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https://github.com/opencv/opencv.git
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1016 lines
35 KiB
C++
1016 lines
35 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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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
|
|
= UserDefinedType()) {
|
|
if(node.empty())
|
|
{
|
|
value = default_value;
|
|
}
|
|
else
|
|
{
|
|
node["a"] >> value.a;
|
|
node["b"] >> value.b;
|
|
}
|
|
}
|
|
|
|
class CV_MiscIOTest : public cvtest::BaseTest
|
|
{
|
|
public:
|
|
CV_MiscIOTest() {}
|
|
~CV_MiscIOTest() {}
|
|
protected:
|
|
void run(int)
|
|
{
|
|
const char * suffix[3] = {
|
|
".yml",
|
|
".xml",
|
|
".json"
|
|
};
|
|
|
|
for ( size_t i = 0u; i < 3u; i++ )
|
|
{
|
|
try
|
|
{
|
|
string fname = cv::tempfile(suffix[i]);
|
|
vector<int> mi, mi2, mi3, mi4;
|
|
vector<Mat> mv, mv2, mv3, mv4;
|
|
vector<UserDefinedType> vudt, vudt2, vudt3, vudt4;
|
|
Mat m(10, 9, CV_32F);
|
|
Mat empty;
|
|
UserDefinedType udt = { 8, 3.3f };
|
|
randu(m, 0, 1);
|
|
mi3.push_back(5);
|
|
mv3.push_back(m);
|
|
vudt3.push_back(udt);
|
|
Point_<float> p1(1.1f, 2.2f), op1;
|
|
Point3i p2(3, 4, 5), op2;
|
|
Size s1(6, 7), os1;
|
|
Complex<int> c1(9, 10), oc1;
|
|
Rect r1(11, 12, 13, 14), or1;
|
|
Vec<int, 5> v1(15, 16, 17, 18, 19), ov1;
|
|
Scalar sc1(20.0, 21.1, 22.2, 23.3), osc1;
|
|
Range g1(7, 8), og1;
|
|
|
|
FileStorage fs(fname, FileStorage::WRITE);
|
|
fs << "mi" << mi;
|
|
fs << "mv" << mv;
|
|
fs << "mi3" << mi3;
|
|
fs << "mv3" << mv3;
|
|
fs << "vudt" << vudt;
|
|
fs << "vudt3" << vudt3;
|
|
fs << "empty" << empty;
|
|
fs << "p1" << p1;
|
|
fs << "p2" << p2;
|
|
fs << "s1" << s1;
|
|
fs << "c1" << c1;
|
|
fs << "r1" << r1;
|
|
fs << "v1" << v1;
|
|
fs << "sc1" << sc1;
|
|
fs << "g1" << g1;
|
|
fs.release();
|
|
|
|
fs.open(fname, FileStorage::READ);
|
|
fs["mi"] >> mi2;
|
|
fs["mv"] >> mv2;
|
|
fs["mi3"] >> mi4;
|
|
fs["mv3"] >> mv4;
|
|
fs["vudt"] >> vudt2;
|
|
fs["vudt3"] >> vudt4;
|
|
fs["empty"] >> empty;
|
|
fs["p1"] >> op1;
|
|
fs["p2"] >> op2;
|
|
fs["s1"] >> os1;
|
|
fs["c1"] >> oc1;
|
|
fs["r1"] >> or1;
|
|
fs["v1"] >> ov1;
|
|
fs["sc1"] >> osc1;
|
|
fs["g1"] >> og1;
|
|
CV_Assert( mi2.empty() );
|
|
CV_Assert( mv2.empty() );
|
|
CV_Assert( cvtest::norm(Mat(mi3), Mat(mi4), CV_C) == 0 );
|
|
CV_Assert( mv4.size() == 1 );
|
|
double n = cvtest::norm(mv3[0], mv4[0], CV_C);
|
|
CV_Assert( vudt2.empty() );
|
|
CV_Assert( vudt3 == vudt4 );
|
|
CV_Assert( n == 0 );
|
|
CV_Assert( op1 == p1 );
|
|
CV_Assert( op2 == p2 );
|
|
CV_Assert( os1 == s1 );
|
|
CV_Assert( oc1 == c1 );
|
|
CV_Assert( or1 == r1 );
|
|
CV_Assert( ov1 == v1 );
|
|
CV_Assert( osc1 == sc1 );
|
|
CV_Assert( og1 == g1 );
|
|
}
|
|
catch(...)
|
|
{
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
TEST(Core_InputOutput, misc) { CV_MiscIOTest test; test.safe_run(); }
|
|
|
|
/*class CV_BigMatrixIOTest : public cvtest::BaseTest
|
|
{
|
|
public:
|
|
CV_BigMatrixIOTest() {}
|
|
~CV_BigMatrixIOTest() {}
|
|
protected:
|
|
void run(int)
|
|
{
|
|
try
|
|
{
|
|
RNG& rng = theRNG();
|
|
int N = 1000, M = 1200000;
|
|
Mat mat(M, N, CV_32F);
|
|
rng.fill(mat, RNG::UNIFORM, 0, 1);
|
|
FileStorage fs(cv::tempfile(".xml"), FileStorage::WRITE);
|
|
fs << "mat" << mat;
|
|
fs.release();
|
|
}
|
|
catch(...)
|
|
{
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
|
|
}
|
|
}
|
|
};
|
|
|
|
TEST(Core_InputOutput, huge) { CV_BigMatrixIOTest test; test.safe_run(); }
|
|
*/
|
|
|
|
TEST(Core_globbing, accuracy)
|
|
{
|
|
std::string patternLena = cvtest::TS::ptr()->get_data_path() + "lena*.*";
|
|
std::string patternLenaPng = cvtest::TS::ptr()->get_data_path() + "lena.png";
|
|
|
|
std::vector<String> lenas, pngLenas;
|
|
cv::glob(patternLena, lenas, true);
|
|
cv::glob(patternLenaPng, pngLenas, true);
|
|
|
|
ASSERT_GT(lenas.size(), pngLenas.size());
|
|
|
|
for (size_t i = 0; i < pngLenas.size(); ++i)
|
|
{
|
|
ASSERT_NE(std::find(lenas.begin(), lenas.end(), pngLenas[i]), lenas.end());
|
|
}
|
|
}
|
|
|
|
TEST(Core_InputOutput, FileStorage)
|
|
{
|
|
std::string file = cv::tempfile(".xml");
|
|
cv::FileStorage f(file, cv::FileStorage::WRITE);
|
|
|
|
char arr[66];
|
|
sprintf(arr, "sprintf is hell %d", 666);
|
|
EXPECT_NO_THROW(f << arr);
|
|
}
|
|
|
|
TEST(Core_InputOutput, FileStorageKey)
|
|
{
|
|
cv::FileStorage f("dummy.yml", cv::FileStorage::WRITE | cv::FileStorage::MEMORY);
|
|
|
|
EXPECT_NO_THROW(f << "key1" << "value1");
|
|
EXPECT_NO_THROW(f << "_key2" << "value2");
|
|
EXPECT_NO_THROW(f << "key_3" << "value3");
|
|
const std::string expected = "%YAML:1.0\n---\nkey1: value1\n_key2: value2\nkey_3: value3\n";
|
|
ASSERT_STREQ(f.releaseAndGetString().c_str(), expected.c_str());
|
|
}
|
|
|
|
TEST(Core_InputOutput, FileStorageSpaces)
|
|
{
|
|
cv::FileStorage f("dummy.yml", cv::FileStorage::WRITE | cv::FileStorage::MEMORY);
|
|
const int valueCount = 5;
|
|
std::string values[5] = { "", " ", " ", " a", " some string" };
|
|
for (size_t i = 0; i < valueCount; i++) {
|
|
EXPECT_NO_THROW(f << cv::format("key%d", i) << values[i]);
|
|
}
|
|
cv::FileStorage f2(f.releaseAndGetString(), cv::FileStorage::READ | cv::FileStorage::MEMORY);
|
|
std::string valuesRead[valueCount];
|
|
for (size_t i = 0; i < valueCount; i++) {
|
|
EXPECT_NO_THROW(f2[cv::format("key%d", i)] >> valuesRead[i]);
|
|
ASSERT_STREQ(values[i].c_str(), valuesRead[i].c_str());
|
|
}
|
|
}
|
|
|
|
struct data_t
|
|
{
|
|
typedef uchar u;
|
|
typedef char b;
|
|
typedef ushort w;
|
|
typedef short s;
|
|
typedef int i;
|
|
typedef float f;
|
|
typedef double d;
|
|
|
|
u u1 ;u u2 ; i i1 ;
|
|
i i2 ;i i3 ;
|
|
d d1 ;
|
|
d d2 ;
|
|
i i4 ;
|
|
|
|
static inline const char * signature() { 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();
|
|
}
|
|
|
|
for (int i = 0; i < 1000; i++) {
|
|
// TODO: Solve this bug in `cvReadRawData`
|
|
//EXPECT_EQ(rawdata[i].u1, 1);
|
|
//EXPECT_EQ(rawdata[i].u2, 2);
|
|
//EXPECT_EQ(rawdata[i].i1, 1);
|
|
//EXPECT_EQ(rawdata[i].i2, 2);
|
|
//EXPECT_EQ(rawdata[i].i3, 3);
|
|
//EXPECT_EQ(rawdata[i].d1, 0.1);
|
|
//EXPECT_EQ(rawdata[i].d2, 0.2);
|
|
//EXPECT_EQ(rawdata[i].i4, i);
|
|
}
|
|
|
|
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());
|
|
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));
|
|
|
|
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.);
|
|
}
|