#include <iostream>
#include <vector>
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/search/search.h>
#include <pcl/search/kdtree.h>
#include <pcl/features/normal_3d.h>
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/filters/passthrough.h>
#include <pcl/segmentation/region_growing.h>
#include "o3d3xx_camera.h"
#include "o3d3xx_framegrabber.h"
#include "o3d3xx_image.h"

using namespace std;

int
main (int argc, char** argv)
{
  //logging method
  o3d3xx::Logging::Init();
  //initialise camera constructor expects IP address
  o3d3xx::Camera::Ptr cam = std::make_shared<o3d3xx::Camera>("192.168.1.69");
  //create buffer to fetch image
  o3d3xx::ImageBuffer::Ptr img = std::make_shared<o3d3xx::ImageBuffer>();
  //framegrabber
  o3d3xx::FrameGrabber::Ptr fg =
    std::make_shared<o3d3xx::FrameGrabber>(
      cam, o3d3xx::IMG_AMP|o3d3xx::IMG_RDIS|o3d3xx::IMG_CART);

  //get frame from camera
  if (! fg->WaitForFrame(img.get(), 2000))
    {
      std::cerr << "Timeout waiting for camera!" << std::endl;
      return -1;	
    }
  pcl::PCDWriter writer;
  
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointCloud<pcl::PointXYZI>::Ptr cloudIn (new pcl::PointCloud<pcl::PointXYZI>);
  cloudIn = img->Cloud();
  writer.write<pcl::PointXYZI>("region_growing_tutorial.pcd", *cloudIn, false);
 
  if ( pcl::io::loadPCDFile <pcl::PointXYZ> ("region_growing_tutorial.pcd", *cloud) == -1)
  {
    std::cout << "Cloud reading failed." << std::endl;
    return (-1);
  }
  vector<int> temp;
  pcl::removeNaNFromPointCloud(*cloud,*cloud, temp);
  cout << temp.size() << endl;
  cout << cloud->size() << endl;

  pcl::search::Search<pcl::PointXYZ>::Ptr tree = boost::shared_ptr<pcl::search::Search<pcl::PointXYZ> > (new pcl::search::KdTree<pcl::PointXYZ>);
  pcl::PointCloud <pcl::Normal>::Ptr normals (new pcl::PointCloud <pcl::Normal>);
  pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normal_estimator;
  normal_estimator.setSearchMethod (tree);
  normal_estimator.setInputCloud (cloud);
  normal_estimator.setKSearch (50);
  normal_estimator.compute (*normals);
  
  pcl::IndicesPtr indices (new std::vector <int>);
  pcl::PassThrough<pcl::PointXYZ> pass;
  pass.setInputCloud (cloud);
  pass.setFilterFieldName ("z");
  pass.setFilterLimits (0.0, 1.0);
  pass.filter (*indices);

  pcl::RegionGrowing<pcl::PointXYZ, pcl::Normal> reg;
  reg.setMinClusterSize (50);
  reg.setMaxClusterSize (1000000);
  reg.setSearchMethod (tree);
  reg.setNumberOfNeighbours (30);
  reg.setInputCloud (cloud);
  //reg.setIndices (indices);
  reg.setInputNormals (normals);
  reg.setSmoothnessThreshold (3.0 / 180.0 * M_PI);// graden naar radial
  reg.setCurvatureThreshold (1.0);

  std::vector <pcl::PointIndices> clusters;
  reg.extract (clusters);

  std::cout << "Number of clusters is equal to " << clusters.size () << std::endl;
  std::cout << "First cluster has " << clusters[0].indices.size () << " points." << endl;
  std::cout << "These are the indices of the points of the initial" <<
    std::endl << "cloud that belong to the first cluster:" << std::endl;
  int counter = 0;
  while (counter < clusters[0].indices.size ())
  {
    std::cout << clusters[0].indices[counter] << ", ";
    counter++;
    if (counter % 10 == 0)
      std::cout << std::endl;
  }
  std::cout << std::endl;

  pcl::PointCloud <pcl::PointXYZRGB>::Ptr colored_cloud = reg.getColoredCloud ();
  pcl::visualization::PCLVisualizer viewer ("Cluster viewer");
  viewer.addPointCloud<pcl::PointXYZRGB>(colored_cloud,"cloud");
  //viewer.addPointCloudNormals<pcl::PointXYZRGB, pcl::Normal>(colored_cloud, normals, 10, 0.05, "normals",0);
  while (!viewer.wasStopped ())
  {
      viewer.spinOnce(100);
  }

  return (0);
}