random_sample.cpp

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/*
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 *  Point Cloud Library (PCL) - www.pointclouds.org
 *  Copyright (c) 2009, Willow Garage, Inc.
 *  Copyright (c) 2012-, Open Perception, Inc.
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 * $Id: extract_indices.cpp 1385 2011-06-19 19:15:56Z rusu $
 *
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#include <pcl/filters/impl/random_sample.hpp>

void
pcl::RandomSample<pcl::PCLPointCloud2>::applyFilter (PCLPointCloud2 &output)
{
  unsigned N = input_->width * input_->height;
  // If sample size is 0 or if the sample size is greater then input cloud size
  //   then return entire copy of cloud
  if (sample_ >= N)
  {
    output = *input_;
  }
  else
  {
    // Resize output cloud to sample size
    output.data.resize (sample_ * input_->point_step);

    // Copy the common fields
    output.fields = input_->fields;
    output.is_bigendian = input_->is_bigendian;
    output.row_step = input_->row_step;
    output.point_step = input_->point_step;
    output.height = 1;

    // Set random seed so derived indices are the same each time the filter runs
    std::srand (seed_);

    unsigned top = N - sample_;
    unsigned i = 0;
    unsigned index = 0;

    // Algorithm A
    for (size_t n = sample_; n >= 2; n--)
    {
      float V = unifRand ();
      unsigned S = 0;
      float quot = float (top) / float (N);
      while (quot > V)
      {
        S++;
        top--;
        N--;
        quot = quot * float (top) / float (N);
      }
      index += S;
      memcpy (&output.data[i++ * output.point_step], &input_->data[index++ * output.point_step], output.point_step);
      N--;
    }

    index += N * static_cast<unsigned> (unifRand ());
    memcpy (&output.data[i++ * output.point_step], &input_->data[index++ * output.point_step], output.point_step);

    output.width = sample_;
    output.row_step = output.point_step * output.width;
  }
}

void
pcl::RandomSample<pcl::PCLPointCloud2>::applyFilter (std::vector<int> &indices)
{
  unsigned N = input_->width * input_->height;
  // If sample size is 0 or if the sample size is greater then input cloud size
  //   then return all indices
  if (sample_ >= N)
  {
    indices = *indices_;
  }
  else
  {
    // Resize output indices to sample size
    indices.resize (sample_);

    // Set random seed so derived indices are the same each time the filter runs
    std::srand (seed_);

    unsigned top = N - sample_;
    unsigned i = 0;
    unsigned index = 0;

    // Algorithm A
    for (size_t n = sample_; n >= 2; n--)
    {
      float V = unifRand ();
      unsigned S = 0;
      float quot = float (top) / float (N);
      while (quot > V)
      {
        S++;
        top--;
        N--;
        quot = quot * float (top) / float (N);
      }
      index += S;
      indices[i++] = (*indices_)[index++];
      N--;
    }

    index += N * static_cast<unsigned> (unifRand ());
    indices[i++] = (*indices_)[index++];
  }
}

#ifndef PCL_NO_PRECOMPILE
#include <pcl/impl/instantiate.hpp>
#include <pcl/point_types.h>

PCL_INSTANTIATE(RandomSample, PCL_POINT_TYPES)

#endif    // PCL_NO_PRECOMPILE