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cluster_extraction.cpp
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191 lines (163 loc) · 6.61 KB
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/*
* Software License Agreement (BSD License)
*
* Point Cloud Library (PCL) - www.pointclouds.org
* Copyright (c) 2011, Willow Garage, Inc.
*
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the copyright holder(s) nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*
*/
#include <pcl/PCLPointCloud2.h>
#include <pcl/memory.h> // for pcl::make_shared
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/segmentation/extract_clusters.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/console/print.h>
#include <pcl/console/parse.h>
#include <pcl/console/time.h>
#include <vector>
using namespace pcl;
using namespace pcl::io;
using namespace pcl::console;
int default_min = 100;
int default_max = 25000;
double default_tolerance = 0.02;
Eigen::Vector4f translation;
Eigen::Quaternionf orientation;
void
printHelp (int, char **argv)
{
print_error ("Syntax is: %s input.pcd output.pcd <options>\n", argv[0]);
print_info (" where options are:\n");
print_info (" -min X = use a minimum of X points peer cluster (default: ");
print_value ("%d", default_min); print_info (")\n");
print_info (" -max X = use a maximum of X points peer cluster (default: ");
print_value ("%d", default_max); print_info (")\n");
print_info (" -tolerance X = the spatial distance between clusters (default: ");
print_value ("%lf", default_tolerance); print_info (")\n");
}
bool
loadCloud (const std::string &filename, pcl::PCLPointCloud2 &cloud)
{
TicToc tt;
print_highlight ("Loading "); print_value ("%s ", filename.c_str ());
tt.tic ();
if (loadPCDFile (filename, cloud, translation, orientation) < 0)
return (false);
print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", cloud.width * cloud.height); print_info (" points]\n");
print_info ("Available dimensions: "); print_value ("%s\n", pcl::getFieldsList (cloud).c_str ());
return (true);
}
void
compute (const pcl::PCLPointCloud2::ConstPtr &input, std::vector<pcl::PCLPointCloud2::Ptr> &output,
int min, int max, double tolerance)
{
// Convert data to PointCloud<T>
PointCloud<pcl::PointXYZ>::Ptr xyz (new PointCloud<pcl::PointXYZ>);
fromPCLPointCloud2 (*input, *xyz);
// Estimate
TicToc tt;
tt.tic ();
print_highlight (stderr, "Computing ");
// Creating the KdTree object for the search method of the extraction
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);
tree->setInputCloud (xyz);
std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
ec.setClusterTolerance (tolerance);
ec.setMinClusterSize (min);
ec.setMaxClusterSize (max);
ec.setSearchMethod (tree);
ec.setInputCloud (xyz);
ec.extract (cluster_indices);
print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", cluster_indices.size ()); print_info (" clusters]\n");
output.reserve (cluster_indices.size ());
for (const auto& cluster : cluster_indices)
{
pcl::ExtractIndices<pcl::PCLPointCloud2> extract;
extract.setInputCloud (input);
extract.setIndices (pcl::make_shared<const pcl::PointIndices> (cluster));
pcl::PCLPointCloud2::Ptr out (new pcl::PCLPointCloud2);
extract.filter (*out);
output.push_back (out);
}
}
void
saveCloud (const std::string &filename, const std::vector<pcl::PCLPointCloud2::Ptr> &output)
{
TicToc tt;
tt.tic ();
std::string basename = filename.substr (0, filename.length () - 4);
for (std::size_t i = 0; i < output.size (); i++)
{
std::string clustername = basename + std::to_string(i) + ".pcd";
print_highlight ("Saving "); print_value ("%s ", clustername.c_str ());
pcl::io::savePCDFile (clustername, *(output[i]), translation, orientation, false);
print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", output[i]->width * output[i]->height); print_info (" points]\n");
}
}
/* ---[ */
int
main (int argc, char** argv)
{
print_info ("Extract point clusters using pcl::EuclideanClusterExtraction. For more information, use: %s -h\n", argv[0]);
bool help = false;
parse_argument (argc, argv, "-h", help);
if (argc < 3 || help)
{
printHelp (argc, argv);
return (-1);
}
// Parse the command line arguments for .pcd files
std::vector<int> p_file_indices;
p_file_indices = parse_file_extension_argument (argc, argv, ".pcd");
if (p_file_indices.size () != 2)
{
print_error ("Need one input PCD file and one output PCD file to continue.\n");
return (-1);
}
// Command line parsing
int min = default_min;
int max = default_max;
double tolerance = default_tolerance;
parse_argument (argc, argv, "-min", min);
parse_argument (argc, argv, "-max", max);
parse_argument (argc, argv, "-tolerance", tolerance);
// Load the first file
pcl::PCLPointCloud2::Ptr cloud (new pcl::PCLPointCloud2);
if (!loadCloud (argv[p_file_indices[0]], *cloud))
return (-1);
// Perform the feature estimation
std::vector<pcl::PCLPointCloud2::Ptr> output;
compute (cloud, output, min, max, tolerance);
// Save into the second file
saveCloud (argv[p_file_indices[1]], output);
}