SpectralDecomposition.cpp

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// kate: replace-tabs off; indent-width 4; indent-mode normal
// vim: ts=4:sw=4:noexpandtab
/*
 
Copyright (c) 2010--2018,
François Pomerleau and Stephane Magnenat, ASL, ETHZ, Switzerland
You can contact the authors at <f dot pomerleau at gmail dot com> and
<stephane at magnenat dot net>
 
All rights reserved.
 
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modification, are permitted provided that the following conditions are met:
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      names of its contributors may be used to endorse or promote products
      derived from this software without specific prior written permission.
 
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*/
#include "SpectralDecomposition.h"
 
#include <random>
 
// SpectralDecomposition
template <typename T>
SpectralDecompositionDataPointsFilter<T>::SpectralDecompositionDataPointsFilter(const Parameters& params) :
        PointMatcher<T>::DataPointsFilter("SpectralDecompositionDataPointsFilter", 
                SpectralDecompositionDataPointsFilter::availableParameters(), params),
        k{Parametrizable::get<std::size_t>("k")},
        sigma{Parametrizable::get<T>("sigma")},
        radius{Parametrizable::get<T>("radius")},
        itMax{Parametrizable::get<std::size_t>("itMax")},
        keepNormals{Parametrizable::get<bool>("keepNormals")},
        keepLabels{Parametrizable::get<bool>("keepLabels")},
        keepLambdas{Parametrizable::get<bool>("keepLambdas")},
        keepTensors{Parametrizable::get<bool>("keepTensors")}
{
}
 
template <typename T>
typename PointMatcher<T>::DataPoints
SpectralDecompositionDataPointsFilter<T>::filter(const DataPoints& input)
{
        DataPoints output(input);
        inPlaceFilter(output);
        return output;
}
 
template <typename T>
void SpectralDecompositionDataPointsFilter<T>::inPlaceFilter(DataPoints& cloud)
{
        const std::size_t nbPts = cloud.getNbPoints();
        
        if(k > nbPts) return;
        
        TensorVoting<T> tv{sigma, k};
        
//--- 1. Vote to determine prefered orientation + density estimation -----------
        tv.encode(cloud, TensorVoting<T>::Encoding::BALL);
        tv.cfvote(cloud, true);
        tv.decompose();
        tv.toDescriptors();
        addDescriptor(cloud, tv, false /*normals*/, false /*labels*/, true /*lambdas*/, false /*tensors*/);
        
//--- 2. Filter iteratively on each measure (surfaceness, curveness, pointness) to uniformize density
        std::size_t it = 0;
        const std::size_t itMax_ = itMax;
        const std::size_t k_ = k;
        std::size_t oldnbPts = nbPts;
        
        auto checkConvergence = [&oldnbPts, &it, &itMax_, &k_](const DataPoints& pts, const std::size_t threshold) mutable ->bool{              
                const std::size_t nbPts = pts.getNbPoints();
                bool ret = (oldnbPts - nbPts) < threshold;
                
                oldnbPts = nbPts;
                
                return ret or ++it >= itMax_ or k_ >= nbPts;            
        };
        
        const T xi3 = xi_expectation(3, sigma, radius);
        const T xi2 = xi_expectation(2, sigma, radius);
        const T xi1 = xi_expectation(1, sigma, radius);
        
        do 
        {       
        // 2.1 On pointness
                filterPointness(cloud, xi3, tv.k);
        // 2.2 On curveness
                filterCurveness(cloud, xi1, tv.k);
        // 2.3 On surfaceness
                filterSurfaceness(cloud, xi2, tv.k);
 
        //Re-compute vote...
                tv.encode(cloud, TensorVoting<T>::Encoding::BALL);
                tv.cfvote(cloud, true);
                tv.decompose();
                tv.toDescriptors();
                
                addDescriptor(cloud, tv, false /*normals*/, false /*labels*/, true /*lambdas*/, false /*tensors*/);
        } 
        while(not checkConvergence(cloud, 5 /*delta points*/));
        
//--- 3. Re-encode as Aware tensors + Re-vote ----------------------------------
        addDescriptor(cloud, tv, false /*normals*/, false /*labels*/, false /*lambdas*/, true /*tensors*/);
        tv.encode(cloud, TensorVoting<T>::Encoding::AWARE_TENSOR);
        tv.cfvote(cloud);
        tv.decompose();
        tv.toDescriptors();
 
//--- 4. Add descriptors
        addDescriptor(cloud, tv, keepNormals, true, keepLambdas, keepTensors);//TODO: add remove not kept descriptors
        
//--- 5. Remove outliers
        removeOutlier(cloud, tv);
}
 
 
template <typename T>
void SpectralDecompositionDataPointsFilter<T>::addDescriptor(DataPoints& pts, const TensorVoting<T> &tv, bool keepNormals_, bool keepLabels_, bool keepLambdas_, bool keepTensors_) const
{
        const std::size_t nbPts = pts.getNbPoints();
 
        Matrix labels  = Matrix::Zero(1, nbPts);
        Matrix l1  = PM::Matrix::Zero(1, nbPts);        
        Matrix l2  = PM::Matrix::Zero(1, nbPts);        
        Matrix l3  = PM::Matrix::Zero(1, nbPts);
        
        if(keepLabels_ or keepLambdas_)
        {
        #pragma omp parallel for
                for(std::size_t i = 0; i < nbPts; ++i)
                {
                        const T lambda1 = tv.surfaceness(i) + tv.curveness(i) + tv.pointness(i);
                        const T lambda2 = tv.curveness(i) + tv.pointness(i);
                        const T lambda3 = tv.pointness(i);
        
                        int index;
                        Vector coeff = (Vector(3) << lambda3, (lambda2 - lambda3), (lambda1 - lambda2)).finished();
                        coeff.maxCoeff(&index);  
 
                        labels(i) = index + 1 ;
                
                        l1(i) = lambda1 * k;
                        l2(i) = lambda2 * k;
                        l3(i) = lambda3 * k;
                }
        }
        try
        {       
            pts.addDescriptor("surfaceness", tv.surfaceness);
            pts.addDescriptor("curveness", tv.curveness);
            pts.addDescriptor("pointness", tv.pointness);
            
            if(keepLambdas_)
            {
                        pts.addDescriptor("lambda1", l1);
                        pts.addDescriptor("lambda2", l2);
                        pts.addDescriptor("lambda3", l3);
            }
            
                if(keepNormals_)
                {    
                        pts.addDescriptor("normals", tv.normals);
                        pts.addDescriptor("tangents", tv.tangents);
                }    
            if(keepLabels_)
            {
                        pts.addDescriptor("labels", labels);
            }  
            if(keepTensors_)
            {
                        pts.addDescriptor("sticks", tv.sticks);
                        pts.addDescriptor("plates", tv.plates);
                        pts.addDescriptor("balls", tv.balls);
            }
        }
        catch (...) {
            std::cerr << "SpectralDecomposition<T>::inPlaceFilter::addDescriptor: Cannot add descriptors to pointcloud" << std::endl;
        }
 
}
 
 
//------------------------------------------------------------------------------
// Outlier filter
//------------------------------------------------------------------------------
template <typename T>
void SpectralDecompositionDataPointsFilter<T>::removeOutlier(DataPoints& pts, const TensorVoting<T> &tv) const
{
        static constexpr int POINT = 0;
        static constexpr int CURVE = 1;
        static constexpr int SURFACE = 2;
        
        static constexpr T th = 0.1; //threshold at 10%
        
        const std::size_t nbPts = pts.getNbPoints();
                
        const T th_p = (tv.pointness.maxCoeff() - tv.pointness.minCoeff()) * th + tv.pointness.minCoeff();
        const T th_c = (tv.curveness.maxCoeff() - tv.curveness.minCoeff()) * th + tv.curveness.minCoeff();
        const T th_s = (tv.surfaceness.maxCoeff() - tv.surfaceness.minCoeff()) * th + tv.surfaceness.minCoeff();
        
        
        std::size_t j = 0;
        for (std::size_t i = 0; i < nbPts; ++i)
        {
                const T surfaceness = tv.surfaceness(i);
                const T curveness = tv.curveness(i);
                const T pointness = tv.pointness(i);
 
                int label;
                (Vector(3) << pointness, curveness, surfaceness).finished().maxCoeff(&label); 
 
                bool keepPt = ((label == POINT) and  (pointness >= th_p)) 
                        or ((label == CURVE) and  (curveness >= th_c)) 
                        or ((label == SURFACE) and  (surfaceness >= th_s));
                
                if (keepPt)
                {
                        pts.setColFrom(j, pts, i);
                        ++j;
                }
        }
        pts.conservativeResize(j);      
}
 
//------------------------------------------------------------------------------
// Filter
//------------------------------------------------------------------------------
template <typename T>
void SpectralDecompositionDataPointsFilter<T>::filterSurfaceness(DataPoints& pts, T xi, std::size_t k) const
{
        constexpr std::size_t seed = 1;
        std::mt19937 gen(seed); //Standard mersenne_twister_engine seeded with seed
        std::uniform_real_distribution<> uni01(0., 1.);
        
        const std::size_t nbPts = pts.getNbPoints();
        
        // Check field exists
        if (!pts.descriptorExists("lambda1") or !pts.descriptorExists("lambda2") or !pts.descriptorExists("lambda3"))
        {
                throw InvalidField("SpectralDecomposition<T>::filter: Error, lambdas field not found in descriptors.");
        }
        
        const auto& lambda1 = pts.getDescriptorViewByName("lambda1");
        const auto& lambda2 = pts.getDescriptorViewByName("lambda2");
        const auto& lambda3 = pts.getDescriptorViewByName("lambda3");
 
        std::size_t j = 0;
        for (std::size_t i = 0; i < nbPts; ++i)
        {
                const T randv = uni01(gen);     
                
                const T nl1 = lambda1(0,i) / k;
                const T nl2 = lambda2(0,i) / k;
                const T nl3 = lambda3(0,i) / k;
                
                if (nl1 < xi or nl2 < 0.75 * xi or nl3 < 0.75 * xi or randv < 0.5)
                {
                        pts.setColFrom(j, pts, i);
                        ++j;
                }
        }
        pts.conservativeResize(j);
}
 
template <typename T>
void SpectralDecompositionDataPointsFilter<T>::filterCurveness(DataPoints& pts, T xi, std::size_t k) const
{
        constexpr std::size_t seed = 1;
        std::mt19937 gen(seed); //Standard mersenne_twister_engine seeded with seed
        std::uniform_real_distribution<> uni01(0., 1.);
        
        const std::size_t nbPts = pts.getNbPoints();
        
        // Check field exists
        if (!pts.descriptorExists("lambda1") or !pts.descriptorExists("lambda2") or !pts.descriptorExists("lambda3"))
        {
                throw InvalidField("SpectralDecomposition<T>::filter: Error, lambdas field not found in descriptors.");
        }
        
        const auto& lambda1 = pts.getDescriptorViewByName("lambda1");
        const auto& lambda2 = pts.getDescriptorViewByName("lambda2");
        const auto& lambda3 = pts.getDescriptorViewByName("lambda3");
 
        std::size_t j = 0;
        for (std::size_t i = 0; i < nbPts; ++i)
        {
                const T randv = uni01(gen);     
                
                const T nl1 = lambda1(0,i) / k;
                const T nl2 = lambda2(0,i) / k;
                const T nl3 = lambda3(0,i) / k;
                
                if (nl1 < xi or nl2 < xi or nl3 < 0.5 * xi or randv < 0.5)
                {
                        pts.setColFrom(j, pts, i);
                        ++j;
                }
        }
        pts.conservativeResize(j);
}
 
template <typename T>
void SpectralDecompositionDataPointsFilter<T>::filterPointness(DataPoints& pts, T xi, std::size_t k) const
{
        constexpr std::size_t seed = 1;
        std::mt19937 gen(seed); //Standard mersenne_twister_engine seeded with seed
        std::uniform_real_distribution<> uni01(0., 1.);
        
        const std::size_t nbPts = pts.getNbPoints();
        
        // Check field exists
        if (!pts.descriptorExists("lambda1") or !pts.descriptorExists("lambda2") or !pts.descriptorExists("lambda3"))
        {
                throw InvalidField("SpectralDecomposition<T>::filter: Error, lambdas field not found in descriptors.");
        }
        
        const auto& lambda1 = pts.getDescriptorViewByName("lambda1");
        const auto& lambda2 = pts.getDescriptorViewByName("lambda2");
        const auto& lambda3 = pts.getDescriptorViewByName("lambda3");
 
        std::size_t j = 0;
        for (std::size_t i = 0; i < nbPts; ++i)
        {
                const T randv = uni01(gen);     
                
                const T nl1 = lambda1(0,i) / k;
                const T nl2 = lambda2(0,i) / k;
                const T nl3 = lambda3(0,i) / k;
                
                if (nl1 < (5./6.) * xi or nl2 < (5./6.) * xi or nl3 < (5./6.) * xi or randv < 0.2)
                {
                        pts.setColFrom(j, pts, i);
                        ++j;
                }
        }
        pts.conservativeResize(j);
}
 
template struct SpectralDecompositionDataPointsFilter<float>;
template struct SpectralDecompositionDataPointsFilter<double>;