/*
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 *  Point Cloud Library (PCL) - www.pointclouds.org
 *  Copyright (c) 2012, Willow Garage, Inc.
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#include <pcl/apps/modeler/cloud_mesh.h>
#include <pcl/apps/modeler/cloud_mesh_item.h>
#include <pcl/apps/modeler/normal_estimation_worker.h>
#include <pcl/apps/modeler/parameter.h>
#include <pcl/apps/modeler/parameter_dialog.h>
#include <pcl/common/common.h>
#include <pcl/features/normal_3d.h>
#include <pcl/filters/filter_indices.h>
#include <pcl/filters/voxel_grid.h>

//////////////////////////////////////////////////////////////////////////////////////////////
pcl::modeler::NormalEstimationWorker::NormalEstimationWorker(
    const QList<CloudMeshItem*>& cloud_mesh_items, QWidget* parent)
: AbstractWorker(cloud_mesh_items, parent)
, x_min_(std::numeric_limits<double>::max())
, x_max_(std::numeric_limits<double>::min())
, y_min_(std::numeric_limits<double>::max())
, y_max_(std::numeric_limits<double>::min())
, z_min_(std::numeric_limits<double>::max())
, z_max_(std::numeric_limits<double>::min())
, search_radius_(nullptr)
{}

//////////////////////////////////////////////////////////////////////////////////////////////
pcl::modeler::NormalEstimationWorker::~NormalEstimationWorker()
{
  delete search_radius_;
}

//////////////////////////////////////////////////////////////////////////////////////////////
void
pcl::modeler::NormalEstimationWorker::initParameters(CloudMeshItem* cloud_mesh_item)
{
  Eigen::Vector4f min_pt, max_pt;
  pcl::getMinMax3D(*(cloud_mesh_item->getCloudMesh()->getCloud()), min_pt, max_pt);

  x_min_ = std::min(double(min_pt.x()), x_min_);
  x_max_ = std::max(double(max_pt.x()), x_max_);

  y_min_ = std::min(double(min_pt.y()), y_min_);
  y_max_ = std::max(double(max_pt.y()), y_max_);

  z_min_ = std::min(double(min_pt.z()), z_min_);
  z_max_ = std::max(double(max_pt.z()), z_max_);
}

//////////////////////////////////////////////////////////////////////////////////////////////
void
pcl::modeler::NormalEstimationWorker::setupParameters()
{
  double x_range = x_max_ - x_min_;
  double y_range = y_max_ - y_min_;
  double z_range = z_max_ - z_min_;

  double range_max = std::max(x_range, std::max(y_range, z_range));
  double radius = range_max / 100;
  double step = range_max / 1000;

  // clang-format off
  search_radius_ = new DoubleParameter(
      "Search Radius",
      "The sphere radius that is to be used for determining the nearest neighbors",
      radius, 0, x_max_ - x_min_, step);
  // clang-format on

  parameter_dialog_->addParameter(search_radius_);
}

//////////////////////////////////////////////////////////////////////////////////////////////
void
pcl::modeler::NormalEstimationWorker::processImpl(CloudMeshItem* cloud_mesh_item)
{
  CloudMesh::PointCloudPtr cloud = cloud_mesh_item->getCloudMesh()->getCloud();

  // Create the normal estimation class, and pass the input dataset to it
  pcl::NormalEstimation<pcl::PointSurfel, pcl::PointNormal> normal_estimator;
  normal_estimator.setInputCloud(cloud);

  pcl::IndicesPtr indices(new std::vector<int>());
  pcl::removeNaNFromPointCloud(*cloud, *indices);
  normal_estimator.setIndices(indices);

  // Create an empty kdtree representation, and pass it to the normal estimation object.
  // Its content will be filled inside the object, based on the given input dataset (as
  // no other search surface is given).
  pcl::search::KdTree<pcl::PointSurfel>::Ptr tree(
      new pcl::search::KdTree<pcl::PointSurfel>());
  normal_estimator.setSearchMethod(tree);

  // Use all neighbors in a sphere of the search radius
  normal_estimator.setRadiusSearch(*search_radius_);

  pcl::PointCloud<pcl::PointNormal> normals;
  normal_estimator.compute(normals);

  for (std::size_t i = 0, i_end = indices->size(); i < i_end; ++i) {
    std::size_t dest = (*indices)[i];
    cloud->points[dest].normal_x = normals.points[i].normal_x;
    cloud->points[dest].normal_y = normals.points[i].normal_y;
    cloud->points[dest].normal_z = normals.points[i].normal_z;
  }
}
