outlier_removal.cpp

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#include <sensor_msgs/PointCloud2.h>
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/console/print.h>
#include <pcl/console/parse.h>
#include <pcl/console/time.h>
#include <pcl/filters/radius_outlier_removal.h>
#include <pcl/filters/statistical_outlier_removal.h>

using namespace pcl;
using namespace pcl::io;
using namespace pcl::console;

std::string default_method = "radius";

int default_mean_k = 2;
double default_std_dev_mul = 0.0;
int default_negative = 1;

double default_radius = 0.0;
int default_min_pts = 0;

void
printHelp (int, char **argv)
{
  print_error ("Syntax is: %s input.pcd output.pcd <options>\n", argv[0]);
  print_info ("  where options are:\n");
  print_info ("                     -method X = the outlier removal method to be used (options: radius / statistical) (default: ");
  print_value ("%s", default_method.c_str ()); print_info (")\n");
  print_info ("                     -radius X = (RadiusOutlierRemoval) the sphere radius used for determining the k-nearest neighbors (default: ");
  print_value ("%d", default_min_pts); print_info (")\n");
  print_info ("                     -min_pts X = (RadiusOutlierRemoval) the minimum number of neighbors that a point needs to have in the given search radius in order to be considered an inlier (default: ");
  print_value ("%d", default_min_pts); print_info (")\n");
  print_info ("                     -mean_k X = (StatisticalOutlierRemoval only) the number of points to use for mean distance estimation (default: ");
  print_value ("%d", default_mean_k); print_info (")\n");
  print_info ("                     -std_dev_mul X = (StatisticalOutlierRemoval only) the standard deviation multiplier threshold (default: ");
  print_value ("%f", default_std_dev_mul); print_info (")\n");
  print_info ("                     -inliers X = (StatisticalOutlierRemoval only) decides whether the inliers should be returned (1), or the outliers (0). (default: ");
  print_value ("%d", default_negative); print_info (")\n");
}

bool
loadCloud (const std::string &filename, sensor_msgs::PointCloud2 &cloud)
{
  TicToc tt;
  print_highlight ("Loading "); print_value ("%s ", filename.c_str ());

  tt.tic ();
  if (loadPCDFile (filename, cloud) < 0)
    return (false);
  print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", cloud.width * cloud.height); print_info (" points]\n");
  print_info ("Available dimensions: "); print_value ("%s\n", pcl::getFieldsList (cloud).c_str ());

  return (true);
}

void
compute (const sensor_msgs::PointCloud2::ConstPtr &input, sensor_msgs::PointCloud2 &output,
         std::string method,
         int min_pts, double radius,
         int mean_k, double std_dev_mul, bool negative)
{

  PointCloud<PointXYZ>::Ptr xyz_cloud_pre (new pcl::PointCloud<PointXYZ> ()),
      xyz_cloud (new pcl::PointCloud<PointXYZ> ());
  fromROSMsg (*input, *xyz_cloud_pre);
  
  std::vector<int> index_vector;
  removeNaNFromPointCloud<PointXYZ> (*xyz_cloud_pre, *xyz_cloud, index_vector);

      
  TicToc tt;
  tt.tic ();
  PointCloud<PointXYZ>::Ptr xyz_cloud_filtered (new PointCloud<PointXYZ> ());
  if (method == "statistical")
  {
    StatisticalOutlierRemoval<PointXYZ> filter;
    filter.setInputCloud (xyz_cloud);
    filter.setMeanK (mean_k);
    filter.setStddevMulThresh (std_dev_mul);
    filter.setNegative (negative);
    PCL_INFO ("Computing filtered cloud with mean_k %d, std_dev_mul %f, inliers %d\n", filter.getMeanK (), filter.getStddevMulThresh (), filter.getNegative ());
    filter.filter (*xyz_cloud_filtered);
  }
  else if (method == "radius")
  {
    RadiusOutlierRemoval<PointXYZ> filter;
    filter.setInputCloud (xyz_cloud);
    filter.setRadiusSearch (radius);
    filter.setMinNeighborsInRadius (min_pts);
    PCL_INFO ("Computing filtered cloud with radius %f, min_pts %d\n", radius, min_pts);
    filter.filter (*xyz_cloud_filtered);
  }
  else
  {
    PCL_ERROR ("%s is not a valid filter name! Quitting!\n", method.c_str ());
    return;
  }
    
  print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", xyz_cloud_filtered->width * xyz_cloud_filtered->height); print_info (" points]\n");

  toROSMsg (*xyz_cloud_filtered, output);
}

void
saveCloud (const std::string &filename, const sensor_msgs::PointCloud2 &output)
{
  TicToc tt;
  tt.tic ();

  print_highlight ("Saving "); print_value ("%s ", filename.c_str ());

  pcl::io::savePCDFile (filename, output,  Eigen::Vector4f::Zero (),
                        Eigen::Quaternionf::Identity (), true);

  print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", output.width * output.height); print_info (" points]\n");
}

/* ---[ */
int
main (int argc, char** argv)
{
  print_info ("Statistical Outlier Removal filtering of a point cloud. For more information, use: %s -h\n", argv[0]);

  if (argc < 3)
  {
    printHelp (argc, argv);
    return (-1);
  }

  // Parse the command line arguments for .pcd files
  std::vector<int> p_file_indices;
  p_file_indices = parse_file_extension_argument (argc, argv, ".pcd");
  if (p_file_indices.size () != 2)
  {
    print_error ("Need one input PCD file and one output PCD file to continue.\n");
    return (-1);
  }

  // Command line parsing
  std::string method = default_method;
  int min_pts = default_min_pts;
  double radius = default_radius;
  int mean_k = default_mean_k;
  double std_dev_mul = default_std_dev_mul;
  int negative = default_negative;
  

  parse_argument (argc, argv, "-method", method);
  parse_argument (argc, argv, "-radius", radius);
  parse_argument (argc, argv, "-min_pts", min_pts);
  parse_argument (argc, argv, "-mean_k", mean_k);
  parse_argument (argc, argv, "-std_dev_mul", std_dev_mul);
  parse_argument (argc, argv, "-inliers", negative);
  
  

  // Load the first file
  sensor_msgs::PointCloud2::Ptr cloud (new sensor_msgs::PointCloud2);
  if (!loadCloud (argv[p_file_indices[0]], *cloud))
    return (-1);

  // Do the smoothing
  sensor_msgs::PointCloud2 output;
  compute (cloud, output, method, min_pts, radius, mean_k, std_dev_mul, negative);

  // Save into the second file
  saveCloud (argv[p_file_indices[1]], output);
}