/*
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 *
 * Point Cloud Library (PCL) - www.pointclouds.org
 * Copyright (c) 2009-2011, Willow Garage, Inc.
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#include <iostream>

#include <pcl/io/pcd_io.h>
#include <pcl/features/normal_3d.h>
#include <pcl/segmentation/extract_clusters.h>

int 
main (int argc, char **argv)
{
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_ptr (new pcl::PointCloud<pcl::PointXYZ> ());
  pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal> ());
  pcl::PCDWriter writer;
	
  if (argc < 2)
  {
    std::cout<<"No PCD file given!"<<std::endl;
    return (-1);
  }
  if (pcl::io::loadPCDFile<pcl::PointXYZ> (argv[1], *cloud_ptr) == -1)
  {
    std::cout<<"Couldn't read the file "<<argv[1]<<std::endl;
    return (-1);
  }
  std::cout << "Loaded pcd file " << argv[1] << " with " << cloud_ptr->size () << std::endl;

  // Normal estimation
  pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
  ne.setInputCloud (cloud_ptr);

  pcl::search::KdTree<pcl::PointXYZ>::Ptr tree_n (new pcl::search::KdTree<pcl::PointXYZ>());
  ne.setSearchMethod (tree_n);
  ne.setRadiusSearch (0.03);
  ne.compute (*cloud_normals);
  std::cout << "Estimated the normals" << std::endl;

  // Creating the kdtree object for the search method of the extraction
  pcl::KdTree<pcl::PointXYZ>::Ptr tree_ec  (new pcl::KdTreeFLANN<pcl::PointXYZ> ());
  tree_ec->setInputCloud (cloud_ptr);
  
  // Extracting Euclidean clusters using cloud and its normals
  std::vector<pcl::PointIndices> cluster_indices;
  constexpr float tolerance = 0.5f; // 50cm tolerance in (x, y, z) coordinate system
  constexpr double eps_angle = 5 * (M_PI / 180.0); // 5degree tolerance in normals
  constexpr unsigned int min_cluster_size = 50;
 
  pcl::extractEuclideanClusters (*cloud_ptr, *cloud_normals, tolerance, tree_ec, cluster_indices, eps_angle, min_cluster_size);

  std::cout << "No of clusters formed are " << cluster_indices.size () << std::endl;

  // Saving the clusters in separate pcd files
  int j = 0;
  for (const auto& cluster : cluster_indices)
  {
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster (new pcl::PointCloud<pcl::PointXYZ>);
    for (const auto &index : cluster.indices) {
      cloud_cluster->push_back((*cloud_ptr)[index]);
    }
    cloud_cluster->width = cloud_cluster->size ();
    cloud_cluster->height = 1;
    cloud_cluster->is_dense = true;

    std::cout << "PointCloud representing the Cluster using xyzn: " << cloud_cluster->size () << " data " << std::endl;
    std::stringstream ss;
    ss << "./cloud_cluster_" << j << ".pcd";
    writer.write<pcl::PointXYZ> (ss.str (), *cloud_cluster, false); 
    ++j;
  }

  return (0);
}
