#include <iostream>
#include <string>
#include <fstream>

#include <pcl/io/ply_io.h>
#include <pcl/point_types.h>
#include <pcl/registration/icp.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/console/time.h>   // TicToc

typedef pcl::PointXYZ PointT;
typedef pcl::PointCloud<PointT> PointCloudT;

bool next_iteration = false;

void
print4x4Matrix (const Eigen::Matrix4d & matrix)
{
  printf ("Rotation matrix :\n");
  printf ("    | %6.3f %6.3f %6.3f | \n", matrix (0, 0), matrix (0, 1), matrix (0, 2));
  printf ("R = | %6.3f %6.3f %6.3f | \n", matrix (1, 0), matrix (1, 1), matrix (1, 2));
  printf ("    | %6.3f %6.3f %6.3f | \n", matrix (2, 0), matrix (2, 1), matrix (2, 2));
  printf ("Translation vector :\n");
  printf ("t = < %6.3f, %6.3f, %6.3f >\n\n", matrix (0, 3), matrix (1, 3), matrix (2, 3));
}

void
keyboardEventOccurred (const pcl::visualization::KeyboardEvent& event,
                       void* nothing)
{
  if (event.getKeySym () == "space" && event.keyDown ())
    next_iteration = true;
}

int import_depth_map(std::string const& file_depthmap, PointCloudT::Ptr const& cloud){
   std::ifstream depthmap(file_depthmap.c_str());

   float fx(365.166), fy(365.166), cx(259.653), cy(206.166);
   if(depthmap.is_open())
   {
      std::string line;
      int height(0), width(0), counter(0);
      while(getline(depthmap, line))
      {
         std::istringstream is_before(line);
         width = 0;
         for(std::istream_iterator<float> it = std::istream_iterator<float>(is_before); it !=  std::istream_iterator<float>(); ++it)
         {
           if((*it)*4500.< 1260. && width > 80 && width < 432){ 
               PointT point3d;
               point3d.x = (width-cx)*(*it)*4500.f/fx;
               point3d.y = (cy-height)*(*it)*4500.f/fy;
               point3d.z = (*it)*4500.f;
               cloud->points.push_back(point3d);++counter;}
            width++;
         }
         height++;
      }
      cloud->header.frame_id = file_depthmap.substr(0,file_depthmap.find("."));
      cloud->height = 1;
      cloud->width = counter;
      cloud->is_dense = true;
      cloud->points.resize(counter);
      
      std::cout << file_depthmap << " load" << std::endl;
      return 0;
   }
   else
   {
      return 1;
   }
}

int main (int argc,
      char* argv[])
{
  // The point clouds we will be using
  PointCloudT::Ptr cloud_in (new PointCloudT);  // Original point cloud
  PointCloudT::Ptr cloud_tr (new PointCloudT);  // Transformed point cloud
  PointCloudT::Ptr cloud_icp (new PointCloudT);  // ICP output point cloud

  // Checking program arguments
  if (argc < 4)
  {
    printf ("Usage :\n");
    printf ("\t\t%s point_cloud1.txt point_cloud2.txt number_of_ICP_iterations threshold\n", argv[0]);
    return (-1);
  }

  int iterations = atoi(argv[3]);  // Default number of ICP iterations
  if (iterations < 1)
  {
    PCL_ERROR ("Number of initial iterations must be >= 1\n");
    return (-1);
  }
  // Defining a rotation matrix and translation vector
  Eigen::Matrix4d transformation_matrix = Eigen::Matrix4d::Identity ();


  pcl::console::TicToc time;
  time.tic ();
  if (import_depth_map(argv[1], cloud_in) != 0)
  {
    PCL_ERROR ("Error loading cloud %s.\n", argv[1]);
    return (-1);
  }
  std::cout << "\nLoaded file " << argv[1] << " (" << cloud_in->size () << " points) in " << time.toc () << " ms\n" << std::endl;

  time.tic ();
  if (import_depth_map(argv[2], cloud_icp) != 0)
  {
    PCL_ERROR ("Error loading cloud %s.\n", argv[2]);
    return (-1);
  }
  std::cout << "\nLoaded file " << argv[2] << " (" << cloud_icp->size () << " points) in " << time.toc () << " ms\n" << std::endl;

  *cloud_tr = *cloud_icp;  // We backup cloud_icp into cloud_tr for later use

  // The Iterative Closest Point algorithm
  time.tic ();
  pcl::IterativeClosestPoint<PointT, PointT> icp;
  icp.setMaximumIterations (iterations);
//Avec des kdtrees a la main
//  boost::shared_ptr<pcl::search::KdTree<PointT> > kdtree_icp (new pcl::search::KdTree<PointT>);
//  boost::shared_ptr<pcl::search::KdTree<PointT> > kdtree_in (new pcl::search::KdTree<PointT>);
//  kdtree_icp->setInputCloud(cloud_icp);
//  kdtree_in->setInputCloud(cloud_in);
//  icp.setSearchMethodSource(kdtree_icp, false);
//  icp.setSearchMethodTarget(kdtree_in, false);
  std::cout << "RANSAC : " << icp.getRANSACIterations() << std::endl;
  //icp.setMaxCorrespondenceDistance (100); 
  icp.setUseReciprocalCorrespondences(true);
  icp.setInputSource (cloud_icp);
  icp.setInputTarget (cloud_in);
  icp.setMaxCorrespondenceDistance (atof(argv[4])); 
  icp.align (*cloud_icp);
  icp.setMaximumIterations (1);  // We set this variable to 1 for the next time we will call .align () function
  std::cout << "Applied " << iterations << " ICP iteration(s) in " << time.toc () << " ms" << std::endl;

  if (icp.hasConverged ())
  {
    std::cout << "\nICP has converged, score is " << icp.getFitnessScore () << std::endl;
    std::cout << "\nICP transformation " << iterations << " : cloud_icp -> cloud_in" << std::endl;
    transformation_matrix = icp.getFinalTransformation ().cast<double>();
    print4x4Matrix (transformation_matrix);
  }
  else
  {
    PCL_ERROR ("\nICP has not converged.\n");
    return (-1);
  }

  // Visualization
  pcl::visualization::PCLVisualizer viewer ("ICP");
  // Create two verticaly separated viewports
  int v1 (0);
  int v2 (1);
  viewer.createViewPort (0.0, 0.0, 0.5, 1.0, v1);
  viewer.createViewPort (0.5, 0.0, 1.0, 1.0, v2);

  // The color we will be using
  float bckgr_gray_level = 0.0;  // Black
  float txt_gray_lvl = 1.0 - bckgr_gray_level;

  // Original point cloud is white
  pcl::visualization::PointCloudColorHandlerCustom<PointT> cloud_in_color_h (cloud_in, (int) 255 * txt_gray_lvl, (int) 255 * txt_gray_lvl,
                                                                             (int) 255 * txt_gray_lvl);
  viewer.addPointCloud (cloud_in, cloud_in_color_h, "cloud_in_v1", v1);
  viewer.addPointCloud (cloud_in, cloud_in_color_h, "cloud_in_v2", v2);

  // Transformed point cloud is green
  pcl::visualization::PointCloudColorHandlerCustom<PointT> cloud_tr_color_h (cloud_tr, 20, 180, 20);
  viewer.addPointCloud (cloud_tr, cloud_tr_color_h, "cloud_tr_v1", v1);

  // ICP aligned point cloud is red
  pcl::visualization::PointCloudColorHandlerCustom<PointT> cloud_icp_color_h (cloud_icp, 180, 20, 20);
  viewer.addPointCloud (cloud_icp, cloud_icp_color_h, "cloud_icp_v2", v2);

  // Adding text descriptions in each viewport
  viewer.addText ("White: Point cloud: " + std::string(argv[1]) + "\nGreen: Point cloud " + std::string(argv[2]), 10, 15, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "icp_info_1", v1);
  viewer.addText ("White: Point cloud: " + std::string(argv[1]) + "\nRed: ICP aligned point cloud of " + std::string(argv[2]), 10, 15, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "icp_info_2", v2);

  std::stringstream ss;
  ss << iterations;
  std::string iterations_cnt = "ICP iterations = " + ss.str ();
  viewer.addText (iterations_cnt, 10, 60, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "iterations_cnt", v2);

  // Set background color
  viewer.setBackgroundColor (bckgr_gray_level, bckgr_gray_level, bckgr_gray_level, v1);
  viewer.setBackgroundColor (bckgr_gray_level, bckgr_gray_level, bckgr_gray_level, v2);

  // Set camera position and orientation
  viewer.setCameraPosition (-3.68332, 2.94092, 5.71266, 0.289847, 0.921947, -0.256907, 0);
  viewer.setSize (1280, 1024);  // Visualiser window size

  // Register keyboard callback :
  viewer.registerKeyboardCallback (&keyboardEventOccurred, (void*) NULL);

  // Display the visualiser
  while (!viewer.wasStopped ())
  {
    viewer.spinOnce ();

    // The user pressed "space" :
    if (next_iteration)
    {
      // The Iterative Closest Point algorithm
      time.tic ();
      icp.align (*cloud_icp);
      std::cout << "Applied 1 ICP iteration in " << time.toc () << " ms" << std::endl;

      if (icp.hasConverged ())
      {
        printf ("\033[11A");  // Go up 11 lines in terminal output.
        printf ("\nICP has converged, score is %+.0e\n", icp.getFitnessScore ());
        std::cout << "\nICP transformation " << ++iterations << " : cloud_icp -> cloud_in" << std::endl;
        transformation_matrix *= icp.getFinalTransformation ().cast<double>();  // WARNING /!\ This is not accurate! For "educational" purpose only!
        print4x4Matrix (transformation_matrix);  // Print the transformation between original pose and current pose

        ss.str ("");
        ss << iterations;
        std::string iterations_cnt = "ICP iterations = " + ss.str ();
        viewer.updateText (iterations_cnt, 10, 60, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "iterations_cnt");
        viewer.updatePointCloud (cloud_icp, cloud_icp_color_h, "cloud_icp_v2");
      }
      else
      {
        PCL_ERROR ("\nICP has not converged.\n");
        return (-1);
      }
    }
    next_iteration = false;
  }
  return (0);
}