#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(val); else if( type == CV_8S ) *(schar*)ptr = saturate_cast(val); else if( type == CV_16U ) *(ushort*)ptr = saturate_cast(val); else if( type == CV_16S ) *(short*)ptr = saturate_cast(val); else if( type == CV_32S ) *(int*)ptr = saturate_cast(val); else if( type == CV_32F ) *(float*)ptr = saturate_cast(val); else *(double*)ptr = saturate_cast(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 = 2; 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); string filename = tempfile(idx % 2 ? ".yml" : ".xml"); FileStorage fs(filename.c_str(), FileStorage::WRITE); 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\"&<>&'@#$@$%$%&%IJUKYILFD@#$@%$&*&() "; 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.data, 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[] = {cvtest::randInt(rng)%10+1, cvtest::randInt(rng)%10+1, 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[] = {cvtest::randInt(rng)%10+1, cvtest::randInt(rng)%10+1, cvtest::randInt(rng)%10+1,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); fs.release(); if(!fs.open(filename.c_str(), FileStorage::READ)) { ts->printf( cvtest::TS::LOG, "filename %s can not be read\n", filename.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 pt; if( !m || !CV_IS_MAT(m) || m->rows != test_mat.rows || m->cols != test_mat.cols || cvtest::cmpEps( Mat(&stub1), Mat(&_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( Mat(&stub1), Mat(&_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( Mat(&stub1), Mat(&_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 m_s = (CvSparseMat*)fs["test_sparse_mat"].readObj(); Ptr _test_sparse_ = (CvSparseMat*)test_sparse_mat; Ptr _test_sparse = (CvSparseMat*)cvClone(_test_sparse_); SparseMat m_s2; fs["test_sparse_mat"] >> m_s2; Ptr _m_s2 = (CvSparseMat*)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(); remove(filename.c_str()); } } }; TEST(Core_InputOutput, write_read_consistency) { Core_IOTest test; test.safe_run(); } class CV_MiscIOTest : public cvtest::BaseTest { public: CV_MiscIOTest() {} ~CV_MiscIOTest() {} protected: void run(int) { try { FileStorage fs("test.xml", FileStorage::WRITE); vector mi, mi2, mi3, mi4; vector mv, mv2, mv3, mv4; Mat m(10, 9, CV_32F); Mat empty; randu(m, 0, 1); mi3.push_back(5); mv3.push_back(m); fs << "mi" << mi; fs << "mv" << mv; fs << "mi3" << mi3; fs << "mv3" << mv3; fs << "empty" << empty; fs.release(); fs.open("test.xml", FileStorage::READ); fs["mi"] >> mi2; fs["mv"] >> mv2; fs["mi3"] >> mi4; fs["mv3"] >> mv4; fs["empty"] >> empty; CV_Assert( mi2.empty() ); CV_Assert( mv2.empty() ); CV_Assert( norm(mi3, mi4, CV_C) == 0 ); CV_Assert( mv4.size() == 1 ); double n = norm(mv3[0], mv4[0], CV_C); CV_Assert( n == 0 ); } 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("test.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(); } */