passthrough.hpp

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 * $Id: passthrough.hpp 34776 2010-12-15 10:00:05Z rusu $
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#ifndef PCL_FILTERS_IMPL_PASSTHROUGH_H_
#define PCL_FILTERS_IMPL_PASSTHROUGH_H_

#include "pcl/filters/passthrough.h"
#include "pcl/io/io.h"

template <typename PointT> void
pcl::PassThrough<PointT>::applyFilter (PointCloud &output)
{
  // Has the input dataset been set already?
  if (!input_)
  {
    ROS_WARN ("[pcl::%s::applyFilter] No input dataset given!", getClassName ().c_str ());
    output.width = output.height = 0;
    output.points.clear ();
    return;
  }

  // Check if we're going to keep the organized structure of the cloud or not
  if (keep_organized_)
  {
    if (filter_field_name_.empty ())
    {
      // Silly - if no filtering is actually done, and we want to keep the data organized, 
      // just copy everything. Any optimizations that can be done here???
      output = *input_;
      return;
    }

    output.width  = input_->width;
    output.height = input_->height;
    // Check what the user value is: if !finite, set is_dense to false, true otherwise
    if (!pcl_isfinite (user_filter_value_))
      output.is_dense = false;
    else
      output.is_dense = input_->is_dense;
  }
  else
  {
    output.height = 1;                    // filtering breaks the organized structure
    // Because we're doing explit checks for isfinite, is_dense = true
    output.is_dense = true;
  }
  output.points.resize (input_->points.size ());

  int nr_p = 0;
  // If we don't want to process the entire cloud, but rather filter points far away from the viewpoint first...
  if (!filter_field_name_.empty ())
  {
    // Get the distance field index
    std::vector<sensor_msgs::PointField> fields;
    int distance_idx = pcl::getFieldIndex (*input_, filter_field_name_, fields);
    if (distance_idx == -1)
    {
      ROS_WARN ("[pcl::%s::applyFilter] Invalid filter field name. Index is %d.", getClassName ().c_str (), distance_idx);
      output.width = output.height = 0;
      output.points.clear ();
      return;
    }

    if (keep_organized_)
    {
      for (size_t cp = 0; cp < input_->points.size (); ++cp)
      {
        // Copy the point
        pcl::for_each_type <FieldList> (pcl::NdConcatenateFunctor <PointT, PointT> (input_->points[cp], output.points[cp]));

        // Filter it. Get the distance value
        uint8_t* pt_data = (uint8_t*)&input_->points[cp];
        float distance_value = 0;
        memcpy (&distance_value, pt_data + fields[distance_idx].offset, sizeof (float));

        if (filter_limit_negative_)
        {
          // Use a threshold for cutting out points which inside the interval
          if ((distance_value < filter_limit_max_) && (distance_value > filter_limit_min_))
          {
            output.points[cp].x = output.points[cp].y = output.points[cp].z = user_filter_value_;
            continue;
          }
        }
        else
        {
          // Use a threshold for cutting out points which are too close/far away
          if ((distance_value > filter_limit_max_) || (distance_value < filter_limit_min_))
          {
            output.points[cp].x = output.points[cp].y = output.points[cp].z = user_filter_value_;
            continue;
          }
        }
      }
      nr_p = input_->points.size ();
    }
    // Remove filtered points
    else
    {
      // Go over all points
      for (size_t cp = 0; cp < input_->points.size (); ++cp)
      {
        // Check if the point is invalid
        if (!pcl_isfinite (input_->points[cp].x) || !pcl_isfinite (input_->points[cp].y) || !pcl_isfinite (input_->points[cp].z))
          continue;

        // Get the distance value
        uint8_t* pt_data = (uint8_t*)&input_->points[cp];
        float distance_value = 0;
        memcpy (&distance_value, pt_data + fields[distance_idx].offset, sizeof (float));

        if (filter_limit_negative_)
        {
          // Use a threshold for cutting out points which inside the interval
          if ((distance_value < filter_limit_max_) && (distance_value > filter_limit_min_))
            continue;
        }
        else
        {
          // Use a threshold for cutting out points which are too close/far away
          if ((distance_value > filter_limit_max_) || (distance_value < filter_limit_min_))
            continue;
        }

        pcl::for_each_type <FieldList> (pcl::NdConcatenateFunctor <PointT, PointT> (input_->points[cp], output.points[nr_p]));
        nr_p++;
      }
      output.width = nr_p;
    } // !keep_organized_
  }
  // No distance filtering, process all data. No need to check for is_organized here as we did it above
  else
  {
    // First pass: go over all points and insert them into the right leaf
    for (size_t cp = 0; cp < input_->points.size (); ++cp)
    {
      // Check if the point is invalid
      if (!pcl_isfinite (input_->points[cp].x) || !pcl_isfinite (input_->points[cp].y) || !pcl_isfinite (input_->points[cp].z))
        continue;

      pcl::for_each_type <FieldList> (pcl::NdConcatenateFunctor <PointT, PointT> (input_->points[cp], output.points[nr_p]));
      nr_p++;
    }
    output.width = nr_p;
  }

  output.points.resize (output.width * output.height);
}

#define PCL_INSTANTIATE_PassThrough(T) template class pcl::PassThrough<T>;

#endif    // PCL_FILTERS_IMPL_PASSTHROUGH_H_

---

pcl
Author(s): See http://pcl.ros.org/authors for the complete list of authors.
autogenerated on Fri Jan 11 09:55:22 2013