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#ifndef PCL_FILTERS_BILATERAL_IMPL_H_
#define PCL_FILTERS_BILATERAL_IMPL_H_

#include <pcl/filters/bilateral.h>
#include <pcl/search/organized.h> // for OrganizedNeighbor
#include <pcl/search/kdtree.h> // for KdTree
#include <pcl/common/point_tests.h> // for isXYZFinite

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> double
pcl::BilateralFilter<PointT>::computePointWeight (const int pid, 
                                                  const Indices &indices,
                                                  const std::vector<float> &distances)
{
  double BF = 0, W = 0;

  // For each neighbor
  for (std::size_t n_id = 0; n_id < indices.size (); ++n_id)
  {
    int id = indices[n_id];
    // Compute the difference in intensity
    double intensity_dist = std::abs ((*input_)[pid].intensity - (*input_)[id].intensity);

    // Compute the Gaussian intensity weights both in Euclidean and in intensity space
    double dist = std::sqrt (distances[n_id]);
    double weight = kernel (dist, sigma_s_) * kernel (intensity_dist, sigma_r_);

    // Calculate the bilateral filter response
    BF += weight * (*input_)[id].intensity;
    W += weight;
  }
  return (BF / W);
}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::BilateralFilter<PointT>::applyFilter (PointCloud &output)
{
  // Check if sigma_s has been given by the user
  if (sigma_s_ == 0)
  {
    PCL_ERROR ("[pcl::BilateralFilter::applyFilter] Need a sigma_s value given before continuing.\n");
    return;
  }
  // In case a search method has not been given, initialize it using some defaults
  if (!tree_)
  {
    // For organized datasets, use an OrganizedNeighbor
    if (input_->isOrganized ())
      tree_.reset (new pcl::search::OrganizedNeighbor<PointT> ());
    // For unorganized data, use a FLANN kdtree
    else
      tree_.reset (new pcl::search::KdTree<PointT> (false));
  }
  tree_->setInputCloud (input_);

  Indices k_indices;
  std::vector<float> k_distances;

  // Copy the input data into the output
  output = *input_;

  // For all the indices given (equal to the entire cloud if none given)
  for (const auto& idx : (*indices_))
  {
    if (input_->is_dense || pcl::isXYZFinite((*input_)[idx]))
    {
      // Perform a radius search to find the nearest neighbors
      tree_->radiusSearch (idx, sigma_s_ * 2, k_indices, k_distances);

      // Overwrite the intensity value with the computed average
      output[idx].intensity = static_cast<float> (computePointWeight (idx, k_indices, k_distances));
    }
  }
}
 
#define PCL_INSTANTIATE_BilateralFilter(T) template class PCL_EXPORTS pcl::BilateralFilter<T>;

#endif // PCL_FILTERS_BILATERAL_IMPL_H_

