1697 lines
61 KiB
C++
1697 lines
61 KiB
C++
/****************************************************************************
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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 \/)\/ *
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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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/****************************************************************************
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The sampling Class has a set of static functions, that you can call to sample the surface of a mesh.
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Each function is templated on the mesh and on a Sampler object s.
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Each function calls many time the sample object with the sampling point as parameter.
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Sampler Classes and Sampling algorithms are independent.
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Sampler classes exploits the sample that are generated with various algorithms.
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For example, you can compute Hausdorff distance (that is a sampler) using various
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sampling strategies (montecarlo, stratified etc).
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****************************************************************************/
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#ifndef __VCGLIB_POINT_SAMPLING
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#define __VCGLIB_POINT_SAMPLING
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#include <vcg/math/random_generator.h>
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#include <vcg/complex/algorithms/closest.h>
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#include <vcg/space/index/spatial_hashing.h>
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#include <vcg/complex/algorithms/stat.h>
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#include <vcg/complex/algorithms/create/platonic.h>
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#include <vcg/complex/algorithms/update/topology.h>
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#include <vcg/complex/algorithms/update/normal.h>
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#include <vcg/complex/algorithms/update/bounding.h>
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#include <vcg/complex/algorithms/update/flag.h>
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#include <vcg/space/segment2.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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/// \ingroup trimesh
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/// \headerfile point_sampling.h vcg/complex/algorithms/point_sampling.h
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/**
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\brief An basic sampler class that show the required interface used by the SurfaceSampling class.
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Most of the methods of sampling classes call the AddFace method of this class with the face containing the sample and its barycentric coord.
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Beside being an example of how to write a sampler it provides a simple way to use the various sampling classes.
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For example if you just want to get a vector with positions over the surface You have just to write
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vector<Point3f> myVec;
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TrivialSampler<MyMesh> ts(myVec);
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SurfaceSampling<MyMesh, TrivialSampler<MyMesh> >::Montecarlo(M, ts, SampleNum);
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**/
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template <class MeshType>
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class TrivialSampler
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{
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public:
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typedef typename MeshType::CoordType CoordType;
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typedef typename MeshType::VertexType VertexType;
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typedef typename MeshType::FaceType FaceType;
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TrivialSampler()
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{
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sampleVec = new std::vector<CoordType>();
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vectorOwner=true;
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}
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TrivialSampler(std::vector<CoordType> &Vec)
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{
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sampleVec = &Vec;
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sampleVec->clear();
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vectorOwner=false;
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}
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~TrivialSampler()
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{
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if(vectorOwner) delete sampleVec;
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}
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private:
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std::vector<CoordType> *sampleVec;
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bool vectorOwner;
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public:
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void AddVert(const VertexType &p)
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{
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sampleVec->push_back(p.cP());
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}
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void AddFace(const FaceType &f, const CoordType &p)
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{
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sampleVec->push_back(f.cP(0)*p[0] + f.cP(1)*p[1] +f.cP(2)*p[2] );
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}
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void AddTextureSample(const FaceType &, const CoordType &, const Point2i &, float )
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{
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// Retrieve the color of the sample from the face f using the barycentric coord p
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// and write that color in a texture image at position <tp[0], texHeight-tp[1]>
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// if edgeDist is > 0 then the corrisponding point is affecting face color even if outside the face area (in texture space)
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}
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}; // end class TrivialSampler
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template <class MeshType>
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class MeshSampler
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{
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public:
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typedef typename MeshType::VertexType VertexType;
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typedef typename MeshType::FaceType FaceType;
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typedef typename MeshType::CoordType CoordType;
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MeshSampler(MeshType &_m):m(_m){}
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MeshType &m;
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void AddVert(const VertexType &p)
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{
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tri::Allocator<MeshType>::AddVertices(m,1);
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m.vert.back().ImportData(p);
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}
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void AddFace(const FaceType &f, CoordType p)
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{
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tri::Allocator<MeshType>::AddVertices(m,1);
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m.vert.back().P() = f.cP(0)*p[0] + f.cP(1)*p[1] +f.cP(2)*p[2];
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m.vert.back().N() = f.cV(0)->N()*p[0] + f.cV(1)->N()*p[1] + f.cV(2)->N()*p[2];
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if(tri::HasPerVertexQuality(m) )
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m.vert.back().Q() = f.cV(0)->Q()*p[0] + f.cV(1)->Q()*p[1] + f.cV(2)->Q()*p[2];
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}
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}; // end class BaseSampler
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/**
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\brief Main Class of the Sampling framework.
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This class allows you to perform various kind of random/procedural point sampling over a triangulated surface.
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The class is templated over the PointSampler object that allows to customize the use of the generated samples.
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**/
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template <class MetroMesh, class VertexSampler = TrivialSampler< MetroMesh> >
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class SurfaceSampling
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{
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typedef typename MetroMesh::CoordType CoordType;
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typedef typename MetroMesh::ScalarType ScalarType;
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typedef typename MetroMesh::VertexType VertexType;
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typedef typename MetroMesh::VertexPointer VertexPointer;
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typedef typename MetroMesh::VertexIterator VertexIterator;
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typedef typename MetroMesh::FacePointer FacePointer;
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typedef typename MetroMesh::FaceIterator FaceIterator;
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typedef typename MetroMesh::FaceType FaceType;
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typedef typename MetroMesh::FaceContainer FaceContainer;
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typedef typename vcg::Box3<ScalarType> BoxType;
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typedef typename vcg::SpatialHashTable<FaceType, ScalarType> MeshSHT;
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typedef typename vcg::SpatialHashTable<FaceType, ScalarType>::CellIterator MeshSHTIterator;
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typedef typename vcg::SpatialHashTable<VertexType, ScalarType> MontecarloSHT;
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typedef typename vcg::SpatialHashTable<VertexType, ScalarType>::CellIterator MontecarloSHTIterator;
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typedef typename vcg::SpatialHashTable<VertexType, ScalarType> SampleSHT;
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typedef typename vcg::SpatialHashTable<VertexType, ScalarType>::CellIterator SampleSHTIterator;
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public:
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static math::MarsenneTwisterRNG &SamplingRandomGenerator()
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{
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static math::MarsenneTwisterRNG rnd;
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return rnd;
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}
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// Returns an integer random number in the [0,i-1] interval using the improve Marsenne-Twister method.
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static unsigned int RandomInt(unsigned int i)
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{
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return (SamplingRandomGenerator().generate(0) % i);
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}
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// Returns a random number in the [0,1) real interval using the improved Marsenne-Twister method.
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static double RandomDouble01()
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{
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return SamplingRandomGenerator().generate01();
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}
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#define FAK_LEN 1024
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static double LnFac(int n) {
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// Tabled log factorial function. gives natural logarithm of n!
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// define constants
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static const double // coefficients in Stirling approximation
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C0 = 0.918938533204672722, // ln(sqrt(2*pi))
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C1 = 1./12.,
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C3 = -1./360.;
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// C5 = 1./1260., // use r^5 term if FAK_LEN < 50
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// C7 = -1./1680.; // use r^7 term if FAK_LEN < 20
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// static variables
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static double fac_table[FAK_LEN]; // table of ln(n!):
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static bool initialized = false; // remember if fac_table has been initialized
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if (n < FAK_LEN) {
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if (n <= 1) {
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if (n < 0) assert(0);//("Parameter negative in LnFac function");
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return 0;
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}
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if (!initialized) { // first time. Must initialize table
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// make table of ln(n!)
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double sum = fac_table[0] = 0.;
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for (int i=1; i<FAK_LEN; i++) {
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sum += log(double(i));
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fac_table[i] = sum;
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}
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initialized = true;
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}
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return fac_table[n];
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}
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// not found in table. use Stirling approximation
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double n1, r;
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n1 = n; r = 1. / n1;
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return (n1 + 0.5)*log(n1) - n1 + C0 + r*(C1 + r*r*C3);
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}
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static int PoissonRatioUniforms(double L) {
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/*
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This subfunction generates a integer with the poisson
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distribution using the ratio-of-uniforms rejection method (PRUAt).
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This approach is STABLE even for large L (e.g. it does not suffer from the overflow limit of the classical Knuth implementation)
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Execution time does not depend on L, except that it matters whether
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is within the range where ln(n!) is tabulated.
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Reference:
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E. Stadlober
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"The ratio of uniforms approach for generating discrete random variates".
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Journal of Computational and Applied Mathematics,
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vol. 31, no. 1, 1990, pp. 181-189.
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Partially adapted/inspired from some subfunctions of the Agner Fog stocc library ( www.agner.org/random )
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Same licensing scheme.
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*/
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// constants
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const double SHAT1 = 2.943035529371538573; // 8/e
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const double SHAT2 = 0.8989161620588987408; // 3-sqrt(12/e)
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double u; // uniform random
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double lf; // ln(f(x))
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double x; // real sample
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int k; // integer sample
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double pois_a = L + 0.5; // hat center
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int mode = (int)L; // mode
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double pois_g = log(L);
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double pois_f0 = mode * pois_g - LnFac(mode); // value at mode
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double pois_h = sqrt(SHAT1 * (L+0.5)) + SHAT2; // hat width
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double pois_bound = (int)(pois_a + 6.0 * pois_h); // safety-bound
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while(1) {
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u = RandomDouble01();
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if (u == 0) continue; // avoid division by 0
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x = pois_a + pois_h * (RandomDouble01() - 0.5) / u;
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if (x < 0 || x >= pois_bound) continue; // reject if outside valid range
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k = (int)(x);
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lf = k * pois_g - LnFac(k) - pois_f0;
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if (lf >= u * (4.0 - u) - 3.0) break; // quick acceptance
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if (u * (u - lf) > 1.0) continue; // quick rejection
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if (2.0 * log(u) <= lf) break; // final acceptance
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}
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return k;
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}
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/**
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algorithm poisson random number (Knuth):
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init:
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Let L ← e^−λ, k ← 0 and p ← 1.
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do:
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k ← k + 1.
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Generate uniform random number u in [0,1] and let p ← p × u.
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while p > L.
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return k − 1.
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*/
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static int Poisson(double lambda)
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{
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if(lambda>50) return PoissonRatioUniforms(lambda);
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double L = exp(-lambda);
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int k =0;
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double p = 1.0;
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do
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{
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k = k+1;
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p = p*RandomDouble01();
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} while (p>L);
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return k -1;
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}
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static void AllVertex(MetroMesh & m, VertexSampler &ps)
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{
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VertexIterator vi;
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for(vi=m.vert.begin();vi!=m.vert.end();++vi)
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{
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if(!(*vi).IsD())
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{
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ps.AddVert(*vi);
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}
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}
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}
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/// Sample the vertices in a weighted way. Each vertex has a probability of being chosen
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/// that is proportional to its quality.
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/// It assumes that you are asking a number of vertices smaller than nv;
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/// Algorithm:
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/// 1) normalize quality so that sum q == 1;
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/// 2) shuffle vertices.
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/// 3) for each vertices choose it if rand > thr;
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static void VertexWeighted(MetroMesh & m, VertexSampler &ps, int sampleNum)
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{
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ScalarType qSum = 0;
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VertexIterator vi;
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for(vi = m.vert.begin(); vi != m.vert.end(); ++vi)
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if(!(*vi).IsD())
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qSum += (*vi).Q();
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ScalarType samplePerUnit = sampleNum/qSum;
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ScalarType floatSampleNum =0;
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std::vector<VertexPointer> vertVec;
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FillAndShuffleVertexPointerVector(m,vertVec);
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std::vector<bool> vertUsed(m.vn,false);
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int i=0; int cnt=0;
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while(cnt < sampleNum)
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{
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if(vertUsed[i])
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{
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floatSampleNum += vertVec[i]->Q() * samplePerUnit;
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int vertSampleNum = (int) floatSampleNum;
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floatSampleNum -= (float) vertSampleNum;
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// for every sample p_i in T...
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if(vertSampleNum > 1)
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{
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ps.AddVert(*vertVec[i]);
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cnt++;
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vertUsed[i]=true;
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}
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}
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i = (i+1)%m.vn;
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}
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}
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/// Sample the vertices in a uniform way. Each vertex has a probability of being chosen
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/// that is proportional to the area it represent.
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static void VertexAreaUniform(MetroMesh & m, VertexSampler &ps, int sampleNum)
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{
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VertexIterator vi;
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for(vi = m.vert.begin(); vi != m.vert.end(); ++vi)
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if(!(*vi).IsD())
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(*vi).Q() = 0;
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FaceIterator fi;
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for(fi = m.face.begin(); fi != m.face.end(); ++fi)
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if(!(*fi).IsD())
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{
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ScalarType areaThird = DoubleArea(*fi)/6.0;
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(*fi).V(0)->Q()+=areaThird;
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(*fi).V(1)->Q()+=areaThird;
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(*fi).V(2)->Q()+=areaThird;
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}
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VertexWeighted(m,ps,sampleNum);
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}
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static void FillAndShuffleFacePointerVector(MetroMesh & m, std::vector<FacePointer> &faceVec)
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{
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FaceIterator fi;
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for(fi=m.face.begin();fi!=m.face.end();++fi)
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if(!(*fi).IsD()) faceVec.push_back(&*fi);
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assert((int)faceVec.size()==m.fn);
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unsigned int (*p_myrandom)(unsigned int) = RandomInt;
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std::random_shuffle(faceVec.begin(),faceVec.end(), p_myrandom);
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}
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static void FillAndShuffleVertexPointerVector(MetroMesh & m, std::vector<VertexPointer> &vertVec)
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{
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VertexIterator vi;
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for(vi=m.vert.begin();vi!=m.vert.end();++vi)
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if(!(*vi).IsD()) vertVec.push_back(&*vi);
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||
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assert((int)vertVec.size()==m.vn);
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||
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unsigned int (*p_myrandom)(unsigned int) = RandomInt;
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std::random_shuffle(vertVec.begin(),vertVec.end(), p_myrandom);
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}
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||
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/// Sample the vertices in a uniform way. Each vertex has the same probabiltiy of being chosen.
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static void VertexUniform(MetroMesh & m, VertexSampler &ps, int sampleNum)
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{
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if(sampleNum>=m.vn) {
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AllVertex(m,ps);
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return;
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||
}
|
||
|
||
std::vector<VertexPointer> vertVec;
|
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FillAndShuffleVertexPointerVector(m,vertVec);
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||
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for(int i =0; i< sampleNum; ++i)
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ps.AddVert(*vertVec[i]);
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||
}
|
||
|
||
/// Sample all the crease vertices.
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/// e.g. all the vertices where there are at least three non faux edges.
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||
static void VertexCrease(MetroMesh & m, VertexSampler &ps)
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||
{
|
||
typedef typename UpdateTopology<MetroMesh>::PEdge SimpleEdge;
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||
std::vector< SimpleEdge > Edges;
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||
typename std::vector< SimpleEdge >::iterator ei;
|
||
UpdateTopology<MetroMesh>::FillUniqueEdgeVector(m,Edges,false);
|
||
|
||
typename MetroMesh::template PerVertexAttributeHandle <int> hv = tri::Allocator<MetroMesh>:: template GetPerVertexAttribute<int> (m);
|
||
|
||
for(ei=Edges.begin(); ei!=Edges.end(); ++ei)
|
||
{
|
||
hv[ei->v[0]]++;
|
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hv[ei->v[1]]++;
|
||
}
|
||
|
||
for(VertexIterator vi=m.vert.begin();vi!=m.vert.end();++vi)
|
||
{
|
||
if(hv[vi]>2)
|
||
ps.AddVert(*vi);
|
||
}
|
||
}
|
||
|
||
|
||
static void FaceUniform(MetroMesh & m, VertexSampler &ps, int sampleNum)
|
||
{
|
||
if(sampleNum>=m.fn) {
|
||
AllFace(m,ps);
|
||
return;
|
||
}
|
||
|
||
std::vector<FacePointer> faceVec;
|
||
FillAndShuffleFacePointerVector(m,faceVec);
|
||
|
||
for(int i =0; i< sampleNum; ++i)
|
||
ps.AddFace(*faceVec[i],Barycenter(*faceVec[i]));
|
||
}
|
||
|
||
static void AllFace(MetroMesh & m, VertexSampler &ps)
|
||
{
|
||
FaceIterator fi;
|
||
for(fi=m.face.begin();fi!=m.face.end();++fi)
|
||
if(!(*fi).IsD())
|
||
{
|
||
ps.AddFace(*fi,Barycenter(*fi));
|
||
}
|
||
}
|
||
|
||
|
||
static void AllEdge(MetroMesh & m, VertexSampler &ps)
|
||
{
|
||
// Edge sampling.
|
||
typedef typename UpdateTopology<MetroMesh>::PEdge SimpleEdge;
|
||
std::vector< SimpleEdge > Edges;
|
||
typename std::vector< SimpleEdge >::iterator ei;
|
||
UpdateTopology<MetroMesh>::FillUniqueEdgeVector(m,Edges);
|
||
|
||
for(ei=Edges.begin(); ei!=Edges.end(); ++ei)
|
||
ps.AddFace(*(*ei).f,ei->EdgeBarycentricToFaceBarycentric(0.5));
|
||
}
|
||
|
||
// Regular Uniform Edge sampling
|
||
// Each edge is subdivided in a number of pieces proprtional to its length
|
||
// Sample are choosen without touching the vertices.
|
||
|
||
static void EdgeUniform(MetroMesh & m, VertexSampler &ps,int sampleNum, bool sampleFauxEdge=true)
|
||
{
|
||
typedef typename UpdateTopology<MetroMesh>::PEdge SimpleEdge;
|
||
std::vector< SimpleEdge > Edges;
|
||
UpdateTopology<MetroMesh>::FillUniqueEdgeVector(m,Edges,sampleFauxEdge);
|
||
// First loop compute total edge length;
|
||
float edgeSum=0;
|
||
typename std::vector< SimpleEdge >::iterator ei;
|
||
for(ei=Edges.begin(); ei!=Edges.end(); ++ei)
|
||
edgeSum+=Distance((*ei).v[0]->P(),(*ei).v[1]->P());
|
||
|
||
float sampleLen = edgeSum/sampleNum;
|
||
float rest=0;
|
||
for(ei=Edges.begin(); ei!=Edges.end(); ++ei)
|
||
{
|
||
float len = Distance((*ei).v[0]->P(),(*ei).v[1]->P());
|
||
float samplePerEdge = floor((len+rest)/sampleLen);
|
||
rest = (len+rest) - samplePerEdge * sampleLen;
|
||
float step = 1.0/(samplePerEdge+1);
|
||
for(int i=0;i<samplePerEdge;++i)
|
||
{
|
||
Point3f interp(0,0,0);
|
||
interp[ (*ei).z ]=step*(i+1);
|
||
interp[((*ei).z+1)%3]=1.0-step*(i+1);
|
||
ps.AddFace(*(*ei).f,interp);
|
||
}
|
||
}
|
||
}
|
||
|
||
// Generate the barycentric coords of a random point over a single face,
|
||
// with a uniform distribution over the triangle.
|
||
// It uses the parallelogram folding trick.
|
||
static CoordType RandomBarycentric()
|
||
{
|
||
return math::GenerateBarycentricUniform<ScalarType>(SamplingRandomGenerator());
|
||
}
|
||
|
||
// Given a triangle return a random point over it
|
||
static CoordType RandomPointInTriangle(const FaceType &f)
|
||
{
|
||
CoordType u = RandomBarycentric();
|
||
return f.cP(0)*u[0] + f.cP(1)*u[1] + f.cP(2)*u[2];
|
||
}
|
||
|
||
static void StratifiedMontecarlo(MetroMesh & m, VertexSampler &ps,int sampleNum)
|
||
{
|
||
ScalarType area = Stat<MetroMesh>::ComputeMeshArea(m);
|
||
ScalarType samplePerAreaUnit = sampleNum/area;
|
||
// Montecarlo sampling.
|
||
double floatSampleNum = 0.0;
|
||
|
||
FaceIterator fi;
|
||
for(fi=m.face.begin(); fi != m.face.end(); fi++)
|
||
if(!(*fi).IsD())
|
||
{
|
||
// compute # samples in the current face (taking into account of the remainders)
|
||
floatSampleNum += 0.5*DoubleArea(*fi) * samplePerAreaUnit;
|
||
int faceSampleNum = (int) floatSampleNum;
|
||
|
||
// for every sample p_i in T...
|
||
for(int i=0; i < faceSampleNum; i++)
|
||
ps.AddFace(*fi,RandomBarycentric());
|
||
floatSampleNum -= (double) faceSampleNum;
|
||
}
|
||
}
|
||
|
||
/**
|
||
This function compute montecarlo distribution with an approximate number of
|
||
samples exploiting the poisson distribution approximation of the binomial distribution.
|
||
|
||
For a given triangle t of area a_t, in a Mesh of area A,
|
||
if we take n_s sample over the mesh, the number of samples that falls in t
|
||
follows the poisson distribution of P(lambda ) with lambda = n_s * (a_t/A).
|
||
|
||
To approximate the Binomial we use a Poisson distribution with parameter
|
||
\lambda = np can be used as an approximation to B(n,p)
|
||
(it works if n is sufficiently large and p is sufficiently small).
|
||
|
||
*/
|
||
|
||
static void MontecarloPoisson(MetroMesh & m, VertexSampler &ps,int sampleNum)
|
||
{
|
||
ScalarType area = Stat<MetroMesh>::ComputeMeshArea(m);
|
||
ScalarType samplePerAreaUnit = sampleNum/area;
|
||
|
||
FaceIterator fi;
|
||
for(fi=m.face.begin(); fi != m.face.end(); fi++)
|
||
if(!(*fi).IsD())
|
||
{
|
||
float areaT=DoubleArea(*fi) * 0.5f;
|
||
int faceSampleNum = Poisson(areaT*samplePerAreaUnit);
|
||
|
||
// for every sample p_i in T...
|
||
for(int i=0; i < faceSampleNum; i++)
|
||
ps.AddFace(*fi,RandomBarycentric());
|
||
// SampleNum -= (double) faceSampleNum;
|
||
}
|
||
}
|
||
|
||
|
||
/**
|
||
This function computes a montecarlo distribution with an EXACT number of samples.
|
||
it works by generating a sequence of consecutive segments proportional to the triangle areas
|
||
and actually shooting sample over this line
|
||
*/
|
||
|
||
static void EdgeMontecarlo(MetroMesh & m, VertexSampler &ps, int sampleNum, bool sampleAllEdges)
|
||
{
|
||
typedef typename UpdateTopology<MetroMesh>::PEdge SimpleEdge;
|
||
std::vector< SimpleEdge > Edges;
|
||
UpdateTopology<MetroMesh>::FillUniqueEdgeVector(m,Edges,sampleAllEdges);
|
||
|
||
assert(!Edges.empty());
|
||
|
||
typedef std::pair<ScalarType, SimpleEdge*> IntervalType;
|
||
std::vector< IntervalType > intervals (Edges.size()+1);
|
||
int i=0;
|
||
intervals[i]=std::make_pair(0,(SimpleEdge*)(0));
|
||
// First loop: build a sequence of consecutive segments proportional to the edge lenghts.
|
||
typename std::vector< SimpleEdge >::iterator ei;
|
||
for(ei=Edges.begin(); ei != Edges.end(); ei++)
|
||
{
|
||
intervals[i+1]=std::make_pair(intervals[i].first+Distance((*ei).v[0]->P(),(*ei).v[1]->P()), &*ei);
|
||
++i;
|
||
}
|
||
|
||
// Second Loop get a point on the line 0...Sum(edgeLen) to pick a point;
|
||
ScalarType edgeSum = intervals.back().first;
|
||
for(i=0;i<sampleNum;++i)
|
||
{
|
||
ScalarType val = edgeSum * RandomDouble01();
|
||
// lower_bound returns the furthermost iterator i in [first, last) such that, for every iterator j in [first, i), *j < value.
|
||
// E.g. An iterator pointing to the first element "not less than" val, or end() if every element is less than val.
|
||
typename std::vector<IntervalType>::iterator it = lower_bound(intervals.begin(),intervals.end(),std::make_pair(val,(SimpleEdge*)(0)) );
|
||
assert(it != intervals.end() && it != intervals.begin());
|
||
assert( ( (*(it-1)).first < val ) && ((*(it)).first >= val) );
|
||
SimpleEdge * ep=(*it).second;
|
||
ps.AddFace( *(ep->f), ep->EdgeBarycentricToFaceBarycentric(RandomDouble01()) );
|
||
}
|
||
}
|
||
|
||
/**
|
||
This function computes a montecarlo distribution with an EXACT number of samples.
|
||
it works by generating a sequence of consecutive segments proportional to the triangle areas
|
||
and actually shooting sample over this line
|
||
*/
|
||
|
||
static void Montecarlo(MetroMesh & m, VertexSampler &ps,int sampleNum)
|
||
{
|
||
typedef std::pair<ScalarType, FacePointer> IntervalType;
|
||
std::vector< IntervalType > intervals (m.fn+1);
|
||
FaceIterator fi;
|
||
int i=0;
|
||
intervals[i]=std::make_pair(0,FacePointer(0));
|
||
// First loop: build a sequence of consecutive segments proportional to the triangle areas.
|
||
for(fi=m.face.begin(); fi != m.face.end(); fi++)
|
||
if(!(*fi).IsD())
|
||
{
|
||
intervals[i+1]=std::make_pair(intervals[i].first+0.5*DoubleArea(*fi), &*fi);
|
||
++i;
|
||
}
|
||
ScalarType meshArea = intervals.back().first;
|
||
for(i=0;i<sampleNum;++i)
|
||
{
|
||
ScalarType val = meshArea * RandomDouble01();
|
||
// lower_bound returns the furthermost iterator i in [first, last) such that, for every iterator j in [first, i), *j < value.
|
||
// E.g. An iterator pointing to the first element "not less than" val, or end() if every element is less than val.
|
||
typename std::vector<IntervalType>::iterator it = lower_bound(intervals.begin(),intervals.end(),std::make_pair(val,FacePointer(0)) );
|
||
assert(it != intervals.end());
|
||
assert(it != intervals.begin());
|
||
assert( (*(it-1)).first <val );
|
||
assert( (*(it)).first >= val);
|
||
ps.AddFace( *(*it).second, RandomBarycentric() );
|
||
}
|
||
}
|
||
|
||
static ScalarType WeightedArea(FaceType f)
|
||
{
|
||
ScalarType averageQ = ( f.V(0)->Q() + f.V(1)->Q() + f.V(2)->Q() ) /3.0;
|
||
return DoubleArea(f)*averageQ/2.0;
|
||
}
|
||
|
||
/// Compute a sampling of the surface that is weighted by the quality
|
||
/// the area of each face is multiplied by the average of the quality of the vertices.
|
||
/// So the a face with a zero quality on all its vertices is never sampled and a face with average quality 2 get twice the samples of a face with the same area but with an average quality of 1;
|
||
static void WeightedMontecarlo(MetroMesh & m, VertexSampler &ps, int sampleNum)
|
||
{
|
||
assert(tri::HasPerVertexQuality(m));
|
||
|
||
ScalarType weightedArea = 0;
|
||
FaceIterator fi;
|
||
for(fi = m.face.begin(); fi != m.face.end(); ++fi)
|
||
if(!(*fi).IsD())
|
||
weightedArea += WeightedArea(*fi);
|
||
|
||
ScalarType samplePerAreaUnit = sampleNum/weightedArea;
|
||
// Montecarlo sampling.
|
||
double floatSampleNum = 0.0;
|
||
for(fi=m.face.begin(); fi != m.face.end(); fi++)
|
||
if(!(*fi).IsD())
|
||
{
|
||
// compute # samples in the current face (taking into account of the remainders)
|
||
floatSampleNum += WeightedArea(*fi) * samplePerAreaUnit;
|
||
int faceSampleNum = (int) floatSampleNum;
|
||
|
||
// for every sample p_i in T...
|
||
for(int i=0; i < faceSampleNum; i++)
|
||
ps.AddFace(*fi,RandomBarycentric());
|
||
|
||
floatSampleNum -= (double) faceSampleNum;
|
||
}
|
||
}
|
||
|
||
|
||
// Subdivision sampling of a single face.
|
||
// return number of added samples
|
||
|
||
static int SingleFaceSubdivision(int sampleNum, const CoordType & v0, const CoordType & v1, const CoordType & v2, VertexSampler &ps, FacePointer fp, bool randSample)
|
||
{
|
||
// recursive face subdivision.
|
||
if(sampleNum == 1)
|
||
{
|
||
// ground case.
|
||
CoordType SamplePoint;
|
||
if(randSample)
|
||
{
|
||
CoordType rb=RandomBarycentric();
|
||
SamplePoint=v0*rb[0]+v1*rb[1]+v2*rb[2];
|
||
}
|
||
else SamplePoint=((v0+v1+v2)*(1.0f/3.0f));
|
||
|
||
ps.AddFace(*fp,SamplePoint);
|
||
return 1;
|
||
}
|
||
|
||
int s0 = sampleNum /2;
|
||
int s1 = sampleNum-s0;
|
||
assert(s0>0);
|
||
assert(s1>0);
|
||
|
||
ScalarType w0 = ScalarType(s1)/ScalarType(sampleNum);
|
||
ScalarType w1 = 1.0-w0;
|
||
// compute the longest edge.
|
||
ScalarType maxd01 = SquaredDistance(v0,v1);
|
||
ScalarType maxd12 = SquaredDistance(v1,v2);
|
||
ScalarType maxd20 = SquaredDistance(v2,v0);
|
||
int res;
|
||
if(maxd01 > maxd12)
|
||
if(maxd01 > maxd20) res = 0;
|
||
else res = 2;
|
||
else
|
||
if(maxd12 > maxd20) res = 1;
|
||
else res = 2;
|
||
|
||
int faceSampleNum=0;
|
||
// break the input triangle along the midpoint of the longest edge.
|
||
CoordType pp;
|
||
switch(res)
|
||
{
|
||
case 0 : pp = v0*w0 + v1*w1;
|
||
faceSampleNum+=SingleFaceSubdivision(s0,v0,pp,v2,ps,fp,randSample);
|
||
faceSampleNum+=SingleFaceSubdivision(s1,pp,v1,v2,ps,fp,randSample);
|
||
break;
|
||
case 1 : pp = v1*w0 + v2*w1;
|
||
faceSampleNum+=SingleFaceSubdivision(s0,v0,v1,pp,ps,fp,randSample);
|
||
faceSampleNum+=SingleFaceSubdivision(s1,v0,pp,v2,ps,fp,randSample);
|
||
break;
|
||
case 2 : pp = v0*w0 + v2*w1;
|
||
faceSampleNum+=SingleFaceSubdivision(s0,v0,v1,pp,ps,fp,randSample);
|
||
faceSampleNum+=SingleFaceSubdivision(s1,pp,v1,v2,ps,fp,randSample);
|
||
break;
|
||
}
|
||
return faceSampleNum;
|
||
}
|
||
|
||
|
||
/// Compute a sampling of the surface where the points are regularly scattered over the face surface using a recursive longest-edge subdivision rule.
|
||
static void FaceSubdivision(MetroMesh & m, VertexSampler &ps,int sampleNum, bool randSample)
|
||
{
|
||
|
||
ScalarType area = Stat<MetroMesh>::ComputeMeshArea(m);
|
||
ScalarType samplePerAreaUnit = sampleNum/area;
|
||
std::vector<FacePointer> faceVec;
|
||
FillAndShuffleFacePointerVector(m,faceVec);
|
||
vcg::tri::UpdateNormal<MetroMesh>::PerFaceNormalized(m);
|
||
double floatSampleNum = 0.0;
|
||
int faceSampleNum;
|
||
// Subdivision sampling.
|
||
typename std::vector<FacePointer>::iterator fi;
|
||
for(fi=faceVec.begin(); fi!=faceVec.end(); fi++)
|
||
{
|
||
const CoordType b0(1.0, 0.0, 0.0);
|
||
const CoordType b1(0.0, 1.0, 0.0);
|
||
const CoordType b2(0.0, 0.0, 1.0);
|
||
// compute # samples in the current face.
|
||
floatSampleNum += 0.5*DoubleArea(**fi) * samplePerAreaUnit;
|
||
faceSampleNum = (int) floatSampleNum;
|
||
if(faceSampleNum>0)
|
||
faceSampleNum = SingleFaceSubdivision(faceSampleNum,b0,b1,b2,ps,*fi,randSample);
|
||
floatSampleNum -= (double) faceSampleNum;
|
||
}
|
||
}
|
||
//---------
|
||
// Subdivision sampling of a single face.
|
||
// return number of added samples
|
||
|
||
static int SingleFaceSubdivisionOld(int sampleNum, const CoordType & v0, const CoordType & v1, const CoordType & v2, VertexSampler &ps, FacePointer fp, bool randSample)
|
||
{
|
||
// recursive face subdivision.
|
||
if(sampleNum == 1)
|
||
{
|
||
// ground case.
|
||
CoordType SamplePoint;
|
||
if(randSample)
|
||
{
|
||
CoordType rb=RandomBarycentric();
|
||
SamplePoint=v0*rb[0]+v1*rb[1]+v2*rb[2];
|
||
}
|
||
else SamplePoint=((v0+v1+v2)*(1.0f/3.0f));
|
||
|
||
CoordType SampleBary;
|
||
InterpolationParameters(*fp,SamplePoint,SampleBary);
|
||
ps.AddFace(*fp,SampleBary);
|
||
return 1;
|
||
}
|
||
|
||
int s0 = sampleNum /2;
|
||
int s1 = sampleNum-s0;
|
||
assert(s0>0);
|
||
assert(s1>0);
|
||
|
||
ScalarType w0 = ScalarType(s1)/ScalarType(sampleNum);
|
||
ScalarType w1 = 1.0-w0;
|
||
// compute the longest edge.
|
||
ScalarType maxd01 = SquaredDistance(v0,v1);
|
||
ScalarType maxd12 = SquaredDistance(v1,v2);
|
||
ScalarType maxd20 = SquaredDistance(v2,v0);
|
||
int res;
|
||
if(maxd01 > maxd12)
|
||
if(maxd01 > maxd20) res = 0;
|
||
else res = 2;
|
||
else
|
||
if(maxd12 > maxd20) res = 1;
|
||
else res = 2;
|
||
|
||
int faceSampleNum=0;
|
||
// break the input triangle along the midpoint of the longest edge.
|
||
CoordType pp;
|
||
switch(res)
|
||
{
|
||
case 0 : pp = v0*w0 + v1*w1;
|
||
faceSampleNum+=SingleFaceSubdivision(s0,v0,pp,v2,ps,fp,randSample);
|
||
faceSampleNum+=SingleFaceSubdivision(s1,pp,v1,v2,ps,fp,randSample);
|
||
break;
|
||
case 1 : pp = v1*w0 + v2*w1;
|
||
faceSampleNum+=SingleFaceSubdivision(s0,v0,v1,pp,ps,fp,randSample);
|
||
faceSampleNum+=SingleFaceSubdivision(s1,v0,pp,v2,ps,fp,randSample);
|
||
break;
|
||
case 2 : pp = v0*w0 + v2*w1;
|
||
faceSampleNum+=SingleFaceSubdivision(s0,v0,v1,pp,ps,fp,randSample);
|
||
faceSampleNum+=SingleFaceSubdivision(s1,pp,v1,v2,ps,fp,randSample);
|
||
break;
|
||
}
|
||
return faceSampleNum;
|
||
}
|
||
|
||
|
||
/// Compute a sampling of the surface where the points are regularly scattered over the face surface using a recursive longest-edge subdivision rule.
|
||
static void FaceSubdivisionOld(MetroMesh & m, VertexSampler &ps,int sampleNum, bool randSample)
|
||
{
|
||
|
||
ScalarType area = Stat<MetroMesh>::ComputeMeshArea(m);
|
||
ScalarType samplePerAreaUnit = sampleNum/area;
|
||
std::vector<FacePointer> faceVec;
|
||
FillAndShuffleFacePointerVector(m,faceVec);
|
||
tri::UpdateNormal<MetroMesh>::PerFaceNormalized(m);
|
||
double floatSampleNum = 0.0;
|
||
int faceSampleNum;
|
||
// Subdivision sampling.
|
||
typename std::vector<FacePointer>::iterator fi;
|
||
for(fi=faceVec.begin(); fi!=faceVec.end(); fi++)
|
||
{
|
||
// compute # samples in the current face.
|
||
floatSampleNum += 0.5*DoubleArea(**fi) * samplePerAreaUnit;
|
||
faceSampleNum = (int) floatSampleNum;
|
||
if(faceSampleNum>0)
|
||
faceSampleNum = SingleFaceSubdivision(faceSampleNum,(**fi).V(0)->cP(), (**fi).V(1)->cP(), (**fi).V(2)->cP(),ps,*fi,randSample);
|
||
floatSampleNum -= (double) faceSampleNum;
|
||
}
|
||
}
|
||
|
||
|
||
//---------
|
||
|
||
// Similar Triangles sampling.
|
||
// Skip vertex and edges
|
||
// Sample per edges includes vertexes, so here we should expect n_samples_per_edge >=4
|
||
|
||
static int SingleFaceSimilar(FacePointer fp, VertexSampler &ps, int n_samples_per_edge)
|
||
{
|
||
int n_samples=0;
|
||
int i, j;
|
||
float segmentNum=n_samples_per_edge -1 ;
|
||
float segmentLen = 1.0/segmentNum;
|
||
// face sampling.
|
||
for(i=1; i < n_samples_per_edge-1; i++)
|
||
for(j=1; j < n_samples_per_edge-1-i; j++)
|
||
{
|
||
//AddSample( v0 + (V1*(double)i + V2*(double)j) );
|
||
CoordType sampleBary(i*segmentLen,j*segmentLen, 1.0 - (i*segmentLen+j*segmentLen) ) ;
|
||
n_samples++;
|
||
ps.AddFace(*fp,sampleBary);
|
||
}
|
||
return n_samples;
|
||
}
|
||
static int SingleFaceSimilarDual(FacePointer fp, VertexSampler &ps, int n_samples_per_edge, bool randomFlag)
|
||
{
|
||
int n_samples=0;
|
||
float i, j;
|
||
float segmentNum=n_samples_per_edge -1 ;
|
||
float segmentLen = 1.0/segmentNum;
|
||
// face sampling.
|
||
for(i=0; i < n_samples_per_edge-1; i++)
|
||
for(j=0; j < n_samples_per_edge-1-i; j++)
|
||
{
|
||
//AddSample( v0 + (V1*(double)i + V2*(double)j) );
|
||
CoordType V0((i+0)*segmentLen,(j+0)*segmentLen, 1.0 - ((i+0)*segmentLen+(j+0)*segmentLen) ) ;
|
||
CoordType V1((i+1)*segmentLen,(j+0)*segmentLen, 1.0 - ((i+1)*segmentLen+(j+0)*segmentLen) ) ;
|
||
CoordType V2((i+0)*segmentLen,(j+1)*segmentLen, 1.0 - ((i+0)*segmentLen+(j+1)*segmentLen) ) ;
|
||
n_samples++;
|
||
if(randomFlag) {
|
||
CoordType rb=RandomBarycentric();
|
||
ps.AddFace(*fp, V0*rb[0]+V1*rb[1]+V2*rb[2]);
|
||
} else ps.AddFace(*fp,(V0+V1+V2)/3.0);
|
||
|
||
if( j < n_samples_per_edge-i-2 )
|
||
{
|
||
CoordType V3((i+1)*segmentLen,(j+1)*segmentLen, 1.0 - ((i+1)*segmentLen+(j+1)*segmentLen) ) ;
|
||
n_samples++;
|
||
if(randomFlag) {
|
||
CoordType rb=RandomBarycentric();
|
||
ps.AddFace(*fp, V3*rb[0]+V1*rb[1]+V2*rb[2]);
|
||
} else ps.AddFace(*fp,(V3+V1+V2)/3.0);
|
||
}
|
||
}
|
||
return n_samples;
|
||
}
|
||
|
||
// Similar sampling
|
||
// Each triangle is subdivided into similar triangles following a generalization of the classical 1-to-4 splitting rule of triangles.
|
||
// According to the level of subdivision <k> you get 1, 4 , 9, 16 , <k^2> triangles.
|
||
// Depending on the kind of the sampling strategies we can have two different approach to choosing the sample points.
|
||
// 1) you have already sampled both edges and vertices
|
||
// 2) you are not going to take samples on edges and vertices.
|
||
//
|
||
// In the first case you have to consider only internal vertices of the subdivided triangles (to avoid multiple sampling of edges and vertices).
|
||
// Therefore the number of internal points is ((k-3)*(k-2))/2. where k is the number of points on an edge (vertex included)
|
||
// E.g. for k=4 you get 3 segments on each edges and the original triangle is subdivided
|
||
// into 9 smaller triangles and you get (1*2)/2 == 1 only a single internal point.
|
||
// So if you want N samples in a triangle you have to solve k^2 -5k +6 - 2N = 0
|
||
// from which you get:
|
||
//
|
||
// 5 + sqrt( 1 + 8N )
|
||
// k = -------------------
|
||
// 2
|
||
//
|
||
// In the second case if you are not interested to skip the sampling on edges and vertices you have to consider as sample number the number of triangles.
|
||
// So if you want N samples in a triangle, the number <k> of points on an edge (vertex included) should be simply:
|
||
// k = 1 + sqrt(N)
|
||
// examples:
|
||
// N = 4 -> k = 3
|
||
// N = 9 -> k = 4
|
||
|
||
|
||
|
||
//template <class MetroMesh>
|
||
//void Sampling<MetroMesh>::SimilarFaceSampling()
|
||
static void FaceSimilar(MetroMesh & m, VertexSampler &ps,int sampleNum, bool dualFlag, bool randomFlag)
|
||
{
|
||
ScalarType area = Stat<MetroMesh>::ComputeMeshArea(m);
|
||
ScalarType samplePerAreaUnit = sampleNum/area;
|
||
|
||
// Similar Triangles sampling.
|
||
int n_samples_per_edge;
|
||
double n_samples_decimal = 0.0;
|
||
FaceIterator fi;
|
||
|
||
for(fi=m.face.begin(); fi != m.face.end(); fi++)
|
||
{
|
||
// compute # samples in the current face.
|
||
n_samples_decimal += 0.5*DoubleArea(*fi) * samplePerAreaUnit;
|
||
int n_samples = (int) n_samples_decimal;
|
||
if(n_samples>0)
|
||
{
|
||
// face sampling.
|
||
if(dualFlag)
|
||
{
|
||
n_samples_per_edge = (int)((sqrt(1.0+8.0*(double)n_samples) +5.0)/2.0); // original for non dual case
|
||
n_samples = SingleFaceSimilar(&*fi,ps, n_samples_per_edge);
|
||
} else {
|
||
n_samples_per_edge = (int)(sqrt((double)n_samples) +1.0);
|
||
n_samples = SingleFaceSimilarDual(&*fi,ps, n_samples_per_edge,randomFlag);
|
||
}
|
||
}
|
||
n_samples_decimal -= (double) n_samples;
|
||
}
|
||
}
|
||
|
||
|
||
// Rasterization fuction
|
||
// Take a triangle
|
||
// T deve essere una classe funzionale che ha l'operatore ()
|
||
// con due parametri x,y di tipo S esempio:
|
||
// class Foo { public void operator()(int x, int y ) { ??? } };
|
||
|
||
// This function does rasterization with a safety buffer area, thus accounting some points actually outside triangle area
|
||
// The safety area samples are generated according to face flag BORDER which should be true for texture space border edges
|
||
// Use correctSafePointsBaryCoords = true to map safety texels to closest point barycentric coords (on edge).
|
||
static void SingleFaceRaster(typename MetroMesh::FaceType &f, VertexSampler &ps,
|
||
const Point2<typename MetroMesh::ScalarType> & v0,
|
||
const Point2<typename MetroMesh::ScalarType> & v1,
|
||
const Point2<typename MetroMesh::ScalarType> & v2,
|
||
bool correctSafePointsBaryCoords=true)
|
||
{
|
||
typedef typename MetroMesh::ScalarType S;
|
||
// Calcolo bounding box
|
||
Box2i bbox;
|
||
Box2<S> bboxf;
|
||
bboxf.Add(v0);
|
||
bboxf.Add(v1);
|
||
bboxf.Add(v2);
|
||
|
||
bbox.min[0] = floor(bboxf.min[0]);
|
||
bbox.min[1] = floor(bboxf.min[1]);
|
||
bbox.max[0] = ceil(bboxf.max[0]);
|
||
bbox.max[1] = ceil(bboxf.max[1]);
|
||
|
||
// Calcolo versori degli spigoli
|
||
Point2<S> d10 = v1 - v0;
|
||
Point2<S> d21 = v2 - v1;
|
||
Point2<S> d02 = v0 - v2;
|
||
|
||
// Preparazione prodotti scalari
|
||
S b0 = (bbox.min[0]-v0[0])*d10[1] - (bbox.min[1]-v0[1])*d10[0];
|
||
S b1 = (bbox.min[0]-v1[0])*d21[1] - (bbox.min[1]-v1[1])*d21[0];
|
||
S b2 = (bbox.min[0]-v2[0])*d02[1] - (bbox.min[1]-v2[1])*d02[0];
|
||
// Preparazione degli steps
|
||
S db0 = d10[1];
|
||
S db1 = d21[1];
|
||
S db2 = d02[1];
|
||
// Preparazione segni
|
||
S dn0 = -d10[0];
|
||
S dn1 = -d21[0];
|
||
S dn2 = -d02[0];
|
||
|
||
//Calculating orientation
|
||
bool flipped = !(d02 * vcg::Point2<S>(-d10[1], d10[0]) >= 0);
|
||
|
||
// Calculating border edges
|
||
Segment2<S> borderEdges[3];
|
||
S edgeLength[3];
|
||
unsigned char edgeMask = 0;
|
||
|
||
if (f.IsB(0)) {
|
||
borderEdges[0] = Segment2<S>(v0, v1);
|
||
edgeLength[0] = borderEdges[0].Length();
|
||
edgeMask |= 1;
|
||
}
|
||
if (f.IsB(1)) {
|
||
borderEdges[1] = Segment2<S>(v1, v2);
|
||
edgeLength[1] = borderEdges[1].Length();
|
||
edgeMask |= 2;
|
||
}
|
||
if (f.IsB(2)) {
|
||
borderEdges[2] = Segment2<S>(v2, v0);
|
||
edgeLength[2] = borderEdges[2].Length();
|
||
edgeMask |= 4;
|
||
}
|
||
|
||
// Rasterizzazione
|
||
double de = v0[0]*v1[1]-v0[0]*v2[1]-v1[0]*v0[1]+v1[0]*v2[1]-v2[0]*v1[1]+v2[0]*v0[1];
|
||
|
||
for(int x=bbox.min[0]-1;x<=bbox.max[0]+1;++x)
|
||
{
|
||
bool in = false;
|
||
S n[3] = { b0-db0-dn0, b1-db1-dn1, b2-db2-dn2};
|
||
for(int y=bbox.min[1]-1;y<=bbox.max[1]+1;++y)
|
||
{
|
||
if( ((n[0]>=0 && n[1]>=0 && n[2]>=0) || (n[0]<=0 && n[1]<=0 && n[2]<=0)) && (de != 0))
|
||
{
|
||
typename MetroMesh::CoordType baryCoord;
|
||
baryCoord[0] = double(-y*v1[0]+v2[0]*y+v1[1]*x-v2[0]*v1[1]+v1[0]*v2[1]-x*v2[1])/de;
|
||
baryCoord[1] = -double( x*v0[1]-x*v2[1]-v0[0]*y+v0[0]*v2[1]-v2[0]*v0[1]+v2[0]*y)/de;
|
||
baryCoord[2] = 1-baryCoord[0]-baryCoord[1];
|
||
|
||
ps.AddTextureSample(f, baryCoord, Point2i(x,y), 0);
|
||
in = true;
|
||
} else {
|
||
// Check whether a pixel outside (on a border edge side) triangle affects color inside it
|
||
Point2<S> px(x, y);
|
||
Point2<S> closePoint;
|
||
int closeEdge = -1;
|
||
S minDst = FLT_MAX;
|
||
|
||
// find the closest point (on some edge) that lies on the 2x2 squared neighborhood of the considered point
|
||
for (int i=0; i<3; ++i)
|
||
{
|
||
if (edgeMask & (1 << i))
|
||
{
|
||
Point2<S> close;
|
||
S dst;
|
||
if ( ((!flipped) && (n[i]<0)) ||
|
||
( flipped && (n[i]>0)) )
|
||
{
|
||
dst = ((close = ClosestPoint(borderEdges[i], px)) - px).Norm();
|
||
if(dst < minDst &&
|
||
close.X() > px.X()-1 && close.X() < px.X()+1 &&
|
||
close.Y() > px.Y()-1 && close.Y() < px.Y()+1)
|
||
{
|
||
minDst = dst;
|
||
closePoint = close;
|
||
closeEdge = i;
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
if (closeEdge >= 0)
|
||
{
|
||
typename MetroMesh::CoordType baryCoord;
|
||
if (correctSafePointsBaryCoords)
|
||
{
|
||
// Add x,y sample with closePoint barycentric coords (on edge)
|
||
baryCoord[closeEdge] = (closePoint - borderEdges[closeEdge].P(1)).Norm()/edgeLength[closeEdge];
|
||
baryCoord[(closeEdge+1)%3] = 1 - baryCoord[closeEdge];
|
||
baryCoord[(closeEdge+2)%3] = 0;
|
||
} else {
|
||
// Add x,y sample with his own barycentric coords (off edge)
|
||
baryCoord[0] = double(-y*v1[0]+v2[0]*y+v1[1]*x-v2[0]*v1[1]+v1[0]*v2[1]-x*v2[1])/de;
|
||
baryCoord[1] = -double( x*v0[1]-x*v2[1]-v0[0]*y+v0[0]*v2[1]-v2[0]*v0[1]+v2[0]*y)/de;
|
||
baryCoord[2] = 1-baryCoord[0]-baryCoord[1];
|
||
}
|
||
ps.AddTextureSample(f, baryCoord, Point2i(x,y), minDst);
|
||
in = true;
|
||
}
|
||
}
|
||
n[0] += dn0;
|
||
n[1] += dn1;
|
||
n[2] += dn2;
|
||
}
|
||
b0 += db0;
|
||
b1 += db1;
|
||
b2 += db2;
|
||
}
|
||
}
|
||
|
||
// check the radius constrain
|
||
static bool checkPoissonDisk(SampleSHT & sht, const Point3<ScalarType> & p, ScalarType radius)
|
||
{
|
||
// get the samples closest to the given one
|
||
std::vector<VertexType*> closests;
|
||
typedef VertTmark<MetroMesh> MarkerVert;
|
||
static MarkerVert mv;
|
||
|
||
Box3f bb(p-Point3f(radius,radius,radius),p+Point3f(radius,radius,radius));
|
||
GridGetInBox(sht, mv, bb, closests);
|
||
|
||
ScalarType r2 = radius*radius;
|
||
for(int i=0; i<closests.size(); ++i)
|
||
if(SquaredDistance(p,closests[i]->cP()) < r2)
|
||
return false;
|
||
|
||
return true;
|
||
}
|
||
|
||
struct PoissonDiskParam
|
||
{
|
||
PoissonDiskParam()
|
||
{
|
||
adaptiveRadiusFlag = false;
|
||
bestSampleChoiceFlag = true;
|
||
bestSamplePoolSize = 10;
|
||
radiusVariance =1;
|
||
MAXLEVELS = 5;
|
||
invertQuality = false;
|
||
preGenFlag = false;
|
||
preGenMesh = NULL;
|
||
geodesicDistanceFlag = false;
|
||
pds=NULL;
|
||
}
|
||
|
||
struct Stat
|
||
{
|
||
int montecarloTime;
|
||
int gridTime;
|
||
int pruneTime;
|
||
int totalTime;
|
||
Point3i gridSize;
|
||
int gridCellNum;
|
||
int sampleNum;
|
||
int montecarloSampleNum;
|
||
};
|
||
|
||
bool geodesicDistanceFlag;
|
||
bool bestSampleChoiceFlag; // In poisson disk pruning when we choose a sample in a cell, we choose the sample that remove the minimal number of other samples. This previlege the "on boundary" samples.
|
||
int bestSamplePoolSize;
|
||
bool adaptiveRadiusFlag;
|
||
float radiusVariance;
|
||
bool invertQuality;
|
||
bool preGenFlag; // when generating a poisson distribution, you can initialize the set of computed points with ALL the vertices of another mesh. Useful for building progressive refinements.
|
||
MetroMesh *preGenMesh;
|
||
int MAXLEVELS;
|
||
|
||
Stat *pds;
|
||
};
|
||
|
||
|
||
// generate Poisson-disk sample using a set of pre-generated samples (with the Montecarlo algorithm)
|
||
// It always return a point.
|
||
static VertexPointer getSampleFromCell(Point3i &cell, MontecarloSHT & samplepool)
|
||
{
|
||
MontecarloSHTIterator cellBegin, cellEnd;
|
||
samplepool.Grid(cell, cellBegin, cellEnd);
|
||
return *cellBegin;
|
||
}
|
||
|
||
// Given a cell of the grid it search the point that remove the minimum number of other samples
|
||
// it linearly scan all the points of a cell.
|
||
|
||
static VertexPointer getBestPrecomputedMontecarloSample(Point3i &cell, MontecarloSHT & samplepool, ScalarType diskRadius, const PoissonDiskParam &pp)
|
||
{
|
||
MontecarloSHTIterator cellBegin,cellEnd;
|
||
samplepool.Grid(cell, cellBegin, cellEnd);
|
||
VertexPointer bestSample=0;
|
||
int minRemoveCnt = std::numeric_limits<int>::max();
|
||
std::vector<typename MontecarloSHT::HashIterator> inSphVec;
|
||
int i=0;
|
||
for(MontecarloSHTIterator ci=cellBegin; ci!=cellEnd && i<pp.bestSamplePoolSize; ++ci,i++)
|
||
{
|
||
VertexPointer sp = *ci;
|
||
if(pp.adaptiveRadiusFlag) diskRadius = sp->Q();
|
||
int curRemoveCnt = samplepool.CountInSphere(sp->cP(),diskRadius,inSphVec);
|
||
if(curRemoveCnt < minRemoveCnt)
|
||
{
|
||
bestSample = sp;
|
||
minRemoveCnt = curRemoveCnt;
|
||
}
|
||
}
|
||
return bestSample;
|
||
}
|
||
|
||
|
||
static ScalarType ComputePoissonDiskRadius(MetroMesh &origMesh, int sampleNum)
|
||
{
|
||
ScalarType meshArea = Stat<MetroMesh>::ComputeMeshArea(origMesh);
|
||
// Manage approximately the PointCloud Case, use the half a area of the bbox.
|
||
// TODO: If you had the radius a much better approximation could be done.
|
||
if(meshArea ==0)
|
||
{
|
||
meshArea = (origMesh.bbox.DimX()*origMesh.bbox.DimY() +
|
||
origMesh.bbox.DimX()*origMesh.bbox.DimZ() +
|
||
origMesh.bbox.DimY()*origMesh.bbox.DimZ());
|
||
}
|
||
ScalarType diskRadius = sqrt(meshArea / (0.7 * M_PI * sampleNum)); // 0.7 is a density factor
|
||
return diskRadius;
|
||
}
|
||
|
||
static int ComputePoissonSampleNum(MetroMesh &origMesh, ScalarType diskRadius)
|
||
{
|
||
ScalarType meshArea = Stat<MetroMesh>::ComputeMeshArea(origMesh);
|
||
int sampleNum = meshArea / (diskRadius*diskRadius *M_PI *0.7) ; // 0.7 is a density factor
|
||
return sampleNum;
|
||
}
|
||
|
||
// Note that this function actually CHANGE the quality of the montecarlo samples so that it represents the expected radius of each sample
|
||
// the expected radius of the sample is computed so that it linearly maps the quality between diskradius / variance and diskradius*variance
|
||
|
||
static void ComputePoissonSampleRadii(MetroMesh &sampleMesh, ScalarType diskRadius, ScalarType radiusVariance, bool invert)
|
||
{
|
||
VertexIterator vi;
|
||
std::pair<float,float> minmax = tri::Stat<MetroMesh>::ComputePerVertexQualityMinMax( sampleMesh);
|
||
float minRad = diskRadius / radiusVariance;
|
||
float maxRad = diskRadius * radiusVariance;
|
||
float deltaQ = minmax.second-minmax.first;
|
||
float deltaRad = maxRad-minRad;
|
||
for (vi = sampleMesh.vert.begin(); vi != sampleMesh.vert.end(); vi++)
|
||
{
|
||
(*vi).Q() = minRad + deltaRad*((invert ? minmax.second - (*vi).Q() : (*vi).Q() - minmax.first )/deltaQ);
|
||
}
|
||
}
|
||
|
||
// initialize spatial hash table for searching
|
||
// radius is the radius of empty disk centered over the samples (e.g. twice of the empty space disk)
|
||
// This radius implies that when we pick a sample in a cell all that cell probably will not be touched again.
|
||
// Howvever we must ensure that we do not put too many vertices inside each hash cell
|
||
|
||
static void InitSpatialHashTable(MetroMesh &montecarloMesh, MontecarloSHT &montecarloSHT, ScalarType diskRadius,
|
||
const struct PoissonDiskParam pp=PoissonDiskParam())
|
||
{
|
||
ScalarType cellsize = 2.0f* diskRadius / sqrt(3.0);
|
||
float occupancyRatio=0;
|
||
do
|
||
{
|
||
// inflating
|
||
BoxType bb=montecarloMesh.bbox;
|
||
assert(!bb.IsNull());
|
||
bb.Offset(cellsize);
|
||
|
||
int sizeX = std::max(1.0f,bb.DimX() / cellsize);
|
||
int sizeY = std::max(1.0f,bb.DimY() / cellsize);
|
||
int sizeZ = std::max(1.0f,bb.DimZ() / cellsize);
|
||
Point3i gridsize(sizeX, sizeY, sizeZ);
|
||
|
||
montecarloSHT.InitEmpty(bb, gridsize);
|
||
|
||
for (VertexIterator vi = montecarloMesh.vert.begin(); vi != montecarloMesh.vert.end(); vi++)
|
||
montecarloSHT.Add(&(*vi));
|
||
montecarloSHT.UpdateAllocatedCells();
|
||
if(pp.pds)
|
||
{
|
||
pp.pds->gridSize = gridsize;
|
||
pp.pds->gridCellNum = (int)montecarloSHT.AllocatedCells.size();
|
||
}
|
||
cellsize/=2.0f;
|
||
occupancyRatio = float(montecarloMesh.vn) / float(montecarloSHT.AllocatedCells.size());
|
||
// qDebug(" %i / %i = %6.3f", montecarloMesh.vn , montecarloSHT.AllocatedCells.size(),occupancyRatio);
|
||
}
|
||
while( occupancyRatio> 100);
|
||
}
|
||
|
||
// Trivial approach that puts all the samples in a UG and removes all the ones that surely do not fit the
|
||
static void PoissonDiskPruning(VertexSampler &ps, MetroMesh &montecarloMesh,
|
||
ScalarType diskRadius, const struct PoissonDiskParam pp=PoissonDiskParam())
|
||
{
|
||
int t0 = clock();
|
||
// spatial index of montecarlo samples - used to choose a new sample to insert
|
||
MontecarloSHT montecarloSHT;
|
||
InitSpatialHashTable(montecarloMesh,montecarloSHT,diskRadius,pp);
|
||
|
||
// if we are doing variable density sampling we have to prepare the random samples quality with the correct expected radii.
|
||
// At this point we just assume that there is the quality values as sampled from the base mesh
|
||
if(pp.adaptiveRadiusFlag)
|
||
ComputePoissonSampleRadii(montecarloMesh, diskRadius, pp.radiusVariance, pp.invertQuality);
|
||
|
||
unsigned int (*p_myrandom)(unsigned int) = RandomInt;
|
||
std::random_shuffle(montecarloSHT.AllocatedCells.begin(),montecarloSHT.AllocatedCells.end(), p_myrandom);
|
||
int t1 = clock();
|
||
if(pp.pds) {
|
||
pp.pds->montecarloSampleNum = montecarloMesh.vn;
|
||
}
|
||
int removedCnt=0;
|
||
if(pp.preGenFlag && pp.preGenMesh !=0)
|
||
{
|
||
// Initial pass for pruning the Hashed grid with the an eventual pre initialized set of samples
|
||
for(VertexIterator vi =pp.preGenMesh->vert.begin(); vi!=pp.preGenMesh->vert.end();++vi)
|
||
{
|
||
ps.AddVert(*vi);
|
||
removedCnt += montecarloSHT.RemoveInSphere(vi->cP(),diskRadius);
|
||
}
|
||
montecarloSHT.UpdateAllocatedCells();
|
||
}
|
||
vertex::ApproximateGeodesicDistanceFunctor<VertexType> GDF;
|
||
while(!montecarloSHT.AllocatedCells.empty())
|
||
{
|
||
removedCnt=0;
|
||
for (size_t i = 0; i < montecarloSHT.AllocatedCells.size(); i++)
|
||
{
|
||
if( montecarloSHT.EmptyCell(montecarloSHT.AllocatedCells[i]) ) continue;
|
||
ScalarType currentRadius =diskRadius;
|
||
VertexPointer sp;
|
||
if(pp.bestSampleChoiceFlag)
|
||
sp = getBestPrecomputedMontecarloSample(montecarloSHT.AllocatedCells[i], montecarloSHT, diskRadius, pp);
|
||
else
|
||
sp = getSampleFromCell(montecarloSHT.AllocatedCells[i], montecarloSHT);
|
||
|
||
if(pp.adaptiveRadiusFlag)
|
||
currentRadius = sp->Q();
|
||
|
||
ps.AddVert(*sp);
|
||
if(pp.geodesicDistanceFlag) removedCnt += montecarloSHT.RemoveInSphereNormal(sp->cP(),sp->cN(),GDF,currentRadius);
|
||
else removedCnt += montecarloSHT.RemoveInSphere(sp->cP(),currentRadius);
|
||
}
|
||
montecarloSHT.UpdateAllocatedCells();
|
||
}
|
||
int t2 = clock();
|
||
if(pp.pds)
|
||
{
|
||
pp.pds->gridTime = t1-t0;
|
||
pp.pds->pruneTime = t2-t1;
|
||
}
|
||
}
|
||
|
||
/** Compute a Poisson-disk sampling of the surface.
|
||
* The radius of the disk is computed according to the estimated sampling density.
|
||
*
|
||
* This algorithm is an adaptation of the algorithm of White et al. :
|
||
*
|
||
* "Poisson Disk Point Set by Hierarchical Dart Throwing"
|
||
* K. B. White, D. Cline, P. K. Egbert,
|
||
* IEEE Symposium on Interactive Ray Tracing, 2007,
|
||
* 10-12 Sept. 2007, pp. 129-132.
|
||
*/
|
||
static void HierarchicalPoissonDisk(MetroMesh &origMesh, VertexSampler &ps, MetroMesh &montecarloMesh, ScalarType diskRadius, const struct PoissonDiskParam pp=PoissonDiskParam())
|
||
{
|
||
// int t0=clock();
|
||
// spatial index of montecarlo samples - used to choose a new sample to insert
|
||
MontecarloSHT montecarloSHTVec[5];
|
||
|
||
|
||
|
||
// initialize spatial hash table for searching
|
||
// radius is the radius of empty disk centered over the samples (e.g. twice of the empty space disk)
|
||
// This radius implies that when we pick a sample in a cell all that cell will not be touched again.
|
||
ScalarType cellsize = 2.0f* diskRadius / sqrt(3.0);
|
||
|
||
// inflating
|
||
BoxType bb=origMesh.bbox;
|
||
bb.Offset(cellsize);
|
||
|
||
int sizeX = std::max(1.0f,bb.DimX() / cellsize);
|
||
int sizeY = std::max(1.0f,bb.DimY() / cellsize);
|
||
int sizeZ = std::max(1.0f,bb.DimZ() / cellsize);
|
||
Point3i gridsize(sizeX, sizeY, sizeZ);
|
||
|
||
// spatial hash table of the generated samples - used to check the radius constrain
|
||
SampleSHT checkSHT;
|
||
checkSHT.InitEmpty(bb, gridsize);
|
||
|
||
|
||
// sampling algorithm
|
||
// ------------------
|
||
//
|
||
// - generate millions of samples using montecarlo algorithm
|
||
// - extract a cell (C) from the active cell list (with probability proportional to cell's volume)
|
||
// - generate a sample inside C by choosing one of the contained pre-generated samples
|
||
// - if the sample violates the radius constrain discard it, and add the cell to the cells-to-subdivide list
|
||
// - iterate until the active cell list is empty or a pre-defined number of subdivisions is reached
|
||
//
|
||
|
||
int level = 0;
|
||
|
||
// initialize spatial hash to index pre-generated samples
|
||
montecarloSHTVec[0].InitEmpty(bb, gridsize);
|
||
// create active cell list
|
||
for (VertexIterator vi = montecarloMesh.vert.begin(); vi != montecarloMesh.vert.end(); vi++)
|
||
montecarloSHTVec[0].Add(&(*vi));
|
||
montecarloSHTVec[0].UpdateAllocatedCells();
|
||
|
||
// if we are doing variable density sampling we have to prepare the random samples quality with the correct expected radii.
|
||
if(pp.adaptiveRadiusFlag)
|
||
ComputePoissonSampleRadii(montecarloMesh, diskRadius, pp.radiusVariance, pp.invertQuality);
|
||
|
||
do
|
||
{
|
||
MontecarloSHT &montecarloSHT = montecarloSHTVec[level];
|
||
|
||
if(level>0)
|
||
{// initialize spatial hash with the remaining points
|
||
montecarloSHT.InitEmpty(bb, gridsize);
|
||
// create active cell list
|
||
for (typename MontecarloSHT::HashIterator hi = montecarloSHTVec[level-1].hash_table.begin(); hi != montecarloSHTVec[level-1].hash_table.end(); hi++)
|
||
montecarloSHT.Add((*hi).second);
|
||
montecarloSHT.UpdateAllocatedCells();
|
||
}
|
||
// shuffle active cells
|
||
unsigned int (*p_myrandom)(unsigned int) = RandomInt;
|
||
std::random_shuffle(montecarloSHT.AllocatedCells.begin(),montecarloSHT.AllocatedCells.end(), p_myrandom);
|
||
|
||
// generate a sample inside C by choosing one of the contained pre-generated samples
|
||
//////////////////////////////////////////////////////////////////////////////////////////
|
||
int removedCnt=montecarloSHT.hash_table.size();
|
||
int addedCnt=checkSHT.hash_table.size();
|
||
for (int i = 0; i < montecarloSHT.AllocatedCells.size(); i++)
|
||
{
|
||
for(int j=0;j<4;j++)
|
||
{
|
||
if( montecarloSHT.EmptyCell(montecarloSHT.AllocatedCells[i]) ) continue;
|
||
|
||
// generate a sample chosen from the pre-generated one
|
||
typename MontecarloSHT::HashIterator hi = montecarloSHT.hash_table.find(montecarloSHT.AllocatedCells[i]);
|
||
|
||
if(hi==montecarloSHT.hash_table.end()) {break;}
|
||
VertexPointer sp = (*hi).second;
|
||
// vr spans between 3.0*r and r / 4.0 according to vertex quality
|
||
ScalarType sampleRadius = diskRadius;
|
||
if(pp.adaptiveRadiusFlag) sampleRadius = sp->Q();
|
||
if (checkPoissonDisk(checkSHT, sp->cP(), sampleRadius))
|
||
{
|
||
ps.AddVert(*sp);
|
||
montecarloSHT.RemoveCell(sp);
|
||
checkSHT.Add(sp);
|
||
break;
|
||
}
|
||
else
|
||
montecarloSHT.RemovePunctual(sp);
|
||
}
|
||
}
|
||
addedCnt = checkSHT.hash_table.size()-addedCnt;
|
||
removedCnt = removedCnt-montecarloSHT.hash_table.size();
|
||
|
||
// proceed to the next level of subdivision
|
||
// increase grid resolution
|
||
gridsize *= 2;
|
||
|
||
//
|
||
level++;
|
||
} while(level < 5);
|
||
}
|
||
|
||
//template <class MetroMesh>
|
||
//void Sampling<MetroMesh>::SimilarFaceSampling()
|
||
|
||
// This function also generates samples outside faces if those affects faces in texture space.
|
||
// Use correctSafePointsBaryCoords = true to map safety texels to closest point barycentric coords (on edge)
|
||
// otherwise obtained samples will map to barycentric coord actually outside face
|
||
//
|
||
// If you don't need to get those extra points clear faces Border Flags
|
||
// vcg::tri::UpdateFlags<Mesh>::FaceClearB(m);
|
||
//
|
||
// Else make sure to update border flags from texture space FFadj
|
||
// vcg::tri::UpdateTopology<Mesh>::FaceFaceFromTexCoord(m);
|
||
// vcg::tri::UpdateFlags<Mesh>::FaceBorderFromFF(m);
|
||
static void Texture(MetroMesh & m, VertexSampler &ps, int textureWidth, int textureHeight, bool correctSafePointsBaryCoords=true)
|
||
{
|
||
FaceIterator fi;
|
||
|
||
printf("Similar Triangles face sampling\n");
|
||
for(fi=m.face.begin(); fi != m.face.end(); fi++)
|
||
if (!fi->IsD())
|
||
{
|
||
Point2f ti[3];
|
||
for(int i=0;i<3;++i)
|
||
ti[i]=Point2f((*fi).WT(i).U() * textureWidth - 0.5, (*fi).WT(i).V() * textureHeight - 0.5);
|
||
// - 0.5 constants are used to obtain correct texture mapping
|
||
|
||
SingleFaceRaster(*fi, ps, ti[0],ti[1],ti[2], correctSafePointsBaryCoords);
|
||
}
|
||
}
|
||
|
||
typedef GridStaticPtr<FaceType, ScalarType > TriMeshGrid;
|
||
|
||
class RRParam
|
||
{
|
||
public:
|
||
float offset;
|
||
float minDiag;
|
||
tri::FaceTmark<MetroMesh> markerFunctor;
|
||
TriMeshGrid gM;
|
||
};
|
||
|
||
static void RegularRecursiveOffset(MetroMesh & m, std::vector<Point3f> &pvec, ScalarType offset, float minDiag)
|
||
{
|
||
Box3<ScalarType> bb=m.bbox;
|
||
bb.Offset(offset*2.0);
|
||
|
||
RRParam rrp;
|
||
|
||
rrp.markerFunctor.SetMesh(&m);
|
||
|
||
rrp.gM.Set(m.face.begin(),m.face.end(),bb);
|
||
|
||
|
||
rrp.offset=offset;
|
||
rrp.minDiag=minDiag;
|
||
SubdivideAndSample(m, pvec, bb, rrp, bb.Diag());
|
||
}
|
||
|
||
static void SubdivideAndSample(MetroMesh & m, std::vector<Point3f> &pvec, const Box3<ScalarType> bb, RRParam &rrp, float curDiag)
|
||
{
|
||
Point3f startPt = bb.Center();
|
||
|
||
ScalarType dist;
|
||
// Compute mesh point nearest to bb center
|
||
FaceType *nearestF=0;
|
||
float dist_upper_bound = curDiag+rrp.offset;
|
||
Point3f closestPt;
|
||
vcg::face::PointDistanceBaseFunctor<ScalarType> PDistFunct;
|
||
dist=dist_upper_bound;
|
||
nearestF = rrp.gM.GetClosest(PDistFunct,rrp.markerFunctor,startPt,dist_upper_bound,dist,closestPt);
|
||
curDiag /=2;
|
||
if(dist < dist_upper_bound)
|
||
{
|
||
if(curDiag/3 < rrp.minDiag) //store points only for the last level of recursion (?)
|
||
{
|
||
if(rrp.offset==0)
|
||
pvec.push_back(closestPt);
|
||
else
|
||
{
|
||
if(dist>rrp.offset) // points below the offset threshold cannot be displaced at the right offset distance, we can only make points nearer.
|
||
{
|
||
Point3f delta = startPt-closestPt;
|
||
pvec.push_back(closestPt+delta*(rrp.offset/dist));
|
||
}
|
||
}
|
||
}
|
||
if(curDiag < rrp.minDiag) return;
|
||
Point3f hs = (bb.max-bb.min)/2;
|
||
for(int i=0;i<2;i++)
|
||
for(int j=0;j<2;j++)
|
||
for(int k=0;k<2;k++)
|
||
SubdivideAndSample(m,pvec,
|
||
Box3f(Point3f( bb.min[0]+i*hs[0], bb.min[1]+j*hs[1], bb.min[2]+k*hs[2]),
|
||
Point3f(startPt[0]+i*hs[0],startPt[1]+j*hs[1],startPt[2]+k*hs[2])),rrp,curDiag);
|
||
|
||
}
|
||
}
|
||
}; // end class
|
||
|
||
|
||
template <class MeshType>
|
||
void MontecarloSampling(MeshType &m, // the mesh that has to be sampled
|
||
MeshType &mm, // the mesh that will contain the samples
|
||
int sampleNum) // the desired number sample, if zero you must set the radius to the wanted value
|
||
{
|
||
typedef tri::MeshSampler<MeshType> BaseSampler;
|
||
MeshSampler<MeshType> mcSampler(&mm);
|
||
tri::SurfaceSampling<MeshType,BaseSampler>::Montecarlo(m, mcSampler, sampleNum);
|
||
}
|
||
|
||
|
||
template <class MeshType>
|
||
void MontecarloSampling(MeshType &m, // the mesh that has to be sampled
|
||
std::vector<Point3f> &montercarloSamples, // the vector that will contain the set of points
|
||
int sampleNum) // the desired number sample, if zero you must set the radius to the wanted value
|
||
{
|
||
typedef tri::TrivialSampler<MeshType> BaseSampler;
|
||
BaseSampler mcSampler(montercarloSamples);
|
||
tri::SurfaceSampling<MeshType,BaseSampler>::Montecarlo(m, mcSampler, sampleNum);
|
||
}
|
||
|
||
// Yet another simpler wrapper for the generation of a poisson disk distribution over a mesh.
|
||
//
|
||
template <class MeshType>
|
||
void PoissonSampling(MeshType &m, // the mesh that has to be sampled
|
||
std::vector<Point3f> &poissonSamples, // the vector that will contain the set of points
|
||
int sampleNum, // the desired number sample, if zero you must set the radius to the wanted value
|
||
float &radius, // the Poisson Disk Radius (used if sampleNum==0, setted if sampleNum!=0)
|
||
float radiusVariance=1)
|
||
|
||
{
|
||
typedef tri::TrivialSampler<MeshType> BaseSampler;
|
||
typedef tri::MeshSampler<MeshType> MontecarloSampler;
|
||
|
||
typename tri::SurfaceSampling<MeshType, BaseSampler>::PoissonDiskParam pp;
|
||
typename tri::SurfaceSampling<MeshType, BaseSampler>::PoissonDiskParam::Stat stat;
|
||
pp.pds = &stat;
|
||
int t0=clock();
|
||
|
||
if(sampleNum>0) radius = tri::SurfaceSampling<MeshType,BaseSampler>::ComputePoissonDiskRadius(m,sampleNum);
|
||
if(radius>0 && sampleNum==0) sampleNum = tri::SurfaceSampling<MeshType,BaseSampler>::ComputePoissonSampleNum(m,radius);
|
||
|
||
pp.pds->sampleNum = sampleNum;
|
||
poissonSamples.clear();
|
||
// std::vector<Point3f> MontecarloSamples;
|
||
MeshType MontecarloMesh;
|
||
|
||
// First step build the sampling
|
||
MontecarloSampler mcSampler(MontecarloMesh);
|
||
BaseSampler pdSampler(poissonSamples);
|
||
|
||
tri::SurfaceSampling<MeshType,MontecarloSampler>::Montecarlo(m, mcSampler, std::max(10000,sampleNum*40));
|
||
tri::UpdateBounding<MeshType>::Box(MontecarloMesh);
|
||
// tri::Build(MontecarloMesh, MontecarloSamples);
|
||
int t1=clock();
|
||
pp.pds->montecarloTime = t1-t0;
|
||
|
||
if(radiusVariance !=1)
|
||
{
|
||
pp.adaptiveRadiusFlag=true;
|
||
pp.radiusVariance=radiusVariance;
|
||
}
|
||
tri::SurfaceSampling<MeshType,BaseSampler>::PoissonDiskPruning(pdSampler, MontecarloMesh, radius,pp);
|
||
int t2=clock();
|
||
pp.pds->totalTime = t2-t0;
|
||
}
|
||
// simpler wrapper for the pruning
|
||
//
|
||
template <class MeshType>
|
||
void PoissonPruning(MeshType &m, // the mesh that has to be pruned
|
||
std::vector<Point3f> &poissonSamples, // the vector that will contain the chosen set of points
|
||
float & radius)
|
||
{
|
||
typedef tri::TrivialSampler<MeshType> BaseSampler;
|
||
typename tri::SurfaceSampling<MeshType, BaseSampler>::PoissonDiskParam pp;
|
||
typename tri::SurfaceSampling<MeshType, BaseSampler>::PoissonDiskParam::Stat stat;
|
||
pp.pds = &stat;
|
||
tri::UpdateBounding<MeshType>::Box(m);
|
||
BaseSampler pdSampler(poissonSamples);
|
||
tri::SurfaceSampling<MeshType,BaseSampler>::PoissonDiskPruning(pdSampler, m, radius,pp);
|
||
}
|
||
} // end namespace tri
|
||
} // end namespace vcg
|
||
|
||
#endif
|
||
|