opencv/modules/core/test/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;
        int progress = 0;
        MemStorage storage(cvCreateMemStorage(0));
        const char * suffixs[3] = {".yml", ".xml", ".json" };
        test_case_count = 6;

        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 % test_case_count) >= (test_case_count >> 1);
            string filename = tempfile(suffixs[idx % (test_case_count >> 1)]);

            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)
    {
        const char * suffix[3] = {
            ".yml",
            ".xml",
            ".json"
        };

        for ( size_t i = 0u; i < 3u; i++ )
        {
            try
            {
                string fname = cv::tempfile(suffix[i]);
                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, FileStorageSpaces)
{
    cv::FileStorage f("dummy.yml", cv::FileStorage::WRITE | cv::FileStorage::MEMORY);
    const int valueCount = 5;
    std::string values[5] = { "", " ", " ", "  a", " some string" };
    for (size_t i = 0; i < valueCount; i++) {
        EXPECT_NO_THROW(f << cv::format("key%d", i) << values[i]);
    }
    cv::FileStorage f2(f.releaseAndGetString(), cv::FileStorage::READ | cv::FileStorage::MEMORY);
    std::string valuesRead[valueCount];
    for (size_t i = 0; i < valueCount; i++) {
        EXPECT_NO_THROW(f2[cv::format("key%d", i)] >> valuesRead[i]);
        ASSERT_STREQ(values[i].c_str(), valuesRead[i].c_str());
    }
}

struct data_t
{
    typedef uchar  u;
    typedef char   b;
    typedef ushort w;
    typedef short  s;
    typedef int    i;
    typedef float  f;
    typedef double d;

    /*0x00*/ u u1   ;u u2   ;                i i1                           ;
    /*0x08*/ i i2                           ;i i3                           ;
    /*0x10*/ d d1                                                           ;
    /*0x18*/ d d2                                                           ;
    /*0x20*/ i i4                           ;i required_alignment_field_for_linux32;
    /*
     * OpenCV persistence.cpp stuff expects: sizeof(data_t) = alignSize(36, sizeof(largest type = double)) = 40
     * Some compilers on some archs returns sizeof(data_t) = 36 due struct packaging UB
     */

    static inline const char * signature() {
        if (sizeof(data_t) != 40)
        {
            printf("sizeof(data_t)=%d, u1=%p u2=%p i1=%p i2=%p i3=%p d1=%p d2=%p i4=%p\n", (int)sizeof(data_t),
                    &(((data_t*)0)->u1),
                    &(((data_t*)0)->u2),
                    &(((data_t*)0)->i1),
                    &(((data_t*)0)->i2),
                    &(((data_t*)0)->i3),
                    &(((data_t*)0)->d1),
                    &(((data_t*)0)->d2),
                    &(((data_t*)0)->i4)
            );
        }
        CV_Assert(sizeof(data_t) == 40);
        CV_Assert((size_t)&(((data_t*)0)->u1) == 0x0);
        CV_Assert((size_t)&(((data_t*)0)->u2) == 0x1);
        CV_Assert((size_t)&(((data_t*)0)->i1) == 0x4);
        CV_Assert((size_t)&(((data_t*)0)->i2) == 0x8);
        CV_Assert((size_t)&(((data_t*)0)->i3) == 0xc);
        CV_Assert((size_t)&(((data_t*)0)->d1) == 0x10);
        CV_Assert((size_t)&(((data_t*)0)->d2) == 0x18);
        CV_Assert((size_t)&(((data_t*)0)->i4) == 0x20);
        return "2u3i2di";
    }
};

TEST(Core_InputOutput, filestorage_base64_basic)
{
    char const * filenames[] = {
        "core_io_base64_basic_test.yml",
        "core_io_base64_basic_test.xml",
        "core_io_base64_basic_test.json",
        0
    };

    for (char const ** ptr = filenames; *ptr; ptr++)
    {
        char const * name = *ptr;

        std::vector<data_t> rawdata;

        cv::Mat _em_out, _em_in;
        cv::Mat _2d_out, _2d_in;
        cv::Mat _nd_out, _nd_in;
        cv::Mat _rd_out(64, 64, CV_64FC1), _rd_in;

        bool no_type_id = true;

        {   /* init */

            /* a 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 + j) % 256;

            /* a 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);

            /* a random mat */
            cv::randu(_rd_out, cv::Scalar(0.0), cv::Scalar(1.0));

            /* raw data */
            for (int i = 0; i < 1000; i++) {
                data_t tmp;
                tmp.u1 = 1;
                tmp.u2 = 2;
                tmp.i1 = 1;
                tmp.i2 = 2;
                tmp.i3 = 3;
                tmp.d1 = 0.1;
                tmp.d2 = 0.2;
                tmp.i4 = i;
                rawdata.push_back(tmp);
            }
        }

        {   /* write */
            cv::FileStorage fs(name, cv::FileStorage::WRITE_BASE64);
            fs << "normal_2d_mat" << _2d_out;
            fs << "normal_nd_mat" << _nd_out;
            fs << "empty_2d_mat"  << _em_out;
            fs << "random_mat"    << _rd_out;

            cvStartWriteStruct( *fs, "rawdata", CV_NODE_SEQ | CV_NODE_FLOW, "binary" );
            for (int i = 0; i < 10; i++)
                cvWriteRawDataBase64(*fs, rawdata.data() + i * 100, 100, data_t::signature());
            cvEndWriteStruct( *fs );

            fs.release();
        }

        {   /* read */
            cv::FileStorage fs(name, cv::FileStorage::READ);

            /* mat */
            fs["empty_2d_mat"]  >> _em_in;
            fs["normal_2d_mat"] >> _2d_in;
            fs["normal_nd_mat"] >> _nd_in;
            fs["random_mat"]    >> _rd_in;

            if ( !fs["empty_2d_mat"]["type_id"].empty() ||
                !fs["normal_2d_mat"]["type_id"].empty() ||
                !fs["normal_nd_mat"]["type_id"].empty() ||
                !fs[   "random_mat"]["type_id"].empty() )
                no_type_id = false;

            /* raw data */
            std::vector<data_t>(1000).swap(rawdata);
            cvReadRawData(*fs, fs["rawdata"].node, rawdata.data(), data_t::signature());

            fs.release();
        }

        int errors = 0;
        for (int i = 0; i < 1000; i++)
        {
            EXPECT_EQ((int)rawdata[i].u1, 1);
            EXPECT_EQ((int)rawdata[i].u2, 2);
            EXPECT_EQ((int)rawdata[i].i1, 1);
            EXPECT_EQ((int)rawdata[i].i2, 2);
            EXPECT_EQ((int)rawdata[i].i3, 3);
            EXPECT_EQ(rawdata[i].d1, 0.1);
            EXPECT_EQ(rawdata[i].d2, 0.2);
            EXPECT_EQ((int)rawdata[i].i4, i);
            if (::testing::Test::HasNonfatalFailure())
            {
                printf("i = %d\n", i);
                errors++;
            }
            if (errors >= 3)
                break;
        }

        EXPECT_TRUE(no_type_id);

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

        EXPECT_EQ(_2d_in.rows   , _2d_out.rows);
        EXPECT_EQ(_2d_in.cols   , _2d_out.cols);
        EXPECT_EQ(_2d_in.dims   , _2d_out.dims);
        EXPECT_EQ(_2d_in.depth(), _2d_out.depth());

        errors = 0;
        for(int i = 0; i < _2d_out.rows; ++i)
        {
            for (int j = 0; j < _2d_out.cols; ++j)
            {
                EXPECT_EQ(_2d_in.at<cv::Vec3b>(i, j), _2d_out.at<cv::Vec3b>(i, j));
                if (::testing::Test::HasNonfatalFailure())
                {
                    printf("i = %d, j = %d\n", i, j);
                    errors++;
                }
                if (errors >= 3)
                {
                    i = _2d_out.rows;
                    break;
                }
            }
        }

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

        EXPECT_EQ(_rd_in.rows   , _rd_out.rows);
        EXPECT_EQ(_rd_in.cols   , _rd_out.cols);
        EXPECT_EQ(_rd_in.dims   , _rd_out.dims);
        EXPECT_EQ(_rd_in.depth(), _rd_out.depth());
        EXPECT_EQ(cv::countNonZero(cv::mean(_rd_in != _rd_out)), 0);

        remove(name);
    }
}

TEST(Core_InputOutput, filestorage_base64_valid_call)
{
    char const * filenames[] = {
        "core_io_base64_other_test.yml",
        "core_io_base64_other_test.xml",
        "core_io_base64_other_test.json",
        "core_io_base64_other_test.yml?base64",
        "core_io_base64_other_test.xml?base64",
        "core_io_base64_other_test.json?base64",
        0
    };
    char const * real_name[] = {
        "core_io_base64_other_test.yml",
        "core_io_base64_other_test.xml",
        "core_io_base64_other_test.json",
        "core_io_base64_other_test.yml",
        "core_io_base64_other_test.xml",
        "core_io_base64_other_test.json",
        0
    };

    std::vector<int> rawdata(10, static_cast<int>(0x00010203));
    cv::String str_out = "test_string";

    for (char const ** ptr = filenames; *ptr; ptr++)
    {
        char const * name = *ptr;

        EXPECT_NO_THROW(
        {
            cv::FileStorage fs(name, cv::FileStorage::WRITE_BASE64);

            cvStartWriteStruct(*fs, "manydata", CV_NODE_SEQ);
            cvStartWriteStruct(*fs, 0, CV_NODE_SEQ | CV_NODE_FLOW);
            for (int i = 0; i < 10; i++)
                cvWriteRawData(*fs, rawdata.data(), static_cast<int>(rawdata.size()), "i");
            cvEndWriteStruct(*fs);
            cvWriteString(*fs, 0, str_out.c_str(), 1);
            cvEndWriteStruct(*fs);

            fs.release();
        });

        {
            cv::FileStorage fs(name, cv::FileStorage::READ);
            std::vector<int> data_in(rawdata.size());
            fs["manydata"][0].readRaw("i", (uchar *)data_in.data(), data_in.size());
            EXPECT_TRUE(fs["manydata"][0].isSeq());
            EXPECT_TRUE(std::equal(rawdata.begin(), rawdata.end(), data_in.begin()));
            cv::String str_in;
            fs["manydata"][1] >> str_in;
            EXPECT_TRUE(fs["manydata"][1].isString());
            EXPECT_EQ(str_in, str_out);
            fs.release();
        }

        EXPECT_NO_THROW(
        {
            cv::FileStorage fs(name, cv::FileStorage::WRITE);

            cvStartWriteStruct(*fs, "manydata", CV_NODE_SEQ);
            cvWriteString(*fs, 0, str_out.c_str(), 1);
            cvStartWriteStruct(*fs, 0, CV_NODE_SEQ | CV_NODE_FLOW, "binary");
            for (int i = 0; i < 10; i++)
                cvWriteRawData(*fs, rawdata.data(), static_cast<int>(rawdata.size()), "i");
            cvEndWriteStruct(*fs);
            cvEndWriteStruct(*fs);

            fs.release();
        });

        {
            cv::FileStorage fs(name, cv::FileStorage::READ);
            cv::String str_in;
            fs["manydata"][0] >> str_in;
            EXPECT_TRUE(fs["manydata"][0].isString());
            EXPECT_EQ(str_in, str_out);
            std::vector<int> data_in(rawdata.size());
            fs["manydata"][1].readRaw("i", (uchar *)data_in.data(), data_in.size());
            EXPECT_TRUE(fs["manydata"][1].isSeq());
            EXPECT_TRUE(std::equal(rawdata.begin(), rawdata.end(), data_in.begin()));
            fs.release();
        }

        remove(real_name[ptr - filenames]);
    }
}

TEST(Core_InputOutput, filestorage_base64_invalid_call)
{
    char const * filenames[] = {
        "core_io_base64_other_test.yml",
        "core_io_base64_other_test.xml",
        "core_io_base64_other_test.json",
        0
    };

    for (char const ** ptr = filenames; *ptr; ptr++)
    {
        char const * name = *ptr;

        EXPECT_ANY_THROW({
            cv::FileStorage fs(name, cv::FileStorage::WRITE);
            cvStartWriteStruct(*fs, "rawdata", CV_NODE_SEQ, "binary");
            cvStartWriteStruct(*fs, 0, CV_NODE_SEQ | CV_NODE_FLOW);
        });

        EXPECT_ANY_THROW({
            cv::FileStorage fs(name, cv::FileStorage::WRITE);
            cvStartWriteStruct(*fs, "rawdata", CV_NODE_SEQ);
            cvStartWriteStruct(*fs, 0, CV_NODE_SEQ | CV_NODE_FLOW);
            cvWriteRawDataBase64(*fs, name, 1, "u");
        });

        remove(name);
    }
}

TEST(Core_InputOutput, filestorage_yml_vec2i)
{
    const std::string file_name = "vec2i.yml";
    cv::Vec2i vec(2, 1), ovec;

    /* write */
    {
        cv::FileStorage fs(file_name, cv::FileStorage::WRITE);
        fs << "prms0" << "{" << "vec0" << vec << "}";
        fs.release();
    }

    /* read */
    {
        cv::FileStorage fs(file_name, cv::FileStorage::READ);
        fs["prms0"]["vec0"] >> ovec;
        fs.release();
    }

    EXPECT_EQ(vec(0), ovec(0));
    EXPECT_EQ(vec(1), ovec(1));

    remove(file_name.c_str());
}

TEST(Core_InputOutput, filestorage_json_comment)
{
    String mem_str =
        "{ /* comment */\n"
        "  \"key\": \"value\"\n"
        "  /************\n"
        "   * multiline comment\n"
        "   ************/\n"
        "  // 233\n"
        "  // \n"
        "}\n"
        ;

    String str;

    EXPECT_NO_THROW(
    {
        cv::FileStorage fs(mem_str, cv::FileStorage::READ | cv::FileStorage::MEMORY);
        fs["key"] >> str;
        fs.release();
    });

    EXPECT_EQ(str, String("value"));
}

TEST(Core_InputOutput, filestorage_utf8_bom)
{
    EXPECT_NO_THROW(
    {
        String content ="\xEF\xBB\xBF<?xml version=\"1.0\"?>\n<opencv_storage>\n</opencv_storage>\n";
        cv::FileStorage fs(content, cv::FileStorage::READ | cv::FileStorage::MEMORY);
        fs.release();
    });
    EXPECT_NO_THROW(
    {
        String content ="\xEF\xBB\xBF%YAML:1.0\n";
        cv::FileStorage fs(content, cv::FileStorage::READ | cv::FileStorage::MEMORY);
        fs.release();
    });
    EXPECT_NO_THROW(
    {
        String content ="\xEF\xBB\xBF{\n}\n";
        cv::FileStorage fs(content, cv::FileStorage::READ | cv::FileStorage::MEMORY);
        fs.release();
    });
}

TEST(Core_InputOutput, filestorage_vec_vec_io)
{
    std::vector<std::vector<Mat> > outputMats(3);
    for(size_t i = 0; i < outputMats.size(); i++)
    {
        outputMats[i].resize(i+1);
        for(size_t j = 0; j < outputMats[i].size(); j++)
        {
            outputMats[i][j] = Mat::eye((int)i + 1, (int)i + 1, CV_8U);
        }
    }

    String fileName = "vec_test.";

    std::vector<String> formats;
    formats.push_back("xml");
    formats.push_back("yml");
    formats.push_back("json");

    for(size_t i = 0; i < formats.size(); i++)
    {
        FileStorage writer(fileName + formats[i], FileStorage::WRITE);
        writer << "vecVecMat" << outputMats;
        writer.release();

        FileStorage reader(fileName + formats[i], FileStorage::READ);
        std::vector<std::vector<Mat> > testMats;
        reader["vecVecMat"] >> testMats;

        ASSERT_EQ(testMats.size(), testMats.size());

        for(size_t j = 0; j < testMats.size(); j++)
        {
            ASSERT_EQ(testMats[j].size(), outputMats[j].size());

            for(size_t k = 0; k < testMats[j].size(); k++)
            {
                ASSERT_TRUE(norm(outputMats[j][k] - testMats[j][k], NORM_INF) == 0);
            }
        }

        reader.release();
        remove((fileName + formats[i]).c_str());
    }
}

TEST(Core_InputOutput, filestorage_yaml_advanvced_type_heading)
{
    String content = "%YAML:1.0\n cameraMatrix: !<tag:yaml.org,2002:opencv-matrix>\n"
            "   rows: 1\n"
            "   cols: 1\n"
            "   dt: d\n"
            "   data: [ 1. ]";

    cv::FileStorage fs(content, cv::FileStorage::READ | cv::FileStorage::MEMORY);

    cv::Mat inputMatrix;
    cv::Mat actualMatrix = cv::Mat::eye(1, 1, CV_64F);
    fs["cameraMatrix"] >> inputMatrix;

    ASSERT_EQ(cv::norm(inputMatrix, actualMatrix, NORM_INF), 0.);
}

TEST(Core_InputOutput, filestorage_keypoints_vec_vec_io)
{
    vector<vector<KeyPoint> > kptsVec;
    vector<KeyPoint> kpts;
    kpts.push_back(KeyPoint(0, 0, 1.1f));
    kpts.push_back(KeyPoint(1, 1, 1.1f));
    kptsVec.push_back(kpts);
    kpts.clear();
    kpts.push_back(KeyPoint(0, 0, 1.1f, 10.1f, 34.5f, 10, 11));
    kptsVec.push_back(kpts);

    FileStorage writer("", FileStorage::WRITE + FileStorage::MEMORY + FileStorage::FORMAT_XML);
    writer << "keypoints" << kptsVec;
    String content = writer.releaseAndGetString();

    FileStorage reader(content, FileStorage::READ + FileStorage::MEMORY);
    vector<vector<KeyPoint> > readKptsVec;
    reader["keypoints"] >> readKptsVec;

    ASSERT_EQ(kptsVec.size(), readKptsVec.size());

    for(size_t i = 0; i < kptsVec.size(); i++)
    {
        ASSERT_EQ(kptsVec[i].size(), readKptsVec[i].size());
        for(size_t j = 0; j < kptsVec[i].size(); j++)
        {
            ASSERT_FLOAT_EQ(kptsVec[i][j].pt.x, readKptsVec[i][j].pt.x);
            ASSERT_FLOAT_EQ(kptsVec[i][j].pt.y, readKptsVec[i][j].pt.y);
            ASSERT_FLOAT_EQ(kptsVec[i][j].angle, readKptsVec[i][j].angle);
            ASSERT_FLOAT_EQ(kptsVec[i][j].size, readKptsVec[i][j].size);
            ASSERT_FLOAT_EQ(kptsVec[i][j].response, readKptsVec[i][j].response);
            ASSERT_EQ(kptsVec[i][j].octave, readKptsVec[i][j].octave);
            ASSERT_EQ(kptsVec[i][j].class_id, readKptsVec[i][j].class_id);
        }
    }
}

TEST(Core_InputOutput, FileStorage_DMatch)
{
    cv::FileStorage fs("dmatch.yml", cv::FileStorage::WRITE | cv::FileStorage::MEMORY);

    cv::DMatch d(1, 2, 3, -1.5f);

    EXPECT_NO_THROW(fs << "d" << d);
    cv::String fs_result = fs.releaseAndGetString();
    EXPECT_STREQ(fs_result.c_str(), "%YAML:1.0\n---\nd: [ 1, 2, 3, -1.5000000000000000e+00 ]\n");

    cv::FileStorage fs_read(fs_result, cv::FileStorage::READ | cv::FileStorage::MEMORY);

    cv::DMatch d_read;
    ASSERT_NO_THROW(fs_read["d"] >> d_read);

    EXPECT_EQ(d.queryIdx, d_read.queryIdx);
    EXPECT_EQ(d.trainIdx, d_read.trainIdx);
    EXPECT_EQ(d.imgIdx, d_read.imgIdx);
    EXPECT_EQ(d.distance, d_read.distance);
}

TEST(Core_InputOutput, FileStorage_DMatch_vector)
{
    cv::FileStorage fs("dmatch.yml", cv::FileStorage::WRITE | cv::FileStorage::MEMORY);

    cv::DMatch d1(1, 2, 3, -1.5f);
    cv::DMatch d2(2, 3, 4, 1.5f);
    cv::DMatch d3(3, 2, 1, 0.5f);
    std::vector<cv::DMatch> dv;
    dv.push_back(d1);
    dv.push_back(d2);
    dv.push_back(d3);

    EXPECT_NO_THROW(fs << "dv" << dv);
    cv::String fs_result = fs.releaseAndGetString();
    EXPECT_STREQ(fs_result.c_str(),
"%YAML:1.0\n"
"---\n"
"dv: [ 1, 2, 3, -1.5000000000000000e+00, 2, 3, 4, 1.5000000000000000e+00,\n"
"    3, 2, 1, 5.0000000000000000e-01 ]\n"
);

    cv::FileStorage fs_read(fs_result, cv::FileStorage::READ | cv::FileStorage::MEMORY);

    std::vector<cv::DMatch> dv_read;
    ASSERT_NO_THROW(fs_read["dv"] >> dv_read);

    ASSERT_EQ(dv.size(), dv_read.size());
    for (size_t i = 0; i < dv.size(); i++)
    {
        EXPECT_EQ(dv[i].queryIdx, dv_read[i].queryIdx);
        EXPECT_EQ(dv[i].trainIdx, dv_read[i].trainIdx);
        EXPECT_EQ(dv[i].imgIdx, dv_read[i].imgIdx);
        EXPECT_EQ(dv[i].distance, dv_read[i].distance);
    }
}

TEST(Core_InputOutput, FileStorage_DMatch_vector_vector)
{
    cv::FileStorage fs("dmatch.yml", cv::FileStorage::WRITE | cv::FileStorage::MEMORY);

    cv::DMatch d1(1, 2, 3, -1.5f);
    cv::DMatch d2(2, 3, 4, 1.5f);
    cv::DMatch d3(3, 2, 1, 0.5f);
    std::vector<cv::DMatch> dv1;
    dv1.push_back(d1);
    dv1.push_back(d2);
    dv1.push_back(d3);

    std::vector<cv::DMatch> dv2;
    dv2.push_back(d3);
    dv2.push_back(d1);

    std::vector< std::vector<cv::DMatch> > dvv;
    dvv.push_back(dv1);
    dvv.push_back(dv2);

    EXPECT_NO_THROW(fs << "dvv" << dvv);
    cv::String fs_result = fs.releaseAndGetString();
    EXPECT_STREQ(fs_result.c_str(),
"%YAML:1.0\n"
"---\n"
"dvv:\n"
"   - [ 1, 2, 3, -1.5000000000000000e+00, 2, 3, 4, 1.5000000000000000e+00,\n"
"       3, 2, 1, 5.0000000000000000e-01 ]\n"
"   - [ 3, 2, 1, 5.0000000000000000e-01, 1, 2, 3, -1.5000000000000000e+00 ]\n"
);

    cv::FileStorage fs_read(fs_result, cv::FileStorage::READ | cv::FileStorage::MEMORY);

    std::vector< std::vector<cv::DMatch> > dvv_read;
    ASSERT_NO_THROW(fs_read["dvv"] >> dvv_read);

    ASSERT_EQ(dvv.size(), dvv_read.size());
    for (size_t j = 0; j < dvv.size(); j++)
    {
        const std::vector<cv::DMatch>& dv = dvv[j];
        const std::vector<cv::DMatch>& dv_read = dvv_read[j];
        ASSERT_EQ(dvv.size(), dvv_read.size());
        for (size_t i = 0; i < dv.size(); i++)
        {
            EXPECT_EQ(dv[i].queryIdx, dv_read[i].queryIdx);
            EXPECT_EQ(dv[i].trainIdx, dv_read[i].trainIdx);
            EXPECT_EQ(dv[i].imgIdx, dv_read[i].imgIdx);
            EXPECT_EQ(dv[i].distance, dv_read[i].distance);
        }
    }
}