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
                    return;
                }
            }

            fs.release();
            if( !mem )
                remove(filename.c_str());
        }
    }
};

TEST(Core_InputOutput, write_read_consistency) { Core_IOTest test; test.safe_run(); }

extern void testFormatter();


struct UserDefinedType
{
    int a;
    float b;
};

static inline bool operator==(const UserDefinedType &x,
                              const UserDefinedType &y) {
    return (x.a == y.a) && (x.b == y.b);
}

static inline void write(FileStorage &fs,
                         const String&,
                         const UserDefinedType &value)
{
    fs << "{:" << "a" << value.a << "b" << value.b << "}";
}

static inline void read(const FileNode& node,
                        UserDefinedType& value,
                        const UserDefinedType& default_value
                          = UserDefinedType()) {
    if(node.empty())
    {
        value = default_value;
    }
    else
    {
        node["a"] >> value.a;
        node["b"] >> value.b;
    }
}

class CV_MiscIOTest : public cvtest::BaseTest
{
public:
    CV_MiscIOTest() {}
    ~CV_MiscIOTest() {}
protected:
    void run(int)
    {
        try
        {
            string fname = cv::tempfile(".xml");
            vector<int> mi, mi2, mi3, mi4;
            vector<Mat> mv, mv2, mv3, mv4;
            vector<UserDefinedType> vudt, vudt2, vudt3, vudt4;
            Mat m(10, 9, CV_32F);
            Mat empty;
            UserDefinedType udt = { 8, 3.3f };
            randu(m, 0, 1);
            mi3.push_back(5);
            mv3.push_back(m);
            vudt3.push_back(udt);
            Point_<float> p1(1.1f, 2.2f), op1;
            Point3i p2(3, 4, 5), op2;
            Size s1(6, 7), os1;
            Complex<int> c1(9, 10), oc1;
            Rect r1(11, 12, 13, 14), or1;
            Vec<int, 5> v1(15, 16, 17, 18, 19), ov1;
            Scalar sc1(20.0, 21.1, 22.2, 23.3), osc1;
            Range g1(7, 8), og1;

            FileStorage fs(fname, FileStorage::WRITE);
            fs << "mi" << mi;
            fs << "mv" << mv;
            fs << "mi3" << mi3;
            fs << "mv3" << mv3;
            fs << "vudt" << vudt;
            fs << "vudt3" << vudt3;
            fs << "empty" << empty;
            fs << "p1" << p1;
            fs << "p2" << p2;
            fs << "s1" << s1;
            fs << "c1" << c1;
            fs << "r1" << r1;
            fs << "v1" << v1;
            fs << "sc1" << sc1;
            fs << "g1" << g1;
            fs.release();

            fs.open(fname, FileStorage::READ);
            fs["mi"] >> mi2;
            fs["mv"] >> mv2;
            fs["mi3"] >> mi4;
            fs["mv3"] >> mv4;
            fs["vudt"] >> vudt2;
            fs["vudt3"] >> vudt4;
            fs["empty"] >> empty;
            fs["p1"] >> op1;
            fs["p2"] >> op2;
            fs["s1"] >> os1;
            fs["c1"] >> oc1;
            fs["r1"] >> or1;
            fs["v1"] >> ov1;
            fs["sc1"] >> osc1;
            fs["g1"] >> og1;
            CV_Assert( mi2.empty() );
            CV_Assert( mv2.empty() );
            CV_Assert( cvtest::norm(Mat(mi3), Mat(mi4), CV_C) == 0 );
            CV_Assert( mv4.size() == 1 );
            double n = cvtest::norm(mv3[0], mv4[0], CV_C);
            CV_Assert( vudt2.empty() );
            CV_Assert( vudt3 == vudt4 );
            CV_Assert( n == 0 );
            CV_Assert( op1 == p1 );
            CV_Assert( op2 == p2 );
            CV_Assert( os1 == s1 );
            CV_Assert( oc1 == c1 );
            CV_Assert( or1 == r1 );
            CV_Assert( ov1 == v1 );
            CV_Assert( osc1 == sc1 );
            CV_Assert( og1 == g1 );
        }
        catch(...)
        {
            ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
        }
    }
};

TEST(Core_InputOutput, misc) { CV_MiscIOTest test; test.safe_run(); }

/*class CV_BigMatrixIOTest : public cvtest::BaseTest
{
public:
    CV_BigMatrixIOTest() {}
    ~CV_BigMatrixIOTest() {}
protected:
    void run(int)
    {
        try
        {
            RNG& rng = theRNG();
            int N = 1000, M = 1200000;
            Mat mat(M, N, CV_32F);
            rng.fill(mat, RNG::UNIFORM, 0, 1);
            FileStorage fs(cv::tempfile(".xml"), FileStorage::WRITE);
            fs << "mat" << mat;
            fs.release();
        }
        catch(...)
        {
            ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
        }
    }
};

TEST(Core_InputOutput, huge) { CV_BigMatrixIOTest test; test.safe_run(); }
*/

TEST(Core_globbing, accuracy)
{
    std::string patternLena    = cvtest::TS::ptr()->get_data_path() + "lena*.*";
    std::string patternLenaPng = cvtest::TS::ptr()->get_data_path() + "lena.png";

    std::vector<String> lenas, pngLenas;
    cv::glob(patternLena, lenas, true);
    cv::glob(patternLenaPng, pngLenas, true);

    ASSERT_GT(lenas.size(), pngLenas.size());

    for (size_t i = 0; i < pngLenas.size(); ++i)
    {
        ASSERT_NE(std::find(lenas.begin(), lenas.end(), pngLenas[i]), lenas.end());
    }
}

TEST(Core_InputOutput, FileStorage)
{
    std::string file = cv::tempfile(".xml");
    cv::FileStorage f(file, cv::FileStorage::WRITE);

    char arr[66];
    sprintf(arr, "sprintf is hell %d", 666);
    EXPECT_NO_THROW(f << arr);
}

TEST(Core_InputOutput, FileStorageKey)
{
    cv::FileStorage f("dummy.yml", cv::FileStorage::WRITE | cv::FileStorage::MEMORY);

    EXPECT_NO_THROW(f << "key1" << "value1");
    EXPECT_NO_THROW(f << "_key2" << "value2");
    EXPECT_NO_THROW(f << "key_3" << "value3");
    const std::string expected = "%YAML 1.0\n---\nkey1: value1\n_key2: value2\nkey_3: value3\n";
    ASSERT_STREQ(f.releaseAndGetString().c_str(), expected.c_str());
}

TEST(Core_InputOutput, filestorage_yml_compatibility)
{
    //EXPECT_ANY_THROW();
}

TEST(Core_InputOutput, filestorage_yml_base64)
{
    cv::Mat _em_out, _em_in;
    cv::Mat _2d_out, _2d_in;
    cv::Mat _nd_out, _nd_in;

    {   /* init */

        /* normal mat */
        _2d_out = cv::Mat(100, 100, CV_8UC3, cvScalar(1U, 2U, 127U));
        for (int i = 0; i < _2d_out.rows; ++i)
            for (int j = 0; j < _2d_out.cols; ++j)
                _2d_out.at<cv::Vec3b>(i, j)[1] = i % 256;

        /* 4d mat */
        const int Size[] = {4, 4, 4, 4};
        cv::Mat _4d(4, Size, CV_64FC4, cvScalar(0.888, 0.111, 0.666, 0.444));
        const cv::Range ranges[] = {
            cv::Range(0, 2),
            cv::Range(0, 2),
            cv::Range(1, 2),
            cv::Range(0, 2) };
        _nd_out = _4d(ranges);
    }

    {   /* write */
        cv::FileStorage fs("test.yml", cv::FileStorage::WRITE);
        CvMat holder = _2d_out;
        cv::cvWriteMat_Base64(*fs, "normal_2d_mat", &holder);
        CvMatND holder_nd = _nd_out;
        cv::cvWriteMatND_Base64(*fs, "normal_nd_mat", &holder_nd);
        holder = _em_out;
        cv::cvWriteMat_Base64(*fs, "empty_2d_mat", &holder);
        fs.release();
    }

    {   /* read */
        cv::FileStorage fs("test.yml", cv::FileStorage::READ);
        fs["empty_2d_mat"]  >> _em_in;
        fs["normal_2d_mat"] >> _2d_in;
        fs["normal_nd_mat"] >> _nd_in;
        fs.release();
    }

    EXPECT_EQ(_em_in.rows   , _em_out.rows);
    EXPECT_EQ(_em_in.cols   , _em_out.cols);
    EXPECT_EQ(_em_in.dims   , _em_out.dims);
    EXPECT_EQ(_em_in.depth(), _em_out.depth());
    EXPECT_TRUE(_em_in.empty());

    EXPECT_EQ(_2d_in.rows   , _2d_in.rows);
    EXPECT_EQ(_2d_in.cols   , _2d_in.cols);
    EXPECT_EQ(_2d_in.dims   , _2d_in.dims);
    EXPECT_EQ(_2d_in.depth(), _2d_in.depth());
    for(int i = 0; i < _2d_in.rows; ++i)
        for (int j = 0; j < _2d_in.cols; ++j)
            EXPECT_EQ(_2d_in.at<cv::Vec3b>(i, j), _2d_out.at<cv::Vec3b>(i, j));

    EXPECT_EQ(_nd_in.rows   , _nd_in.rows);
    EXPECT_EQ(_nd_in.cols   , _nd_in.cols);
    EXPECT_EQ(_nd_in.dims   , _nd_in.dims);
    EXPECT_EQ(_nd_in.depth(), _nd_in.depth());
    EXPECT_EQ(cv::countNonZero(cv::mean(_nd_in != _nd_out)), 0);
}

TEST(Core_InputOutput, filestorage_xml_base64)
{
    cv::Mat _em_out, _em_in;
    cv::Mat _2d_out, _2d_in;
    cv::Mat _nd_out, _nd_in;

    {   /* init */

        /* normal mat */
        _2d_out = cv::Mat(100, 100, CV_8UC3, cvScalar(1U, 2U, 127U));
        for (int i = 0; i < _2d_out.rows; ++i)
            for (int j = 0; j < _2d_out.cols; ++j)
                _2d_out.at<cv::Vec3b>(i, j)[1] = i % 256;

        /* 4d mat */
        const int Size[] = {4, 4, 4, 4};
        cv::Mat _4d(4, Size, CV_64FC4, cvScalar(0.888, 0.111, 0.666, 0.444));
        const cv::Range ranges[] = {
            cv::Range(0, 2),
            cv::Range(0, 2),
            cv::Range(1, 2),
            cv::Range(0, 2) };
        _nd_out = _4d(ranges);
    }

    {   /* write */
        cv::FileStorage fs("test.xml", cv::FileStorage::WRITE);
        CvMat holder = _2d_out;
        cv::cvWriteMat_Base64(*fs, "normal_2d_mat", &holder);
        CvMatND holder_nd = _nd_out;
        cv::cvWriteMatND_Base64(*fs, "normal_nd_mat", &holder_nd);
        holder = _em_out;
        cv::cvWriteMat_Base64(*fs, "empty_2d_mat", &holder);
        fs.release();
    }

    {   /* read */
        cv::FileStorage fs("test.xml", cv::FileStorage::READ);
        fs["empty_2d_mat"]  >> _em_in;
        fs["normal_2d_mat"] >> _2d_in;
        fs["normal_nd_mat"] >> _nd_in;
        fs.release();
    }

    EXPECT_EQ(_em_in.rows   , _em_out.rows);
    EXPECT_EQ(_em_in.cols   , _em_out.cols);
    EXPECT_EQ(_em_in.dims   , _em_out.dims);
    EXPECT_EQ(_em_in.depth(), _em_out.depth());
    EXPECT_TRUE(_em_in.empty());

    EXPECT_EQ(_2d_in.rows   , _2d_in.rows);
    EXPECT_EQ(_2d_in.cols   , _2d_in.cols);
    EXPECT_EQ(_2d_in.dims   , _2d_in.dims);
    EXPECT_EQ(_2d_in.depth(), _2d_in.depth());
    for(int i = 0; i < _2d_in.rows; ++i)
        for (int j = 0; j < _2d_in.cols; ++j)
            EXPECT_EQ(_2d_in.at<cv::Vec3b>(i, j), _2d_out.at<cv::Vec3b>(i, j));

    EXPECT_EQ(_nd_in.rows   , _nd_in.rows);
    EXPECT_EQ(_nd_in.cols   , _nd_in.cols);
    EXPECT_EQ(_nd_in.dims   , _nd_in.dims);
    EXPECT_EQ(_nd_in.depth(), _nd_in.depth());
    EXPECT_EQ(cv::countNonZero(cv::sum(_nd_in != _nd_out)), 0);
}