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 * $Id:$
 * @author: Koen Buys
 */

#pragma once
#include <pcl/common/copy_point.h>
#include <pcl/gpu/segmentation/gpu_extract_clusters.h>

namespace pcl {
namespace detail {

//// Downloads only the neccssary cluster indices from the device to the host.
void
economical_download(const pcl::gpu::NeighborIndices& source_indices,
                    const pcl::Indices& buffer_indices,
                    std::size_t buffer_size,
                    pcl::Indices& downloaded_indices);
} // namespace detail
} // namespace pcl

template <typename PointT>
void
pcl::gpu::extractEuclideanClusters(
    const typename pcl::PointCloud<PointT>::Ptr& host_cloud_,
    const pcl::gpu::Octree::Ptr& tree,
    float tolerance,
    std::vector<PointIndices>& clusters,
    unsigned int min_pts_per_cluster,
    unsigned int max_pts_per_cluster)
{

  // Create a bool vector of processed point indices, and initialize it to false
  // cloud is a DeviceArray<PointType>
  PCL_DEBUG("[pcl::gpu::extractEuclideanClusters]\n");
  std::vector<bool> processed(host_cloud_->size(), false);

  int max_answers;

  if (max_pts_per_cluster > host_cloud_->size())
    max_answers = host_cloud_->size();
  else
    max_answers = max_pts_per_cluster;
  PCL_DEBUG("Max_answers: %i\n", max_answers);

  // to store the current cluster
  pcl::PointIndices r;

  DeviceArray<PointXYZ> queries_device_buffer;
  queries_device_buffer.create(max_answers);

  // Host buffer for results
  pcl::Indices sizes, data, cpu_tmp;
  // Process all points in the cloud
  for (std::size_t i = 0; i < host_cloud_->size(); ++i) {
    // if we already processed this point continue with the next one
    if (processed[i])
      continue;
    // now we will process this point
    processed[i] = true;

    // Create the query queue on the device, point based not indices
    pcl::gpu::Octree::Queries queries_device;
    // Create the query queue on the host
    pcl::PointCloud<pcl::PointXYZ>::VectorType queries_host;

    // Buffer in a new PointXYZ type
    PointXYZ p;
    pcl::copyPoint((*host_cloud_)[i], p);

    // Push the starting point in the vector
    queries_host.push_back(p);
    // Clear vector
    r.indices.clear();
    // Push the starting point in
    r.indices.push_back(i);

    unsigned int found_points = queries_host.size();
    unsigned int previous_found_points = 0;

    pcl::gpu::NeighborIndices result_device;

    // once the area stop growing, stop also iterating.
    do {
      data.clear();
      // if the number of queries is not high enough implement search on Host here
      if (queries_host.size() <=
          10) ///@todo: adjust this to a variable number settable with method
      {
        PCL_DEBUG(" CPU: ");
        for (std::size_t p = 0; p < queries_host.size(); p++) {
          // Execute the radiusSearch on the host
          cpu_tmp.clear();
          tree->radiusSearchHost(queries_host[p], tolerance, cpu_tmp, max_answers);
          std::copy(cpu_tmp.begin(), cpu_tmp.end(), std::back_inserter(data));
        }
      }
      // If number of queries is high enough do it here
      else {
        PCL_DEBUG(" GPU: ");
        sizes.clear();
        // Copy buffer
        queries_device =
            DeviceArray<PointXYZ>(queries_device_buffer.ptr(), queries_host.size());
        // Move queries to GPU
        queries_device.upload(queries_host);
        // Execute search
        tree->radiusSearch(queries_device, tolerance, max_answers, result_device);
        // Copy results from GPU to Host
        result_device.sizes.download(sizes);
        pcl::detail::economical_download(result_device, sizes, max_answers, data);
      }
      // Store the previously found number of points
      previous_found_points = found_points;
      // Clear queries list
      queries_host.clear();

      if (data.size() == 1)
        continue;

      // Process the results
      for (auto idx : data) {
        if (processed[idx])
          continue;
        processed[idx] = true;
        PointXYZ p;
        pcl::copyPoint((*host_cloud_)[idx], p);
        queries_host.push_back(p);
        found_points++;
        r.indices.push_back(idx);
      }
      PCL_DEBUG(" data.size: %i, foundpoints: %i, previous: %i",
                data.size(),
                found_points,
                previous_found_points);
      PCL_DEBUG(" new points: %i, next queries size: %i\n",
                found_points - previous_found_points,
                queries_host.size());
    } while (previous_found_points < found_points);
    // If this queue is satisfactory, add to the clusters
    if (found_points >= min_pts_per_cluster && found_points <= max_pts_per_cluster) {
      std::sort(r.indices.begin(), r.indices.end());
      // @todo: check if the following is actually still needed
      // r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()),
      // r.indices.end ());

      r.header = host_cloud_->header;
      clusters.push_back(r); // We could avoid a copy by working directly in the vector
    }
  }
}

template <typename PointT>
void
pcl::gpu::EuclideanClusterExtraction<PointT>::extract(
    std::vector<pcl::PointIndices>& clusters)
{
  /*
    // Initialize the GPU search tree
    if (!tree_)
    {
      tree_.reset (new pcl::gpu::Octree());
      ///@todo what do we do if input isn't a PointXYZ cloud?
      tree_.setCloud(input_);
    }
  */
  if (!tree_->isBuilt()) {
    tree_->build();
  }
  /*
    if(tree_->cloud_.size() != host_cloud.size ())
    {
      PCL_ERROR("[pcl::gpu::EuclideanClusterExtraction] size of host cloud and device
    cloud don't match!\n"); return;
    }
  */
  // Extract the actual clusters
  extractEuclideanClusters<PointT>(host_cloud_,
                                   tree_,
                                   cluster_tolerance_,
                                   clusters,
                                   min_pts_per_cluster_,
                                   max_pts_per_cluster_);
  PCL_DEBUG("INFO: end of extractEuclideanClusters\n");
  // Sort the clusters based on their size (largest one first)
  // std::sort (clusters.rbegin (), clusters.rend (), comparePointClusters);
}

#define PCL_INSTANTIATE_extractEuclideanClusters(T)                                    \
  template void PCL_EXPORTS pcl::gpu::extractEuclideanClusters<T>(                     \
      const typename pcl::PointCloud<T>::Ptr&,                                         \
      const pcl::gpu::Octree::Ptr&,                                                    \
      float,                                                                           \
      std::vector<PointIndices>&,                                                      \
      unsigned int,                                                                    \
      unsigned int);
#define PCL_INSTANTIATE_EuclideanClusterExtraction(T)                                  \
  template class PCL_EXPORTS pcl::gpu::EuclideanClusterExtraction<T>;
