text: #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);
  }
  