passthrough.cpp

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#include <pcl/filters/impl/passthrough.hpp>

void
pcl::PassThrough<pcl::PCLPointCloud2>::applyFilter (PCLPointCloud2 &output)
{
  if (!input_)
  {
    PCL_ERROR ("[pcl::%s::applyFilter] Input dataset not given!\n", getClassName ().c_str ());
    output.width = output.height = 0;
    output.data.clear ();
    return;
  }

  // If fields x/y/z are not present, we cannot filter
  if (x_idx_ == -1 || y_idx_ == -1 || z_idx_ == -1)
  {
    PCL_ERROR ("[pcl::%s::applyFilter] Input dataset doesn't have x-y-z coordinates!\n", getClassName ().c_str ());
    output.width = output.height = 0;
    output.data.clear ();
    return;
  }

  int nr_points = input_->width * input_->height;

  // 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
  {
    // Copy the header (and thus the frame_id) + allocate enough space for points
    output.height = 1; // filtering breaks the organized structure
    // Because we're doing explit checks for isfinite, is_dense = true
    output.is_dense = true;
  }
  output.row_step = input_->row_step;
  output.point_step = input_->point_step;
  output.is_bigendian = input_->is_bigendian;
  output.data.resize (input_->data.size ());

  removed_indices_->resize (input_->data.size ());

  int nr_p = 0;
  int nr_removed_p = 0;
  // Create the first xyz_offset
  Eigen::Array4i xyz_offset (input_->fields[x_idx_].offset, input_->fields[y_idx_].offset,
                             input_->fields[z_idx_].offset, 0);

  Eigen::Vector4f pt = Eigen::Vector4f::Zero ();
  // 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
    int distance_idx = pcl::getFieldIndex (*input_, filter_field_name_);
    if (distance_idx == -1)
    {
      PCL_WARN ("[pcl::%s::applyFilter] Invalid filter field name. Index is %d.\n", getClassName ().c_str (), distance_idx);
      output.width = output.height = 0;
      output.data.clear ();
      return;
    }

    // @todo fixme
    if (input_->fields[distance_idx].datatype != pcl::PCLPointField::FLOAT32)
    {
      PCL_ERROR ("[pcl::%s::downsample] Distance filtering requested, but distances are not float/double in the dataset! Only FLOAT32/FLOAT64 distances are supported right now.\n", getClassName ().c_str ());
      output.width = output.height = 0;
      output.data.clear ();
      return;
    }

    float badpt = std::numeric_limits<float>::quiet_NaN ();
    // Check whether we need to store filtered valued in place
    if (keep_organized_)
    {
      float distance_value = 0;
      // Go over all points
      for (int cp = 0; cp < nr_points; ++cp, xyz_offset += input_->point_step)
      {
        // Copy all the fields
        memcpy (&output.data[cp * output.point_step], &input_->data[cp * output.point_step], output.point_step);

        // Get the distance value
        memcpy (&distance_value, &input_->data[cp * input_->point_step + input_->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_))
          {
            // Unoptimized memcpys: assume fields x, y, z are in random order
            memcpy (&output.data[xyz_offset[0]], &badpt, sizeof (float));
            memcpy (&output.data[xyz_offset[1]], &badpt, sizeof (float));
            memcpy (&output.data[xyz_offset[2]], &badpt, sizeof (float));
            continue;
          }
          else
          {
            if (extract_removed_indices_)
              (*removed_indices_)[nr_removed_p++] = cp;
          }
        }
        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_))
          {
            // Unoptimized memcpys: assume fields x, y, z are in random order
            memcpy (&output.data[xyz_offset[0]], &badpt, sizeof (float));
            memcpy (&output.data[xyz_offset[1]], &badpt, sizeof (float));
            memcpy (&output.data[xyz_offset[2]], &badpt, sizeof (float));
            continue;
          }
          else
          {
            if (extract_removed_indices_)
              (*removed_indices_)[nr_removed_p++] = cp;
          }
        }
      }
    }
    // Remove filtered points
    else
    {
      // Go over all points
      float distance_value = 0;
      for (int cp = 0; cp < nr_points; ++cp, xyz_offset += input_->point_step)
      {
        // Get the distance value
        memcpy (&distance_value, &input_->data[cp * input_->point_step + input_->fields[distance_idx].offset],
                sizeof(float));

        // Remove NAN/INF/-INF values. We expect passthrough to output clean valid data.
        if (!pcl_isfinite (distance_value))
        {
          if (extract_removed_indices_)
            (*removed_indices_)[nr_removed_p++] = cp;
          continue;
        }

        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_)
          {
            if (extract_removed_indices_)
            {
              (*removed_indices_)[nr_removed_p] = cp;
              nr_removed_p++;
            }
            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_)
          {
            if (extract_removed_indices_)
            {
              (*removed_indices_)[nr_removed_p] = cp;
              nr_removed_p++;
            }
            continue;
          }
        }

        // Unoptimized memcpys: assume fields x, y, z are in random order
        memcpy (&pt[0], &input_->data[xyz_offset[0]], sizeof(float));
        memcpy (&pt[1], &input_->data[xyz_offset[1]], sizeof(float));
        memcpy (&pt[2], &input_->data[xyz_offset[2]], sizeof(float));

        // Check if the point is invalid
        if (!pcl_isfinite (pt[0]) || !pcl_isfinite (pt[1]) || !pcl_isfinite (pt[2]))
        {
          if (extract_removed_indices_)
          {
            (*removed_indices_)[nr_removed_p] = cp;
            nr_removed_p++;
          }
          continue;
        }

        // Copy all the fields
        memcpy (&output.data[nr_p * output.point_step], &input_->data[cp * output.point_step], output.point_step);
        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
  {
    for (int cp = 0; cp < nr_points; ++cp, xyz_offset += input_->point_step)
    {
      // Unoptimized memcpys: assume fields x, y, z are in random order
      memcpy (&pt[0], &input_->data[xyz_offset[0]], sizeof(float));
      memcpy (&pt[1], &input_->data[xyz_offset[1]], sizeof(float));
      memcpy (&pt[2], &input_->data[xyz_offset[2]], sizeof(float));

      // Check if the point is invalid
      if (!pcl_isfinite (pt[0]) || !pcl_isfinite (pt[1]) || !pcl_isfinite (pt[2]))
      {
        if (extract_removed_indices_)
        {
          (*removed_indices_)[nr_removed_p] = cp;
          nr_removed_p++;
        }
        continue;
      }

      // Copy all the fields
      memcpy (&output.data[nr_p * output.point_step], &input_->data[cp * output.point_step], output.point_step);
      nr_p++;
    }
    output.width = nr_p;
  }

  output.row_step = output.point_step * output.width;
  output.data.resize (output.width * output.height * output.point_step);

  removed_indices_->resize (nr_removed_p);
}

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

// Instantiations of specific point types
PCL_INSTANTIATE(PassThrough, PCL_XYZ_POINT_TYPES)

#endif    // PCL_NO_PRECOMPILE