147 lines
6.2 KiB
C++
147 lines
6.2 KiB
C++
/****************************************************************************
|
|
* VCGLib o o *
|
|
* Visual and Computer Graphics Library o o *
|
|
* _ O _ *
|
|
* Copyright(C) 2004-2016 \/)\/ *
|
|
* Visual Computing Lab /\/| *
|
|
* ISTI - Italian National Research Council | *
|
|
* \ *
|
|
* All rights reserved. *
|
|
* *
|
|
* This program is free software; you can redistribute it and/or modify *
|
|
* it under the terms of the GNU General Public License as published by *
|
|
* the Free Software Foundation; either version 2 of the License, or *
|
|
* (at your option) any later version. *
|
|
* *
|
|
* This program is distributed in the hope that it will be useful, *
|
|
* but WITHOUT ANY WARRANTY; without even the implied warranty of *
|
|
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the *
|
|
* GNU General Public License (http://www.gnu.org/licenses/gpl.txt) *
|
|
* for more details. *
|
|
* *
|
|
****************************************************************************/
|
|
#ifndef VCG_TRI_OUTLIERS__H
|
|
#define VCG_TRI_OUTLIERS__H
|
|
|
|
#include <vcg/space/index/kdtree/kdtree.h>
|
|
|
|
|
|
namespace vcg
|
|
{
|
|
|
|
namespace tri
|
|
{
|
|
|
|
template <class MeshType>
|
|
class OutlierRemoval
|
|
{
|
|
public:
|
|
|
|
typedef typename MeshType::ScalarType ScalarType;
|
|
typedef typename vcg::KdTree<ScalarType> KdTreeType;
|
|
typedef typename vcg::KdTree<ScalarType>::PriorityQueue PriorityQueue;
|
|
|
|
|
|
/**
|
|
Compute an outlier probability value for each vertex of the mesh using the approch
|
|
in the paper "LoOP: Local Outlier Probabilities". The outlier probability is stored in the
|
|
vertex attribute "outlierScore". It use the input kdtree to find the kNearest of each vertex.
|
|
|
|
"LoOP: local outlier probabilities" by Hans-Peter Kriegel et al.
|
|
Proceedings of the 18th ACM conference on Information and knowledge management
|
|
*/
|
|
static void ComputeLoOPScore(MeshType& mesh, KdTreeType& kdTree, int kNearest)
|
|
{
|
|
vcg::tri::RequireCompactness(mesh);
|
|
typename MeshType::template PerVertexAttributeHandle<ScalarType> outlierScore = tri::Allocator<MeshType>:: template GetPerVertexAttribute<ScalarType>(mesh, std::string("outlierScore"));
|
|
typename MeshType::template PerVertexAttributeHandle<ScalarType> sigma = tri::Allocator<MeshType>:: template GetPerVertexAttribute<ScalarType>(mesh, std::string("sigma"));
|
|
typename MeshType::template PerVertexAttributeHandle<ScalarType> plof = tri::Allocator<MeshType>:: template GetPerVertexAttribute<ScalarType>(mesh, std::string("plof"));
|
|
|
|
#pragma omp parallel for schedule(dynamic, 10)
|
|
for (size_t i = 0; i < mesh.vert.size(); i++)
|
|
{
|
|
PriorityQueue queue;
|
|
kdTree.doQueryK(mesh.vert[i].cP(), kNearest, queue);
|
|
ScalarType sum = 0;
|
|
for (int j = 0; j < queue.getNofElements(); j++)
|
|
sum += queue.getWeight(j);
|
|
sum /= (queue.getNofElements());
|
|
sigma[i] = sqrt(sum);
|
|
}
|
|
|
|
float mean = 0;
|
|
#pragma omp parallel for reduction(+: mean) schedule(dynamic, 10)
|
|
for (size_t i = 0; i < mesh.vert.size(); i++)
|
|
{
|
|
PriorityQueue queue;
|
|
kdTree.doQueryK(mesh.vert[i].cP(), kNearest, queue);
|
|
ScalarType sum = 0;
|
|
for (int j = 0; j < queue.getNofElements(); j++)
|
|
sum += sigma[queue.getIndex(j)];
|
|
sum /= (queue.getNofElements());
|
|
plof[i] = sigma[i] / sum - 1.0f;
|
|
mean += plof[i] * plof[i];
|
|
}
|
|
|
|
mean /= mesh.vert.size();
|
|
mean = sqrt(mean);
|
|
|
|
#pragma omp parallel for schedule(dynamic, 10)
|
|
for (size_t i = 0; i < mesh.vert.size(); i++)
|
|
{
|
|
ScalarType value = plof[i] / (mean * sqrt(2.0f));
|
|
double dem = 1.0 + 0.278393 * value;
|
|
dem += 0.230389 * value * value;
|
|
dem += 0.000972 * value * value * value;
|
|
dem += 0.078108 * value * value * value * value;
|
|
ScalarType op = max(0.0, 1.0 - 1.0 / dem);
|
|
outlierScore[i] = op;
|
|
}
|
|
|
|
tri::Allocator<MeshType>::DeletePerVertexAttribute(mesh, std::string("sigma"));
|
|
tri::Allocator<MeshType>::DeletePerVertexAttribute(mesh, std::string("plof"));
|
|
};
|
|
|
|
/**
|
|
Select all the vertex of the mesh with an outlier probability above the input threshold [0.0, 1.0].
|
|
*/
|
|
static int SelectLoOPOutliers(MeshType& mesh, KdTreeType& kdTree, int kNearest, float threshold)
|
|
{
|
|
ComputeLoOPScore(mesh, kdTree, kNearest);
|
|
int count = 0;
|
|
typename MeshType:: template PerVertexAttributeHandle<ScalarType> outlierScore = tri::Allocator<MeshType>::template GetPerVertexAttribute<ScalarType>(mesh, std::string("outlierScore"));
|
|
for (int i = 0; i < mesh.vert.size(); i++)
|
|
{
|
|
if (outlierScore[i] > threshold)
|
|
{
|
|
mesh.vert[i].SetS();
|
|
count++;
|
|
}
|
|
}
|
|
return count;
|
|
}
|
|
|
|
|
|
|
|
/**
|
|
Delete all the vertex of the mesh with an outlier probability above the input threshold [0.0, 1.0].
|
|
*/
|
|
static int DeleteLoOPOutliers(MeshType& m, KdTreeType& kdTree, int kNearest, float threshold)
|
|
{
|
|
SelectLoOPOutliers(m,kdTree,kNearest,threshold);
|
|
int ovn = m.vn;
|
|
|
|
for(typename MeshType::VertexIterator vi=m.vert.begin();vi!=m.vert.end();++vi)
|
|
if((*vi).IsS() ) tri::Allocator<MeshType>::DeleteVertex(m,*vi);
|
|
tri::Allocator<MeshType>::CompactVertexVector(m);
|
|
tri::Allocator<MeshType>::DeletePerVertexAttribute(m, std::string("outlierScore"));
|
|
return m.vn - ovn;
|
|
}
|
|
};
|
|
|
|
} // end namespace tri
|
|
|
|
} // end namespace vcg
|
|
|
|
#endif // VCG_TRI_OUTLIERS_H
|