statistical_outlier_removal_worker.cpp

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#include <pcl/apps/modeler/statistical_outlier_removal_worker.h>
#include <pcl/apps/modeler/parameter.h>
#include <pcl/apps/modeler/parameter_dialog.h>
#include <pcl/apps/modeler/cloud_mesh.h>
#include <pcl/apps/modeler/cloud_mesh_item.h>
#include <pcl/filters/statistical_outlier_removal.h>


pcl::modeler::StatisticalOutlierRemovalWorker::StatisticalOutlierRemovalWorker(const QList<CloudMeshItem*>& cloud_mesh_items, QWidget* parent) :
  AbstractWorker(cloud_mesh_items, parent),
  mean_k_(NULL), stddev_mul_thresh_(NULL)
{
}

pcl::modeler::StatisticalOutlierRemovalWorker::~StatisticalOutlierRemovalWorker(void)
{
  delete mean_k_;
  delete stddev_mul_thresh_;
}

void
pcl::modeler::StatisticalOutlierRemovalWorker::initParameters(CloudMeshItem *)
{
  return;
}

void
pcl::modeler::StatisticalOutlierRemovalWorker::setupParameters()
{
  mean_k_ = new IntParameter("Mean K", "The number of nearest neighbors to use for mean distance estimation", 8, 1, 1024, 1);
  stddev_mul_thresh_ = new DoubleParameter("Standard Deviation Multiplier", "The distance threshold will be equal to: mean + stddev_mult * stddev. Points will be classified as inlier or outlier if their average neighbor distance is below or above this threshold respectively.", 1.0, 0.1, 10, 0.1);

  parameter_dialog_->addParameter(mean_k_);
  parameter_dialog_->addParameter(stddev_mul_thresh_);

  return;
}

void
pcl::modeler::StatisticalOutlierRemovalWorker::processImpl(CloudMeshItem* cloud_mesh_item)
{
  pcl::StatisticalOutlierRemoval<pcl::PointSurfel> sor;
  sor.setInputCloud(cloud_mesh_item->getCloudMesh()->getCloud());
  sor.setMeanK(*mean_k_);
  sor.setStddevMulThresh(*stddev_mul_thresh_);

  CloudMesh::PointCloudPtr cloud(new CloudMesh::PointCloud());
  sor.filter(*cloud);

  cloud_mesh_item->getCloudMesh()->getCloud() = cloud;

  emitDataUpdated(cloud_mesh_item);

  return;
}