MinDist.cpp

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/*
 
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 "MinDist.h"
 
#include "Functions.h"
 
// MinDistDataPointsFilter
// Constructor
template<typename T>
MinDistDataPointsFilter<T>::MinDistDataPointsFilter(const Parameters& params):
        PointMatcher<T>::DataPointsFilter("MinDistDataPointsFilter",
                MinDistDataPointsFilter::availableParameters(), params),
        dim(Parametrizable::get<unsigned>("dim")),
        minDist(Parametrizable::get<T>("minDist"))
{
}
 
// Compute
template<typename T>
typename PointMatcher<T>::DataPoints MinDistDataPointsFilter<T>::filter(
        const DataPoints& input)
{
        DataPoints output(input);
        inPlaceFilter(output);
        return output;
}
 
// In-place filter
template<typename T>
void MinDistDataPointsFilter<T>::inPlaceFilter(
        DataPoints& cloud)
{
        using namespace PointMatcherSupport;
        
        if (dim >= cloud.features.rows() - 1)
                throw InvalidParameter((boost::format("MinDistDataPointsFilter: Error, filtering on dimension number %1%, larger than feature dimensionality %2%") % dim % (cloud.features.rows() - 2)).str());
 
        const int nbPointsIn = cloud.features.cols();
        const int nbRows = cloud.features.rows();
 
        int j = 0;
        if(dim == -1) // Euclidean distance
        {
                const T absMinDist = anyabs(minDist);
                for (int i = 0; i < nbPointsIn; ++i)
                {
                        if (cloud.features.col(i).head(nbRows-1).norm() > absMinDist)
                        {
                                cloud.setColFrom(j, cloud, i);
                                ++j;
                        }
                }
        }
        else // Single axis distance
        {
                for (int i = 0; i < nbPointsIn; ++i)
                {
                        if ((cloud.features(dim, i)) > minDist)
                        {
                                cloud.setColFrom(j, cloud, i);
                                ++j;
                        }
                }
        }
 
        cloud.conservativeResize(j);
 
}
 
template struct MinDistDataPointsFilter<float>;
template struct MinDistDataPointsFilter<double>;