/*
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 *
 *  Point Cloud Library (PCL) - www.pointclouds.org
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#include <pcl/apps/modeler/cloud_mesh.h>
#include <pcl/apps/modeler/cloud_mesh_item.h>
#include <pcl/apps/modeler/icp_registration_worker.h>
#include <pcl/apps/modeler/parameter.h>
#include <pcl/apps/modeler/parameter_dialog.h>
#include <pcl/common/common.h>
#include <pcl/registration/icp.h>

//////////////////////////////////////////////////////////////////////////////////////////////
pcl::modeler::ICPRegistrationWorker::ICPRegistrationWorker(
    CloudMesh::PointCloudPtr cloud,
    const QList<CloudMeshItem*>& cloud_mesh_items,
    QWidget* parent)
: AbstractWorker(cloud_mesh_items, parent)
, cloud_(std::move(cloud))
, 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())
, max_correspondence_distance_(nullptr)
, max_iterations_(nullptr)
, transformation_epsilon_(nullptr)
, euclidean_fitness_epsilon_(nullptr)
{}

//////////////////////////////////////////////////////////////////////////////////////////////
void
pcl::modeler::ICPRegistrationWorker::initParameters(CloudMeshItem* cloud_mesh_item)
{
  cloud_->clear();

  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::ICPRegistrationWorker::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 max_correspondence_distance = range_max / 2;
  double step = range_max / 1000;

  // clang-format off
  max_correspondence_distance_ =
      new DoubleParameter("Max Correspondence Distance",
                          "If the distance is larger than this threshold, the points "
                          "will be ignored in the alignment process.",
                          max_correspondence_distance, 0, x_max_ - x_min_, step);

  max_iterations_ = new IntParameter(
      "Max Iterations",
      "Set the maximum number of iterations the internal optimization should run for.",
      10, 0, 256);

  double transformation_epsilon = range_max / 2;
  transformation_epsilon_ = new DoubleParameter(
      "Transformation Epsilon",
      "Maximum allowable difference between two consecutive transformations.",
      0.0, 0, transformation_epsilon, step);

  double euclidean_fitness_epsilon = range_max / 2;
  euclidean_fitness_epsilon_ = new DoubleParameter(
      "Euclidean Fitness Epsilon",
      "Maximum allowed Euclidean error between two consecutive steps in the ICP loop.",
      0.0, 0, euclidean_fitness_epsilon, step);
  // clang-format on

  parameter_dialog_->addParameter(max_correspondence_distance_);
  parameter_dialog_->addParameter(max_iterations_);
  parameter_dialog_->addParameter(transformation_epsilon_);
  parameter_dialog_->addParameter(euclidean_fitness_epsilon_);
}

//////////////////////////////////////////////////////////////////////////////////////////////
void
pcl::modeler::ICPRegistrationWorker::processImpl(CloudMeshItem* cloud_mesh_item)
{
  if (cloud_->empty()) {
    *cloud_ = *(cloud_mesh_item->getCloudMesh()->getCloud());
    return;
  }

  pcl::IterativeClosestPoint<CloudMesh::PointT, CloudMesh::PointT> icp;

  // Set the max correspondence distance to 5cm (e.g., correspondences with higher
  // distances will be ignored)
  icp.setMaxCorrespondenceDistance(*max_correspondence_distance_);
  // Set the maximum number of iterations (criterion 1)
  icp.setMaximumIterations(*max_iterations_);
  // Set the transformation epsilon (criterion 2)
  icp.setTransformationEpsilon(*transformation_epsilon_);
  // Set the euclidean distance difference epsilon (criterion 3)
  icp.setEuclideanFitnessEpsilon(*euclidean_fitness_epsilon_);

  icp.setInputSource(cloud_mesh_item->getCloudMesh()->getCloud());
  icp.setInputTarget(cloud_);
  pcl::PointCloud<CloudMesh::PointT> result;
  icp.align(result);

  result.sensor_origin_ = cloud_mesh_item->getCloudMesh()->getCloud()->sensor_origin_;
  result.sensor_orientation_ =
      cloud_mesh_item->getCloudMesh()->getCloud()->sensor_orientation_;

  *(cloud_mesh_item->getCloudMesh()->getCloud()) = result;
  *cloud_ += result;
}
