target_adaptive_tracking_nodelet.cpp

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// Copyright (C) 2015 by Krishneel Chaudhary, JSK Lab,
// The University of Tokyo, Japan
 
#include <jsk_pcl_ros/target_adaptive_tracking.h>
#include <map>
#include <string>
 
namespace jsk_pcl_ros 
{
   TargetAdaptiveTracking::TargetAdaptiveTracking() :
      DiagnosticNodelet("target_adaptive_tracking"),
      init_counter_(0),
      update_counter_(0),
      growth_rate_(1.15)
   {
      this->object_reference_ = ModelsPtr(new Models);
      this->background_reference_ = ModelsPtr(new Models);
      this->previous_template_ = pcl::PointCloud<PointT>::Ptr(
         new pcl::PointCloud<PointT>);
   }
 
   TargetAdaptiveTracking::~TargetAdaptiveTracking() {
     // message_filters::Synchronizer needs to be called reset
     // before message_filters::Subscriber is freed.
     // Calling reset fixes the following error on shutdown of the nodelet:
     // terminate called after throwing an instance of
     // 'boost::exception_detail::clone_impl<boost::exception_detail::error_info_injector<boost::lock_error> >'
     //     what():  boost: mutex lock failed in pthread_mutex_lock: Invalid argument
     // Also see https://github.com/ros/ros_comm/issues/720 .
     sync_.reset();
     obj_sync_.reset();
   }
 
   void TargetAdaptiveTracking::onInit()
   {
      DiagnosticNodelet::onInit();
 
      pnh_->param("use_tf", use_tf_, false);
      pnh_->param("parent_frame_id", parent_frame_id_, std::string("/track_result"));
      pnh_->param("child_frame_id", child_frame_id_, std::string("/camera_rgb_optical_frame"));
 
      srv_ = boost::make_shared<dynamic_reconfigure::Server<Config> >(*pnh_);
      dynamic_reconfigure::Server<Config>::CallbackType f =
       boost::bind(&TargetAdaptiveTracking::configCallback, this, _1, _2);
      srv_->setCallback(f);
 
      this->pub_cloud_ = advertise<sensor_msgs::PointCloud2>(
         *pnh_, "/target_adaptive_tracking/output/cloud", 1);
 
      // this->pub_templ_ = advertise<sensor_msgs::PointCloud2>(
      //    *pnh_, "/target_adaptive_tracking/output/template", 1);
 
      this->pub_templ_ = advertise<sensor_msgs::PointCloud2>(
         *pnh_, "/selected_pointcloud", 1);
    
      this->pub_sindices_ = advertise<
         jsk_recognition_msgs::ClusterPointIndices>(
            *pnh_, "/target_adaptive_tracking/supervoxel/indices", 1);
    
      this->pub_scloud_ = advertise<sensor_msgs::PointCloud2>(
         *pnh_, "/target_adaptive_tracking/supervoxel/cloud", 1);
 
      this->pub_normal_ = advertise<sensor_msgs::PointCloud2>(
         *pnh_, "/target_adaptive_tracking/output/normal", 1);
 
      this->pub_tdp_ = advertise<jsk_recognition_msgs::ClusterPointIndices>(
            *pnh_, "/target_adaptive_tracking/supervoxel/tdp_indices", 1);
 
      this->pub_inliers_ = advertise<sensor_msgs::PointCloud2>(
         *pnh_, "/target_adaptive_tracking/output/inliers", 1);
 
      this->pub_centroids_ = advertise<sensor_msgs::PointCloud2>(
         *pnh_, "/target_adaptive_tracking/output/centroids", 1);
 
      this->pub_pose_ = advertise<geometry_msgs::PoseStamped>(
         *pnh_, "/target_adaptive_tracking/output/object_pose", 1);
 
      this->pub_prob_ = advertise<sensor_msgs::PointCloud2>(
         *pnh_, "/target_adaptive_tracking/output/probability_map", 1);
 
      onInitPostProcess();
   }
 
   void TargetAdaptiveTracking::subscribe()
   {
      this->sub_obj_cloud_.subscribe(*pnh_, "input_obj_cloud", 1);
      this->sub_bkgd_cloud_.subscribe(*pnh_, "input_bkgd_cloud", 1);
      this->sub_obj_pose_.subscribe(*pnh_, "input_obj_pose", 1);
      this->obj_sync_ = boost::make_shared<message_filters::Synchronizer<
         ObjectSyncPolicy> >(100);
      this->obj_sync_->connectInput(
         sub_obj_cloud_, sub_bkgd_cloud_, sub_obj_pose_);
      this->obj_sync_->registerCallback(
         boost::bind(&TargetAdaptiveTracking::objInitCallback,
                     this, _1, _2, _3));
      this->sub_cloud_.subscribe(*pnh_, "input_cloud", 1);
      this->sub_pose_.subscribe(*pnh_, "input_pose", 1);
      this->sync_ = boost::make_shared<message_filters::Synchronizer<
         SyncPolicy> >(100);
      this->sync_->connectInput(sub_cloud_, sub_pose_);
      this->sync_->registerCallback(
         boost::bind(&TargetAdaptiveTracking::callback,
                     this, _1, _2));
   }
 
   void TargetAdaptiveTracking::unsubscribe()
   {
      this->sub_cloud_.unsubscribe();
      this->sub_pose_.unsubscribe();
      this->sub_obj_cloud_.unsubscribe();
      this->sub_obj_pose_.unsubscribe();
   }
 
   void TargetAdaptiveTracking::objInitCallback(
      const sensor_msgs::PointCloud2::ConstPtr &cloud_msg,
      const sensor_msgs::PointCloud2::ConstPtr &bkgd_msg,
      const geometry_msgs::PoseStamped::ConstPtr &pose_msg)
   {
      pcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PointT>);
      pcl::fromROSMsg(*cloud_msg, *cloud);
      pcl::PointCloud<PointT>::Ptr bkgd_cloud (new pcl::PointCloud<PointT>);
      pcl::fromROSMsg(*bkgd_msg, *bkgd_cloud);
      if (this->init_counter_++ > 0) {
         ROS_WARN("Object is re-initalized! stopping & reseting...");
      }
      if (!cloud->empty() && !bkgd_cloud->empty()) {
         this->motion_history_.clear();
         PointXYZRPY motion_displacement;
         this->estimatedPFPose(pose_msg, motion_displacement);
         this->previous_pose_ = this->current_pose_;
         this->object_reference_ = ModelsPtr(new Models);
         this->processInitCloud(cloud, this->object_reference_);
         this->background_reference_ = ModelsPtr(new Models);
         this->processInitCloud(bkgd_cloud, this->background_reference_);
         previous_distance_ = this->templateCloudFilterLenght(cloud);
         this->previous_template_->clear();
         pcl::copyPointCloud<PointT, PointT>(*cloud, *previous_template_);
         this->previous_transform_ = tf::Transform::getIdentity();
         
         sensor_msgs::PointCloud2 ros_templ;
         pcl::toROSMsg(*cloud, ros_templ);
         ros_templ.header = cloud_msg->header;
         this->pub_templ_.publish(ros_templ);
      }
   }
 
   void TargetAdaptiveTracking::processInitCloud(
      const pcl::PointCloud<PointT>::Ptr cloud,
      ModelsPtr object_reference)
   {
      if (cloud->empty()) {
         ROS_ERROR("OBJECT INIT CLOUD IS EMPTY");
         return;
      }
      float seed_resolution = static_cast<float>(this->seed_resolution_) / 2.0f;
      float seed_factor = seed_resolution;
      for (int i = 0; i < 3; i++) {
         std::map <uint32_t, pcl::Supervoxel<PointT>::Ptr> supervoxel_clusters;
         std::multimap<uint32_t, uint32_t> supervoxel_adjacency;
         this->supervoxelSegmentation(
            cloud, supervoxel_clusters, supervoxel_adjacency, seed_resolution);
         ModelsPtr obj_ref(new Models);
         std::vector<AdjacentInfo> supervoxel_list;
         this->voxelizeAndProcessPointCloud(
            cloud, supervoxel_clusters, supervoxel_adjacency,
            supervoxel_list, obj_ref, true, true, true, true);
         for (int j = 0; j < obj_ref->size(); j++) {
            object_reference->push_back(obj_ref->operator[](j));
         }
         seed_resolution += seed_factor;
      }
   }
 
   void TargetAdaptiveTracking::callback(
      const sensor_msgs::PointCloud2::ConstPtr &cloud_msg,
      const geometry_msgs::PoseStamped::ConstPtr &pose_msg) {
      if (this->object_reference_->empty())
      {
         ROS_WARN("No Model To Track Selected");
         return;
      }
      ROS_INFO("\n\n\033[34m------------RUNNING CALLBACK-------------\033[0m");
      ros::Time begin = ros::Time::now();
      PointXYZRPY motion_displacement;
      this->estimatedPFPose(pose_msg, motion_displacement);
      std::cout << "Motion Displacement: " << motion_displacement << std::endl;
      
      pcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PointT>);
      pcl::fromROSMsg(*cloud_msg, *cloud);
      tf::TransformListener tf_listener;
      tf::StampedTransform transform;
      ros::Time now = ros::Time(0);
      Eigen::Affine3f transform_model = Eigen::Affine3f::Identity();
      tf::Transform update_transform;
      if (use_tf_) {
         bool wft_ok = tf_listener.waitForTransform(
            child_frame_id_, parent_frame_id_, now, ros::Duration(2.0f));
         if (!wft_ok) {
            ROS_ERROR("CANNOT TRANSFORM SOURCE AND TARGET FRAMES");
            return;
         }
         tf_listener.lookupTransform(
            child_frame_id_, parent_frame_id_, now, transform);
         tf::Quaternion tf_quaternion =  transform.getRotation();
         transform_model = Eigen::Affine3f::Identity();
         transform_model.translation() <<
            transform.getOrigin().getX(),
            transform.getOrigin().getY(),
            transform.getOrigin().getZ();
         Eigen::Quaternion<float> quaternion = Eigen::Quaternion<float>(
            tf_quaternion.w(), tf_quaternion.x(),
            tf_quaternion.y(), tf_quaternion.z());
         transform_model.rotate(quaternion);
         tf::Vector3 origin = tf::Vector3(transform.getOrigin().getX(),
                                          transform.getOrigin().getY(),
                                          transform.getOrigin().getZ());
         update_transform.setOrigin(origin);
         tf::Quaternion update_quaternion = tf_quaternion;
         update_transform.setRotation(update_quaternion +
                                      this->previous_transform_.getRotation());
        
      } else {
         transform_model = Eigen::Affine3f::Identity();
         transform_model.translation() << pose_msg->pose.position.x,
            pose_msg->pose.position.y, pose_msg->pose.position.z;
         Eigen::Quaternion<float> pf_quat = Eigen::Quaternion<float>(
            pose_msg->pose.orientation.w, pose_msg->pose.orientation.x,
            pose_msg->pose.orientation.y, pose_msg->pose.orientation.z);
         transform_model.rotate(pf_quat);
         tf::Vector3 origin = tf::Vector3(
            pose_msg->pose.position.x,
            pose_msg->pose.position.y,
            pose_msg->pose.position.z);
         update_transform.setOrigin(origin);
         tf::Quaternion update_quaternion = tf::Quaternion(
            pose_msg->pose.orientation.x, pose_msg->pose.orientation.y,
            pose_msg->pose.orientation.z, pose_msg->pose.orientation.w);
         update_transform.setRotation(update_quaternion *
                                      this->previous_transform_.getRotation());
      }
    
      Eigen::Affine3f transform_reference = Eigen::Affine3f::Identity();
      const int motion_hist_index = static_cast<int>(
         this->motion_history_.size()) - 1;
      transform_reference.translation() <<
         motion_history_[motion_hist_index].x,
         motion_history_[motion_hist_index].y,
         motion_history_[motion_hist_index].z;
      Eigen::Affine3f transformation_matrix = transform_model *
         transform_reference.inverse();
      bool is_cloud_exist = this->filterPointCloud(
         cloud, this->current_pose_, this->object_reference_, 1.5f);
      if (is_cloud_exist && this->update_filter_template_) {
         this->targetDescriptiveSurfelsEstimationAndUpdate(
            cloud, transformation_matrix, motion_displacement,
            cloud_msg->header);
      }
      ros::Time end = ros::Time::now();
      std::cout << "Processing Time: " << end - begin << std::endl;
      
      static tf::TransformBroadcaster br;
      br.sendTransform(tf::StampedTransform(
                          update_transform, cloud_msg->header.stamp,
                          cloud_msg->header.frame_id, "object_pose"));
      this->previous_transform_ = update_transform;
 
      geometry_msgs::PoseStamped update_pose;
      tf::poseTFToMsg(update_transform, update_pose.pose);
      update_pose.header.stamp = cloud_msg->header.stamp;
      update_pose.header.frame_id = child_frame_id_;
      this->pub_pose_.publish(update_pose);
    
      sensor_msgs::PointCloud2 ros_cloud;
      pcl::toROSMsg(*cloud, ros_cloud);
      ros_cloud.header.stamp = cloud_msg->header.stamp;
      ros_cloud.header.frame_id = child_frame_id_;
      this->pub_cloud_.publish(ros_cloud);
   }
 
   void TargetAdaptiveTracking::voxelizeAndProcessPointCloud(
      const pcl::PointCloud<PointT>::Ptr cloud,
      const std::map <uint32_t, pcl::Supervoxel<PointT>::Ptr> &
      supervoxel_clusters,
      const std::multimap<uint32_t, uint32_t> &supervoxel_adjacency,
      std::vector<AdjacentInfo> &supervoxel_list,
      TargetAdaptiveTracking::ModelsPtr &models,
      bool norm_flag, bool feat_flag, bool cent_flag, bool neigh_pfh)
   {
      if (cloud->empty() || supervoxel_clusters.empty()) {
         return;
      }
      models = ModelsPtr(new Models);
      int icounter = 0;
      for (std::multimap<uint32_t, pcl::Supervoxel<PointT>::Ptr>::const_iterator
              label_itr = supervoxel_clusters.begin(); label_itr !=
              supervoxel_clusters.end(); label_itr++) {
         ReferenceModel ref_model;
         ref_model.flag = true;
         ref_model.supervoxel_index = label_itr->first;
         uint32_t supervoxel_label = label_itr->first;
         pcl::Supervoxel<PointT>::Ptr supervoxel =
            supervoxel_clusters.at(supervoxel_label);
         if (supervoxel->voxels_->size() > min_cluster_size_) {
            std::vector<uint32_t> adjacent_voxels;
            for (std::multimap<uint32_t, uint32_t>::const_iterator
                    adjacent_itr = supervoxel_adjacency.equal_range(
                       supervoxel_label).first; adjacent_itr !=
                    supervoxel_adjacency.equal_range(
                       supervoxel_label).second; ++adjacent_itr) {
               pcl::Supervoxel<PointT>::Ptr neighbor_supervoxel =
                  supervoxel_clusters.at(adjacent_itr->second);
               if (neighbor_supervoxel->voxels_->size() >
                   min_cluster_size_) {
                  adjacent_voxels.push_back(adjacent_itr->second);
               }
               icounter++;
            }
            AdjacentInfo a_info;
            a_info.adjacent_voxel_indices[supervoxel_label] = adjacent_voxels;
            supervoxel_list.push_back(a_info);
            a_info.voxel_index = supervoxel_label;
            ref_model.cluster_neigbors = a_info;
            ref_model.cluster_cloud = pcl::PointCloud<PointT>::Ptr(
               new pcl::PointCloud<PointT>);
            ref_model.cluster_cloud = supervoxel->voxels_;
            if (norm_flag) {
               ref_model.cluster_normals = pcl::PointCloud<pcl::Normal>::Ptr(
                  new pcl::PointCloud<pcl::Normal>);
               ref_model.cluster_normals = supervoxel->normals_;
            }
            if (feat_flag) {
               this->computeCloudClusterRPYHistogram(
                  ref_model.cluster_cloud,
                  ref_model.cluster_normals,
                  ref_model.cluster_vfh_hist);
               this->computeColorHistogram(
                  ref_model.cluster_cloud,
                  ref_model.cluster_color_hist);
            }
            if (cent_flag) {
               ref_model.cluster_centroid = Eigen::Vector4f();
               ref_model.cluster_centroid =
                  supervoxel->centroid_.getVector4fMap();
            }
            ref_model.flag = false;
            ref_model.match_counter = 0;
            models->push_back(ref_model);
         }
      }
      std::cout << "Cloud Voxel size: "  << models->size() << std::endl;
 
      // compute the local pfh
      if (neigh_pfh) {
         for (int i = 0; i < models->size(); i++) {
            this->computeLocalPairwiseFeautures(
               supervoxel_clusters,
               models->operator[](i).cluster_neigbors.adjacent_voxel_indices,
               models->operator[](i).neigbour_pfh);
         }
      }
   }
 
   void TargetAdaptiveTracking::targetDescriptiveSurfelsEstimationAndUpdate(
      pcl::PointCloud<PointT>::Ptr cloud,
      const Eigen::Affine3f &transformation_matrix,
      const TargetAdaptiveTracking::PointXYZRPY &motion_disp,
      const std_msgs::Header header)
   {
      if (cloud->empty()) {
         ROS_ERROR("ERROR: Global Layer Input Empty");
         return;
      }
      pcl::PointCloud<PointT>::Ptr n_cloud(new pcl::PointCloud<PointT>);
      Models obj_ref = *object_reference_;
      std::map <uint32_t, pcl::Supervoxel<PointT>::Ptr> supervoxel_clusters;
      std::multimap<uint32_t, uint32_t> supervoxel_adjacency;
      this->supervoxelSegmentation(cloud,
                                   supervoxel_clusters,
                                   supervoxel_adjacency);
      Eigen::Matrix<float, 3, 3> rotation_matrix;
      rotation_matrix = transformation_matrix.rotation();
    
      std::vector<AdjacentInfo> supervoxel_list;
      ModelsPtr t_voxels = ModelsPtr(new Models);
      this->voxelizeAndProcessPointCloud(
         cloud, supervoxel_clusters, supervoxel_adjacency,
         supervoxel_list, t_voxels, true, false, true);
      Models target_voxels = *t_voxels;
    
      ROS_INFO("\033[35m MODEL TRANSITION FOR MATCHING \033[0m");    
      std::map<int, int> matching_indices;
      pcl::PointCloud<PointT>::Ptr template_cloud(new pcl::PointCloud<PointT>);
      for (int j = 0; j < obj_ref.size(); j++) {
         if (!obj_ref[j].flag) {
            float distance = FLT_MAX;
            int nearest_index = -1;
            Eigen::Vector4f obj_centroid;
            obj_centroid = transformation_matrix * obj_ref[j].cluster_centroid;
            for (int i = 0; i < target_voxels.size(); i++) {
               if (!target_voxels[i].flag) {
                  Eigen::Vector4f t_centroid =
                     target_voxels[i].cluster_centroid;
                  t_centroid(3) = 1.0f;
                  float dist = static_cast<float>(
                     pcl::distances::l2(obj_centroid, t_centroid));
                  if (dist < distance) {
                     distance = dist;
                     nearest_index = i;
                  }
               }
            }
            if (nearest_index != -1) {
               matching_indices[j] = nearest_index;
            }
            const int motion_hist_index = this->motion_history_.size() - 1;
            obj_ref[j].centroid_distance(0) = obj_ref[j].cluster_centroid(0) -
               this->motion_history_[motion_hist_index].x;
            obj_ref[j].centroid_distance(1) = obj_ref[j].cluster_centroid(1) -
               this->motion_history_[motion_hist_index].y;
            obj_ref[j].centroid_distance(2) = obj_ref[j].cluster_centroid(2) -
               this->motion_history_[motion_hist_index].z;
         }
      }
      ROS_INFO("\033[35m MATCHING THROUGH NIEGBOUR SEARCH \033[0m");
      int counter = 0;
      float connectivity_lenght = 2.0f;
      pcl::PointCloud<PointT>::Ptr est_centroid_cloud(
         new pcl::PointCloud<PointT>);
      std::multimap<uint32_t, Eigen::Vector3f> estimated_centroids;
      std::multimap<uint32_t, float> estimated_match_prob;
      std::multimap<uint32_t, ReferenceModel*> estimated_match_info;
      std::vector<uint32_t> best_match_index;
      std::multimap<uint32_t, float> all_probabilites;
      for (std::map<int, int>::iterator itr = matching_indices.begin();
           itr != matching_indices.end(); itr++) {
         if (!target_voxels[itr->second].flag) {
            std::map<uint32_t, std::vector<uint32_t> > neigb =
               target_voxels[itr->second].cluster_neigbors.adjacent_voxel_indices;
            uint32_t v_ind = target_voxels[
               itr->second].cluster_neigbors.voxel_index;
            uint32_t bm_index = v_ind;
            float probability = 0.0f;
            ReferenceModel *voxel_model = new ReferenceModel;
            probability = this->targetCandidateToReferenceLikelihood<float>(
               obj_ref[itr->first], target_voxels[itr->second].cluster_cloud,
               target_voxels[itr->second].cluster_normals,
               target_voxels[itr->second].cluster_centroid, voxel_model);
            voxel_model->query_index = itr->first;
 
            // TODO(.) collect the neigbours here instead of next for
            // loop
 
            bool is_voxel_adjacency_info = true;
            float local_weight = 0.0f;
            if (is_voxel_adjacency_info) {
               cv::Mat histogram_phf;
               this->computeLocalPairwiseFeautures(
                  supervoxel_clusters, neigb, histogram_phf);
               voxel_model->cluster_neigbors.adjacent_voxel_indices = neigb;
               voxel_model->neigbour_pfh = histogram_phf.clone();
               float dist_phf = static_cast<float>(
                  cv::compareHist(obj_ref[itr->first].neigbour_pfh,
                                  histogram_phf, CV_COMP_BHATTACHARYYA));
               local_weight = std::exp(-this->structure_scaling_ * dist_phf);
               probability *= local_weight;
            }
            for (std::vector<uint32_t>::iterator it =
                    neigb.find(v_ind)->second.begin();
                 it != neigb.find(v_ind)->second.end(); it++) {
               ReferenceModel *voxel_mod = new ReferenceModel;
               float prob = this->targetCandidateToReferenceLikelihood<float>(
                  obj_ref[itr->first], supervoxel_clusters.at(*it)->voxels_,
                  supervoxel_clusters.at(*it)->normals_,
                  supervoxel_clusters.at(*it)->centroid_.getVector4fMap(),
                  voxel_mod);
               voxel_mod->query_index = itr->first;
               if (is_voxel_adjacency_info) {
                  std::map<uint32_t, std::vector<uint32_t> > local_adjacency;
                  std::vector<uint32_t> list_adj;
                  for (std::multimap<uint32_t, uint32_t>::const_iterator
                          adjacent_itr = supervoxel_adjacency.equal_range(
                             *it).first; adjacent_itr !=
                          supervoxel_adjacency.equal_range(*it).second;
                       ++adjacent_itr) {
                     list_adj.push_back(adjacent_itr->second);
                  }
                  local_adjacency[*it] = list_adj;
                  cv::Mat local_phf;
                  this->computeLocalPairwiseFeautures(
                     supervoxel_clusters, local_adjacency, local_phf);
                  voxel_mod->neigbour_pfh = local_phf.clone();
                  voxel_mod->cluster_neigbors.adjacent_voxel_indices =
                     local_adjacency;
                  float dist_phf = static_cast<float>(
                     cv::compareHist(obj_ref[itr->first].neigbour_pfh,
                                     local_phf, CV_COMP_BHATTACHARYYA));
                  float phf_prob = std::exp(-this->structure_scaling_ * dist_phf);
                  local_weight = phf_prob;
                  prob *= phf_prob;
               }
 
               float matching_dist = static_cast<float>(pcl::distances::l2(
                     supervoxel_clusters.at(v_ind)->centroid_.getVector4fMap(),
                     supervoxel_clusters.at(*it)->centroid_.getVector4fMap()));
               if (matching_dist > this->seed_resolution_ / connectivity_lenght) {
                  prob *= 0.0f;
               }
               if (prob > probability) {
                  probability = prob;
                  bm_index = *it;
                  voxel_model = voxel_mod;
               }
            }
            all_probabilites.insert(
               std::pair<uint32_t, float>(bm_index, probability));
            if (probability > threshold_) {
               Eigen::Vector3f estimated_position = supervoxel_clusters.at(
                  bm_index)->centroid_.getVector3fMap() - rotation_matrix *
                  obj_ref[itr->first].centroid_distance /* local_weight*/;
               Eigen::Vector4f estimated_pos = Eigen::Vector4f(
                  estimated_position(0), estimated_position(1),
                  estimated_position(2), 0.0f);
               float match_dist = static_cast<float>(
                  pcl::distances::l2(estimated_pos, current_pose_));
 
               if (match_dist < this->seed_resolution_ / connectivity_lenght) {
                  best_match_index.push_back(bm_index);
                  estimated_centroids.insert(
                     std::pair<uint32_t, Eigen::Vector3f>(
                        bm_index, estimated_position));
                  estimated_match_prob.insert(
                     std::pair<uint32_t, float>(bm_index, probability));
                  estimated_match_info.insert(
                     std::pair<uint32_t, ReferenceModel*>(
                        bm_index, voxel_model));
                  obj_ref[itr->first].history_window.push_back(1);
                  PointT pt;
                  pt.x = estimated_position(0);
                  pt.y = estimated_position(1);
                  pt.z = estimated_position(2);
                  pt.r = 255;
                  est_centroid_cloud->push_back(pt);
                  counter++;
               } else {
                  ROS_WARN("-- Outlier not added...");
               }
            } else {
               obj_ref[itr->first].history_window.push_back(0);
            }
         }
      }
      pcl::PointCloud<PointT>::Ptr prob_cloud(new pcl::PointCloud<PointT>);
      for (std::multimap<uint32_t, float>::iterator it = all_probabilites.begin();
           it != all_probabilites.end(); it++) {
         if (it->second > eps_distance_) {
            for (int i = 0; i < supervoxel_clusters.at(
                    it->first)->voxels_->size(); i++) {
               PointT pt = supervoxel_clusters.at(it->first)->voxels_->points[i];
               pt.r = 255 * it->second;
               pt.g = 255 * it->second;
               pt.b = 255 * it->second;
               prob_cloud->push_back(pt);
            }
         }
      }
      sensor_msgs::PointCloud2 ros_prob;
      pcl::toROSMsg(*prob_cloud, ros_prob);
      ros_prob.header = header;
      this->pub_prob_.publish(ros_prob);
    
      pcl::PointCloud<PointT>::Ptr inliers(new pcl::PointCloud<PointT>);
      std::vector<uint32_t> outlier_index;
 
      ROS_INFO("\033[35m OUTLIER FILTERING VIA BACKPROJECTION \033[0m");
      Eigen::Matrix<float, 3, 3> inv_rotation_matrix = rotation_matrix.inverse();
      PointT ptt;
      ptt.x = previous_pose_(0);
      ptt.y = previous_pose_(1);
      ptt.z = previous_pose_(2);
      ptt.b = 255;
      inliers->push_back(ptt);
      std::vector<uint32_t> matching_indx = best_match_index;
      best_match_index.clear();
      for (std::vector<uint32_t>::iterator it = matching_indx.begin();
           it != matching_indx.end(); it++) {
         Eigen::Vector4f cur_pt = supervoxel_clusters.at(
            *it)->centroid_.getVector4fMap();
         Eigen::Vector3f demean_pts = Eigen::Vector3f();
         demean_pts(0) = cur_pt(0) - this->current_pose_(0);
         demean_pts(1) = cur_pt(1) - this->current_pose_(1);
         demean_pts(2) = cur_pt(2) - this->current_pose_(2);
         int query_idx = estimated_match_info.find(*it)->second->query_index;
         Eigen::Vector3f abs_position = -(inv_rotation_matrix * demean_pts) +
            obj_ref[query_idx].cluster_centroid.head<3>();
         Eigen::Vector4f prev_vote = Eigen::Vector4f(
            abs_position(0), abs_position(1), abs_position(2), 0.0f);
         float matching_dist = static_cast<float>(
            pcl::distances::l2(prev_vote, this->previous_pose_));
 
         PointT pt;
         pt.x = abs_position(0);
         pt.y = abs_position(1);
         pt.z = abs_position(2);
         if (matching_dist < this->seed_resolution_ / connectivity_lenght) {
            best_match_index.push_back(*it);
            pt.r = 255;
            pt.b = 255;
         } else {
            pt.g = 255;
            pt.b = 255;
         }
         inliers->push_back(pt);
      }
      /*
        std::cout << "TOTAL POINTS: " << estimated_centroids.size() << std::endl;
        std::cout << "Cloud Size: " << est_centroid_cloud->size() << "\t"
        << inliers->size() << "\t" << counter << "\t Best Match: "
        << best_match_index.size() << "\t Query-Test"
        << matching_indices.size() << std::endl;
      */
      
      pcl::PointCloud<pcl::PointXYZRGBNormal>::Ptr centroid_normal(
         new pcl::PointCloud<pcl::PointXYZRGBNormal>);
 
      // visualization of removed normal
      for (std::vector<uint32_t>::iterator it = outlier_index.begin();
           it != outlier_index.end(); it++) {
         Eigen::Vector4f c_centroid = supervoxel_clusters.at(
            *it)->centroid_.getVector4fMap();
         Eigen::Vector4f c_normal = this->cloudMeanNormal(
            supervoxel_clusters.at(*it)->normals_);
         centroid_normal->push_back(
            this->convertVector4fToPointXyzRgbNormal(
               c_centroid, c_normal, cv::Scalar(0, 0, 255)));
      }
      
      ROS_INFO("\033[35m CONVEX VOXELS \033[0m");
      pcl::PointCloud<PointT>::Ptr output(new pcl::PointCloud<PointT>);
      std::vector<uint32_t> neigb_lookup;
      neigb_lookup = best_match_index;
      std::vector<uint32_t> convex_ok;
      std::map<uint32_t, ReferenceModel*> convex_local_voxels;
    
      for (std::vector<uint32_t>::iterator it = best_match_index.begin();
           it != best_match_index.end(); it++) {
         std::pair<std::multimap<uint32_t, uint32_t>::iterator,
                   std::multimap<uint32_t, uint32_t>::iterator> ret;
         ret = supervoxel_adjacency.equal_range(*it);
         Eigen::Vector4f c_centroid = supervoxel_clusters.at(
            *it)->centroid_.getVector4fMap();
         Eigen::Vector4f c_normal = this->cloudMeanNormal(
            supervoxel_clusters.at(*it)->normals_);
 
         centroid_normal->push_back(
            this->convertVector4fToPointXyzRgbNormal(
               c_centroid, c_normal, cv::Scalar(255, 0, 0)));
         
         for (std::multimap<uint32_t, uint32_t>::iterator itr = ret.first;
              itr != ret.second; itr++) {
            bool is_process_neigh = true;
            for (std::vector<uint32_t>::iterator lup_it = neigb_lookup.begin();
                 lup_it != neigb_lookup.end(); lup_it++) {
               if (*lup_it == itr->second) {
                  is_process_neigh = false;
                  break;
               }
            }
            if (!supervoxel_clusters.at(itr->second)->voxels_->empty() &&
                is_process_neigh) {
               bool is_common_neigh = false;
               if (!is_common_neigh) {
                  neigb_lookup.push_back(itr->second);
                  Eigen::Vector4f n_centroid = supervoxel_clusters.at(
                     itr->second)->centroid_.getVector4fMap();
                  Eigen::Vector4f n_normal = this->cloudMeanNormal(
                     supervoxel_clusters.at(itr->second)->normals_);
                  float convx_weight = this->localVoxelConvexityLikelihood<
                     float>(c_centroid, c_normal, n_centroid, n_normal);
                  if (convx_weight > 0.0f) {
                     *output = *output + *supervoxel_clusters.at(
                        itr->second)->voxels_;
 
                     ReferenceModel *ref_model = new ReferenceModel;
                     this->processVoxelForReferenceModel(
                        supervoxel_clusters, supervoxel_adjacency,
                        itr->second, ref_model);
                     if (!ref_model->flag) {
                        Eigen::Vector4f convx_centroid = Eigen::Vector4f();
                        convx_centroid = transformation_matrix.inverse() *
                           ref_model->cluster_centroid;
                        for (int j = 0; j < this->object_reference_->size();
                             j++) {
                           float rev_match_dist = static_cast<float>(
                              pcl::distances::l2(convx_centroid,
                               this->object_reference_->operator[](
                                  j).cluster_centroid));
                           if (rev_match_dist < this->seed_resolution_) {
                              float convx_dist = static_cast<float>(
                                 cv::compareHist(ref_model->cluster_vfh_hist,
                                                 object_reference_->operator[](
                                                    j).cluster_vfh_hist,
                                                 CV_COMP_BHATTACHARYYA));
                              float convx_prob = std::exp(
                                 -1 * this->vfh_scaling_ * convx_dist);
                              if (convx_prob > this->threshold_) {
                                 ref_model->query_index = static_cast<int>(j);
                                 estimated_match_info.insert(
                                    std::pair<int32_t, ReferenceModel*>(
                                       itr->second, ref_model));
                                 convex_ok.push_back(itr->second);
                                 estimated_match_prob.insert(
                                    std::pair<uint32_t, float>(
                                       itr->second, convx_prob));
                                 centroid_normal->push_back(
                                    this->convertVector4fToPointXyzRgbNormal(
                                       n_centroid, n_normal,
                                       cv::Scalar(0, 255, 0)));
                                 ROS_INFO("\033[34m Added VOXEL \033[0m");
                                 break;
                              }
                           } else {
                              // TODO(complete): here
                              // mark and test on object in next (t +
                              // 1)
                              convex_local_voxels[itr->second] = ref_model;
                              centroid_normal->push_back(
                                 this->convertVector4fToPointXyzRgbNormal(
                                    n_centroid, n_normal, cv::Scalar(0, 255, 0)));
                           }
                        }
                     }
                  }
               } else {
                  std::pair<
                     std::multimap<uint32_t, uint32_t>::iterator,
                     std::multimap<uint32_t, uint32_t>::iterator> comm_neigb;
                  comm_neigb = supervoxel_adjacency.equal_range(itr->second);
                  uint32_t common_neigbour_index = 0;
                  for (std::multimap<uint32_t, uint32_t>::iterator c_itr =
                          comm_neigb.first; c_itr != comm_neigb.second;
                       c_itr++) {
                     if (!supervoxel_clusters.at(
                            c_itr->second)->voxels_->empty()) {
                        bool is_common_neigh = false;
                        for (std::map<uint32_t, uint32_t>::iterator itr_ret =
                                supervoxel_adjacency.equal_range(c_itr->first).
                                first; itr_ret !=supervoxel_adjacency.equal_range(
                                   c_itr->first).second; itr_ret++) {
                           if (itr_ret->second == *it) {
                              is_common_neigh = true;
                              common_neigbour_index = c_itr->second;
                              break;
                           }
                        }
                        if (is_common_neigh) {
                           break;
                        }
                     }
                  }
                  if (common_neigbour_index > 0) {
                     Eigen::Vector4f n_centroid_b = supervoxel_clusters.at(
                        itr->second)->centroid_.getVector4fMap();
                     Eigen::Vector4f n_normal_b = this->cloudMeanNormal(
                        supervoxel_clusters.at(itr->second)->normals_);
                     Eigen::Vector4f n_centroid_c = supervoxel_clusters.at(
                        common_neigbour_index)->centroid_.getVector4fMap();
                     Eigen::Vector4f n_normal_c = this->cloudMeanNormal(
                        supervoxel_clusters.at(common_neigbour_index)->normals_);
                     float convx_weight_ab = this->localVoxelConvexityLikelihood<
                        float>(c_centroid, c_normal, n_centroid_b, n_normal_b);
                     float convx_weight_ac = this->localVoxelConvexityLikelihood<
                        float>(c_centroid, c_normal, n_centroid_c, n_normal_c);
                     float convx_weight_bc = this->localVoxelConvexityLikelihood<
                        float>(n_centroid_b, n_normal_b, n_centroid_c,
                               n_normal_c);
                     if (convx_weight_ab != 0.0f &&
                         convx_weight_ac != 0.0f &&
                         convx_weight_bc != 0.0f) {
                        *output = *output + *supervoxel_clusters.at(
                           itr->second)->voxels_;
                        centroid_normal->push_back(
                           this->convertVector4fToPointXyzRgbNormal(
                              n_centroid_b, n_normal_b, cv::Scalar(0, 255, 0)));
                        neigb_lookup.push_back(itr->second);
                        ReferenceModel *ref_model = new ReferenceModel;
                        this->processVoxelForReferenceModel(
                           supervoxel_clusters, supervoxel_adjacency,
                           itr->second, ref_model);
                        if (!ref_model->flag) {
                           Eigen::Vector4f convx_centroid = Eigen::Vector4f();
                           convx_centroid = transformation_matrix.inverse() *
                              ref_model->cluster_centroid;
                           for (int j = 0; j < this->object_reference_->size(); j++) {
                              float rev_match_dist = static_cast<float>(
                                 pcl::distances::l2(convx_centroid,
                                    this->object_reference_->operator[](
                                       j).cluster_centroid));
                              if (rev_match_dist < this->seed_resolution_) {
                                 float convx_dist = static_cast<float>(
                                    cv::compareHist(ref_model->cluster_vfh_hist,
                                       object_reference_->operator[](
                                          j).cluster_vfh_hist, CV_COMP_BHATTACHARYYA));
                                 float convx_prob = std::exp(
                                    -1 * this->vfh_scaling_ * convx_dist);
                                 if (convx_prob > this->threshold_) {
                                    ref_model->query_index = static_cast<int>(j);
                                    estimated_match_info.insert(
                                       std::pair<int32_t, ReferenceModel*>(
                                          itr->second, ref_model));
                                    convex_ok.push_back(itr->second);
                                    estimated_match_prob.insert(
                                       std::pair<uint32_t, float>(
                                          itr->second, convx_prob));
                                    centroid_normal->push_back(
                                       this->convertVector4fToPointXyzRgbNormal(
                                          n_centroid_b, n_normal_b,
                                          cv::Scalar(0, 255, 0)));
                                    break;
                                 }
                              } else {
                                 convex_local_voxels[itr->second] = ref_model;
                              }
                           }
                        }
                     }
                  }
               }
            }
         }
         *output = *output + *supervoxel_clusters.at(*it)->voxels_;
      }
      for (int i = 0; i < convex_ok.size(); i++) {
         best_match_index.push_back(convex_ok[i]);
      }
 
      ModelsPtr transform_model (new Models);
      this->transformModelPrimitives(
         this->object_reference_, transform_model, transformation_matrix);
      obj_ref.clear();
      obj_ref = *transform_model;
      if (best_match_index.size() > 2 && this->update_tracker_reference_) {
         ROS_INFO("\n\033[32mUpdating Tracking Reference Model\033[0m \n");
         std::map<int, ReferenceModel> matching_surfels;
         for (std::vector<uint32_t>::iterator it = best_match_index.begin();
              it != best_match_index.end(); it++) {
            float adaptive_factor = estimated_match_prob.find(*it)->second;
            std::pair<std::multimap<uint32_t, ReferenceModel*>::iterator,
                      std::multimap<uint32_t, ReferenceModel*>::iterator> ret;
            ret = estimated_match_info.equal_range(*it);
            for (std::multimap<uint32_t, ReferenceModel*>::iterator itr =
                    ret.first; itr != ret.second; ++itr) {
               cv::Mat nvfh_hist = cv::Mat::zeros(
                  itr->second->cluster_vfh_hist.size(), CV_32F);
               nvfh_hist = itr->second->cluster_vfh_hist * adaptive_factor +
                  obj_ref[itr->second->query_index].cluster_vfh_hist *
                  (1 - adaptive_factor);
               cv::normalize(nvfh_hist, nvfh_hist, 0, 1,
                             cv::NORM_MINMAX, -1, cv::Mat());
               cv::Mat ncolor_hist = cv::Mat::zeros(
                  itr->second->cluster_color_hist.size(),
                  itr->second->cluster_color_hist.type());
               ncolor_hist = itr->second->cluster_color_hist * adaptive_factor +
                  obj_ref[itr->second->query_index].cluster_color_hist *
                  (1 - adaptive_factor);
               cv::normalize(ncolor_hist, ncolor_hist, 0, 1,
                             cv::NORM_MINMAX, -1, cv::Mat());
               cv::Mat local_phf = cv::Mat::zeros(
                  itr->second->neigbour_pfh.size(),
                  itr->second->neigbour_pfh.type());
               local_phf = itr->second->neigbour_pfh * adaptive_factor +
                  obj_ref[itr->second->query_index].neigbour_pfh *
                  (1 - adaptive_factor);
               cv::normalize(local_phf, local_phf, 0, 1,
                             cv::NORM_MINMAX, -1, cv::Mat());
               int query_idx = estimated_match_info.find(
                  *it)->second->query_index;
               obj_ref[query_idx].cluster_cloud = supervoxel_clusters.at(
                  *it)->voxels_;
               obj_ref[query_idx].cluster_vfh_hist = nvfh_hist.clone();
               obj_ref[query_idx].cluster_color_hist = ncolor_hist.clone();
               obj_ref[query_idx].cluster_normals = supervoxel_clusters.at(
                  *it)->normals_;
               obj_ref[query_idx].cluster_centroid = supervoxel_clusters.at(
                  *it)->centroid_.getVector4fMap();
               obj_ref[query_idx].neigbour_pfh = local_phf.clone();
               obj_ref[query_idx].flag = false;
               matching_surfels[query_idx] = obj_ref[query_idx];
               obj_ref[query_idx].match_counter++;
            }
         }
         this->motion_history_.push_back(this->tracker_pose_);
         this->previous_pose_ = this->current_pose_;
         // std::cout << "Updating Ref Model: " << matching_surfels.size()
         //           << "\t Convex: " << convex_local_voxels.size()
         //           << std::endl;
       
         for (std::map<int, ReferenceModel>::iterator it =
                 matching_surfels.begin(); it != matching_surfels.end(); it++) {
            this->object_reference_->operator[](it->first) = it->second;
         }
         for (std::map<uint32_t, ReferenceModel*>::iterator it =
                 convex_local_voxels.begin(); it != convex_local_voxels.begin();
              it++) {
            this->object_reference_->push_back(*(it->second));
         }
         ModelsPtr tmp_model(new Models);
         if (this->update_counter_++ == this->history_window_size_) {
            for (int i = 0; i < this->object_reference_->size(); i++) {
               if (this->object_reference_->operator[](i).match_counter > 0) {
                  ReferenceModel renew_model;
                  renew_model = this->object_reference_->operator[](i);
                  tmp_model->push_back(renew_model);
               } else {
                  std::cout << "\033[033m OUTDATED MODEL \033[0m" << std::endl;
               }
            }
            this->update_counter_ = 0;
            this->object_reference_->clear();
            this->object_reference_ = tmp_model;
         }       
     
      } else {
         ROS_WARN("TRACKING MODEL CURRENTLY SET TO STATIC\n");
      }
      template_cloud->clear();
      int tmp_counter = 0;
      float argmax_lenght = 0.0f;
      for (int i = 0; i < this->object_reference_->size(); i++) {
         Eigen::Vector4f surfel_centroid = Eigen::Vector4f();
         surfel_centroid = this->object_reference_->operator[](
            i).cluster_centroid;
         surfel_centroid(3) = 0.0f;
         float surfel_dist = static_cast<float>(
            pcl::distances::l2(surfel_centroid, current_pose_));
         if (surfel_dist > argmax_lenght) {
            argmax_lenght = surfel_dist;
         }
         if (surfel_dist < (this->previous_distance_ * growth_rate_)) {            
            float probability = 0.0f;
            for (int j = 0; j < this->background_reference_->size(); j++) {
               ReferenceModel *r_mod = new ReferenceModel;
               float prob = this->targetCandidateToReferenceLikelihood<float>(
                  this->object_reference_->operator[](i),
                  this->background_reference_->operator[](j).cluster_cloud,
                  this->background_reference_->operator[](j).cluster_normals,
                  this->background_reference_->operator[](j).cluster_centroid,
                  r_mod);
               if (prob > probability) {
                  probability = prob;
               }
            }
            if (probability < 0.60f) {
               *template_cloud = *template_cloud + *(
                  this->object_reference_->operator[](i).cluster_cloud);   
               tmp_counter++;
            } else {
               ROS_INFO("\033[35m SURFEL REMOVED AS BACKGRND \033[0m");
            }
         } else {
            ROS_INFO("\033[35m SURFEL REMOVED \033[0m]");
         }
      }
      if (argmax_lenght > (growth_rate_ * previous_distance_)) {
         argmax_lenght = previous_distance_ * growth_rate_;
      } else if (argmax_lenght < ((1 - growth_rate_) * previous_distance_)) {
         argmax_lenght = (1 - growth_rate_) * previous_distance_;
      }
      this->filterCloudForBoundingBoxViz(output, this->background_reference_);
      this->previous_distance_ = argmax_lenght;
    
      // std::cout << "\033[031m TEMPLATE SIZE:  \033[0m" << template_cloud->size()
      //           << std::endl;    
 
      if (tmp_counter < 1) {
         template_cloud->clear();
         pcl::copyPointCloud<PointT, PointT>(*previous_template_, *template_cloud);
      } else {
         ROS_INFO("\033[34m UPDATING INFO...\033[0m");
         // previous_distance_ = this->templateCloudFilterLenght(template_cloud);
         pcl::copyPointCloud<PointT, PointT>(*template_cloud, *previous_template_);
         this->object_reference_ = ModelsPtr(new Models);
         this->processInitCloud(template_cloud, this->object_reference_);
      }
      std::cout << "\033[038m REFERENCE INFO \033[0m"
                << object_reference_->size() << std::endl;
      cloud->clear();
      pcl::copyPointCloud<PointT, PointT>(*output, *cloud);
      pcl::PointIndices tdp_ind;
      for (int i = 0; i < cloud->size(); i++) {
         tdp_ind.indices.push_back(i);
      }
      if (this->update_filter_template_) {
         sensor_msgs::PointCloud2 ros_templ;
         pcl::toROSMsg(*template_cloud, ros_templ);
         ros_templ.header = header;
         this->pub_templ_.publish(ros_templ);
      }
      // visualization of target surfels
      std::vector<pcl::PointIndices> all_indices;
      all_indices.push_back(tdp_ind);
      jsk_recognition_msgs::ClusterPointIndices tdp_indices;
      tdp_indices.cluster_indices = pcl_conversions::convertToROSPointIndices(
         all_indices, header);
      tdp_indices.header = header;
      this->pub_tdp_.publish(tdp_indices);
    
      // for visualization of supervoxel
      sensor_msgs::PointCloud2 ros_svcloud;
      jsk_recognition_msgs::ClusterPointIndices ros_svindices;
      this->publishSupervoxel(
         supervoxel_clusters, ros_svcloud, ros_svindices, header);
      pub_scloud_.publish(ros_svcloud);
      pub_sindices_.publish(ros_svindices);
 
      // for visualization of inliers
      sensor_msgs::PointCloud2 ros_inliers;
      pcl::toROSMsg(*inliers, ros_inliers);
      ros_inliers.header = header;
      this->pub_inliers_.publish(ros_inliers);
    
      // for visualization of initial centroids
      sensor_msgs::PointCloud2 ros_centroids;
      pcl::toROSMsg(*est_centroid_cloud, ros_centroids);
      ros_centroids.header = header;
      this->pub_centroids_.publish(ros_centroids);
    
      // for visualization of normal
      sensor_msgs::PointCloud2 rviz_normal;
      pcl::toROSMsg(*centroid_normal, rviz_normal);
      rviz_normal.header = header;
      this->pub_normal_.publish(rviz_normal);
   }
 
   void TargetAdaptiveTracking::processVoxelForReferenceModel(
      const std::map <uint32_t, pcl::Supervoxel<PointT>::Ptr> supervoxel_clusters,
      const std::multimap<uint32_t, uint32_t> supervoxel_adjacency,
      const uint32_t match_index,
      TargetAdaptiveTracking::ReferenceModel *ref_model)
   {
      if (supervoxel_clusters.empty() || supervoxel_adjacency.empty()) {
         ROS_ERROR("ERROR: empty data for updating voxel ref model");
         return;
      }
      if (supervoxel_clusters.at(
             match_index)->voxels_->size() > this->min_cluster_size_) {
         ref_model->flag = false;
         ref_model->cluster_cloud = supervoxel_clusters.at(
            match_index)->voxels_;
         ref_model->cluster_normals = supervoxel_clusters.at(
            match_index)->normals_;
         ref_model->cluster_centroid = supervoxel_clusters.at(
            match_index)->centroid_.getVector4fMap();
         this->computeCloudClusterRPYHistogram(
            ref_model->cluster_cloud,
            ref_model->cluster_normals,
            ref_model->cluster_vfh_hist);        
         this->computeColorHistogram(
            ref_model->cluster_cloud,
            ref_model->cluster_color_hist);
         std::vector<uint32_t> adjacent_voxels;
         for (std::multimap<uint32_t, uint32_t>::const_iterator adjacent_itr =
                 supervoxel_adjacency.equal_range(match_index).first;
              adjacent_itr != supervoxel_adjacency.equal_range(
                 match_index).second; ++adjacent_itr) {
            pcl::Supervoxel<PointT>::Ptr neighbor_supervoxel =
               supervoxel_clusters.at(adjacent_itr->second);
            if (neighbor_supervoxel->voxels_->size() >
                min_cluster_size_) {
               adjacent_voxels.push_back(adjacent_itr->second);
            }
         }
         AdjacentInfo a_info;
         a_info.adjacent_voxel_indices[match_index] = adjacent_voxels;
         a_info.voxel_index = match_index;
         ref_model->cluster_neigbors = a_info;
         std::map<uint32_t, std::vector<uint32_t> > local_adj;
         local_adj[match_index] = adjacent_voxels;
         this->computeLocalPairwiseFeautures(
            supervoxel_clusters, local_adj, ref_model->neigbour_pfh);
      } else {
         ref_model->flag = true;
      }
   }
 
   template<class T>
   T TargetAdaptiveTracking::targetCandidateToReferenceLikelihood(
      const TargetAdaptiveTracking::ReferenceModel &reference_model,
      const pcl::PointCloud<PointT>::Ptr cloud,
      const pcl::PointCloud<pcl::Normal>::Ptr normal,
      const Eigen::Vector4f &centroid,
      ReferenceModel *voxel_model)
   {
      if (cloud->empty() || normal->empty()) {
         return 0.0f;
      }
      cv::Mat vfh_hist;
      this->computeCloudClusterRPYHistogram(cloud, normal, vfh_hist);
      cv::Mat color_hist;
      this->computeColorHistogram(cloud, color_hist);
      T dist_vfh = static_cast<T>(
         cv::compareHist(vfh_hist,
                         reference_model.cluster_vfh_hist,
                         CV_COMP_BHATTACHARYYA));
      T dist_col = static_cast<T>(
         cv::compareHist(color_hist,
                         reference_model.cluster_color_hist,
                         CV_COMP_BHATTACHARYYA));
      T probability = std::exp(-1 * this->vfh_scaling_ * dist_vfh) *
         std::exp(-1 * this->color_scaling_ * dist_col);
      bool convex_weight = false;
      if (convex_weight) {
         Eigen::Vector4f n_normal = this->cloudMeanNormal(normal);
         Eigen::Vector4f n_centroid = centroid;
         Eigen::Vector4f c_normal = this->cloudMeanNormal(
            reference_model.cluster_normals);
         Eigen::Vector4f c_centroid = reference_model.cluster_centroid;
         float convx_prob = this->localVoxelConvexityLikelihood<float>(
            c_centroid, c_normal, n_centroid, n_normal);
         probability * convx_prob;
      }
      voxel_model->cluster_vfh_hist = vfh_hist.clone();
      voxel_model->cluster_color_hist = color_hist.clone();
      return probability;
   }
 
   template<class T>
   T TargetAdaptiveTracking::localVoxelConvexityLikelihood(
      Eigen::Vector4f c_centroid,
      Eigen::Vector4f c_normal,
      Eigen::Vector4f n_centroid,
      Eigen::Vector4f n_normal) {
      c_centroid(3) = 0.0f;
      c_normal(3) = 0.0f;
      n_centroid(3) = 0.0f;
      n_normal(3) = 0.0f;
      T weight = 1.0f;
      if ((n_centroid - c_centroid).dot(n_normal) > 0) {
         weight = static_cast<T>(std::pow(1 - (c_normal.dot(n_normal)), 2));
         return 1.0f;
      } else {
         // weight = static_cast<T>(1 - (c_normal.dot(n_normal)));
         return 0.0f;
      }
      if (std::isnan(weight)) {
         return 0.0f;
      }
      T probability = std::exp(-1 * weight);
      return probability;
   }
 
   void TargetAdaptiveTracking::backgroundReferenceLikelihood(
      const ModelsPtr background_reference,
      const ModelsPtr target_voxels,
      std::map<uint32_t, float> max_prob)
   {
      if (background_reference->empty() || target_voxels->empty()) {
         ROS_ERROR("INPUT DATA IS EMPTY");
      }
      for (int j = 0; j < target_voxels->size(); j++) {
         float probability = 0.0f;
         for (int i = 0; i < background_reference->size(); i++) {
            ReferenceModel *mod = new ReferenceModel;
            float prob = this->targetCandidateToReferenceLikelihood<float>(
               background_reference->operator[](i),
               target_voxels->operator[](j).cluster_cloud,
               target_voxels->operator[](j).cluster_normals,
               target_voxels->operator[](j).cluster_centroid,
               mod);
            if (prob > probability) {
               probability = prob;
            }
         }
         max_prob[target_voxels->operator[](j).supervoxel_index] = probability;
      }
   }
 
   template<class T>
   void TargetAdaptiveTracking::estimatePointCloudNormals(
      const pcl::PointCloud<PointT>::Ptr cloud,
      pcl::PointCloud<pcl::Normal>::Ptr normals,
      const T k, bool use_knn) const {
      if (cloud->empty()) {
         ROS_ERROR("ERROR: The Input cloud is Empty.....");
         return;
      }
      pcl::NormalEstimationOMP<PointT, pcl::Normal> ne;
      ne.setInputCloud(cloud);
      ne.setNumberOfThreads(8);
      pcl::search::KdTree<PointT>::Ptr tree (new pcl::search::KdTree<PointT> ());
      ne.setSearchMethod(tree);
      if (use_knn) {
         ne.setKSearch(k);
      } else {
         ne.setRadiusSearch(k);
      }    ne.compute(*normals);
   }
 
   void TargetAdaptiveTracking::computeCloudClusterRPYHistogram(
      const pcl::PointCloud<PointT>::Ptr cloud,
      const pcl::PointCloud<pcl::Normal>::Ptr normal,
      cv::Mat &histogram) const
   {
      if (cloud->empty() || normal->empty()){
         ROS_ERROR("ERROR: Empty Input");
         return;
      }
      bool is_gfpfh = false;
      bool is_vfh = true;
      bool is_cvfh = false;
      if (is_gfpfh) {
         pcl::PointCloud<pcl::PointXYZL>::Ptr object(
            new pcl::PointCloud<pcl::PointXYZL>);
         for (int i = 0; i < cloud->size(); i++) {
            pcl::PointXYZL pt;
            pt.x = cloud->points[i].x;
            pt.y = cloud->points[i].y;
            pt.z = cloud->points[i].z;
            pt.label = 1;
            object->push_back(pt);
         }
         pcl::GFPFHEstimation<
            pcl::PointXYZL, pcl::PointXYZL, pcl::GFPFHSignature16> gfpfh;
         gfpfh.setInputCloud(object);
         gfpfh.setInputLabels(object);
         gfpfh.setOctreeLeafSize(0.01);
         gfpfh.setNumberOfClasses(1);
         pcl::PointCloud<pcl::GFPFHSignature16>::Ptr descriptor(
            new pcl::PointCloud<pcl::GFPFHSignature16>);
         gfpfh.compute(*descriptor);
         histogram = cv::Mat(sizeof(char), 16, CV_32F);
         for (int i = 0; i < histogram.cols; i++) {
            histogram.at<float>(0, i) = descriptor->points[0].histogram[i];
 
         }
      }
      if (is_vfh) {
         pcl::VFHEstimation<PointT,
                            pcl::Normal,
                            pcl::VFHSignature308> vfh;
         vfh.setInputCloud(cloud);
         vfh.setInputNormals(normal);
         pcl::search::KdTree<PointT>::Ptr tree(
            new pcl::search::KdTree<PointT>);
         vfh.setSearchMethod(tree);
         pcl::PointCloud<pcl::VFHSignature308>::Ptr vfhs(
            new pcl::PointCloud<pcl::VFHSignature308>());
         vfh.compute(*vfhs);
         histogram = cv::Mat(sizeof(char), 308, CV_32F);
         for (int i = 0; i < histogram.cols; i++) {
            histogram.at<float>(0, i) = vfhs->points[0].histogram[i];
         }
      }
      if (is_cvfh) {
         pcl::CVFHEstimation<PointT,
                             pcl::Normal,
                             pcl::VFHSignature308> cvfh;
         cvfh.setInputCloud(cloud);
         cvfh.setInputNormals(normal);
         pcl::search::KdTree<PointT>::Ptr tree(
            new pcl::search::KdTree<PointT>);
         cvfh.setSearchMethod(tree);
         cvfh.setEPSAngleThreshold(5.0f / 180.0f * M_PI);
         cvfh.setCurvatureThreshold(1.0f);
         cvfh.setNormalizeBins(false);
         pcl::PointCloud<pcl::VFHSignature308>::Ptr cvfhs(
            new pcl::PointCloud<pcl::VFHSignature308>());
         cvfh.compute(*cvfhs);
         histogram = cv::Mat(sizeof(char), 308, CV_32F);
         for (int i = 0; i < histogram.cols; i++) {
            histogram.at<float>(0, i) = cvfhs->points[0].histogram[i];
         }
      }
   }
 
   void TargetAdaptiveTracking::computeColorHistogram(
      const pcl::PointCloud<PointT>::Ptr cloud,
      cv::Mat &hist, const int hBin, const int sBin, bool is_norm) const
   {
      cv::Mat pixels = cv::Mat::zeros(
         sizeof(char), static_cast<int>(cloud->size()), CV_8UC3);
      for (int i = 0; i < cloud->size(); i++) {
         cv::Vec3b pix_val;
         pix_val[0] = cloud->points[i].b;
         pix_val[1] = cloud->points[i].g;
         pix_val[2] = cloud->points[i].r;
         pixels.at<cv::Vec3b>(0, i) = pix_val;
      }
      cv::Mat hsv;
      cv::cvtColor(pixels, hsv, CV_BGR2HSV);
      int histSize[] = {hBin, sBin};
      float h_ranges[] = {0, 180};
      float s_ranges[] = {0, 256};
      const float* ranges[] = {h_ranges, s_ranges};
      int channels[] = {0, 1};
      cv::calcHist(
         &hsv, 1, channels, cv::Mat(), hist, 2, histSize, ranges, true, false);
      if (is_norm) {
         cv::normalize(hist, hist, 0, 1, cv::NORM_MINMAX, -1, cv::Mat());
      }
   }
 
   void TargetAdaptiveTracking::computePointFPFH(
      const pcl::PointCloud<PointT>::Ptr cloud,
      const pcl::PointCloud<pcl::Normal>::Ptr normals,
      cv::Mat &histogram, bool holistic) const
   {
      if (cloud->empty() || normals->empty()) {
         ROS_ERROR("-- ERROR: cannot compute FPFH");
         return;
      }
      pcl::FPFHEstimationOMP<PointT, pcl::Normal, pcl::FPFHSignature33> fpfh;
      fpfh.setInputCloud(cloud);
      fpfh.setInputNormals(normals);
      pcl::search::KdTree<PointT>::Ptr tree (new pcl::search::KdTree<PointT>);
      fpfh.setSearchMethod(tree);
      pcl::PointCloud<pcl::FPFHSignature33>::Ptr fpfhs(
         new pcl::PointCloud<pcl::FPFHSignature33> ());
      fpfh.setRadiusSearch(0.05);
      fpfh.compute(*fpfhs);
      const int hist_dim = 33;
      if (holistic) {
         histogram = cv::Mat::zeros(1, hist_dim, CV_32F);
         for (int i = 0; i < fpfhs->size(); i++) {
            for (int j = 0; j < hist_dim; j++) {
               histogram.at<float>(0, j) += fpfhs->points[i].histogram[j];
            }
         }
      } else {
         histogram = cv::Mat::zeros(
            static_cast<int>(fpfhs->size()), hist_dim, CV_32F);
         for (int i = 0; i < fpfhs->size(); i++) {
            for (int j = 0; j < hist_dim; j++) {
               histogram.at<float>(i, j) = fpfhs->points[i].histogram[j];
            }
         }
      }
      cv::normalize(histogram, histogram, 0, 1, cv::NORM_MINMAX, -1, cv::Mat());
   }
 
   std::vector<pcl::PointIndices::Ptr>
   TargetAdaptiveTracking::clusterPointIndicesToPointIndices(
      const jsk_recognition_msgs::ClusterPointIndicesConstPtr &indices_mgs)
   {
      std::vector<pcl::PointIndices::Ptr> ret;
      for (int i = 0; i < indices_mgs->cluster_indices.size(); i++) {
         std::vector<int> indices = indices_mgs->cluster_indices[i].indices;
         pcl::PointIndices::Ptr pcl_indices (new pcl::PointIndices);
         pcl_indices->indices = indices;
         ret.push_back(pcl_indices);
      }
      return ret;
   }
 
   void TargetAdaptiveTracking::estimatedPFPose(
      const geometry_msgs::PoseStamped::ConstPtr &pose_msg,
      PointXYZRPY &motion_displacement)
   {
      PointXYZRPY current_pose;
      current_pose.x = pose_msg->pose.position.x;
      current_pose.y = pose_msg->pose.position.y;
      current_pose.z = pose_msg->pose.position.z;
      current_pose.roll = pose_msg->pose.orientation.x;
      current_pose.pitch = pose_msg->pose.orientation.y;
      current_pose.yaw = pose_msg->pose.orientation.z;
      current_pose.weight = pose_msg->pose.orientation.w;
      this->tracker_pose_ = current_pose;
      this->current_pose_(0) = current_pose.x;
      this->current_pose_(1) = current_pose.y;
      this->current_pose_(2) = current_pose.z;
      this->current_pose_(3) = 0.0f;
      if (!std::isnan(current_pose.x) && !std::isnan(current_pose.y) &&
          !std::isnan(current_pose.z)) {
         if (!this->motion_history_.empty()) {
            int last_index = static_cast<int>(
               this->motion_history_.size()) - 1;
            motion_displacement.x = current_pose.x -
               this->motion_history_[last_index].x;
            motion_displacement.y = current_pose.y -
               this->motion_history_[last_index].y;
            motion_displacement.z = current_pose.z -
               this->motion_history_[last_index].z;
            motion_displacement.roll = current_pose.roll -
               this->motion_history_[last_index].roll;
            motion_displacement.pitch = current_pose.pitch -
               this->motion_history_[last_index].pitch;
            motion_displacement.yaw = current_pose.yaw -
               this->motion_history_[last_index].yaw;
         } else {
            this->motion_history_.push_back(current_pose);
         }
      } else {
         // pertubate with history error weight
      }
   }
 
   void TargetAdaptiveTracking::compute3DCentroids(
      const pcl::PointCloud<PointT>::Ptr cloud,
      Eigen::Vector4f &centre) const
   {
      if (cloud->empty()) {
         ROS_ERROR("ERROR: empty cloud for centroid");
         centre = Eigen::Vector4f(-1, -1, -1, -1);
         return;
      }
      Eigen::Vector4f centroid;
      pcl::compute3DCentroid<PointT, float>(*cloud, centroid);
      if (!std::isnan(centroid(0)) && !std::isnan(centroid(1)) && !std::isnan(centroid(2))) {
         centre = centroid;
      }
   }
 
   Eigen::Vector4f TargetAdaptiveTracking::cloudMeanNormal(
      const pcl::PointCloud<pcl::Normal>::Ptr normal,
      bool isnorm)
   {
      if (normal->empty()) {
         return Eigen::Vector4f(0, 0, 0, 0);
      }
      float x = 0.0f;
      float y = 0.0f;
      float z = 0.0f;
      int icounter = 0;
      for (int i = 0; i < normal->size(); i++) {
         if ((!std::isnan(normal->points[i].normal_x)) &&
             (!std::isnan(normal->points[i].normal_y)) &&
             (!std::isnan(normal->points[i].normal_z))) {
            x += normal->points[i].normal_x;
            y += normal->points[i].normal_y;
            z += normal->points[i].normal_z;
            icounter++;
         }
      }
      Eigen::Vector4f n_mean = Eigen::Vector4f(
         x/static_cast<float>(icounter),
         y/static_cast<float>(icounter),
         z/static_cast<float>(icounter),
         0.0f);
      if (isnorm) {
         n_mean.normalize();
      }
      return n_mean;
   }
 
   void TargetAdaptiveTracking::computeScatterMatrix(
      const pcl::PointCloud<PointT>::Ptr cloud,
      const Eigen::Vector4f centroid)
   {
      if (cloud->empty()) {
         ROS_ERROR("Empty input for computing Scatter Matrix");
         return;
      }
      const int rows = 3;
      const int cols = 3;
      Eigen::MatrixXf scatter_matrix = Eigen::Matrix3f::Zero(cols, rows);
      for (int i = 0; i < cloud->size(); i++) {
         Eigen::Vector3f de_mean = Eigen::Vector3f();
         de_mean(0) = cloud->points[i].x - centroid(0);
         de_mean(1) = cloud->points[i].y - centroid(1);
         de_mean(2) = cloud->points[i].z - centroid(2);
         Eigen::Vector3f t_de_mean = de_mean.transpose();
         for (int y = 0; y < rows; y++) {
            for (int x = 0; x < cols; x++) {
               scatter_matrix(y, x) += de_mean(y) * t_de_mean(x);
            }
         }
      }
      Eigen::EigenSolver<Eigen::MatrixXf> eigen_solver(scatter_matrix, true);
      // Eigen::complex<float> eigen_values;
   }
 
   float TargetAdaptiveTracking::computeCoherency(
      const float dist, const float weight) {
      if (std::isnan(dist)) {
         return 0.0f;
      }
      return static_cast<float>(1/(1 + (weight * std::pow(dist, 2))));
   }
 
   pcl::PointXYZRGBNormal
   TargetAdaptiveTracking::convertVector4fToPointXyzRgbNormal(
      const Eigen::Vector4f &centroid,
      const Eigen::Vector4f &normal,
      const cv::Scalar color) {
      pcl::PointXYZRGBNormal pt;
      pt.x = centroid(0);
      pt.y = centroid(1);
      pt.z = centroid(2);
      pt.r = color.val[2];
      pt.g = color.val[1];
      pt.b = color.val[0];
      pt.normal_x = normal(0);
      pt.normal_y = normal(1);
      pt.normal_z = normal(2);
      return pt;
   }
 
   template<typename T>
   void TargetAdaptiveTracking::getRotationMatrixFromRPY(
      const PointXYZRPY &motion_displacement,
      Eigen::Matrix<T, 3, 3> &rotation)
   {
      tf::Quaternion tf_quaternion;
      tf_quaternion.setEulerZYX(motion_displacement.yaw,
                                motion_displacement.pitch,
                                motion_displacement.roll);
      Eigen::Quaternion<float> quaternion = Eigen::Quaternion<float>(
         tf_quaternion.w(), tf_quaternion.x(),
         tf_quaternion.y(), tf_quaternion.z());
      rotation.template block<3, 3>(0, 0) =
         quaternion.normalized().toRotationMatrix();
   }
 
   void TargetAdaptiveTracking::computeLocalPairwiseFeautures(
      const std::map <uint32_t, pcl::Supervoxel<PointT>::Ptr> &
      supervoxel_clusters, const std::map<uint32_t, std::vector<uint32_t> > &
      adjacency_list, cv::Mat &histogram, const int feature_count)
   {
      if (supervoxel_clusters.empty() || adjacency_list.empty()) {
         std::cout << supervoxel_clusters.size()  << "\t"
                   << adjacency_list.size() << std::endl;
         ROS_ERROR("ERROR: empty data returing no local feautures");
         return;
      }
      float d_pi = 1.0f / (2.0f * static_cast<float> (M_PI));
      histogram = cv::Mat::zeros(1, this->bin_size_ * feature_count, CV_32F);
      for (std::map<uint32_t, std::vector<uint32_t> >::const_iterator it =
              adjacency_list.begin(); it != adjacency_list.end(); it++) {
         pcl::Supervoxel<PointT>::Ptr supervoxel =
            supervoxel_clusters.at(it->first);
         Eigen::Vector4f c_normal = this->cloudMeanNormal(supervoxel->normals_);
         std::map<uint32_t, Eigen::Vector4f> cache_normals;
         int icounter = 0;
         for (std::vector<uint32_t>::const_iterator itr = it->second.begin();
              itr != it->second.end(); itr++) {
            pcl::Supervoxel<PointT>::Ptr neighbor_supervoxel =
               supervoxel_clusters.at(*itr);
            Eigen::Vector4f n_normal = this->cloudMeanNormal(
               neighbor_supervoxel->normals_);
            float alpha;
            float phi;
            float theta;
            float distance;
            pcl::computePairFeatures(
               supervoxel->centroid_.getVector4fMap(), c_normal,
               neighbor_supervoxel->centroid_.getVector4fMap(),
               n_normal, alpha, phi, theta, distance);
            int bin_index[feature_count];
            bin_index[0] = static_cast<int>(
               std::floor(this->bin_size_ * ((alpha + M_PI) * d_pi)));
            bin_index[1] = static_cast<int>(
               std::floor(this->bin_size_ * ((phi + 1.0f) * 0.5f)));
            bin_index[2] = static_cast<int>(
               std::floor(this->bin_size_ * ((theta + 1.0f) * 0.5f)));
            for (int i = 0; i < feature_count; i++) {
               if (bin_index[i] < 0) {
                  bin_index[i] = 0;
               }
               if (bin_index[i] >= bin_size_) {
                  bin_index[i] = bin_size_ - sizeof(char);
               }
               // h_index += h_p + bin_index[i];
               // h_p *= this->bin_size_;
               histogram.at<float>(
                  0, bin_index[i] + (i * this->bin_size_)) += 1;
            }
            cache_normals[*itr] = n_normal;
            icounter++;
         }
         for (std::vector<uint32_t>::const_iterator itr = it->second.begin();
              itr != it->second.end(); itr++) {
            cache_normals.find(*itr)->second;
            for (std::vector<uint32_t>::const_iterator itr_in =
                    it->second.begin(); itr_in != it->second.end(); itr_in++) {
               if (*itr != *itr_in) {
                  float alpha;
                  float phi;
                  float theta;
                  float distance;
                  pcl::computePairFeatures(
                     supervoxel_clusters.at(*itr)->centroid_.getVector4fMap(),
                     cache_normals.find(*itr)->second, supervoxel_clusters.at(
                        *itr_in)->centroid_.getVector4fMap(),
                     cache_normals.find(*itr_in)->second,
                     alpha, phi, theta, distance);
                  int bin_index[feature_count];
                  bin_index[0] = static_cast<int>(
                     std::floor(this->bin_size_ * ((alpha + M_PI) * d_pi)));
                  bin_index[1] = static_cast<int>(
                     std::floor(this->bin_size_ * ((phi + 1.0f) * 0.5f)));
                  bin_index[2] = static_cast<int>(
                     std::floor(this->bin_size_ * ((theta + 1.0f) * 0.5f)));
                  for (int i = 0; i < feature_count; i++) {
                     if (bin_index[i] < 0) {
                        bin_index[i] = 0;
                     }
                     if (bin_index[i] >= bin_size_) {
                        bin_index[i] = bin_size_ - sizeof(char);
                     }
                     histogram.at<float>(
                        0, bin_index[i] + (i * this->bin_size_)) += 1;
                  }
               }
            }
         }
         cv::normalize(
            histogram, histogram, 0, 1, cv::NORM_MINMAX, -1, cv::Mat());
      }
   }
 
 
   float TargetAdaptiveTracking::templateCloudFilterLenght(
      const pcl::PointCloud<PointT>::Ptr cloud)
   {
      if (cloud->empty()) {
         ROS_ERROR("ERROR! Input Cloud is Empty");
         return -1.0f;
      }
      Eigen::Vector4f pivot_pt;
      pcl::compute3DCentroid<PointT, float>(*cloud, pivot_pt);
      Eigen::Vector4f max_pt;
      pcl::getMaxDistance<PointT>(*cloud, pivot_pt, max_pt);
      pivot_pt(3) = 0.0f;
      max_pt(3) = 0.0f;
      float dist = static_cast<float>(pcl::distances::l2(max_pt, pivot_pt));
      return (dist);
   }
 
   bool TargetAdaptiveTracking::filterPointCloud(
      pcl::PointCloud<PointT>::Ptr cloud,
      const Eigen::Vector4f tracker_position,
      const ModelsPtr template_model,
      const float scaling_factor)
   {
      if (cloud->empty() || template_model->empty()) {
         ROS_ERROR("ERROR! Input data is empty is Empty");
         return false;
      }
      pcl::PointCloud<PointT>::Ptr template_cloud(new pcl::PointCloud<PointT>);
      for (int i = 0; i < template_model->size(); i++) {
         *template_cloud = *template_cloud + *(
            template_model->operator[](i).cluster_cloud);
      }
      float filter_distance = this->templateCloudFilterLenght(template_cloud);
      filter_distance *= scaling_factor;
      if (filter_distance < 0.05f) {
         return false;
      }
      pcl::PointCloud<PointT>::Ptr cloud_filter(new pcl::PointCloud<PointT>);
      pcl::PassThrough<PointT> pass;
      pass.setInputCloud(cloud);
      pass.setFilterFieldName("x");
      float min_x = tracker_position(0) - filter_distance;
      float max_x = tracker_position(0) + filter_distance;
      pass.setFilterLimits(min_x, max_x);
      pass.filter(*cloud_filter);
      pass.setInputCloud(cloud_filter);
      pass.setFilterFieldName("y");
      float min_y = tracker_position(1) - filter_distance;
      float max_y = tracker_position(1) + filter_distance;
      pass.setFilterLimits(min_y, max_y);
      pass.filter(*cloud_filter);
      pass.setInputCloud(cloud_filter);
      pass.setFilterFieldName("z");
      float min_z = tracker_position(2) - filter_distance;
      float max_z = tracker_position(2) + filter_distance;
      pass.setFilterLimits(min_z, max_z);
      pass.filter(*cloud_filter);
      if (cloud_filter->empty()) {
         return false;
      }
      cloud->empty();
      pcl::copyPointCloud<PointT, PointT>(*cloud_filter, *cloud);
      return true;
   }
 
   void TargetAdaptiveTracking::transformModelPrimitives(
      const ModelsPtr &obj_ref,
      ModelsPtr trans_models,
      const Eigen::Affine3f &transform_model)
   {
      if (obj_ref->empty()) {
         ROS_ERROR("ERROR! No Object Model to Transform");
         return;
      }
      for (int i = 0; i < obj_ref->size(); i++) {
         pcl::PointCloud<PointT>::Ptr trans_cloud(
            new pcl::PointCloud<PointT>);
         pcl::transformPointCloud(*(obj_ref->operator[](i).cluster_cloud),
                                  *trans_cloud, transform_model);
         Eigen::Vector4f trans_centroid = Eigen::Vector4f();
         pcl::compute3DCentroid<PointT, float>(
            *trans_cloud, trans_centroid);
         trans_models->push_back(obj_ref->operator[](i));
         trans_models->operator[](i).cluster_cloud = trans_cloud;
         trans_models->operator[](i).cluster_centroid = trans_centroid;
      }
   }
 
   void TargetAdaptiveTracking::filterCloudForBoundingBoxViz(
      pcl::PointCloud<PointT>::Ptr cloud,
      const ModelsPtr background_reference,
      const float threshold)
   {
      if (cloud->empty() || background_reference->empty()) {
         ROS_ERROR("ERROR! EMPTY DATA FOR BOUNDING BOX CLOUD");
         return;
      }
      ModelsPtr tmp_model(new Models);
      this->processInitCloud(cloud, tmp_model);
      pcl::PointCloud<PointT>::Ptr tmp_cloud(new pcl::PointCloud<PointT>);
      for (int i = 0; i < tmp_model->size(); i++) {
         Eigen::Vector4f surfel_centroid = Eigen::Vector4f();
         surfel_centroid = tmp_model->operator[](i).cluster_centroid;
         surfel_centroid(3) = 0.0f;
         float surfel_dist = static_cast<float>(
            pcl::distances::l2(surfel_centroid, current_pose_));
         if (surfel_dist < (this->previous_distance_ * growth_rate_)) {
            float probability = 0.0f;
            for (int j = 0; j < background_reference->size(); j++) {
               ReferenceModel *r_mod = new ReferenceModel;
               float prob = this->targetCandidateToReferenceLikelihood<float>(
                  tmp_model->operator[](i),
                  background_reference->operator[](j).cluster_cloud,
                  background_reference->operator[](j).cluster_normals,
                  background_reference->operator[](j).cluster_centroid,
                  r_mod);
               if (prob > probability) {
                  probability = prob;
               }
            }
            if (probability < 0.60f) {
               *tmp_cloud = *tmp_cloud + *(
                  tmp_model->operator[](i).cluster_cloud);
            } else {
               this->object_reference_->push_back(tmp_model->operator[](i));
            }
         }
      }
      if (tmp_cloud->size() > (static_cast<int>(cloud->size() / 4))) {
         cloud->clear();
         pcl::copyPointCloud<PointT, PointT>(*tmp_cloud, *cloud);
      }
   }
 
   template<typename T, typename U, typename V>
   cv::Scalar TargetAdaptiveTracking::plotJetColour(
      T v, U vmin, V vmax)
   {
      cv::Scalar c = cv::Scalar(0.0, 0.0, 0.0);
      T dv;
      if (v < vmin)
         v = vmin;
      if (v > vmax)
         v = vmax;
      dv = vmax - vmin;
      if (v < (vmin + 0.25 * dv)) {
         c.val[0] = 0;
         c.val[1] = 4 * (v - vmin) / dv;
      } else if (v < (vmin + 0.5 * dv)) {
         c.val[0] = 0;
         c.val[2] = 1 + 4 * (vmin + 0.25 * dv - v) / dv;
      } else if (v < (vmin + 0.75 * dv)) {
         c.val[0] = 4 * (v - vmin - 0.5 * dv) / dv;
         c.val[2] = 0;
      } else {
         c.val[1] = 1 + 4 * (vmin + 0.75 * dv - v) / dv;
         c.val[2] = 0;
      }
      return(c);
   }
 
   void TargetAdaptiveTracking::supervoxelSegmentation(
      const pcl::PointCloud<PointT>::Ptr cloud,
      std::map<uint32_t, pcl::Supervoxel<PointT>::Ptr > &supervoxel_clusters,
      std::multimap<uint32_t, uint32_t> &supervoxel_adjacency,
      const float seed_resolution)
   {
      if (cloud->empty() || seed_resolution <= 0.0f) {
         ROS_ERROR("ERROR: Supervoxel input cloud empty...\n Incorrect Seed");
         return;
      } 
      boost::mutex::scoped_lock lock(mutex_);
      pcl::SupervoxelClustering<PointT> super(
         voxel_resolution_,
         static_cast<double>(seed_resolution)
#if ( PCL_MAJOR_VERSION >= 1 && PCL_MINOR_VERSION >= 12 )
         );
#else
         , use_transform_);
#endif
      super.setInputCloud(cloud);
      super.setColorImportance(color_importance_);
      super.setSpatialImportance(spatial_importance_);
      super.setNormalImportance(normal_importance_);
      supervoxel_clusters.clear();
      super.extract(supervoxel_clusters);
      super.getSupervoxelAdjacency(supervoxel_adjacency);
   }
 
   void TargetAdaptiveTracking::supervoxelSegmentation(
      const pcl::PointCloud<PointT>::Ptr cloud,
      std::map<uint32_t, pcl::Supervoxel<PointT>::Ptr > &supervoxel_clusters,
      std::multimap<uint32_t, uint32_t> &supervoxel_adjacency)
   {
      if (cloud->empty()) {
         ROS_ERROR("ERROR: Supervoxel input cloud empty...");
         return;
      }
      boost::mutex::scoped_lock lock(mutex_);
      pcl::SupervoxelClustering<PointT> super(voxel_resolution_,
                                              seed_resolution_
#if ( PCL_MAJOR_VERSION >= 1 && PCL_MINOR_VERSION >= 12 )
                                              );
#else
                                              , use_transform_);
#endif
      super.setInputCloud(cloud);
      super.setColorImportance(color_importance_);
      super.setSpatialImportance(spatial_importance_);
      super.setNormalImportance(normal_importance_);
      supervoxel_clusters.clear();
      super.extract(supervoxel_clusters);
      super.getSupervoxelAdjacency(supervoxel_adjacency);
   }
 
   void TargetAdaptiveTracking::publishSupervoxel(
      const std::map<uint32_t, pcl::Supervoxel<PointT>::Ptr> supervoxel_clusters,
      sensor_msgs::PointCloud2 &ros_cloud,
      jsk_recognition_msgs::ClusterPointIndices &ros_indices,
      const std_msgs::Header &header)
   {
      pcl::PointCloud<PointT>::Ptr output (new pcl::PointCloud<PointT>);
      std::vector<pcl::PointIndices> all_indices;
      for (std::map<uint32_t, pcl::Supervoxel<PointT>::Ptr >::const_iterator
              it = supervoxel_clusters.begin();
           it != supervoxel_clusters.end();
           ++it) {
         pcl::Supervoxel<PointT>::Ptr super_voxel = it->second;
         pcl::PointCloud<PointT>::Ptr super_voxel_cloud = super_voxel->voxels_;
         pcl::PointIndices indices;
         for (size_t i = 0; i < super_voxel_cloud->size(); i++) {
            indices.indices.push_back(i + output->points.size());
         }
         all_indices.push_back(indices);
         *output = *output + *super_voxel_cloud;
      }
      ros_indices.cluster_indices.clear();
      ros_indices.cluster_indices = pcl_conversions::convertToROSPointIndices(
         all_indices, header);
      ros_cloud.data.clear();
      pcl::toROSMsg(*output, ros_cloud);
      ros_indices.header = header;
      ros_cloud.header = header;
   }
 
   void TargetAdaptiveTracking::targetDescriptiveSurfelsIndices(
      const jsk_recognition_msgs::ClusterPointIndices &sv_indices,
      const std::vector<uint32_t> &tdp_list,
      jsk_recognition_msgs::ClusterPointIndices &ros_indices)
   {
      ros_indices.cluster_indices.clear();
      for (std::vector<uint32_t>::const_iterator it = tdp_list.begin();
           it != tdp_list.end(); it++) {
         ros_indices.cluster_indices.push_back(sv_indices.cluster_indices[*it]);
      }
      ros_indices.header = sv_indices.header;
   }
   
   void TargetAdaptiveTracking::configCallback(
      Config &config, uint32_t level)
   {
      boost::mutex::scoped_lock lock(mutex_);
      this->color_importance_ = config.color_importance;
      this->spatial_importance_ = config.spatial_importance;
      this->normal_importance_ = config.normal_importance;
      this->voxel_resolution_ = config.voxel_resolution;
      this->seed_resolution_ = config.seed_resolution;
      this->use_transform_ = config.use_transform;
 
      this->min_cluster_size_ = static_cast<int>(config.min_cluster_size);
      this->threshold_ = static_cast<float>(config.threshold);
      this->bin_size_ = static_cast<int>(config.bin_size);
      this->eps_distance_ = static_cast<float>(config.eps_distance);
      this->eps_min_samples_ = static_cast<float>(config.eps_min_samples);
      this->vfh_scaling_ = static_cast<float>(config.vfh_scaling);
      this->color_scaling_ = static_cast<float>(config.color_scaling);
      this->structure_scaling_ = static_cast<float>(config.structure_scaling);
      this->update_tracker_reference_ = config.update_tracker_reference;
      this->update_filter_template_ = config.update_filter_template;
      this->history_window_size_ = static_cast<int>(config.history_window_size);
   }
}
 
#include <pluginlib/class_list_macros.h>
PLUGINLIB_EXPORT_CLASS(jsk_pcl_ros::TargetAdaptiveTracking, nodelet::Nodelet);