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145 lines
6.3 KiB
145 lines
6.3 KiB
/**************************************************************************** |
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* VCGLib o o * |
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* Visual and Computer Graphics Library o o * |
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* _ O _ * |
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* Copyright(C) 2004-2016 \/)\/ * |
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* Visual Computing Lab /\/| * |
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* ISTI - Italian National Research Council | * |
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* \ * |
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* All rights reserved. * |
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* * |
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* This program is free software; you can redistribute it and/or modify * |
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* it under the terms of the GNU General Public License as published by * |
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* the Free Software Foundation; either version 2 of the License, or * |
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* (at your option) any later version. * |
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* * |
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* This program is distributed in the hope that it will be useful, * |
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* but WITHOUT ANY WARRANTY; without even the implied warranty of * |
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * |
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* GNU General Public License (http://www.gnu.org/licenses/gpl.txt) * |
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* for more details. * |
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* * |
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****************************************************************************/ |
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#ifndef VCG_TRI_OUTLIERS__H |
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#define VCG_TRI_OUTLIERS__H |
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#include <vcg/space/index/kdtree/kdtree.h> |
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namespace vcg |
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{ |
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namespace tri |
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{ |
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template <class MeshType> |
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class OutlierRemoval |
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{ |
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public: |
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typedef typename MeshType::ScalarType ScalarType; |
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typedef typename vcg::KdTree<ScalarType> KdTreeType; |
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typedef typename vcg::KdTree<ScalarType>::PriorityQueue PriorityQueue; |
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/** |
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Compute an outlier probability value for each vertex of the mesh using the approch |
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in the paper "LoOP: Local Outlier Probabilities". The outlier probability is stored in the |
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vertex attribute "outlierScore". It use the input kdtree to find the kNearest of each vertex. |
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"LoOP: local outlier probabilities" by Hans-Peter Kriegel et al. |
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Proceedings of the 18th ACM conference on Information and knowledge management |
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*/ |
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static void ComputeLoOPScore(MeshType& mesh, KdTreeType& kdTree, int kNearest) |
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{ |
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vcg::tri::RequireCompactness(mesh); |
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typename MeshType::template PerVertexAttributeHandle<ScalarType> outlierScore = tri::Allocator<MeshType>:: template GetPerVertexAttribute<ScalarType>(mesh, std::string("outlierScore")); |
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typename MeshType::template PerVertexAttributeHandle<ScalarType> sigma = tri::Allocator<MeshType>:: template GetPerVertexAttribute<ScalarType>(mesh, std::string("sigma")); |
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typename MeshType::template PerVertexAttributeHandle<ScalarType> plof = tri::Allocator<MeshType>:: template GetPerVertexAttribute<ScalarType>(mesh, std::string("plof")); |
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#pragma omp parallel for schedule(dynamic, 10) //MSVC supports only OMP 2 -> no unsigned int allowed in parallel for... |
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for (int i = 0; i < (int)mesh.vert.size(); i++) |
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{ |
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PriorityQueue queue; |
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kdTree.doQueryK(mesh.vert[i].cP(), kNearest, queue); |
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ScalarType sum = 0; |
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for (int j = 0; j < queue.getNofElements(); j++) |
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sum += queue.getWeight(j); |
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sum /= (queue.getNofElements()); |
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sigma[i] = sqrt(sum); |
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} |
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float mean = 0; |
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#pragma omp parallel for reduction(+: mean) schedule(dynamic, 10) |
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for (int i = 0; i < (int)mesh.vert.size(); i++) |
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{ |
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PriorityQueue queue; |
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kdTree.doQueryK(mesh.vert[i].cP(), kNearest, queue); |
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ScalarType sum = 0; |
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for (int j = 0; j < queue.getNofElements(); j++) |
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sum += sigma[queue.getIndex(j)]; |
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sum /= (queue.getNofElements()); |
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plof[i] = sigma[i] / sum - 1.0f; |
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mean += plof[i] * plof[i]; |
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} |
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mean /= mesh.vert.size(); |
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mean = sqrt(mean); |
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#pragma omp parallel for schedule(dynamic, 10) |
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for (int i = 0; i < (int)mesh.vert.size(); i++) |
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{ |
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ScalarType value = plof[i] / (mean * sqrt(2.0f)); |
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double dem = 1.0 + 0.278393 * value; |
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dem += 0.230389 * value * value; |
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dem += 0.000972 * value * value * value; |
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dem += 0.078108 * value * value * value * value; |
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ScalarType op = std::max(0.0, 1.0 - 1.0 / dem); |
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outlierScore[i] = op; |
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} |
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tri::Allocator<MeshType>::DeletePerVertexAttribute(mesh, std::string("sigma")); |
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tri::Allocator<MeshType>::DeletePerVertexAttribute(mesh, std::string("plof")); |
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}; |
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/** |
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Select all the vertex of the mesh with an outlier probability above the input threshold [0.0, 1.0]. |
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*/ |
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static int SelectLoOPOutliers(MeshType& mesh, KdTreeType& kdTree, int kNearest, float threshold) |
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{ |
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ComputeLoOPScore(mesh, kdTree, kNearest); |
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int count = 0; |
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typename MeshType:: template PerVertexAttributeHandle<ScalarType> outlierScore = tri::Allocator<MeshType>::template GetPerVertexAttribute<ScalarType>(mesh, std::string("outlierScore")); |
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for (int i = 0; i < mesh.vert.size(); i++) |
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{ |
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if (outlierScore[i] > threshold) |
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{ |
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mesh.vert[i].SetS(); |
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count++; |
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} |
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} |
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return count; |
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} |
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/** |
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Delete all the vertex of the mesh with an outlier probability above the input threshold [0.0, 1.0]. |
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*/ |
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static int DeleteLoOPOutliers(MeshType& m, KdTreeType& kdTree, int kNearest, float threshold) |
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{ |
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SelectLoOPOutliers(m,kdTree,kNearest,threshold); |
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int ovn = m.vn; |
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for(typename MeshType::VertexIterator vi=m.vert.begin();vi!=m.vert.end();++vi) |
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if((*vi).IsS() ) tri::Allocator<MeshType>::DeleteVertex(m,*vi); |
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tri::Allocator<MeshType>::CompactVertexVector(m); |
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tri::Allocator<MeshType>::DeletePerVertexAttribute(m, std::string("outlierScore")); |
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return m.vn - ovn; |
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} |
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}; |
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} // end namespace tri |
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} // end namespace vcg |
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#endif // VCG_TRI_OUTLIERS_H
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