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#ifndef PCL_FILTERS_IMPL_VOXEL_GRID_H_
#define PCL_FILTERS_IMPL_VOXEL_GRID_H_

#include <pcl/common/centroid.h>
#include <pcl/common/common.h>
#include <pcl/common/io.h>
#include <pcl/filters/voxel_grid.h>
#include  <boost/sort/spreadsort/integer_sort.hpp>

///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::getMinMax3D (const typename pcl::PointCloud<PointT>::ConstPtr &cloud,
                  const std::string &distance_field_name, float min_distance, float max_distance,
                  Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt, bool limit_negative)
{
  Eigen::Array4f min_p, max_p;
  min_p.setConstant (FLT_MAX);
  max_p.setConstant (-FLT_MAX);

  // Get the fields list and the distance field index
  std::vector<pcl::PCLPointField> fields;
  int distance_idx = pcl::getFieldIndex<PointT> (distance_field_name, fields);

  float distance_value;
  // If dense, no need to check for NaNs
  if (cloud->is_dense)
  {
    for (std::size_t i = 0; i < cloud->points.size (); ++i)
    {
      // Get the distance value
      const std::uint8_t* pt_data = reinterpret_cast<const std::uint8_t*> (&cloud->points[i]);
      memcpy (&distance_value, pt_data + fields[distance_idx].offset, sizeof (float));

      if (limit_negative)
      {
        // Use a threshold for cutting out points which inside the interval
        if ((distance_value < max_distance) && (distance_value > min_distance))
          continue;
      }
      else
      {
        // Use a threshold for cutting out points which are too close/far away
        if ((distance_value > max_distance) || (distance_value < min_distance))
          continue;
      }
      // Create the point structure and get the min/max
      pcl::Array4fMapConst pt = cloud->points[i].getArray4fMap ();
      min_p = min_p.min (pt);
      max_p = max_p.max (pt);
    }
  }
  else
  {
    for (std::size_t i = 0; i < cloud->points.size (); ++i)
    {
      // Get the distance value
      const std::uint8_t* pt_data = reinterpret_cast<const std::uint8_t*> (&cloud->points[i]);
      memcpy (&distance_value, pt_data + fields[distance_idx].offset, sizeof (float));

      if (limit_negative)
      {
        // Use a threshold for cutting out points which inside the interval
        if ((distance_value < max_distance) && (distance_value > min_distance))
          continue;
      }
      else
      {
        // Use a threshold for cutting out points which are too close/far away
        if ((distance_value > max_distance) || (distance_value < min_distance))
          continue;
      }

      // Check if the point is invalid
      if (!std::isfinite (cloud->points[i].x) || 
          !std::isfinite (cloud->points[i].y) || 
          !std::isfinite (cloud->points[i].z))
        continue;
      // Create the point structure and get the min/max
      pcl::Array4fMapConst pt = cloud->points[i].getArray4fMap ();
      min_p = min_p.min (pt);
      max_p = max_p.max (pt);
    }
  }
  min_pt = min_p;
  max_pt = max_p;
}

///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::getMinMax3D (const typename pcl::PointCloud<PointT>::ConstPtr &cloud,
                  const std::vector<int> &indices,
                  const std::string &distance_field_name, float min_distance, float max_distance,
                  Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt, bool limit_negative)
{
  Eigen::Array4f min_p, max_p;
  min_p.setConstant (FLT_MAX);
  max_p.setConstant (-FLT_MAX);

  // Get the fields list and the distance field index
  std::vector<pcl::PCLPointField> fields;
  int distance_idx = pcl::getFieldIndex<PointT> (distance_field_name, fields);

  float distance_value;
  // If dense, no need to check for NaNs
  if (cloud->is_dense)
  {
    for (const int &index : indices)
    {
      // Get the distance value
      const std::uint8_t* pt_data = reinterpret_cast<const std::uint8_t*> (&cloud->points[index]);
      memcpy (&distance_value, pt_data + fields[distance_idx].offset, sizeof (float));

      if (limit_negative)
      {
        // Use a threshold for cutting out points which inside the interval
        if ((distance_value < max_distance) && (distance_value > min_distance))
          continue;
      }
      else
      {
        // Use a threshold for cutting out points which are too close/far away
        if ((distance_value > max_distance) || (distance_value < min_distance))
          continue;
      }
      // Create the point structure and get the min/max
      pcl::Array4fMapConst pt = cloud->points[index].getArray4fMap ();
      min_p = min_p.min (pt);
      max_p = max_p.max (pt);
    }
  }
  else
  {
    for (const int &index : indices)
    {
      // Get the distance value
      const std::uint8_t* pt_data = reinterpret_cast<const std::uint8_t*> (&cloud->points[index]);
      memcpy (&distance_value, pt_data + fields[distance_idx].offset, sizeof (float));

      if (limit_negative)
      {
        // Use a threshold for cutting out points which inside the interval
        if ((distance_value < max_distance) && (distance_value > min_distance))
          continue;
      }
      else
      {
        // Use a threshold for cutting out points which are too close/far away
        if ((distance_value > max_distance) || (distance_value < min_distance))
          continue;
      }

      // Check if the point is invalid
      if (!std::isfinite (cloud->points[index].x) || 
          !std::isfinite (cloud->points[index].y) || 
          !std::isfinite (cloud->points[index].z))
        continue;
      // Create the point structure and get the min/max
      pcl::Array4fMapConst pt = cloud->points[index].getArray4fMap ();
      min_p = min_p.min (pt);
      max_p = max_p.max (pt);
    }
  }
  min_pt = min_p;
  max_pt = max_p;
}

struct cloud_point_index_idx 
{
  unsigned int idx;
  unsigned int cloud_point_index;

  cloud_point_index_idx() = default;
  cloud_point_index_idx (unsigned int idx_, unsigned int cloud_point_index_) : idx (idx_), cloud_point_index (cloud_point_index_) {}
  bool operator < (const cloud_point_index_idx &p) const { return (idx < p.idx); }
};

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::VoxelGrid<PointT>::applyFilter (PointCloud &output)
{
  // Has the input dataset been set already?
  if (!input_)
  {
    PCL_WARN ("[pcl::%s::applyFilter] No input dataset given!\n", getClassName ().c_str ());
    output.width = output.height = 0;
    output.points.clear ();
    return;
  }

  // Copy the header (and thus the frame_id) + allocate enough space for points
  output.height       = 1;                    // downsampling breaks the organized structure
  output.is_dense     = true;                 // we filter out invalid points

  Eigen::Vector4f min_p, max_p;
  // Get the minimum and maximum dimensions
  if (!filter_field_name_.empty ()) // If we don't want to process the entire cloud...
    getMinMax3D<PointT> (input_, *indices_, filter_field_name_, static_cast<float> (filter_limit_min_), static_cast<float> (filter_limit_max_), min_p, max_p, filter_limit_negative_);
  else
    getMinMax3D<PointT> (*input_, *indices_, min_p, max_p);

  // Check that the leaf size is not too small, given the size of the data
  std::int64_t dx = static_cast<std::int64_t>((max_p[0] - min_p[0]) * inverse_leaf_size_[0])+1;
  std::int64_t dy = static_cast<std::int64_t>((max_p[1] - min_p[1]) * inverse_leaf_size_[1])+1;
  std::int64_t dz = static_cast<std::int64_t>((max_p[2] - min_p[2]) * inverse_leaf_size_[2])+1;

  if ((dx*dy*dz) > static_cast<std::int64_t>(std::numeric_limits<std::int32_t>::max()))
  {
    PCL_WARN("[pcl::%s::applyFilter] Leaf size is too small for the input dataset. Integer indices would overflow.", getClassName().c_str());
    output = *input_;
    return;
  }

  // Compute the minimum and maximum bounding box values
  min_b_[0] = static_cast<int> (std::floor (min_p[0] * inverse_leaf_size_[0]));
  max_b_[0] = static_cast<int> (std::floor (max_p[0] * inverse_leaf_size_[0]));
  min_b_[1] = static_cast<int> (std::floor (min_p[1] * inverse_leaf_size_[1]));
  max_b_[1] = static_cast<int> (std::floor (max_p[1] * inverse_leaf_size_[1]));
  min_b_[2] = static_cast<int> (std::floor (min_p[2] * inverse_leaf_size_[2]));
  max_b_[2] = static_cast<int> (std::floor (max_p[2] * inverse_leaf_size_[2]));

  // Compute the number of divisions needed along all axis
  div_b_ = max_b_ - min_b_ + Eigen::Vector4i::Ones ();
  div_b_[3] = 0;

  // Set up the division multiplier
  divb_mul_ = Eigen::Vector4i (1, div_b_[0], div_b_[0] * div_b_[1], 0);

  // Storage for mapping leaf and pointcloud indexes
  std::vector<cloud_point_index_idx> index_vector;
  index_vector.reserve (indices_->size ());

  // If we don't want to process the entire cloud, but rather filter points far away from the viewpoint first...
  if (!filter_field_name_.empty ())
  {
    // Get the distance field index
    std::vector<pcl::PCLPointField> fields;
    int distance_idx = pcl::getFieldIndex<PointT> (filter_field_name_, fields);
    if (distance_idx == -1)
      PCL_WARN ("[pcl::%s::applyFilter] Invalid filter field name. Index is %d.\n", getClassName ().c_str (), distance_idx);

    // First pass: go over all points and insert them into the index_vector vector
    // with calculated idx. Points with the same idx value will contribute to the
    // same point of resulting CloudPoint
    for (std::vector<int>::const_iterator it = indices_->begin (); it != indices_->end (); ++it)
    {
      if (!input_->is_dense)
        // Check if the point is invalid
        if (!std::isfinite (input_->points[*it].x) || 
            !std::isfinite (input_->points[*it].y) || 
            !std::isfinite (input_->points[*it].z))
          continue;

      // Get the distance value
      const std::uint8_t* pt_data = reinterpret_cast<const std::uint8_t*> (&input_->points[*it]);
      float distance_value = 0;
      memcpy (&distance_value, pt_data + fields[distance_idx].offset, sizeof (float));

      if (filter_limit_negative_)
      {
        // Use a threshold for cutting out points which inside the interval
        if ((distance_value < filter_limit_max_) && (distance_value > filter_limit_min_))
          continue;
      }
      else
      {
        // Use a threshold for cutting out points which are too close/far away
        if ((distance_value > filter_limit_max_) || (distance_value < filter_limit_min_))
          continue;
      }
      
      int ijk0 = static_cast<int> (std::floor (input_->points[*it].x * inverse_leaf_size_[0]) - static_cast<float> (min_b_[0]));
      int ijk1 = static_cast<int> (std::floor (input_->points[*it].y * inverse_leaf_size_[1]) - static_cast<float> (min_b_[1]));
      int ijk2 = static_cast<int> (std::floor (input_->points[*it].z * inverse_leaf_size_[2]) - static_cast<float> (min_b_[2]));

      // Compute the centroid leaf index
      int idx = ijk0 * divb_mul_[0] + ijk1 * divb_mul_[1] + ijk2 * divb_mul_[2];
      index_vector.emplace_back(static_cast<unsigned int> (idx), *it);
    }
  }
  // No distance filtering, process all data
  else
  {
    // First pass: go over all points and insert them into the index_vector vector
    // with calculated idx. Points with the same idx value will contribute to the
    // same point of resulting CloudPoint
    for (std::vector<int>::const_iterator it = indices_->begin (); it != indices_->end (); ++it)
    {
      if (!input_->is_dense)
        // Check if the point is invalid
        if (!std::isfinite (input_->points[*it].x) || 
            !std::isfinite (input_->points[*it].y) || 
            !std::isfinite (input_->points[*it].z))
          continue;

      int ijk0 = static_cast<int> (std::floor (input_->points[*it].x * inverse_leaf_size_[0]) - static_cast<float> (min_b_[0]));
      int ijk1 = static_cast<int> (std::floor (input_->points[*it].y * inverse_leaf_size_[1]) - static_cast<float> (min_b_[1]));
      int ijk2 = static_cast<int> (std::floor (input_->points[*it].z * inverse_leaf_size_[2]) - static_cast<float> (min_b_[2]));

      // Compute the centroid leaf index
      int idx = ijk0 * divb_mul_[0] + ijk1 * divb_mul_[1] + ijk2 * divb_mul_[2];
      index_vector.emplace_back(static_cast<unsigned int> (idx), *it);
    }
  }

  // Second pass: sort the index_vector vector using value representing target cell as index
  // in effect all points belonging to the same output cell will be next to each other
  auto rightshift_func = [](const cloud_point_index_idx &x, const unsigned offset) { return x.idx >> offset; };
  boost::sort::spreadsort::integer_sort(index_vector.begin(), index_vector.end(), rightshift_func);
  
  // Third pass: count output cells
  // we need to skip all the same, adjacent idx values
  unsigned int total = 0;
  unsigned int index = 0;
  // first_and_last_indices_vector[i] represents the index in index_vector of the first point in
  // index_vector belonging to the voxel which corresponds to the i-th output point,
  // and of the first point not belonging to.
  std::vector<std::pair<unsigned int, unsigned int> > first_and_last_indices_vector;
  // Worst case size
  first_and_last_indices_vector.reserve (index_vector.size ());
  while (index < index_vector.size ()) 
  {
    unsigned int i = index + 1;
    while (i < index_vector.size () && index_vector[i].idx == index_vector[index].idx) 
      ++i;
    if (i - index >= min_points_per_voxel_)
    {
      ++total;
      first_and_last_indices_vector.emplace_back(index, i);
    }
    index = i;
  }

  // Fourth pass: compute centroids, insert them into their final position
  output.points.resize (total);
  if (save_leaf_layout_)
  {
    try
    { 
      // Resizing won't reset old elements to -1.  If leaf_layout_ has been used previously, it needs to be re-initialized to -1
      std::uint32_t new_layout_size = div_b_[0]*div_b_[1]*div_b_[2];
      //This is the number of elements that need to be re-initialized to -1
      std::uint32_t reinit_size = std::min (static_cast<unsigned int> (new_layout_size), static_cast<unsigned int> (leaf_layout_.size()));
      for (std::uint32_t i = 0; i < reinit_size; i++)
      {
        leaf_layout_[i] = -1;
      }        
      leaf_layout_.resize (new_layout_size, -1);           
    }
    catch (std::bad_alloc&)
    {
      throw PCLException("VoxelGrid bin size is too low; impossible to allocate memory for layout", 
        "voxel_grid.hpp", "applyFilter");	
    }
    catch (std::length_error&)
    {
      throw PCLException("VoxelGrid bin size is too low; impossible to allocate memory for layout", 
        "voxel_grid.hpp", "applyFilter");	
    }
  }
  
  index = 0;
  for (const auto &cp : first_and_last_indices_vector)
  {
    // calculate centroid - sum values from all input points, that have the same idx value in index_vector array
  	unsigned int first_index = cp.first;
  	unsigned int last_index = cp.second;

    // index is centroid final position in resulting PointCloud
    if (save_leaf_layout_)
      leaf_layout_[index_vector[first_index].idx] = index;

    //Limit downsampling to coords
    if (!downsample_all_data_)
    {
      Eigen::Vector4f centroid (Eigen::Vector4f::Zero ());

      for (unsigned int li = first_index; li < last_index; ++li)
        centroid += input_->points[index_vector[li].cloud_point_index].getVector4fMap ();

      centroid /= static_cast<float> (last_index - first_index);
      output.points[index].getVector4fMap () = centroid;
    }
    else
    {
      CentroidPoint<PointT> centroid;

      // fill in the accumulator with leaf points
      for (unsigned int li = first_index; li < last_index; ++li)
        centroid.add (input_->points[index_vector[li].cloud_point_index]);  

      centroid.get (output.points[index]);
    }
     
    ++index;
  }
  output.width = static_cast<std::uint32_t> (output.points.size ());
}

#define PCL_INSTANTIATE_VoxelGrid(T) template class PCL_EXPORTS pcl::VoxelGrid<T>;
#define PCL_INSTANTIATE_getMinMax3D(T) template PCL_EXPORTS void pcl::getMinMax3D<T> (const pcl::PointCloud<T>::ConstPtr &, const std::string &, float, float, Eigen::Vector4f &, Eigen::Vector4f &, bool);

#endif    // PCL_FILTERS_IMPL_VOXEL_GRID_H_

