normal_estimation_integral_image_nodelet.cpp

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// -*- mode: c++ -*-
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 * Software License Agreement (BSD License)
 *
 *  Copyright (c) 2013, Ryohei Ueda and JSK Lab
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#include "jsk_pcl_ros/normal_estimation_integral_image.h"
#include <pluginlib/class_list_macros.h>
 
#include <pcl/features/integral_image_normal.h>
 
 
namespace jsk_pcl_ros
{
  void NormalEstimationIntegralImage::onInit()
  {
    ConnectionBasedNodelet::onInit();
 
    srv_ = boost::make_shared <dynamic_reconfigure::Server<Config> > (*pnh_);
    dynamic_reconfigure::Server<Config>::CallbackType f =
      boost::bind (&NormalEstimationIntegralImage::configCallback, this, _1, _2);
    srv_->setCallback (f);
 
    pub_ = advertise<sensor_msgs::PointCloud2>(*pnh_, "output", 1);
    pub_with_xyz_ = advertise<sensor_msgs::PointCloud2>(*pnh_, "output_with_xyz", 1);
 
    onInitPostProcess();
  }
 
  void NormalEstimationIntegralImage::subscribe()
  {
    sub_input_ = pnh_->subscribe("input", 1, &NormalEstimationIntegralImage::compute, this);
  }
 
  void NormalEstimationIntegralImage::unsubscribe()
  {
    sub_input_.shutdown();
  }
 
  void NormalEstimationIntegralImage::configCallback(Config& config, uint32_t level)
  {
    boost::mutex::scoped_lock lock(mutex_);
    max_depth_change_factor_ = config.max_depth_change_factor;
    normal_smoothing_size_ = config.normal_smoothing_size;
    depth_dependent_smoothing_ = config.depth_dependent_smoothing;
    estimation_method_ = config.estimation_method;
    border_policy_ignore_ = config.border_policy_ignore;
  }
 
  void NormalEstimationIntegralImage::compute(const sensor_msgs::PointCloud2::ConstPtr& msg)
  {
    boost::mutex::scoped_lock lock(mutex_);
 
    pcl::PointCloud<pcl::PointXYZRGB>::Ptr input(new pcl::PointCloud<pcl::PointXYZRGB>());
    pcl::fromROSMsg(*msg, *input);
    pcl::PointCloud<pcl::Normal> output;
    pcl::IntegralImageNormalEstimation<pcl::PointXYZRGB, pcl::Normal> ne;
    if (estimation_method_ == 0) {
      ne.setNormalEstimationMethod (ne.AVERAGE_3D_GRADIENT);
    }
    else if (estimation_method_ == 1) {
      ne.setNormalEstimationMethod (ne.COVARIANCE_MATRIX);
    }
    else if (estimation_method_ == 2) {
      ne.setNormalEstimationMethod (ne.AVERAGE_DEPTH_CHANGE);
    }
    else {
      NODELET_FATAL("unknown estimation method: %d", estimation_method_);
      return;
    }
 
    if (border_policy_ignore_) {
      ne.setBorderPolicy(pcl::IntegralImageNormalEstimation<pcl::PointXYZRGB, pcl::Normal>::BORDER_POLICY_IGNORE);
    }
    else {
      ne.setBorderPolicy(pcl::IntegralImageNormalEstimation<pcl::PointXYZRGB, pcl::Normal>::BORDER_POLICY_MIRROR);
    }
    
    ne.setMaxDepthChangeFactor(max_depth_change_factor_);
    ne.setNormalSmoothingSize(normal_smoothing_size_);
    ne.setDepthDependentSmoothing(depth_dependent_smoothing_);
    ne.setInputCloud(input);
    ne.compute(output);
    sensor_msgs::PointCloud2 ros_output;
    pcl::toROSMsg(output, ros_output);
    pub_.publish(ros_output);
 
    // concatenate
    pcl::PointCloud<pcl::PointXYZRGBNormal>::Ptr concat(new pcl::PointCloud<pcl::PointXYZRGBNormal>());
    concat->points.resize(output.points.size());
    for (size_t i = 0; i < output.points.size(); i++) {
      pcl::PointXYZRGBNormal point;
      point.x = input->points[i].x;
      point.y = input->points[i].y;
      point.z = input->points[i].z;
      point.rgb = input->points[i].rgb;
      point.normal_x = output.points[i].normal_x;
      point.normal_y = output.points[i].normal_y;
      point.normal_z = output.points[i].normal_z;
      point.curvature = output.points[i].curvature;
      concat->points[i] = point;
    }
    concat->header = input->header;
    sensor_msgs::PointCloud2 ros_output_with_xyz;
    pcl::toROSMsg(*concat, ros_output_with_xyz);
    pub_with_xyz_.publish(ros_output_with_xyz);
  }
  
}
 
 
PLUGINLIB_EXPORT_CLASS (jsk_pcl_ros::NormalEstimationIntegralImage,
                        nodelet::Nodelet);