example_rift_estimation.cpp
Go to the documentation of this file.
00001 /*
00002  * Software License Agreement (BSD License)
00003  *
00004  * Point Cloud Library (PCL) - www.pointclouds.org
00005  * Copyright (c) 2009-2011, Willow Garage, Inc.
00006  *
00007  * All rights reserved.
00008  *
00009  * Redistribution and use in source and binary forms, with or without
00010  * modification, are permitted provided that the following conditions
00011  * are met:
00012  *
00013  * * Redistributions of source code must retain the above copyright
00014  *   notice, this list of conditions and the following disclaimer.
00015  * * Redistributions in binary form must reproduce the above
00016  *   copyright notice, this list of conditions and the following
00017  *   disclaimer in the documentation and/or other materials provided
00018  *   with the distribution.
00019  * * Neither the name of Willow Garage, Inc. nor the names of its
00020  *   contributors may be used to endorse or promote products derived
00021  *   from this software without specific prior written permission.
00022  *
00023  * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
00024  * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
00025  * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
00026  * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
00027  * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
00028  * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
00029  * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
00030  * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
00031  * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
00032  * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
00033  * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
00034  * POSSIBILITY OF SUCH DAMAGE.
00035  *
00036  * $Id$
00037  *
00038  *
00039  */
00040 // STL
00041 #include <iostream>
00042 
00043 // PCL
00044 #include <pcl/io/pcd_io.h>
00045 #include <pcl/point_types.h>
00046 #include <pcl/common/io.h>
00047 #include <pcl/features/normal_3d.h>
00048 #include <pcl/kdtree/kdtree_flann.h>
00049 #include <pcl/features/rift.h>
00050 #include <pcl/features/intensity_gradient.h>
00051 
00052 int
00053 main (int, char** argv)
00054 {
00055   std::string filename = argv[1];
00056   std::cout << "Reading " << filename << std::endl;
00057 
00058   pcl::PointCloud<pcl::PointXYZI>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZI>);
00059 
00060   if (pcl::io::loadPCDFile<pcl::PointXYZI> (filename, *cloud) == -1) // load the file
00061   {
00062     PCL_ERROR ("Couldn't read file");
00063     return -1;
00064   }
00065 
00066   std::cout << "points: " << cloud->points.size () << std::endl;
00067 
00068   // Estimate the surface normals
00069   pcl::PointCloud<pcl::Normal>::Ptr cloud_n (new pcl::PointCloud<pcl::Normal>);
00070   pcl::NormalEstimation<pcl::PointXYZI, pcl::Normal> norm_est;
00071   norm_est.setInputCloud(cloud);
00072   pcl::search::KdTree<pcl::PointXYZI>::Ptr treept1 (new pcl::search::KdTree<pcl::PointXYZI> (false));
00073   norm_est.setSearchMethod(treept1);
00074   norm_est.setRadiusSearch(0.25);
00075   norm_est.compute(*cloud_n);
00076 
00077   std::cout<<" Surface normals estimated";
00078   std::cout<<" with size "<< cloud_n->points.size() <<std::endl;
00079  
00080   // Estimate the Intensity Gradient
00081   pcl::PointCloud<pcl::IntensityGradient>::Ptr cloud_ig (new pcl::PointCloud<pcl::IntensityGradient>);
00082   pcl::IntensityGradientEstimation<pcl::PointXYZI, pcl::Normal, pcl::IntensityGradient> gradient_est;
00083   gradient_est.setInputCloud(cloud);
00084   gradient_est.setInputNormals(cloud_n);
00085   pcl::search::KdTree<pcl::PointXYZI>::Ptr treept2 (new pcl::search::KdTree<pcl::PointXYZI> (false));
00086   gradient_est.setSearchMethod(treept2);
00087   gradient_est.setRadiusSearch(0.25);
00088   gradient_est.compute(*cloud_ig);
00089   std::cout<<" Intesity Gradient estimated";
00090   std::cout<<" with size "<< cloud_ig->points.size() <<std::endl;
00091 
00092 
00093   // Estimate the RIFT feature
00094   pcl::RIFTEstimation<pcl::PointXYZI, pcl::IntensityGradient, pcl::Histogram<32> > rift_est;
00095   pcl::search::KdTree<pcl::PointXYZI>::Ptr treept3 (new pcl::search::KdTree<pcl::PointXYZI> (false));
00096   rift_est.setSearchMethod(treept3);
00097   rift_est.setRadiusSearch(10.0);
00098   rift_est.setNrDistanceBins (4);
00099   rift_est.setNrGradientBins (8);
00100   rift_est.setInputCloud(cloud);
00101   rift_est.setInputGradient(cloud_ig);
00102   pcl::PointCloud<pcl::Histogram<32> > rift_output;
00103   rift_est.compute(rift_output);
00104 
00105   std::cout<<" RIFT feature estimated";
00106   std::cout<<" with size "<<rift_output.points.size()<<std::endl;
00107   
00108   // Display and retrieve the rift descriptor vector for the first point
00109   pcl::Histogram<32> first_descriptor = rift_output.points[0];
00110   std::cout << first_descriptor << std::endl;
00111   return 0;
00112 }


pcl
Author(s): Open Perception
autogenerated on Wed Aug 26 2015 15:23:36