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#pragma once

#include <pcl/segmentation/segment_differences.h>

#include <pcl/common/io.h>
#include <pcl/common/point_tests.h> // for pcl::isFinite
#include <pcl/search/organized.h> // for OrganizedNeighbor
#include <pcl/search/kdtree.h> // for KdTree


//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::getPointCloudDifference (
    const pcl::PointCloud<PointT> &src,
    double threshold,
    const typename pcl::search::Search<PointT>::Ptr &tree,
    pcl::PointCloud<PointT> &output)
{
  // We're interested in a single nearest neighbor only
  Indices nn_indices (1);
  std::vector<float> nn_distances (1);

  // The input cloud indices that do not have a neighbor in the target cloud
  Indices src_indices;

  // Iterate through the source data set
  for (index_t i = 0; i < static_cast<index_t> (src.size ()); ++i)
  {
    // Ignore invalid points in the input cloud
    if (!isFinite (src[i]))
      continue;
    // Search for the closest point in the target data set (number of neighbors to find = 1)
    if (!tree->nearestKSearch (src[i], 1, nn_indices, nn_distances))
    {
      PCL_WARN ("No neighbor found for point %lu (%f %f %f)!\n", i, src[i].x, src[i].y, src[i].z);
      continue;
    }
    // Add points without a corresponding point in the target cloud to the output cloud
    if (nn_distances[0] > threshold)
      src_indices.push_back (i);
  }

  // Copy all the data fields from the input cloud to the output one
  copyPointCloud (src, src_indices, output);

  // Output is always dense, as invalid points in the input cloud are ignored
  output.is_dense = true;
}

//////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void 
pcl::SegmentDifferences<PointT>::segment (PointCloud &output)
{
  output.header = input_->header;

  if (!initCompute ()) 
  {
    output.width = output.height = 0;
    output.clear ();
    return;
  }

  // If target is empty, input - target = input
  if (target_->points.empty ())
  {
    output = *input_;
    return;
  }

  // Initialize the spatial locator
  if (!tree_)
  {
    if (target_->isOrganized ())
      tree_.reset (new pcl::search::OrganizedNeighbor<PointT> ());
    else
      tree_.reset (new pcl::search::KdTree<PointT> (false));
  }
  // Send the input dataset to the spatial locator
  tree_->setInputCloud (target_);

  getPointCloudDifference (*input_, distance_threshold_, tree_, output);

  deinitCompute ();
}

#define PCL_INSTANTIATE_SegmentDifferences(T) template class PCL_EXPORTS pcl::SegmentDifferences<T>;
#define PCL_INSTANTIATE_getPointCloudDifference(T) template PCL_EXPORTS void pcl::getPointCloudDifference<T>(const pcl::PointCloud<T> &, double, const typename pcl::search::Search<T>::Ptr &, pcl::PointCloud<T> &);

