RandomSampling.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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      notice, this list of conditions and the following disclaimer.
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      documentation and/or other materials provided with the distribution.
    * Neither the name of the <organization> nor the
      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 "RandomSampling.h"

#include <random>

// RandomSamplingDataPointsFilter
// Constructor
template<typename T>
RandomSamplingDataPointsFilter<T>::RandomSamplingDataPointsFilter(const Parameters& params):
        PointMatcher<T>::DataPointsFilter("RandomSamplingDataPointsFilter", RandomSamplingDataPointsFilter::availableParameters(), params),
        prob(Parametrizable::get<double>("prob")),
        randomSamplingMethod(Parametrizable::get<int>("randomSamplingMethod")),
        seed(Parametrizable::get<int>("seed"))
{
}

// Compute
template<typename T>
typename PointMatcher<T>::DataPoints
RandomSamplingDataPointsFilter<T>::filter(const DataPoints& input)
{
        DataPoints output(input);
        inPlaceFilter(output);
        return output;
}

// In-place filter
template<typename T>
Eigen::VectorXf RandomSamplingDataPointsFilter<T>::sampleRandomIndices(const size_t nbPoints)
{
        std::minstd_rand randomNumberGenerator;
        if (seed == -1)
        {
                std::random_device randomDevice;
                randomNumberGenerator = std::minstd_rand(randomDevice());
        }
        else
        {
                randomNumberGenerator = std::minstd_rand(seed);
        }

        switch(randomSamplingMethod)
        {
                default:        // Direct RNG.
                {
                        const float randomNumberRange{static_cast<float>(randomNumberGenerator.max() - randomNumberGenerator.min())};
                        return Eigen::VectorXf::NullaryExpr(nbPoints, [&](float){return static_cast<float>(randomNumberGenerator() / randomNumberRange);});
                }
                case 1:         // Uniform distribution.
                {
                        std::uniform_real_distribution<float> distribution(0, 1);
                        return Eigen::VectorXf::NullaryExpr(nbPoints, [&](float){return distribution(randomNumberGenerator);});
                }
        }
}

// In-place filter
template<typename T>
void RandomSamplingDataPointsFilter<T>::inPlaceFilter(
        DataPoints& cloud)
{
        const size_t nbPointsIn = cloud.features.cols();
        const size_t nbPointsOut = nbPointsIn * prob;

        const Eigen::VectorXf randomNumbers{sampleRandomIndices(nbPointsIn)};
        size_t j{0u};
        for (size_t i{0u}; i < nbPointsIn && j<=nbPointsOut; ++i)
        {
                if (randomNumbers(i) < prob)
                {
                        cloud.setColFrom(j, cloud, i);
                        ++j;
                }
        }
        cloud.conservativeResize(j);
}

template struct RandomSamplingDataPointsFilter<float>;
template struct RandomSamplingDataPointsFilter<double>;