408 lines
16 KiB
C++
408 lines
16 KiB
C++
#include "test_precomp.hpp"
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using namespace cv;
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using namespace std;
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static SparseMat cvTsGetRandomSparseMat(int dims, const int* sz, int type,
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int nzcount, double a, double b, RNG& rng)
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{
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SparseMat m(dims, sz, type);
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int i, j;
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CV_Assert(CV_MAT_CN(type) == 1);
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for( i = 0; i < nzcount; i++ )
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{
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int idx[CV_MAX_DIM];
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for( j = 0; j < dims; j++ )
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idx[j] = cvtest::randInt(rng) % sz[j];
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double val = cvtest::randReal(rng)*(b - a) + a;
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uchar* ptr = m.ptr(idx, true, 0);
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if( type == CV_8U )
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*(uchar*)ptr = saturate_cast<uchar>(val);
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else if( type == CV_8S )
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*(schar*)ptr = saturate_cast<schar>(val);
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else if( type == CV_16U )
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*(ushort*)ptr = saturate_cast<ushort>(val);
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else if( type == CV_16S )
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*(short*)ptr = saturate_cast<short>(val);
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else if( type == CV_32S )
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*(int*)ptr = saturate_cast<int>(val);
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else if( type == CV_32F )
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*(float*)ptr = saturate_cast<float>(val);
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else
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*(double*)ptr = saturate_cast<double>(val);
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}
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return m;
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}
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static bool cvTsCheckSparse(const CvSparseMat* m1, const CvSparseMat* m2, double eps)
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{
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CvSparseMatIterator it1;
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CvSparseNode* node1;
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int depth = CV_MAT_DEPTH(m1->type);
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if( m1->heap->active_count != m2->heap->active_count ||
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m1->dims != m2->dims || CV_MAT_TYPE(m1->type) != CV_MAT_TYPE(m2->type) )
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return false;
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for( node1 = cvInitSparseMatIterator( m1, &it1 );
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node1 != 0; node1 = cvGetNextSparseNode( &it1 ))
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{
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uchar* v1 = (uchar*)CV_NODE_VAL(m1,node1);
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uchar* v2 = cvPtrND( m2, CV_NODE_IDX(m1,node1), 0, 0, &node1->hashval );
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if( !v2 )
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return false;
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if( depth == CV_8U || depth == CV_8S )
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{
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if( *v1 != *v2 )
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return false;
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}
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else if( depth == CV_16U || depth == CV_16S )
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{
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if( *(ushort*)v1 != *(ushort*)v2 )
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return false;
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}
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else if( depth == CV_32S )
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{
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if( *(int*)v1 != *(int*)v2 )
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return false;
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}
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else if( depth == CV_32F )
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{
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if( fabs(*(float*)v1 - *(float*)v2) > eps*(fabs(*(float*)v2) + 1) )
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return false;
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}
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else if( fabs(*(double*)v1 - *(double*)v2) > eps*(fabs(*(double*)v2) + 1) )
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return false;
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}
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return true;
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}
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class Core_IOTest : public cvtest::BaseTest
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{
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public:
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Core_IOTest() {};
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protected:
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void run(int)
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{
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double ranges[][2] = {{0, 256}, {-128, 128}, {0, 65536}, {-32768, 32768},
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{-1000000, 1000000}, {-10, 10}, {-10, 10}};
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RNG& rng = ts->get_rng();
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RNG rng0;
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test_case_count = 2;
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int progress = 0;
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MemStorage storage(cvCreateMemStorage(0));
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for( int idx = 0; idx < test_case_count; idx++ )
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{
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ts->update_context( this, idx, false );
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progress = update_progress( progress, idx, test_case_count, 0 );
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cvClearMemStorage(storage);
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string filename = tempfile(idx % 2 ? ".yml" : ".xml");
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FileStorage fs(filename.c_str(), FileStorage::WRITE);
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int test_int = (int)cvtest::randInt(rng);
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double test_real = (cvtest::randInt(rng)%2?1:-1)*exp(cvtest::randReal(rng)*18-9);
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string test_string = "vw wv23424rt\"&<>&'@#$@$%$%&%IJUKYILFD@#$@%$&*&() ";
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int depth = cvtest::randInt(rng) % (CV_64F+1);
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int cn = cvtest::randInt(rng) % 4 + 1;
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Mat test_mat(cvtest::randInt(rng)%30+1, cvtest::randInt(rng)%30+1, CV_MAKETYPE(depth, cn));
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rng0.fill(test_mat, CV_RAND_UNI, Scalar::all(ranges[depth][0]), Scalar::all(ranges[depth][1]));
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if( depth >= CV_32F )
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{
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exp(test_mat, test_mat);
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Mat test_mat_scale(test_mat.size(), test_mat.type());
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rng0.fill(test_mat_scale, CV_RAND_UNI, Scalar::all(-1), Scalar::all(1));
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multiply(test_mat, test_mat_scale, test_mat);
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}
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CvSeq* seq = cvCreateSeq(test_mat.type(), (int)sizeof(CvSeq),
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(int)test_mat.elemSize(), storage);
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cvSeqPushMulti(seq, test_mat.data, test_mat.cols*test_mat.rows);
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CvGraph* graph = cvCreateGraph( CV_ORIENTED_GRAPH,
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sizeof(CvGraph), sizeof(CvGraphVtx),
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sizeof(CvGraphEdge), storage );
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int edges[][2] = {{0,1},{1,2},{2,0},{0,3},{3,4},{4,1}};
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int i, vcount = 5, ecount = 6;
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for( i = 0; i < vcount; i++ )
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cvGraphAddVtx(graph);
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for( i = 0; i < ecount; i++ )
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{
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CvGraphEdge* edge;
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cvGraphAddEdge(graph, edges[i][0], edges[i][1], 0, &edge);
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edge->weight = (float)(i+1);
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}
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depth = cvtest::randInt(rng) % (CV_64F+1);
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cn = cvtest::randInt(rng) % 4 + 1;
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int sz[] = {cvtest::randInt(rng)%10+1, cvtest::randInt(rng)%10+1, cvtest::randInt(rng)%10+1};
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MatND test_mat_nd(3, sz, CV_MAKETYPE(depth, cn));
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rng0.fill(test_mat_nd, CV_RAND_UNI, Scalar::all(ranges[depth][0]), Scalar::all(ranges[depth][1]));
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if( depth >= CV_32F )
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{
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exp(test_mat_nd, test_mat_nd);
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MatND test_mat_scale(test_mat_nd.dims, test_mat_nd.size, test_mat_nd.type());
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rng0.fill(test_mat_scale, CV_RAND_UNI, Scalar::all(-1), Scalar::all(1));
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multiply(test_mat_nd, test_mat_scale, test_mat_nd);
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}
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int ssz[] = {cvtest::randInt(rng)%10+1, cvtest::randInt(rng)%10+1,
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cvtest::randInt(rng)%10+1,cvtest::randInt(rng)%10+1};
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SparseMat test_sparse_mat = cvTsGetRandomSparseMat(4, ssz, cvtest::randInt(rng)%(CV_64F+1),
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cvtest::randInt(rng) % 10000, 0, 100, rng);
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fs << "test_int" << test_int << "test_real" << test_real << "test_string" << test_string;
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fs << "test_mat" << test_mat;
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fs << "test_mat_nd" << test_mat_nd;
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fs << "test_sparse_mat" << test_sparse_mat;
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fs << "test_list" << "[" << 0.0000000000001 << 2 << CV_PI << -3435345 << "2-502 2-029 3egegeg" <<
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"{:" << "month" << 12 << "day" << 31 << "year" << 1969 << "}" << "]";
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fs << "test_map" << "{" << "x" << 1 << "y" << 2 << "width" << 100 << "height" << 200 << "lbp" << "[:";
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const uchar arr[] = {0, 1, 1, 0, 1, 1, 0, 1};
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fs.writeRaw("u", arr, (int)(sizeof(arr)/sizeof(arr[0])));
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fs << "]" << "}";
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cvWriteComment(*fs, "test comment", 0);
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fs.writeObj("test_seq", seq);
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fs.writeObj("test_graph",graph);
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CvGraph* graph2 = (CvGraph*)cvClone(graph);
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fs.release();
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if(!fs.open(filename.c_str(), FileStorage::READ))
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{
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ts->printf( cvtest::TS::LOG, "filename %s can not be read\n", filename.c_str() );
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ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
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return;
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}
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int real_int = (int)fs["test_int"];
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double real_real = (double)fs["test_real"];
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string real_string = (string)fs["test_string"];
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if( real_int != test_int ||
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fabs(real_real - test_real) > DBL_EPSILON*(fabs(test_real)+1) ||
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real_string != test_string )
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{
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ts->printf( cvtest::TS::LOG, "the read scalars are not correct\n" );
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
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return;
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}
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CvMat* m = (CvMat*)fs["test_mat"].readObj();
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CvMat _test_mat = test_mat;
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double max_diff = 0;
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CvMat stub1, _test_stub1;
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cvReshape(m, &stub1, 1, 0);
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cvReshape(&_test_mat, &_test_stub1, 1, 0);
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vector<int> pt;
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if( !m || !CV_IS_MAT(m) || m->rows != test_mat.rows || m->cols != test_mat.cols ||
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cvtest::cmpEps( Mat(&stub1), Mat(&_test_stub1), &max_diff, 0, &pt, true) < 0 )
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{
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ts->printf( cvtest::TS::LOG, "the read matrix is not correct: (%.20g vs %.20g) at (%d,%d)\n",
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cvGetReal2D(&stub1, pt[0], pt[1]), cvGetReal2D(&_test_stub1, pt[0], pt[1]),
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pt[0], pt[1] );
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
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return;
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}
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if( m && CV_IS_MAT(m))
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cvReleaseMat(&m);
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CvMatND* m_nd = (CvMatND*)fs["test_mat_nd"].readObj();
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CvMatND _test_mat_nd = test_mat_nd;
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if( !m_nd || !CV_IS_MATND(m_nd) )
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{
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ts->printf( cvtest::TS::LOG, "the read nd-matrix is not correct\n" );
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
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return;
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}
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CvMat stub, _test_stub;
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cvGetMat(m_nd, &stub, 0, 1);
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cvGetMat(&_test_mat_nd, &_test_stub, 0, 1);
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cvReshape(&stub, &stub1, 1, 0);
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cvReshape(&_test_stub, &_test_stub1, 1, 0);
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if( !CV_ARE_TYPES_EQ(&stub, &_test_stub) ||
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!CV_ARE_SIZES_EQ(&stub, &_test_stub) ||
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//cvNorm(&stub, &_test_stub, CV_L2) != 0 )
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cvtest::cmpEps( Mat(&stub1), Mat(&_test_stub1), &max_diff, 0, &pt, true) < 0 )
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{
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ts->printf( cvtest::TS::LOG, "readObj method: the read nd matrix is not correct: (%.20g vs %.20g) vs at (%d,%d)\n",
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cvGetReal2D(&stub1, pt[0], pt[1]), cvGetReal2D(&_test_stub1, pt[0], pt[1]),
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pt[0], pt[1] );
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
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return;
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}
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MatND mat_nd2;
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fs["test_mat_nd"] >> mat_nd2;
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CvMatND m_nd2 = mat_nd2;
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cvGetMat(&m_nd2, &stub, 0, 1);
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cvReshape(&stub, &stub1, 1, 0);
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if( !CV_ARE_TYPES_EQ(&stub, &_test_stub) ||
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!CV_ARE_SIZES_EQ(&stub, &_test_stub) ||
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//cvNorm(&stub, &_test_stub, CV_L2) != 0 )
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cvtest::cmpEps( Mat(&stub1), Mat(&_test_stub1), &max_diff, 0, &pt, true) < 0 )
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{
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ts->printf( cvtest::TS::LOG, "C++ method: the read nd matrix is not correct: (%.20g vs %.20g) vs at (%d,%d)\n",
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cvGetReal2D(&stub1, pt[0], pt[1]), cvGetReal2D(&_test_stub1, pt[1], pt[0]),
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pt[0], pt[1] );
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
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return;
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}
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cvRelease((void**)&m_nd);
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Ptr<CvSparseMat> m_s = (CvSparseMat*)fs["test_sparse_mat"].readObj();
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Ptr<CvSparseMat> _test_sparse_ = (CvSparseMat*)test_sparse_mat;
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Ptr<CvSparseMat> _test_sparse = (CvSparseMat*)cvClone(_test_sparse_);
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SparseMat m_s2;
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fs["test_sparse_mat"] >> m_s2;
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Ptr<CvSparseMat> _m_s2 = (CvSparseMat*)m_s2;
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if( !m_s || !CV_IS_SPARSE_MAT(m_s) ||
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!cvTsCheckSparse(m_s, _test_sparse,0) ||
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!cvTsCheckSparse(_m_s2, _test_sparse,0))
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{
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ts->printf( cvtest::TS::LOG, "the read sparse matrix is not correct\n" );
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
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return;
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}
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FileNode tl = fs["test_list"];
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if( tl.type() != FileNode::SEQ || tl.size() != 6 ||
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fabs((double)tl[0] - 0.0000000000001) >= DBL_EPSILON ||
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(int)tl[1] != 2 ||
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fabs((double)tl[2] - CV_PI) >= DBL_EPSILON ||
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(int)tl[3] != -3435345 ||
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(string)tl[4] != "2-502 2-029 3egegeg" ||
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tl[5].type() != FileNode::MAP || tl[5].size() != 3 ||
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(int)tl[5]["month"] != 12 ||
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(int)tl[5]["day"] != 31 ||
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(int)tl[5]["year"] != 1969 )
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{
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ts->printf( cvtest::TS::LOG, "the test list is incorrect\n" );
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
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return;
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}
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FileNode tm = fs["test_map"];
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FileNode tm_lbp = tm["lbp"];
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int real_x = (int)tm["x"];
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int real_y = (int)tm["y"];
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int real_width = (int)tm["width"];
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int real_height = (int)tm["height"];
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int real_lbp_val = 0;
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FileNodeIterator it;
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it = tm_lbp.begin();
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real_lbp_val |= (int)*it << 0;
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++it;
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real_lbp_val |= (int)*it << 1;
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it++;
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real_lbp_val |= (int)*it << 2;
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it += 1;
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real_lbp_val |= (int)*it << 3;
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FileNodeIterator it2(it);
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it2 += 4;
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real_lbp_val |= (int)*it2 << 7;
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--it2;
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real_lbp_val |= (int)*it2 << 6;
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it2--;
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real_lbp_val |= (int)*it2 << 5;
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it2 -= 1;
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real_lbp_val |= (int)*it2 << 4;
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it2 += -1;
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CV_Assert( it == it2 );
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if( tm.type() != FileNode::MAP || tm.size() != 5 ||
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real_x != 1 ||
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real_y != 2 ||
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real_width != 100 ||
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real_height != 200 ||
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tm_lbp.type() != FileNode::SEQ ||
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tm_lbp.size() != 8 ||
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real_lbp_val != 0xb6 )
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{
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ts->printf( cvtest::TS::LOG, "the test map is incorrect\n" );
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
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return;
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}
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CvGraph* graph3 = (CvGraph*)fs["test_graph"].readObj();
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if(graph2->active_count != vcount || graph3->active_count != vcount ||
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graph2->edges->active_count != ecount || graph3->edges->active_count != ecount)
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{
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ts->printf( cvtest::TS::LOG, "the cloned or read graph have wrong number of vertices or edges\n" );
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
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return;
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}
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for( i = 0; i < ecount; i++ )
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{
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CvGraphEdge* edge2 = cvFindGraphEdge(graph2, edges[i][0], edges[i][1]);
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CvGraphEdge* edge3 = cvFindGraphEdge(graph3, edges[i][0], edges[i][1]);
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if( !edge2 || edge2->weight != (float)(i+1) ||
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!edge3 || edge3->weight != (float)(i+1) )
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{
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ts->printf( cvtest::TS::LOG, "the cloned or read graph do not have the edge (%d, %d)\n", edges[i][0], edges[i][1] );
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
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return;
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}
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}
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fs.release();
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remove(filename.c_str());
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}
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}
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};
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TEST(Core_InputOutput, write_read_consistency) { Core_IOTest test; test.safe_run(); }
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/*class CV_BigMatrixIOTest : public cvtest::BaseTest
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{
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public:
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CV_BigMatrixIOTest() {}
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~CV_BigMatrixIOTest() {}
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protected:
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void run(int)
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{
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try
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{
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RNG& rng = theRNG();
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int N = 1000, M = 1200000;
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Mat mat(M, N, CV_32F);
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rng.fill(mat, RNG::UNIFORM, 0, 1);
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FileStorage fs("test.xml", FileStorage::WRITE);
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fs << "mat" << mat;
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fs.release();
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}
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catch(...)
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{
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ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
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}
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}
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};
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TEST(Core_InputOutput, huge) { CV_BigMatrixIOTest test; test.safe_run(); }
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*/
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