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#include <pcl/filters/extract_indices.h> // for ExtractIndices
#include <pcl/memory.h>                  // for pcl::make_shared

template <typename PointType>
void
pcl::apps::DominantPlaneSegmentation<PointType>::compute_table_plane()
{
  // Has the input dataset been set already?
  if (!input_) {
    PCL_WARN("[pcl::apps::DominantPlaneSegmentation] No input dataset given!\n");
    return;
  }

  CloudConstPtr cloud_;
  CloudPtr cloud_filtered_(new Cloud());
  CloudPtr cloud_downsampled_(new Cloud());
  pcl::PointCloud<pcl::Normal>::Ptr cloud_normals_(new pcl::PointCloud<pcl::Normal>());
  pcl::PointIndices::Ptr table_inliers_(new pcl::PointIndices());
  pcl::ModelCoefficients::Ptr table_coefficients_(new pcl::ModelCoefficients());
  CloudPtr table_projected_(new Cloud());
  CloudPtr table_hull_(new Cloud());

  typename pcl::search::KdTree<PointType>::Ptr normals_tree_(
      new pcl::search::KdTree<PointType>);

  // Normal estimation parameters
  n3d_.setKSearch(k_);
  n3d_.setSearchMethod(normals_tree_);

  // Table model fitting parameters
  seg_.setDistanceThreshold(sac_distance_threshold_);
  seg_.setMaxIterations(2000);
  seg_.setNormalDistanceWeight(0.1);
  seg_.setOptimizeCoefficients(true);
  seg_.setModelType(pcl::SACMODEL_NORMAL_PLANE);
  seg_.setMethodType(pcl::SAC_RANSAC);
  seg_.setProbability(0.99);

  proj_.setModelType(pcl::SACMODEL_NORMAL_PLANE);
  bb_cluster_proj_.setModelType(pcl::SACMODEL_NORMAL_PLANE);

  // ---[ PassThroughFilter
  pass_.setFilterLimits(float(min_z_bounds_), float(max_z_bounds_));
  pass_.setFilterFieldName("z");
  pass_.setInputCloud(input_);
  pass_.filter(*cloud_filtered_);

  if (int(cloud_filtered_->size()) < k_) {
    PCL_WARN("[DominantPlaneSegmentation] Filtering returned %lu points! Aborting.\n",
             cloud_filtered_->size());
    return;
  }

  // Downsample the point cloud
  grid_.setLeafSize(downsample_leaf_, downsample_leaf_, downsample_leaf_);
  grid_.setDownsampleAllData(false);
  grid_.setInputCloud(cloud_filtered_);
  grid_.filter(*cloud_downsampled_);

  // ---[ Estimate the point normals
  n3d_.setInputCloud(cloud_downsampled_);
  n3d_.compute(*cloud_normals_);

  // ---[ Perform segmentation
  seg_.setInputCloud(cloud_downsampled_);
  seg_.setInputNormals(cloud_normals_);
  seg_.segment(*table_inliers_, *table_coefficients_);

  if (table_inliers_->indices.empty()) {
    PCL_WARN("[DominantPlaneSegmentation] No Plane Inliers points! Aborting.\n");
    return;
  }

  // ---[ Extract the plane
  proj_.setInputCloud(cloud_downsampled_);
  proj_.setIndices(table_inliers_);
  proj_.setModelCoefficients(table_coefficients_);
  proj_.filter(*table_projected_);

  // ---[ Estimate the convex hull
  std::vector<pcl::Vertices> polygons;
  CloudPtr table_hull(new Cloud());
  hull_.setInputCloud(table_projected_);
  hull_.reconstruct(*table_hull, polygons);

  // Compute the plane coefficients
  Eigen::Vector4f model_coefficients;
  EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;

  model_coefficients[0] = table_coefficients_->values[0];
  model_coefficients[1] = table_coefficients_->values[1];
  model_coefficients[2] = table_coefficients_->values[2];
  model_coefficients[3] = table_coefficients_->values[3];

  // Need to flip the plane normal towards the viewpoint
  Eigen::Vector4f vp(0, 0, 0, 0);
  // See if we need to flip any plane normals
  vp -= (*table_hull)[0].getVector4fMap();
  vp[3] = 0;
  // Dot product between the (viewpoint - point) and the plane normal
  float cos_theta = vp.dot(model_coefficients);
  // Flip the plane normal
  if (cos_theta < 0) {
    model_coefficients *= -1;
    model_coefficients[3] = 0;
    // Hessian form (D = nc . p_plane (centroid here) + p)
    model_coefficients[3] =
        -1 * (model_coefficients.dot((*table_hull)[0].getVector4fMap()));
  }

  // Set table_coeffs
  table_coeffs_ = model_coefficients;
}

template <typename PointType>
void
pcl::apps::DominantPlaneSegmentation<PointType>::compute_fast(
    std::vector<CloudPtr>& clusters)
{
  // Has the input dataset been set already?
  if (!input_) {
    PCL_WARN("[pcl::apps::DominantPlaneSegmentation] No input dataset given!\n");
    return;
  }

  // Is the input dataset organized?
  if (input_->is_dense) {
    PCL_WARN("[pcl::apps::DominantPlaneSegmentation] compute_fast can only be used "
             "with organized point clouds!\n");
    return;
  }

  CloudConstPtr cloud_;
  CloudPtr cloud_filtered_(new Cloud());
  CloudPtr cloud_downsampled_(new Cloud());
  pcl::PointCloud<pcl::Normal>::Ptr cloud_normals_(new pcl::PointCloud<pcl::Normal>());
  pcl::PointIndices::Ptr table_inliers_(new pcl::PointIndices());
  pcl::ModelCoefficients::Ptr table_coefficients_(new pcl::ModelCoefficients());
  CloudPtr table_projected_(new Cloud());
  CloudPtr table_hull_(new Cloud());
  CloudPtr cloud_objects_(new Cloud());
  CloudPtr cluster_object_(new Cloud());

  typename pcl::search::KdTree<PointType>::Ptr normals_tree_(
      new pcl::search::KdTree<PointType>);
  typename pcl::search::KdTree<PointType>::Ptr clusters_tree_(
      new pcl::search::KdTree<PointType>);
  clusters_tree_->setEpsilon(1);

  // Normal estimation parameters
  n3d_.setKSearch(k_);
  n3d_.setSearchMethod(normals_tree_);

  // Table model fitting parameters
  seg_.setDistanceThreshold(sac_distance_threshold_);
  seg_.setMaxIterations(2000);
  seg_.setNormalDistanceWeight(0.1);
  seg_.setOptimizeCoefficients(true);
  seg_.setModelType(pcl::SACMODEL_NORMAL_PLANE);
  seg_.setMethodType(pcl::SAC_RANSAC);
  seg_.setProbability(0.99);

  proj_.setModelType(pcl::SACMODEL_NORMAL_PLANE);
  bb_cluster_proj_.setModelType(pcl::SACMODEL_NORMAL_PLANE);

  prism_.setHeightLimits(object_min_height_, object_max_height_);

  // Clustering parameters
  cluster_.setClusterTolerance(object_cluster_tolerance_);
  cluster_.setMinClusterSize(object_cluster_min_size_);
  cluster_.setSearchMethod(clusters_tree_);

  // ---[ PassThroughFilter
  pass_.setFilterLimits(float(min_z_bounds_), float(max_z_bounds_));
  pass_.setFilterFieldName("z");
  pass_.setInputCloud(input_);
  pass_.filter(*cloud_filtered_);

  if (int(cloud_filtered_->size()) < k_) {
    PCL_WARN("[DominantPlaneSegmentation] Filtering returned %lu points! Aborting.",
             cloud_filtered_->size());
    return;
  }

  // Downsample the point cloud
  grid_.setLeafSize(downsample_leaf_, downsample_leaf_, downsample_leaf_);
  grid_.setDownsampleAllData(false);
  grid_.setInputCloud(cloud_filtered_);
  grid_.filter(*cloud_downsampled_);

  // ---[ Estimate the point normals
  n3d_.setInputCloud(cloud_downsampled_);
  n3d_.compute(*cloud_normals_);

  // ---[ Perform segmentation
  seg_.setInputCloud(cloud_downsampled_);
  seg_.setInputNormals(cloud_normals_);
  seg_.segment(*table_inliers_, *table_coefficients_);

  if (table_inliers_->indices.empty()) {
    PCL_WARN("[DominantPlaneSegmentation] No Plane Inliers points! Aborting.\n");
    return;
  }

  // ---[ Extract the plane
  proj_.setInputCloud(cloud_downsampled_);
  proj_.setIndices(table_inliers_);
  proj_.setModelCoefficients(table_coefficients_);
  proj_.filter(*table_projected_);

  // ---[ Estimate the convex hull
  std::vector<pcl::Vertices> polygons;
  CloudPtr table_hull(new Cloud());
  hull_.setInputCloud(table_projected_);
  hull_.reconstruct(*table_hull, polygons);

  // Compute the plane coefficients
  Eigen::Vector4f model_coefficients;
  EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;

  model_coefficients[0] = table_coefficients_->values[0];
  model_coefficients[1] = table_coefficients_->values[1];
  model_coefficients[2] = table_coefficients_->values[2];
  model_coefficients[3] = table_coefficients_->values[3];

  // Need to flip the plane normal towards the viewpoint
  Eigen::Vector4f vp(0, 0, 0, 0);
  // See if we need to flip any plane normals
  vp -= (*table_hull)[0].getVector4fMap();
  vp[3] = 0;
  // Dot product between the (viewpoint - point) and the plane normal
  float cos_theta = vp.dot(model_coefficients);
  // Flip the plane normal
  if (cos_theta < 0) {
    model_coefficients *= -1;
    model_coefficients[3] = 0;
    // Hessian form (D = nc . p_plane (centroid here) + p)
    model_coefficients[3] =
        -1 * (model_coefficients.dot((*table_hull)[0].getVector4fMap()));
  }

  // Set table_coeffs
  table_coeffs_ = model_coefficients;

  // ---[ Get the objects on top of the table
  pcl::PointIndices cloud_object_indices;
  prism_.setInputCloud(input_);
  prism_.setInputPlanarHull(table_hull);
  prism_.segment(cloud_object_indices);

  pcl::ExtractIndices<PointType> extract_object_indices;
  extract_object_indices.setInputCloud(input_);
  extract_object_indices.setIndices(
      pcl::make_shared<const pcl::PointIndices>(cloud_object_indices));
  extract_object_indices.filter(*cloud_objects_);

  // create new binary pointcloud with intensity values (0 and 1),
  // 0 for non-object pixels and 1 otherwise
  pcl::PointCloud<pcl::PointXYZI>::Ptr binary_cloud(
      new pcl::PointCloud<pcl::PointXYZI>);

  {
    binary_cloud->width = input_->width;
    binary_cloud->height = input_->height;
    binary_cloud->points.resize(input_->size());
    binary_cloud->is_dense = input_->is_dense;

    for (const auto& idx : cloud_object_indices.indices) {
      (*binary_cloud)[idx].getVector4fMap() = (*input_)[idx].getVector4fMap();
      (*binary_cloud)[idx].intensity = 1.0;
    }
  }

  // connected components on the binary image

  std::map<float, float> connected_labels;
  float c_intensity = 0.1f;
  const float intensity_incr = 0.1f;

  {

    int wsize = wsize_;
    for (int i = 0; i < int(binary_cloud->width); i++) {
      for (int j = 0; j < int(binary_cloud->height); j++) {
        if (binary_cloud->at(i, j).intensity != 0) {
          // check neighboring pixels, first left and then top
          // be aware of margins

          if ((i - 1) < 0 && (j - 1) < 0) {
            // top-left pixel
            (*binary_cloud)(i, j).intensity = c_intensity;
          }
          else {
            if ((j - 1) < 0) {
              // top-row, check on the left of pixel to assign a new label or not
              int left = check((*binary_cloud)(i - 1, j),
                               (*binary_cloud)(i, j),
                               c_intensity,
                               object_cluster_tolerance_);
              if (left) {
                // Nothing found on the left, check bigger window

                bool found = false;
                for (int kk = 2; kk < wsize && !found; kk++) {
                  if ((i - kk) < 0)
                    continue;

                  int left = check((*binary_cloud)(i - kk, j),
                                   (*binary_cloud)(i, j),
                                   c_intensity,
                                   object_cluster_tolerance_);
                  if (left == 0) {
                    found = true;
                  }
                }

                if (!found) {
                  c_intensity += intensity_incr;
                  (*binary_cloud)(i, j).intensity = c_intensity;
                }
              }
            }
            else {
              if ((i - 1) == 0) {
                // check only top
                int top = check((*binary_cloud)(i, j - 1),
                                (*binary_cloud)(i, j),
                                c_intensity,
                                object_cluster_tolerance_);
                if (top) {
                  bool found = false;
                  for (int kk = 2; kk < wsize && !found; kk++) {
                    if ((j - kk) < 0)
                      continue;

                    int top = check((*binary_cloud)(i, j - kk),
                                    (*binary_cloud)(i, j),
                                    c_intensity,
                                    object_cluster_tolerance_);
                    if (top == 0) {
                      found = true;
                    }
                  }

                  if (!found) {
                    c_intensity += intensity_incr;
                    (*binary_cloud)(i, j).intensity = c_intensity;
                  }
                }
              }
              else {
                // check left and top
                int left = check((*binary_cloud)(i - 1, j),
                                 (*binary_cloud)(i, j),
                                 c_intensity,
                                 object_cluster_tolerance_);
                int top = check((*binary_cloud)(i, j - 1),
                                (*binary_cloud)(i, j),
                                c_intensity,
                                object_cluster_tolerance_);

                if (left == 0 && top == 0) {
                  // both top and left had labels, check if they are different
                  // if they are, take the smallest one and mark labels to be
                  // connected..

                  if ((*binary_cloud)(i - 1, j).intensity !=
                      (*binary_cloud)(i, j - 1).intensity) {
                    float smaller_intensity = (*binary_cloud)(i - 1, j).intensity;
                    float bigger_intensity = (*binary_cloud)(i, j - 1).intensity;

                    if ((*binary_cloud)(i - 1, j).intensity >
                        (*binary_cloud)(i, j - 1).intensity) {
                      smaller_intensity = (*binary_cloud)(i, j - 1).intensity;
                      bigger_intensity = (*binary_cloud)(i - 1, j).intensity;
                    }

                    connected_labels[bigger_intensity] = smaller_intensity;
                    (*binary_cloud)(i, j).intensity = smaller_intensity;
                  }
                }

                if (left == 1 && top == 1) {
                  // if none had labels, increment c_intensity
                  // search first on bigger window
                  bool found = false;
                  for (int dist = 2; dist < wsize && !found; dist++) {
                    if (((i - dist) < 0) || ((j - dist) < 0))
                      continue;

                    int left = check((*binary_cloud)(i - dist, j),
                                     (*binary_cloud)(i, j),
                                     c_intensity,
                                     object_cluster_tolerance_);
                    int top = check((*binary_cloud)(i, j - dist),
                                    (*binary_cloud)(i, j),
                                    c_intensity,
                                    object_cluster_tolerance_);

                    if (left == 0 && top == 0) {
                      if ((*binary_cloud)(i - dist, j).intensity !=
                          (*binary_cloud)(i, j - dist).intensity) {
                        float smaller_intensity =
                            (*binary_cloud)(i - dist, j).intensity;
                        float bigger_intensity = (*binary_cloud)(i, j - dist).intensity;

                        if ((*binary_cloud)(i - dist, j).intensity >
                            (*binary_cloud)(i, j - dist).intensity) {
                          smaller_intensity = (*binary_cloud)(i, j - dist).intensity;
                          bigger_intensity = (*binary_cloud)(i - dist, j).intensity;
                        }

                        connected_labels[bigger_intensity] = smaller_intensity;
                        (*binary_cloud)(i, j).intensity = smaller_intensity;
                        found = true;
                      }
                    }
                    else if (left == 0 || top == 0) {
                      // one had label
                      found = true;
                    }
                  }

                  if (!found) {
                    // none had label in the bigger window
                    c_intensity += intensity_incr;
                    (*binary_cloud)(i, j).intensity = c_intensity;
                  }
                }
              }
            }
          }
        }
      }
    }
  }

  std::map<float, pcl::PointIndices> clusters_map;

  {
    std::map<float, float>::iterator it;

    for (int i = 0; i < int(binary_cloud->width); i++) {
      for (int j = 0; j < int(binary_cloud->height); j++) {
        if (binary_cloud->at(i, j).intensity != 0) {
          // check if this is a root label...
          it = connected_labels.find(binary_cloud->at(i, j).intensity);
          while (it != connected_labels.end()) {
            // the label is on the list, change pixel intensity until it
            // has a root label
            (*binary_cloud)(i, j).intensity = (*it).second;
            it = connected_labels.find(binary_cloud->at(i, j).intensity);
          }

          std::map<float, pcl::PointIndices>::iterator it_indices;
          it_indices = clusters_map.find(binary_cloud->at(i, j).intensity);
          if (it_indices == clusters_map.end()) {
            pcl::PointIndices indices;
            clusters_map[binary_cloud->at(i, j).intensity] = indices;
          }

          clusters_map[binary_cloud->at(i, j).intensity].indices.push_back(
              static_cast<int>(j * binary_cloud->width + i));
        }
      }
    }
  }

  clusters.resize(clusters_map.size());

  int k = 0;
  for (const auto& cluster : clusters_map) {

    if (int(cluster.second.indices.size()) >= object_cluster_min_size_) {

      clusters[k] = CloudPtr(new Cloud());
      pcl::copyPointCloud(*input_, cluster.second.indices, *clusters[k]);
      k++;
      indices_clusters_.push_back(cluster.second);
    }
  }

  clusters.resize(k);
}

template <typename PointType>
void
pcl::apps::DominantPlaneSegmentation<PointType>::compute(
    std::vector<CloudPtr>& clusters)
{

  // Has the input dataset been set already?
  if (!input_) {
    PCL_WARN("[pcl::apps::DominantPlaneSegmentation] No input dataset given!\n");
    return;
  }

  CloudConstPtr cloud_;
  CloudPtr cloud_filtered_(new Cloud());
  CloudPtr cloud_downsampled_(new Cloud());
  pcl::PointCloud<pcl::Normal>::Ptr cloud_normals_(new pcl::PointCloud<pcl::Normal>());
  pcl::PointIndices::Ptr table_inliers_(new pcl::PointIndices());
  pcl::ModelCoefficients::Ptr table_coefficients_(new pcl::ModelCoefficients());
  CloudPtr table_projected_(new Cloud());
  CloudPtr table_hull_(new Cloud());
  CloudPtr cloud_objects_(new Cloud());
  CloudPtr cluster_object_(new Cloud());

  typename pcl::search::KdTree<PointType>::Ptr normals_tree_(
      new pcl::search::KdTree<PointType>);
  typename pcl::search::KdTree<PointType>::Ptr clusters_tree_(
      new pcl::search::KdTree<PointType>);
  clusters_tree_->setEpsilon(1);

  // Normal estimation parameters
  n3d_.setKSearch(k_);
  n3d_.setSearchMethod(normals_tree_);

  // Table model fitting parameters
  seg_.setDistanceThreshold(sac_distance_threshold_);
  seg_.setMaxIterations(2000);
  seg_.setNormalDistanceWeight(0.1);
  seg_.setOptimizeCoefficients(true);
  seg_.setModelType(pcl::SACMODEL_NORMAL_PLANE);
  seg_.setMethodType(pcl::SAC_RANSAC);
  seg_.setProbability(0.99);

  proj_.setModelType(pcl::SACMODEL_NORMAL_PLANE);
  bb_cluster_proj_.setModelType(pcl::SACMODEL_NORMAL_PLANE);

  prism_.setHeightLimits(object_min_height_, object_max_height_);

  // Clustering parameters
  cluster_.setClusterTolerance(object_cluster_tolerance_);
  cluster_.setMinClusterSize(object_cluster_min_size_);
  cluster_.setSearchMethod(clusters_tree_);

  // ---[ PassThroughFilter
  pass_.setFilterLimits(float(min_z_bounds_), float(max_z_bounds_));
  pass_.setFilterFieldName("z");
  pass_.setInputCloud(input_);
  pass_.filter(*cloud_filtered_);

  if (int(cloud_filtered_->size()) < k_) {
    PCL_WARN("[DominantPlaneSegmentation] Filtering returned %lu points! Aborting.",
             cloud_filtered_->size());
    return;
  }

  // Downsample the point cloud
  grid_.setLeafSize(downsample_leaf_, downsample_leaf_, downsample_leaf_);
  grid_.setDownsampleAllData(false);
  grid_.setInputCloud(cloud_filtered_);
  grid_.filter(*cloud_downsampled_);

  PCL_INFO("[DominantPlaneSegmentation] Number of points left after filtering "
           "(%f -> %f): %lu out of %lu\n",
           min_z_bounds_,
           max_z_bounds_,
           cloud_downsampled_->size(),
           input_->size());

  // ---[ Estimate the point normals
  n3d_.setInputCloud(cloud_downsampled_);
  n3d_.compute(*cloud_normals_);

  PCL_INFO("[DominantPlaneSegmentation] %lu normals estimated. \n",
           cloud_normals_->size());

  // ---[ Perform segmentation
  seg_.setInputCloud(cloud_downsampled_);
  seg_.setInputNormals(cloud_normals_);
  seg_.segment(*table_inliers_, *table_coefficients_);

  if (table_inliers_->indices.empty()) {
    PCL_WARN("[DominantPlaneSegmentation] No Plane Inliers points! Aborting.");
    return;
  }

  // ---[ Extract the plane
  proj_.setInputCloud(cloud_downsampled_);
  proj_.setIndices(table_inliers_);
  proj_.setModelCoefficients(table_coefficients_);
  proj_.filter(*table_projected_);

  // ---[ Estimate the convex hull
  std::vector<pcl::Vertices> polygons;
  CloudPtr table_hull(new Cloud());
  hull_.setInputCloud(table_projected_);
  hull_.reconstruct(*table_hull, polygons);

  // Compute the plane coefficients
  Eigen::Vector4f model_coefficients;
  EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;

  model_coefficients[0] = table_coefficients_->values[0];
  model_coefficients[1] = table_coefficients_->values[1];
  model_coefficients[2] = table_coefficients_->values[2];
  model_coefficients[3] = table_coefficients_->values[3];

  // Need to flip the plane normal towards the viewpoint
  Eigen::Vector4f vp(0, 0, 0, 0);
  // See if we need to flip any plane normals
  vp -= (*table_hull)[0].getVector4fMap();
  vp[3] = 0;
  // Dot product between the (viewpoint - point) and the plane normal
  float cos_theta = vp.dot(model_coefficients);
  // Flip the plane normal
  if (cos_theta < 0) {
    model_coefficients *= -1;
    model_coefficients[3] = 0;
    // Hessian form (D = nc . p_plane (centroid here) + p)
    model_coefficients[3] =
        -1 * (model_coefficients.dot((*table_hull)[0].getVector4fMap()));
  }

  // Set table_coeffs
  table_coeffs_ = model_coefficients;

  // ---[ Get the objects on top of the table
  pcl::PointIndices cloud_object_indices;
  prism_.setInputCloud(cloud_downsampled_);
  prism_.setInputPlanarHull(table_hull);
  prism_.segment(cloud_object_indices);

  pcl::ExtractIndices<PointType> extract_object_indices;
  extract_object_indices.setInputCloud(cloud_downsampled_);
  extract_object_indices.setIndices(
      pcl::make_shared<const pcl::PointIndices>(cloud_object_indices));
  extract_object_indices.filter(*cloud_objects_);

  if (cloud_objects_->points.empty())
    return;

  // down_.reset(new Cloud(*cloud_downsampled_));

  // ---[ Split the objects into Euclidean clusters
  std::vector<pcl::PointIndices> clusters2;
  cluster_.setInputCloud(cloud_downsampled_);
  cluster_.setIndices(pcl::make_shared<const pcl::PointIndices>(cloud_object_indices));
  cluster_.extract(clusters2);

  PCL_INFO("[DominantPlaneSegmentation::compute()] Number of clusters found matching "
           "the given constraints: %lu.\n",
           clusters2.size());

  clusters.resize(clusters2.size());

  for (std::size_t i = 0; i < clusters2.size(); ++i) {
    clusters[i] = CloudPtr(new Cloud());
    pcl::copyPointCloud(*cloud_downsampled_, clusters2[i].indices, *clusters[i]);
  }
}

template <typename PointType>
void
pcl::apps::DominantPlaneSegmentation<PointType>::compute_full(
    std::vector<CloudPtr>& clusters)
{

  // Has the input dataset been set already?
  if (!input_) {
    PCL_WARN("[pcl::apps::DominantPlaneSegmentation] No input dataset given!\n");
    return;
  }

  CloudConstPtr cloud_;
  CloudPtr cloud_filtered_(new Cloud());
  CloudPtr cloud_downsampled_(new Cloud());
  pcl::PointCloud<pcl::Normal>::Ptr cloud_normals_(new pcl::PointCloud<pcl::Normal>());
  pcl::PointIndices::Ptr table_inliers_(new pcl::PointIndices());
  pcl::ModelCoefficients::Ptr table_coefficients_(new pcl::ModelCoefficients());
  CloudPtr table_projected_(new Cloud());
  CloudPtr table_hull_(new Cloud());
  CloudPtr cloud_objects_(new Cloud());
  CloudPtr cluster_object_(new Cloud());

  typename pcl::search::KdTree<PointType>::Ptr normals_tree_(
      new pcl::search::KdTree<PointType>);
  typename pcl::search::KdTree<PointType>::Ptr clusters_tree_(
      new pcl::search::KdTree<PointType>);
  clusters_tree_->setEpsilon(1);

  // Normal estimation parameters
  n3d_.setKSearch(10);
  n3d_.setSearchMethod(normals_tree_);

  // Table model fitting parameters
  seg_.setDistanceThreshold(sac_distance_threshold_);
  seg_.setMaxIterations(2000);
  seg_.setNormalDistanceWeight(0.1);
  seg_.setOptimizeCoefficients(true);
  seg_.setModelType(pcl::SACMODEL_NORMAL_PLANE);
  seg_.setMethodType(pcl::SAC_MSAC);
  seg_.setProbability(0.98);

  proj_.setModelType(pcl::SACMODEL_NORMAL_PLANE);
  bb_cluster_proj_.setModelType(pcl::SACMODEL_NORMAL_PLANE);

  prism_.setHeightLimits(object_min_height_, object_max_height_);

  // Clustering parameters
  cluster_.setClusterTolerance(object_cluster_tolerance_);
  cluster_.setMinClusterSize(object_cluster_min_size_);
  cluster_.setSearchMethod(clusters_tree_);

  // ---[ PassThroughFilter
  pass_.setFilterLimits(float(min_z_bounds_), float(max_z_bounds_));
  pass_.setFilterFieldName("z");
  pass_.setInputCloud(input_);
  pass_.filter(*cloud_filtered_);

  if (int(cloud_filtered_->size()) < k_) {
    PCL_WARN("[DominantPlaneSegmentation] Filtering returned %lu points! Aborting.\n",
             cloud_filtered_->size());
    return;
  }

  // Downsample the point cloud
  grid_.setLeafSize(downsample_leaf_, downsample_leaf_, downsample_leaf_);
  grid_.setDownsampleAllData(false);
  grid_.setInputCloud(cloud_filtered_);
  grid_.filter(*cloud_downsampled_);

  PCL_INFO("[DominantPlaneSegmentation] Number of points left after "
           "filtering&downsampling (%f -> %f): %lu out of %lu\n",
           min_z_bounds_,
           max_z_bounds_,
           cloud_downsampled_->size(),
           input_->size());

  // ---[ Estimate the point normals
  n3d_.setInputCloud(cloud_downsampled_);
  n3d_.compute(*cloud_normals_);

  PCL_INFO("[DominantPlaneSegmentation] %lu normals estimated. \n",
           cloud_normals_->size());

  // ---[ Perform segmentation
  seg_.setInputCloud(cloud_downsampled_);
  seg_.setInputNormals(cloud_normals_);
  seg_.segment(*table_inliers_, *table_coefficients_);

  if (table_inliers_->indices.empty()) {
    PCL_WARN("[DominantPlaneSegmentation] No Plane Inliers points! Aborting.\n");
    return;
  }

  // ---[ Extract the plane
  proj_.setInputCloud(cloud_downsampled_);
  proj_.setIndices(table_inliers_);
  proj_.setModelCoefficients(table_coefficients_);
  proj_.filter(*table_projected_);

  // ---[ Estimate the convex hull
  std::vector<pcl::Vertices> polygons;
  CloudPtr table_hull(new Cloud());
  hull_.setInputCloud(table_projected_);
  hull_.reconstruct(*table_hull, polygons);

  // Compute the plane coefficients
  Eigen::Vector4f model_coefficients;
  EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;

  model_coefficients[0] = table_coefficients_->values[0];
  model_coefficients[1] = table_coefficients_->values[1];
  model_coefficients[2] = table_coefficients_->values[2];
  model_coefficients[3] = table_coefficients_->values[3];

  // Need to flip the plane normal towards the viewpoint
  Eigen::Vector4f vp(0, 0, 0, 0);
  // See if we need to flip any plane normals
  vp -= (*table_hull)[0].getVector4fMap();
  vp[3] = 0;
  // Dot product between the (viewpoint - point) and the plane normal
  float cos_theta = vp.dot(model_coefficients);
  // Flip the plane normal
  if (cos_theta < 0) {
    model_coefficients *= -1;
    model_coefficients[3] = 0;
    // Hessian form (D = nc . p_plane (centroid here) + p)
    model_coefficients[3] =
        -1 * (model_coefficients.dot((*table_hull)[0].getVector4fMap()));
  }

  // Set table_coeffs
  table_coeffs_ = model_coefficients;

  // ---[ Get the objects on top of the table
  pcl::PointIndices cloud_object_indices;
  prism_.setInputCloud(cloud_filtered_);
  prism_.setInputPlanarHull(table_hull);
  prism_.segment(cloud_object_indices);

  pcl::ExtractIndices<PointType> extract_object_indices;
  extract_object_indices.setInputCloud(cloud_downsampled_);
  extract_object_indices.setIndices(
      pcl::make_shared<const pcl::PointIndices>(cloud_object_indices));
  extract_object_indices.filter(*cloud_objects_);

  if (cloud_objects_->points.empty())
    return;

  // ---[ Split the objects into Euclidean clusters
  std::vector<pcl::PointIndices> clusters2;
  cluster_.setInputCloud(cloud_filtered_);
  cluster_.setIndices(pcl::make_shared<const pcl::PointIndices>(cloud_object_indices));
  cluster_.extract(clusters2);

  PCL_INFO("[DominantPlaneSegmentation::compute_full()] Number of clusters found "
           "matching the given constraints: %lu.\n",
           clusters2.size());

  clusters.resize(clusters2.size());

  for (std::size_t i = 0; i < clusters2.size(); ++i) {
    clusters[i] = CloudPtr(new Cloud());
    pcl::copyPointCloud(*cloud_filtered_, clusters2[i].indices, *clusters[i]);
  }
}

#define PCL_INSTANTIATE_DominantPlaneSegmentation(T)                                   \
  template class PCL_EXPORTS pcl::apps::DominantPlaneSegmentation<T>;
