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unary_classifier_segment.cpp
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/*
* Software License Agreement (BSD License)
*
* Point Cloud Library (PCL) - www.pointclouds.org
* Copyright (c) 2011-2012, 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.
*
* Author : Christian Potthast
* Email : [email protected]
*
*/
#include <pcl/io/pcd_io.h>
#include <pcl/console/print.h>
#include <pcl/console/parse.h>
#include <pcl/console/time.h>
#include <pcl/segmentation/unary_classifier.h>
using namespace pcl;
using namespace pcl::io;
using namespace pcl::console;
double default_feature_threshold = 5.0;
double default_normal_radius_search = 0.01;
double default_fpfh_radius_search = 0.05;
typedef PointXYZRGBA PointT;
typedef PointCloud<PointT> CloudT;
typedef PointCloud<PointXYZRGBL> CloudLT;
typedef PointCloud<FPFHSignature33> FeatureT;
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 (" -d = trained features directory \n");
print_info (" -threshold X = feature threshold (default: ");
print_value ("%f", default_feature_threshold); print_info (")\n");
print_info (" -normal-search X = Normal radius search (default: ");
print_value ("%f", default_normal_radius_search); print_info (")\n");
print_info (" -fpfh-search X = FPFH radius search (default: ");
print_value ("%f", default_fpfh_radius_search); print_info (")\n");
}
bool
loadTrainedFeatures (std::vector<FeatureT::Ptr> &out,
const boost::filesystem::path &base_dir)
{
if (!boost::filesystem::exists (base_dir) && !boost::filesystem::is_directory (base_dir))
return false;
for (boost::filesystem::directory_iterator it (base_dir); it != boost::filesystem::directory_iterator (); ++it)
{
if (!boost::filesystem::is_directory (it->status ()) &&
boost::filesystem::extension (it->path ()) == ".pcd")
{
std::stringstream ss;
ss << it->path ().string ();
print_highlight ("Loading %s \n", ss.str ().c_str ());
FeatureT::Ptr features (new FeatureT);
if (loadPCDFile (ss.str ().c_str (), *features) < 0)
return false;
out.push_back (features);
}
}
return true;
}
bool
loadCloud (const std::string &filename, CloudT::Ptr &cloud)
{
TicToc tt;
print_highlight ("Loading "); print_value ("%s ", filename.c_str ());
tt.tic ();
if (loadPCDFile (filename, *cloud) < 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");
return (true);
}
void
compute (const CloudT::Ptr &input, std::vector<FeatureT::Ptr> &trained_features,
CloudLT::Ptr &out,
float normal_radius_search,
float fpfh_radius_search,
float feature_threshold)
{
TicToc tt;
tt.tic ();
print_highlight ("Computing ");
UnaryClassifier<PointT> classifier;
classifier.setInputCloud (input);
classifier.setTrainedFeatures (trained_features);
classifier.setNormalRadiusSearch (normal_radius_search);
classifier.setFPFHRadiusSearch (fpfh_radius_search);
classifier.setFeatureThreshold (feature_threshold);
classifier.segment (out);
print_info ("[done, ");
print_value ("%g", tt.toc ());
print_info (" ms : "); print_value ("%d", out->width * out->height);
print_info (" points]\n");
}
void
saveCloud (const std::string &filename, CloudLT::Ptr &output)
{
TicToc tt;
tt.tic ();
print_highlight ("Saving "); print_value ("%s ", filename.c_str ());
PCDWriter w;
w.write (filename, *output);
print_info ("[done, ");
print_value ("%g", tt.toc ()); print_info (" ms : ");
print_value ("%d", output->width * output->height); print_info (" points]\n");
}
/* ---[ */
int
main (int argc, char** argv)
{
print_info ("Train unary classifier using FPFH. For more information, use: %s -h\n", argv[0]);
if (argc < 4)
{
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);
}
// Load the input file
CloudT::Ptr cloud (new CloudT);
if (!loadCloud (argv[p_file_indices[0]], cloud))
return (-1);
// TODO:: make this as an optional argument ??
std::vector<int> tmp_indices;
pcl::removeNaNFromPointCloud (*cloud, *cloud, tmp_indices);
// parse optional input arguments from the command line
float normal_radius_search = static_cast<float> (default_normal_radius_search);
float fpfh_radius_search = static_cast<float> (default_fpfh_radius_search);
float feature_threshold = static_cast<float> (default_feature_threshold);
std::string dir_name;
parse_argument (argc, argv, "-d", dir_name);
parse_argument (argc, argv, "-threshold", feature_threshold);
parse_argument (argc, argv, "-normal-radius-search", normal_radius_search);
parse_argument (argc, argv, "-fpfh-radius-search", fpfh_radius_search);
print_info ("trained feature directory: %s \n", dir_name.c_str ());
// load the trained features
std::vector<FeatureT::Ptr> trained_features;
if (!loadTrainedFeatures (trained_features, dir_name.c_str ()))
return (-1);
print_info ("feature_threshold: %f \n", feature_threshold);
print_info ("normal-radius-search: %f \n", normal_radius_search);
print_info ("fpfh-radius-search: %f \n\n", fpfh_radius_search);
CloudLT::Ptr out (new CloudLT);
compute (cloud, trained_features, out, normal_radius_search, fpfh_radius_search, feature_threshold);
saveCloud (argv[p_file_indices[1]], out);
}