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[ERROR] cvflann::set_distance_type only provides backwards compatibility for the L1 and L2 distances. For other distance types you must use cv::flann::GenericIndex NSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEN7cvflann17flann_algorithm_tEN7cvflann20flann_centers_init_tEN7cvflann7anyimpl15base_any_policyEN7cvflann7anyimpl21typed_base_any_policyINS_20flann_centers_init_tEEEN7cvflann7anyimpl14big_any_policyINS_20flann_centers_init_tEEEN7cvflann7anyimpl21typed_base_any_policyINS_17flann_algorithm_tEEEN7cvflann7anyimpl14big_any_policyINS_17flann_algorithm_tEEEN7cvflann7anyimpl21typed_base_any_policyINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN7cvflann7anyimpl14big_any_policyINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN7cvflann7anyimpl21typed_base_any_policyINS0_9empty_anyEEEN7cvflann7anyimpl14big_any_policyINS0_9empty_anyEEEN7cvflann7anyimpl9empty_anyEN7cvflann7anyimpl21typed_base_any_policyIPKcEEN7cvflann7anyimpl16small_any_policyIPKcEEN7cvflann7anyimpl21typed_base_any_policyIiEEN7cvflann7anyimpl16small_any_policyIiEEN7cvflann7anyimpl21typed_base_any_policyIfEEN7cvflann7anyimpl16small_any_policyIfEEN7cvflann7anyimpl21typed_base_any_policyIbEEN7cvflann7anyimpl16small_any_policyIbEEFailed to allocate memory. cannot create std::vector larger than max_size()/code/SuperBuild/src/opencv/modules/flann/include/opencv2/flann/general.hCannot read from file : Building kmeans tree... Building kdtree tree... Invalid index file, cannot readInvalid index file, wrong signatureFLANN_INDEXUnknown/unsupported distance type/code/SuperBuild/src/opencv/modules/flann/src/miniflann.cppreleasevector::_M_default_appendvector::_M_realloc_insert(size_t)knn <= index_->size()runKnnSearch_query.type() == type && indices.type() == CV_32S && dists.type() == dtypequery.isContinuous() && indices.isContinuous() && dists.isContinuous()knnSearchrunRadiusSearch_maxResults > 0radiusSearchLSH index does not support radiusSearch operationwbsaveCan not open file %s for writing FLANN index algorithmbranchingiterationscenters_initcb_indextarget_precisionbuild_weightmemory_weightsample_fractionleaf_sizetable_numberkey_sizemulti_probe_levelfilenamechecksepssortedexplore_all_treesmatches.cols=%d, nn=%d Ground truth is not computed for as many neighbors as requested%8d %10.4g %10.5g %10.5g %10.5g Nodes Precision(%) Time(s) Time/vec(ms) Mean dist --------------------------------------------------------- No need for linear estimation Got as close as I can Start linear estimation ' in the parameters givenMissing parameter 'I can only search one feature at a time for range search queries.cols == veclen()indices.rows >= queries.rowsdists.rows >= queries.rowsint(indices.cols) >= knnint(dists.cols) >= knn/code/SuperBuild/src/opencv/modules/flann/include/opencv2/flann/nn_index.hUnknown algorithm for choosing initial centers./code/SuperBuild/src/opencv/modules/flann/include/opencv2/flann/kdtree_single_index.hUnknown index typerbDatatype of saved index is different than of the one to be created.The index saved belongs to a different datasetDataType::type == data.type() && data.isContinuous()loadIndex_type=%d buildIndex_Only continuous arrays are supporteddimleaf_max_sizereorderb___H`[]\|\y|yy zLuwReading FLANN index error: the saved data size (%d, %d) or type (%d) is different from the passed one (%d, %d), %d Reading FLANN index error: unsupported feature type %d for the index type %d Reading FLANN index error: unsupported distance type %d buildBranching factor must be at least 2LSH is not implemented for that type/code/SuperBuild/src/opencv/modules/flann/include/opencv2/flann/lsh_table.hgetKey/code/SuperBuild/src/opencv/modules/flann/include/opencv2/flann/lsh_index.hKD-TREE, Step 1: Exploring parameter space KDTree using params: trees=%d KDTree buildTime=%g, searchTime=%g KMEANS, Step 1: Exploring parameter space KMeansTree using params: max_iterations=%d, branching=%d KMeansTree buildTime=%g, searchTime=%g, build_weight=%g vector::reserveInvalid key_size (=%d). Valid values for your system are %d <= key_size < %d.initializeLshTableresult.full()findNeighbors/code/SuperBuild/src/opencv/modules/flann/include/opencv2/flann/hierarchical_clustering_index.hEntering autotuning, dataset size: %d, sampleSize: %d, testSampleSize: %d, target precision: %g Choosing linear, dataset too small Computing ground truth... Autotuning parameters... ---------------------------------------------------- bestIndex_ != NULL && "Requires a valid index"/code/SuperBuild/src/opencv/modules/flann/include/opencv2/flann/autotuned_index.hKMeans algorithm, estimating cluster border factor Required number of checks: %d Autotuned parameters: %sestimateSearchParamsComputing ground truth Estimating number of checks Optimum cb_index: %g Search parameters: /code/SuperBuild/src/opencv/modules/flann/include/opencv2/flann/kmeans_index.h/code/SuperBuild/src/opencv/modules/flann/include/opencv2/flann/kdtree_index.hgetNeighborsIt doesn't make any sense to use more than one tree for exact searchgetExactNeighborsN7cvflann11KDTreeIndexINS_2L2IfEEE4NodeEN7cvflann11KDTreeIndexINS_2L1IfEEE4NodeEPN7cvflann11KDTreeIndexINS_2L2IfEEE4NodeEPN7cvflann11KDTreeIndexINS_2L1IfEEE4NodeEPPN7cvflann11KDTreeIndexINS_2L2IfEEE4NodeEPPN7cvflann11KDTreeIndexINS_2L1IfEEE4NodeEN7cvflann18KNNSimpleResultSetIfEEN7cvflann7anyimpl21typed_base_any_policyIPPNS_11KDTreeIndexINS_2L1IfEEE4NodeEEEN7cvflann7anyimpl16small_any_policyIPPNS_11KDTreeIndexINS_2L1IfEEE4NodeEEEN7cvflann7anyimpl21typed_base_any_policyIPPNS_11KDTreeIndexINS_2L2IfEEE4NodeEEEN7cvflann7anyimpl16small_any_policyIPPNS_11KDTreeIndexINS_2L2IfEEE4NodeEEEN7cvflann12KNNResultSetIfEEN7cvflann11KMeansIndexINS_2L1IfEEE22KMeansDistanceComputerINS_6MatrixIdEEEEN7cvflann11KMeansIndexINS_2L2IfEEE22KMeansDistanceComputerINS_6MatrixIdEEEEN7cvflann11KMeansIndexINS_10HammingLUTEE22KMeansDistanceComputerIPPhEEN2cv5flann5IndexEN7cvflann14FLANNExceptionEN7cvflann7anyimpl12bad_any_castEN7cvflann7NNIndexINS_10HammingLUTEEEN7cvflann5IndexINS_10HammingLUTEEEN7cvflann7NNIndexINS_2L2IfEEEEN7cvflann5IndexINS_2L2IfEEEEN7cvflann7NNIndexINS_2L1IfEEEEN7cvflann5IndexINS_2L1IfEEEEN7cvflann7anyimpl21typed_base_any_policyIdEEN7cvflann7anyimpl14big_any_policyIdEEN7cvflann9ResultSetIiEEN7cvflann15UniqueResultSetIiEEN7cvflann18KNNUniqueResultSetIiEEN7cvflann9ResultSetIfEEN7cvflann15UniqueResultSetIfEEN7cvflann18KNNUniqueResultSetIfEEN7cvflann21RadiusUniqueResultSetIiEEN7cvflann21RadiusUniqueResultSetIfEEN7cvflann11LinearIndexINS_10HammingLUTEEEN7cvflann11KMeansIndexINS_10HammingLUTEEEN7cvflann27HierarchicalClusteringIndexINS_10HammingLUTEEEN7cvflann8LshIndexINS_10HammingLUTEEEN7cvflann11LinearIndexINS_2L2IfEEEEN7cvflann17KDTreeSingleIndexINS_2L2IfEEEEN7cvflann11KDTreeIndexINS_2L2IfEEEEN7cvflann11KMeansIndexINS_2L2IfEEEEN7cvflann14CompositeIndexINS_2L2IfEEEEN7cvflann14AutotunedIndexINS_2L2IfEEEEN7cvflann27HierarchicalClusteringIndexINS_2L2IfEEEEN7cvflann8LshIndexINS_2L2IfEEEEN7cvflann11LinearIndexINS_2L1IfEEEEN7cvflann17KDTreeSingleIndexINS_2L1IfEEEEN7cvflann11KDTreeIndexINS_2L1IfEEEEN7cvflann11KMeansIndexINS_2L1IfEEEEN7cvflann14CompositeIndexINS_2L1IfEEEEN7cvflann14AutotunedIndexINS_2L1IfEEEEN7cvflann27HierarchicalClusteringIndexINS_2L1IfEEEEN7cvflann8LshIndexINS_2L1IfEEEEvirtual void cv::flann::Index::save(const String&) constvirtual int cv::flann::Index::radiusSearch(cv::InputArray, cv::OutputArray, cv::OutputArray, double, int, const cv::flann::SearchParams&)virtual void cv::flann::Index::knnSearch(cv::InputArray, cv::OutputArray, cv::OutputArray, int, const cv::flann::SearchParams&)virtual void cv::flann::Index::release()virtual void cv::flann::Index::build(cv::InputArray, const cv::flann::IndexParams&, cvflann::flann_distance_t)ff?@?o:X??__L>̌?BAA?? 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