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normal_estimation.cpp
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247 lines (218 loc) · 8.45 KB
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/*
* Software License Agreement (BSD License)
*
* Point Cloud Library (PCL) - www.pointclouds.org
* Copyright (c) 2010-2011, Willow Garage, Inc.
* Copyright (c) 2012-, Open Perception, 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.
*
* $Id$
*
*/
#include <pcl/PCLPointCloud2.h>
#include <pcl/io/pcd_io.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/integral_image_normal.h>
#include <pcl/common/pcl_filesystem.h>
#include <pcl/console/print.h>
#include <pcl/console/parse.h>
#include <pcl/console/time.h>
#include <boost/algorithm/string/case_conv.hpp> // for to_upper_copy
using namespace pcl;
using namespace pcl::io;
using namespace pcl::console;
int default_k = 0;
double default_radius = 0.0;
void
printHelp (int, char **argv)
{
print_error ("Syntax is: %s input.pcd output.pcd <options> [optional_arguments]\n", argv[0]);
print_info (" where options are:\n");
print_info (" -radius X = use a radius of Xm around each point to determine the neighborhood (default: ");
print_value ("%f", default_radius); print_info (")\n");
print_info (" -k X = use a fixed number of X-nearest neighbors around each point (default: ");
print_value ("%f", default_k); print_info (")\n");
print_info (" For organized datasets, an IntegralImageNormalEstimation approach will be used, with the RADIUS given value as SMOOTHING SIZE.\n");
print_info ("\nOptional arguments are:\n");
print_info (" -input_dir X = batch process all PCD files found in input_dir\n");
print_info (" -output_dir X = save the processed files from input_dir in this directory\n");
}
bool
loadCloud (const std::string &filename, pcl::PCLPointCloud2 &cloud,
Eigen::Vector4f &translation, Eigen::Quaternionf &orientation)
{
if (loadPCDFile (filename, cloud, translation, orientation) < 0)
return (false);
return (true);
}
void
compute (const pcl::PCLPointCloud2::ConstPtr &input, pcl::PCLPointCloud2 &output,
int k, double radius)
{
// Convert data to PointCloud<T>
PointCloud<PointXYZ>::Ptr xyz (new PointCloud<PointXYZ>);
fromPCLPointCloud2 (*input, *xyz);
TicToc tt;
tt.tic ();
PointCloud<Normal> normals;
// Try our luck with organized integral image based normal estimation
if (xyz->isOrganized ())
{
IntegralImageNormalEstimation<PointXYZ, Normal> ne;
ne.setInputCloud (xyz);
ne.setNormalEstimationMethod (IntegralImageNormalEstimation<PointXYZ, Normal>::COVARIANCE_MATRIX);
ne.setNormalSmoothingSize (static_cast<float>(radius));
ne.setDepthDependentSmoothing (true);
ne.compute (normals);
}
else
{
NormalEstimation<PointXYZ, Normal> ne;
ne.setInputCloud (xyz);
ne.setKSearch (k);
ne.setRadiusSearch (radius);
ne.compute (normals);
}
print_highlight ("Computed normals in "); print_value ("%g", tt.toc ()); print_info (" ms for "); print_value ("%d", normals.width * normals.height); print_info (" points.\n");
// Convert data back
pcl::PCLPointCloud2 output_normals;
toPCLPointCloud2 (normals, output_normals);
concatenateFields (*input, output_normals, output);
}
void
saveCloud (const std::string &filename, const pcl::PCLPointCloud2 &output,
const Eigen::Vector4f &translation, const Eigen::Quaternionf &orientation)
{
PCDWriter w;
w.writeBinaryCompressed (filename, output, translation, orientation);
}
int
batchProcess (const std::vector<std::string> &pcd_files, std::string &output_dir, int k, double radius)
{
#pragma omp parallel for \
shared(k, output_dir, pcd_files, radius)
// Disable lint since this 'for' is part of the pragma
// NOLINTNEXTLINE(modernize-loop-convert)
for (int i = 0; i < static_cast<int>(pcd_files.size ()); ++i)
{
// Load the first file
Eigen::Vector4f translation;
Eigen::Quaternionf rotation;
pcl::PCLPointCloud2::Ptr cloud (new pcl::PCLPointCloud2);
if (!loadCloud (pcd_files[i], *cloud, translation, rotation))
continue;
// Perform the feature estimation
pcl::PCLPointCloud2 output;
compute (cloud, output, k, radius);
// Prepare output file name
std::string filename = pcl_fs::path(pcd_files[i]).filename().string();
// Save into the second file
const std::string filepath = output_dir + '/' + filename;
saveCloud (filepath, output, translation, rotation);
}
return (0);
}
/* ---[ */
int
main (int argc, char** argv)
{
print_info ("Estimate surface normals using NormalEstimation. For more information, use: %s -h\n", argv[0]);
if (argc < 3)
{
printHelp (argc, argv);
return (-1);
}
bool batch_mode = false;
// Command line parsing
int k = default_k;
double radius = default_radius;
parse_argument (argc, argv, "-k", k);
parse_argument (argc, argv, "-radius", radius);
std::string input_dir, output_dir;
if (parse_argument (argc, argv, "-input_dir", input_dir) != -1)
{
PCL_INFO ("Input directory given as %s. Batch process mode on.\n", input_dir.c_str ());
if (parse_argument (argc, argv, "-output_dir", output_dir) == -1)
{
PCL_ERROR ("Need an output directory! Please use -output_dir to continue.\n");
return (-1);
}
// Both input dir and output dir given, switch into batch processing mode
batch_mode = true;
}
if (!batch_mode)
{
// 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);
}
print_info ("Estimating normals with a k/radius/smoothing size of: ");
print_value ("%d / %f / %f\n", k, radius, radius);
// Load the first file
Eigen::Vector4f translation;
Eigen::Quaternionf rotation;
pcl::PCLPointCloud2::Ptr cloud (new pcl::PCLPointCloud2);
if (!loadCloud (argv[p_file_indices[0]], *cloud, translation, rotation))
return (-1);
// Perform the feature estimation
pcl::PCLPointCloud2 output;
compute (cloud, output, k, radius);
// Save into the second file
saveCloud (argv[p_file_indices[1]], output, translation, rotation);
}
else
{
if (!input_dir.empty() && pcl_fs::exists (input_dir))
{
std::vector<std::string> pcd_files;
pcl_fs::directory_iterator end_itr;
for (pcl_fs::directory_iterator itr (input_dir); itr != end_itr; ++itr)
{
// Only add PCD files
if (!is_directory (itr->status ()) && boost::algorithm::to_upper_copy (itr->path ().extension ().string ()) == ".PCD" )
{
pcd_files.push_back (itr->path ().string ());
PCL_INFO ("[Batch processing mode] Added %s for processing.\n", itr->path ().string ().c_str ());
}
}
batchProcess (pcd_files, output_dir, k, radius);
}
else
{
PCL_ERROR ("Batch processing mode enabled, but invalid input directory (%s) given!\n", input_dir.c_str ());
return (-1);
}
}
}