OutlierFiltersImpl.cpp

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/*

Copyright (c) 2010--2012,
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>

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#include "OutlierFiltersImpl.h"
#include "PointMatcherPrivate.h"
#include "Functions.h"

#include <algorithm>
#include <vector>
#include <iostream>
#include <limits>

using namespace std;
using namespace PointMatcherSupport;

// NullOutlierFilter
template<typename T>
typename PointMatcher<T>::OutlierWeights OutlierFiltersImpl<T>::NullOutlierFilter::compute(
        const DataPoints& filteredReading,
        const DataPoints& filteredReference,
        const Matches& input)
{
        return OutlierWeights::Constant(input.ids.rows(), input.ids.cols(), 1);
}

template struct OutlierFiltersImpl<float>::NullOutlierFilter;
template struct OutlierFiltersImpl<double>::NullOutlierFilter;


// MaxDistOutlierFilter
template<typename T>
OutlierFiltersImpl<T>::MaxDistOutlierFilter::MaxDistOutlierFilter(const Parameters& params):
        OutlierFilter("MaxDistOutlierFilter", MaxDistOutlierFilter::availableParameters(), params),
        maxDist(pow(Parametrizable::get<T>("maxDist"),2)) // we use the square distance later
{
}


template<typename T>
typename PointMatcher<T>::OutlierWeights OutlierFiltersImpl<T>::MaxDistOutlierFilter::compute(
        const DataPoints& filteredReading,
        const DataPoints& filteredReference,
        const Matches& input)
{
        return OutlierWeights((input.dists.array() <= maxDist).template cast<T>());
}

template struct OutlierFiltersImpl<float>::MaxDistOutlierFilter;
template struct OutlierFiltersImpl<double>::MaxDistOutlierFilter;

// MinDistOutlierFilter
template<typename T>
OutlierFiltersImpl<T>::MinDistOutlierFilter::MinDistOutlierFilter(const Parameters& params):
        OutlierFilter("MinDistOutlierFilter", MinDistOutlierFilter::availableParameters(), params),
        minDist(pow(Parametrizable::get<T>("minDist"),2))// we use the square distance later
{
}

template<typename T>
typename PointMatcher<T>::OutlierWeights OutlierFiltersImpl<T>::MinDistOutlierFilter::compute(
        const DataPoints& filteredReading,
        const DataPoints& filteredReference,
        const Matches& input)
{
        return OutlierWeights((input.dists.array() >= minDist).template cast<T>());
}

template struct OutlierFiltersImpl<float>::MinDistOutlierFilter;
template struct OutlierFiltersImpl<double>::MinDistOutlierFilter;



// MedianDistOutlierFilter
template<typename T>
OutlierFiltersImpl<T>::MedianDistOutlierFilter::MedianDistOutlierFilter(const Parameters& params):
        OutlierFilter("MedianDistOutlierFilter", MedianDistOutlierFilter::availableParameters(), params),
        factor(Parametrizable::get<T>("factor"))
{
}

template<typename T>
typename PointMatcher<T>::OutlierWeights OutlierFiltersImpl<T>::MedianDistOutlierFilter::compute(
        const DataPoints& filteredReading,
        const DataPoints& filteredReference,
        const Matches& input)
{
        const T median = input.getDistsQuantile(0.5);
        const T limit = factor * median;
        return OutlierWeights((input.dists.array() <= limit).template cast<T>());
}

template struct OutlierFiltersImpl<float>::MedianDistOutlierFilter;
template struct OutlierFiltersImpl<double>::MedianDistOutlierFilter;


// TrimmedDistOutlierFilter
template<typename T>
OutlierFiltersImpl<T>::TrimmedDistOutlierFilter::TrimmedDistOutlierFilter(const Parameters& params):
        OutlierFilter("TrimmedDistOutlierFilter", TrimmedDistOutlierFilter::availableParameters(), params),
        ratio(Parametrizable::get<T>("ratio"))
{
}

template<typename T>
typename PointMatcher<T>::OutlierWeights OutlierFiltersImpl<T>::TrimmedDistOutlierFilter::compute(
        const DataPoints& filteredReading,
        const DataPoints& filteredReference,
        const Matches& input)
{
        const T limit = input.getDistsQuantile(ratio);
        return OutlierWeights((input.dists.array() <= limit).template cast<T>());
}

template struct OutlierFiltersImpl<float>::TrimmedDistOutlierFilter;
template struct OutlierFiltersImpl<double>::TrimmedDistOutlierFilter;

// VarTrimmedDistOutlierFilter
template<typename T>
OutlierFiltersImpl<T>::VarTrimmedDistOutlierFilter::VarTrimmedDistOutlierFilter(const Parameters& params):
        OutlierFilter("VarTrimmedDistOutlierFilter", VarTrimmedDistOutlierFilter::availableParameters(), params),
        minRatio(Parametrizable::get<T>("minRatio")),
        maxRatio(Parametrizable::get<T>("maxRatio")),
        lambda(Parametrizable::get<T>("lambda"))
{
        if (this->minRatio >= this->maxRatio)
        {
                throw InvalidParameter((boost::format("VarTrimmedDistOutlierFilter: minRatio (%1%) should be smaller than maxRatio (%2%)") % minRatio % maxRatio).str());
        }
}

template<typename T>
typename PointMatcher<T>::OutlierWeights OutlierFiltersImpl<T>::VarTrimmedDistOutlierFilter::compute(
        const DataPoints& filteredReading,
        const DataPoints& filteredReference,
        const Matches& input)
{
        const T tunedRatio = optimizeInlierRatio(input);
        LOG_INFO_STREAM("Optimized ratio: " << tunedRatio);

        const T limit = input.getDistsQuantile(tunedRatio);
        return OutlierWeights((input.dists.array() <= limit).template cast<T>());
}

template<typename T>
T OutlierFiltersImpl<T>::VarTrimmedDistOutlierFilter::optimizeInlierRatio(const Matches& matches)
{
        typedef typename PointMatcher<T>::ConvergenceError ConvergenceError;
        typedef typename Eigen::Array<T, Eigen::Dynamic, 1> LineArray;
        
        const int points_nbr = matches.dists.rows() * matches.dists.cols();
        
        // vector containing the squared distances of the matches
        std::vector<T> tmpSortedDist;
        tmpSortedDist.reserve(points_nbr);
        for (int x = 0; x < matches.dists.cols(); ++x)
                for (int y = 0; y < matches.dists.rows(); ++y)
                        if ((matches.dists(y, x) != numeric_limits<T>::infinity()) && (matches.dists(y, x) > 0))
                                tmpSortedDist.push_back(matches.dists(y, x));
        if (tmpSortedDist.size() == 0)
                throw ConvergenceError("no outlier to filter");
                        
        std::sort(tmpSortedDist.begin(), tmpSortedDist.end());

        const int minEl = floor(this->minRatio*points_nbr);
        const int maxEl = floor(this->maxRatio*points_nbr);

        // Return std::vector to an eigen::vector
        Eigen::Map<LineArray> sortedDist(&tmpSortedDist[0], points_nbr);

        const LineArray trunkSortedDist = sortedDist.segment(minEl, maxEl-minEl);
        const T lowerSum = sortedDist.head(minEl).sum();
        const LineArray ids = LineArray::LinSpaced(trunkSortedDist.rows(), minEl+1, maxEl);
        const LineArray ratio = ids / points_nbr;
        const LineArray deno = ratio.pow(this->lambda);
        const LineArray FRMS = deno.inverse().square() * ids.inverse() * (lowerSum + trunkSortedDist);
        int minIndex(0);// = FRMS.minCoeff();
        FRMS.minCoeff(&minIndex);
        const T optRatio = (float)(minIndex + minEl)/ (float)points_nbr;
        
        //cout << "Optimized ratio: " << optRatio << endl;
        
        return optRatio;

}

template struct OutlierFiltersImpl<float>::VarTrimmedDistOutlierFilter;
template struct OutlierFiltersImpl<double>::VarTrimmedDistOutlierFilter;

// SurfaceNormalOutlierFilter
template<typename T>
OutlierFiltersImpl<T>::SurfaceNormalOutlierFilter::SurfaceNormalOutlierFilter(const Parameters& params):
        OutlierFilter("SurfaceNormalOutlierFilter", SurfaceNormalOutlierFilter::availableParameters(), params),
        eps(cos(Parametrizable::get<T>("maxAngle"))),
        warningPrinted(false)
{
        //waring: eps is change to cos(maxAngle)!
}

template<typename T>
typename PointMatcher<T>::OutlierWeights OutlierFiltersImpl<T>::SurfaceNormalOutlierFilter::compute(
        const DataPoints& filteredReading,
        const DataPoints& filteredReference,
        const Matches& input)
{
        const BOOST_AUTO(normalsReading, filteredReading.getDescriptorViewByName("normals"));
        const BOOST_AUTO(normalsReference, filteredReference.getDescriptorViewByName("normals"));
        
        // select weight from median
        OutlierWeights w(input.dists.rows(), input.dists.cols());

        if(normalsReading.cols() != 0 && normalsReference.cols() != 0)
        {
                for (int x = 0; x < w.cols(); ++x) // pts in reading
                {
                        const Vector normalRead = normalsReading.col(x).normalized();

                        for (int y = 0; y < w.rows(); ++y) // knn 
                        {
                                const int idRef = input.ids(y, x);

                                const Vector normalRef = normalsReference.col(idRef).normalized();

                                const T value = anyabs(normalRead.dot(normalRef));

                                if(value < eps) // test to keep the points
                                        w(y, x) = 0;
                                else
                                        w(y, x) = 1;
                        }
                }
        }
        else
        {
                if(warningPrinted == false)
                {
                        LOG_INFO_STREAM("SurfaceNormalOutlierFilter: surface normals not available. Skipping filtering");
                        warningPrinted = true;
                }

                w = Matrix::Ones(input.dists.rows(), input.dists.cols());
        }
        //abort();
        return w;
}

template struct OutlierFiltersImpl<float>::SurfaceNormalOutlierFilter;
template struct OutlierFiltersImpl<double>::SurfaceNormalOutlierFilter;

// GenericDescriptorOutlierFilter
template<typename T>
OutlierFiltersImpl<T>::GenericDescriptorOutlierFilter::GenericDescriptorOutlierFilter(const Parameters& params):
        OutlierFilter("GenericDescriptorOutlierFilter", GenericDescriptorOutlierFilter::availableParameters(), params),
        source(Parametrizable::getParamValueString("source")),
        descName(Parametrizable::getParamValueString("descName")),
        useSoftThreshold(Parametrizable::get<bool>("useSoftThreshold")),
        useLargerThan(Parametrizable::get<bool>("useLargerThan")),
        threshold(Parametrizable::get<T>("threshold"))
{
        if(source != "reference" && source != "reading")
        {
                throw InvalidParameter(
                (boost::format("GenericDescriptorOutlierFilter: Error, the parameter named 'source' can only be set to 'reference' or 'reading' but was set to %1%") % source).str());
        }
}

template<typename T>
typename PointMatcher<T>::OutlierWeights OutlierFiltersImpl<T>::GenericDescriptorOutlierFilter::compute(
        const DataPoints& filteredReading,
        const DataPoints& filteredReference,
        const Matches& input)
{
        typedef typename DataPoints::ConstView ConstView;

        const int knn = input.dists.rows();
        const int readPtsCount = input.dists.cols();
        
        OutlierWeights w(knn, readPtsCount);

        const DataPoints *cloud;

        if(source == "reference")
                cloud = &filteredReference;
        else
                cloud = &filteredReference;

        ConstView desc(cloud->getDescriptorViewByName(descName));

        if(desc.rows() != 1)
        {
                throw InvalidParameter(
                (boost::format("GenericDescriptorOutlierFilter: Error, the parameter named 'descName' must be a 1D descriptor but the field %1% is %2%D") % descName % desc.rows()).str());
        }

        for(int k=0; k < knn; k++)
        {
                for(int i=0; i < readPtsCount; i++)
                {
                        if(useSoftThreshold == false)
                        {
                                if(useLargerThan == true)
                                {
                                        if(desc(0, input.ids(k,i)) > threshold)
                                                w(k,i) = 1;
                                        else
                                                w(k,i) = 0;
                                }
                                else
                                {
                                        if(desc(0, input.ids(k,i)) < threshold)
                                                w(k,i) = 1;
                                        else
                                                w(k,i) = 0;
                                }
                        }
                        else
                        {
                                // use soft threshold by assigning the weight using the descriptor
                                w(k,i) = desc(0, input.ids(k,i));
                        }
                }
        }

        //Normalize
        if(useSoftThreshold)
                w = w/w.maxCoeff();

        return w;
}

template struct OutlierFiltersImpl<float>::GenericDescriptorOutlierFilter;
template struct OutlierFiltersImpl<double>::GenericDescriptorOutlierFilter;