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 * $Id$
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#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/apps/vfh_nn_classifier.h>

int 
main (int, char* argv[])
{
  // Load input file
  char* file_name = argv[1];
  pcl::PCLPointCloud2 cloud_blob;
  pcl::io::loadPCDFile (file_name, cloud_blob);

  // Declare variable to hold result
  pcl::NNClassification<pcl::VFHSignature308>::ResultPtr result;
  // same as: pcl::VFHClassifierNN::ResultPtr result;

  // Do general classification using NNClassification or use the VHClassiierNN helper class
  if (false)
  {
    // Estimate your favorite feature
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ> ());
    pcl::fromPCLPointCloud2 (cloud_blob, *cloud);
    /// NOTE: make sure to use same radius as for training data
    pcl::PointCloud<pcl::VFHSignature308>::Ptr feature = pcl::computeVFH<pcl::PointXYZ> (cloud, 0.03);

    // Nearest neighbors classification
    pcl::NNClassification<pcl::VFHSignature308> nn;
    //nn.setTrainingFeatures(cloud);
    //nn.setTrainingLabels(std::vector<std::string>(cloud->points.size(), "bla"));
    nn.loadTrainingFeatures (argv[2], argv[3]);
    result = nn.classify(feature->points[0], 300, 50);
  }
  else
  {
    pcl::VFHClassifierNN vfh_classifier;
    //vfh_classifier.loadTrainingData ("/home/marton/ros/pcl/trunk/apps/data/can.pcd", "can");
    //vfh_classifier.loadTrainingData ("/home/marton/ros/pcl/trunk/apps/data/salt.pcd", "salt");
    //vfh_classifier.loadTrainingData ("/home/marton/ros/pcl/trunk/apps/data/sugar.pcd", "sugar");
    //vfh_classifier.saveTrainingFeatures ("/tmp/vfhs.pcd", "/tmp/vfhs.labels");
    vfh_classifier.loadTrainingFeatures (argv[2], argv[3]);
    vfh_classifier.finalizeTraining ();
    result = vfh_classifier.classify(cloud_blob);
  }

  // Print results
  for (unsigned i = 0; i < result->first.size(); ++i)
    std::cerr << result->first.at (i) << ": " << result->second.at (i) << std::endl;

  return 0;
}
