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 * $Id: vfh_nn_classifier.h 6218 2012-07-06 21:46:51Z aichim $
 *
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#ifndef VFHCLASSIFICATION_H_
#define VFHCLASSIFICATION_H_

#include <fstream>
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/features/vfh.h>
#include <pcl/features/normal_3d.h>
#include <pcl/apps/nn_classification.h>
#include <sensor_msgs/PointCloud2.h>

namespace pcl
{
  /** \brief Helper function to extract the VFH feature describing the given point cloud.
    * \param points point cloud for feature extraction
    * \param radius search radius for normal estimation
    * \return point cloud containing the extracted feature
    */
  template <typename PointT> pcl::PointCloud<pcl::VFHSignature308>::Ptr
  computeVFH (typename PointCloud<PointT>::ConstPtr cloud, double radius)
  {
    using namespace pcl;

    // Create an empty kdtree representation, and pass it to the objects.
    // Its content will be filled inside the object, based on the given input dataset (as no other search surface is given).
    typename pcl::search::KdTree<PointT>::Ptr tree (new pcl::search::KdTree<PointT> ());

    // Create the normal estimation class, and pass the input dataset to it
    NormalEstimation<PointT, Normal> ne;
    ne.setInputCloud (cloud);
    ne.setSearchMethod (tree);

    // Use all neighbors in a sphere of given radius to compute the normals
    PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
    ne.setRadiusSearch (radius);
    ne.compute (*normals);

    // Create the VFH estimation class, and pass the input dataset+normals to it
    VFHEstimation<PointT, Normal, VFHSignature308> vfh;
    vfh.setInputCloud (cloud);
    vfh.setInputNormals (normals);
    vfh.setSearchMethod (tree);

    // Output datasets
    PointCloud<VFHSignature308>::Ptr vfhs (new PointCloud<VFHSignature308>);

    // Compute the features and return
    vfh.compute (*vfhs);
    return vfhs;
  }

  /**
    * \brief Utility class for nearest neighbor search based classification of VFH features.
    * \author Zoltan Csaba Marton
    */
  class VFHClassifierNN
  {
    public:

      typedef pcl::PointCloud<pcl::VFHSignature308> FeatureCloud;
      typedef pcl::PointCloud<pcl::VFHSignature308>::Ptr FeatureCloudPtr;
      typedef pcl::PointCloud<pcl::VFHSignature308>::ConstPtr FeatureCloudConstPtr;
      typedef NNClassification<pcl::VFHSignature308>::Result Result;
      typedef NNClassification<pcl::VFHSignature308>::ResultPtr ResultPtr;

    private:

      /** \brief Point cloud containing the training VFH features */
      FeatureCloudPtr training_features_;
      /** \brief Class label for each training example */
      std::vector<std::string> labels_;
      /** \brief Nearest neighbor classifier instantiated for VFH features */
      NNClassification<pcl::VFHSignature308> classifier_;

    public:

      VFHClassifierNN () : training_features_ (), labels_ (), classifier_ ()
      {
        reset ();
      }

      void reset ()
      {
        training_features_.reset (new FeatureCloud);
        labels_.clear ();
        classifier_ = NNClassification<pcl::VFHSignature308> ();
      }

      /** \brief Set up the classifier with the current training features and labels */
      void finalizeTraining ()
      {
        finalizeTree ();
        finalizeLabels ();
      }

      /** \brief Set up the classifier with the current training features */
      void finalizeTree ()
      {
        classifier_.setTrainingFeatures(training_features_);
      }

      /** \brief Set up the classifier with the current training example labels */
      void finalizeLabels ()
      {
        classifier_.setTrainingLabels(labels_);
      }

      /** \brief Save the list of training examples and corresponding labels.
        * \param file_name file name for writing the training features
        * \param labels_file_name file name for writing the class label for each training example
        * \return true on success, false on failure (write error or number of entries don't match)
        */
      bool saveTrainingFeatures(std::string file_name, std::string labels_file_name)
      {
        if (labels_.size () == training_features_->points.size ())
        {
          if (pcl::io::savePCDFile (file_name.c_str (), *training_features_) != 0)
            return false;
          std::ofstream f (labels_file_name.c_str ());
          BOOST_FOREACH (std::string s, labels_)
            f << s << "\n";
          return true;
        }
        return false;
      }

      /** \brief Fill the list of training examples and corresponding labels.
        * \note this function has a cumulative effect.
        * \param training_features the training features
        * \param labels the class label for each training example
        * \return true on success, false on failure (number of entries don't match)
        */
      bool addTrainingFeatures (const FeatureCloudPtr training_features, const std::vector<std::string> &labels)
      {
        if (labels.size () == training_features->points.size ())
        {
          labels_.insert (labels_.end (), labels.begin (), labels.end ());
          training_features_->points.insert (training_features_->points.end (), training_features->points.begin (), training_features->points.end ());
          training_features_->header = training_features->header;
          training_features_->height = 1;
          training_features_->width  = static_cast<uint32_t> (training_features_->points.size ());
          training_features_->is_dense &= training_features->is_dense;
          training_features_->sensor_origin_ = training_features->sensor_origin_;
          training_features_->sensor_orientation_ = training_features->sensor_orientation_;
          return true;
        }
        return false;
      }

      /** \brief Fill the list of training examples and corresponding labels.
        * \note this function has a cumulative effect.
        * \param file_name PCD file containing the training features
        * \param labels_file_name the class label for each training example
        * \return true on success, false on failure (read error or number of entries don't match)
        */
      bool loadTrainingFeatures(std::string file_name, std::string labels_file_name)
      {
        FeatureCloudPtr cloud (new FeatureCloud);
        if (pcl::io::loadPCDFile (file_name.c_str (), *cloud) != 0)
          return false;
        std::vector<std::string> labels;
        std::ifstream f (labels_file_name.c_str ());
        std::string label;
        while (getline (f, label))
          if (label.size () > 0)
            labels.push_back(label);
        return addTrainingFeatures (cloud, labels);
      }

      /** \brief Add the feature extracted from the cloud at the specified
        * location as a training example with the given labels.
        * \note this function has a cumulative effect.
        * \param file_name PCD file containing the training data
        * \param label the class label for the training example
        * \return true on success, false on failure (read error or number of entries don't match)
        */
      bool loadTrainingData (std::string file_name, std::string label)
      {
        sensor_msgs::PointCloud2 cloud_blob;
        if (pcl::io::loadPCDFile (file_name.c_str (), cloud_blob) != 0)
          return false;
        return addTrainingData (cloud_blob, label);
      }

      /** \brief Add the feature extracted from the cloud as a training example with the given labels.
        * \note this function has a cumulative effect.
        * \param training_data point cloud for training feature extraction
        * \param label the class label for the training example
        * \return true on success, false on failure (read error or number of entries don't match)
        */
      bool addTrainingData (const sensor_msgs::PointCloud2 &training_data, std::string &label)
      {
        // Create label list containing the single label
        std::vector<std::string> labels;
        labels.push_back (label);

        // Compute the feature from the cloud and add it as a training example
        FeatureCloudPtr vfhs = computeFeature (training_data);
        return addTrainingFeatures(vfhs, labels);
      }

      /** \brief Utility function for the default classification process.
        * \param testing_data the point clouds to be classified
        * \param radius the maximum search radius in feature space -- 300 by default
        * \param minimum_score the score to be given to matches at maximum distance (>0) -- 0.002 by default
        * \return pair of label and score for each relevant training class
        */
      ResultPtr classify (const sensor_msgs::PointCloud2 &testing_data, double radius = 300, double min_score = 0.002)
      {
        // compute the VFH feature for this point cloud
        FeatureCloudPtr vfhs = computeFeature (testing_data);
        // compute gaussian parameter producing the desired minimum score (around 50 for the default values)
        float gaussian_param = - static_cast<float> (radius / log (min_score));
        // TODO accept result to be filled in by reference
        return classifier_.classify(vfhs->points.at (0), radius, gaussian_param);
      }

      /** \brief Extract the VFH feature describing the given point cloud.
        * \param points point cloud for feature extraction
        * \param radius search radius for normal estimation -- 0.03 m by default
        * \return point cloud containing the extracted feature
        */
      FeatureCloudPtr computeFeature (const sensor_msgs::PointCloud2 &points, double radius = 0.03)
      {
        pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ> ());
        pcl::fromROSMsg (points, *cloud);
        return pcl::computeVFH<pcl::PointXYZ> (cloud, radius);
      }
  };
}

#endif /* VFHCLASSIFICATION_H_ */
