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Open Source Computer Vision Library
https://opencv.org/
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1238 lines
42 KiB
1238 lines
42 KiB
#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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int progress = 0; |
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MemStorage storage(cvCreateMemStorage(0)); |
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const char * suffixs[3] = {".yml", ".xml", ".json" }; |
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test_case_count = 6; |
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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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bool mem = (idx % test_case_count) >= (test_case_count >> 1); |
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string filename = tempfile(suffixs[idx % (test_case_count >> 1)]); |
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FileStorage fs(filename, FileStorage::WRITE + (mem ? FileStorage::MEMORY : 0)); |
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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.ptr(), 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[] = { |
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static_cast<int>(cvtest::randInt(rng)%10+1), |
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static_cast<int>(cvtest::randInt(rng)%10+1), |
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static_cast<int>(cvtest::randInt(rng)%10+1), |
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}; |
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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[] = { |
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static_cast<int>(cvtest::randInt(rng)%10+1), |
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static_cast<int>(cvtest::randInt(rng)%10+1), |
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static_cast<int>(cvtest::randInt(rng)%10+1), |
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static_cast<int>(cvtest::randInt(rng)%10+1), |
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}; |
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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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string content = fs.releaseAndGetString(); |
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if(!fs.open(mem ? content : filename, FileStorage::READ + (mem ? FileStorage::MEMORY : 0))) |
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{ |
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ts->printf( cvtest::TS::LOG, "filename %s can not be read\n", !mem ? filename.c_str() : content.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( cv::cvarrToMat(&stub1), cv::cvarrToMat(&_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( cv::cvarrToMat(&stub1), cv::cvarrToMat(&_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( cv::cvarrToMat(&stub1), cv::cvarrToMat(&_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_(cvCreateSparseMat(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(cvCreateSparseMat(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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if( !mem ) |
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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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extern void testFormatter(); |
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struct UserDefinedType |
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{ |
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int a; |
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float b; |
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}; |
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static inline bool operator==(const UserDefinedType &x, |
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const UserDefinedType &y) { |
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return (x.a == y.a) && (x.b == y.b); |
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} |
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static inline void write(FileStorage &fs, |
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const String&, |
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const UserDefinedType &value) |
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{ |
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fs << "{:" << "a" << value.a << "b" << value.b << "}"; |
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} |
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static inline void read(const FileNode& node, |
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UserDefinedType& value, |
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const UserDefinedType& default_value |
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= UserDefinedType()) { |
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if(node.empty()) |
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{ |
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value = default_value; |
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} |
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else |
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{ |
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node["a"] >> value.a; |
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node["b"] >> value.b; |
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} |
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} |
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|
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class CV_MiscIOTest : public cvtest::BaseTest |
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{ |
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public: |
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CV_MiscIOTest() {} |
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~CV_MiscIOTest() {} |
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protected: |
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void run(int) |
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{ |
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const char * suffix[3] = { |
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".yml", |
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".xml", |
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".json" |
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}; |
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for ( size_t i = 0u; i < 3u; i++ ) |
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{ |
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try |
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{ |
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string fname = cv::tempfile(suffix[i]); |
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vector<int> mi, mi2, mi3, mi4; |
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vector<Mat> mv, mv2, mv3, mv4; |
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vector<UserDefinedType> vudt, vudt2, vudt3, vudt4; |
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Mat m(10, 9, CV_32F); |
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Mat empty; |
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UserDefinedType udt = { 8, 3.3f }; |
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randu(m, 0, 1); |
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mi3.push_back(5); |
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mv3.push_back(m); |
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vudt3.push_back(udt); |
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Point_<float> p1(1.1f, 2.2f), op1; |
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Point3i p2(3, 4, 5), op2; |
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Size s1(6, 7), os1; |
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Complex<int> c1(9, 10), oc1; |
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Rect r1(11, 12, 13, 14), or1; |
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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(); |
|
#if defined _MSC_VER && _MSC_VER <= 1700 /* MSVC 2012 and older */ |
|
EXPECT_STREQ(fs_result.c_str(), "%YAML:1.0\n---\nd: [ 1, 2, 3, -1.5000000000000000e+000 ]\n"); |
|
#else |
|
EXPECT_STREQ(fs_result.c_str(), "%YAML:1.0\n---\nd: [ 1, 2, 3, -1.5000000000000000e+00 ]\n"); |
|
#endif |
|
|
|
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(); |
|
#if defined _MSC_VER && _MSC_VER <= 1700 /* MSVC 2012 and older */ |
|
EXPECT_STREQ(fs_result.c_str(), |
|
"%YAML:1.0\n" |
|
"---\n" |
|
"dv: [ 1, 2, 3, -1.5000000000000000e+000, 2, 3, 4,\n" |
|
" 1.5000000000000000e+000, 3, 2, 1, 5.0000000000000000e-001 ]\n" |
|
); |
|
#else |
|
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" |
|
); |
|
#endif |
|
|
|
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(); |
|
#if defined _MSC_VER && _MSC_VER <= 1700 /* MSVC 2012 and older */ |
|
EXPECT_STREQ(fs_result.c_str(), |
|
"%YAML:1.0\n" |
|
"---\n" |
|
"dvv:\n" |
|
" - [ 1, 2, 3, -1.5000000000000000e+000, 2, 3, 4,\n" |
|
" 1.5000000000000000e+000, 3, 2, 1, 5.0000000000000000e-001 ]\n" |
|
" - [ 3, 2, 1, 5.0000000000000000e-001, 1, 2, 3,\n" |
|
" -1.5000000000000000e+000 ]\n" |
|
); |
|
#else |
|
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" |
|
); |
|
#endif |
|
|
|
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); |
|
} |
|
} |
|
}
|
|
|