752 lines
26 KiB
C++
752 lines
26 KiB
C++
/*
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* Software License Agreement (BSD License)
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*
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* Point Cloud Library (PCL) - www.pointclouds.org
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* Copyright (c) 2010-2011, Willow Garage, Inc.
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* Copyright (c) 2012-, Open Perception, Inc.
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*
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* * Neither the name of the copyright holder(s) nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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* $Id: cvfh.hpp 5311 2012-03-26 22:02:04Z aaldoma $
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*
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*/
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#ifndef PCL_FEATURES_IMPL_OURCVFH_H_
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#define PCL_FEATURES_IMPL_OURCVFH_H_
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#include <pcl/features/our_cvfh.h>
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#include <pcl/features/vfh.h>
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#include <pcl/features/normal_3d.h>
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#include <pcl/common/io.h> // for copyPointCloud
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#include <pcl/common/common.h> // for getMaxDistance
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#include <pcl/common/transforms.h>
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//////////////////////////////////////////////////////////////////////////////////////////////
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template<typename PointInT, typename PointNT, typename PointOutT> void
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pcl::OURCVFHEstimation<PointInT, PointNT, PointOutT>::compute (PointCloudOut &output)
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{
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if (!Feature<PointInT, PointOutT>::initCompute ())
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{
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output.width = output.height = 0;
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output.clear ();
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return;
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}
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// Resize the output dataset
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// Important! We should only allocate precisely how many elements we will need, otherwise
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// we risk at pre-allocating too much memory which could lead to bad_alloc
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// (see http://dev.pointclouds.org/issues/657)
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output.width = output.height = 1;
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output.resize (1);
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// Perform the actual feature computation
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computeFeature (output);
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Feature<PointInT, PointOutT>::deinitCompute ();
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template<typename PointInT, typename PointNT, typename PointOutT> void
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pcl::OURCVFHEstimation<PointInT, PointNT, PointOutT>::extractEuclideanClustersSmooth (const pcl::PointCloud<pcl::PointNormal> &cloud,
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const pcl::PointCloud<pcl::PointNormal> &normals,
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float tolerance,
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const pcl::search::Search<pcl::PointNormal>::Ptr &tree,
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std::vector<pcl::PointIndices> &clusters, double eps_angle,
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unsigned int min_pts_per_cluster,
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unsigned int max_pts_per_cluster)
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{
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if (tree->getInputCloud ()->size () != cloud.size ())
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{
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PCL_ERROR("[pcl::extractEuclideanClusters] Tree built for a different point cloud "
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"dataset (%zu) than the input cloud (%zu)!\n",
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static_cast<std::size_t>(tree->getInputCloud()->size()),
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static_cast<std::size_t>(cloud.size()));
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return;
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}
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if (cloud.size () != normals.size ())
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{
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PCL_ERROR("[pcl::extractEuclideanClusters] Number of points in the input point "
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"cloud (%zu) different than normals (%zu)!\n",
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static_cast<std::size_t>(cloud.size()),
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static_cast<std::size_t>(normals.size()));
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return;
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}
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// Create a bool vector of processed point indices, and initialize it to false
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std::vector<bool> processed (cloud.size (), false);
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pcl::Indices nn_indices;
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std::vector<float> nn_distances;
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// Process all points in the indices vector
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for (std::size_t i = 0; i < cloud.size (); ++i)
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{
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if (processed[i])
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continue;
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std::vector<std::size_t> seed_queue;
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std::size_t sq_idx = 0;
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seed_queue.push_back (i);
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processed[i] = true;
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while (sq_idx < seed_queue.size ())
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{
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// Search for sq_idx
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if (!tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances))
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{
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sq_idx++;
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continue;
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}
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for (std::size_t j = 1; j < nn_indices.size (); ++j) // nn_indices[0] should be sq_idx
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{
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if (processed[nn_indices[j]]) // Has this point been processed before ?
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continue;
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//processed[nn_indices[j]] = true;
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// [-1;1]
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double dot_p = normals[seed_queue[sq_idx]].normal[0] * normals[nn_indices[j]].normal[0]
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+ normals[seed_queue[sq_idx]].normal[1] * normals[nn_indices[j]].normal[1] + normals[seed_queue[sq_idx]].normal[2]
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* normals[nn_indices[j]].normal[2];
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if (std::abs (std::acos (dot_p)) < eps_angle)
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{
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processed[nn_indices[j]] = true;
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seed_queue.push_back (nn_indices[j]);
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}
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}
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sq_idx++;
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}
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// If this queue is satisfactory, add to the clusters
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if (seed_queue.size () >= min_pts_per_cluster && seed_queue.size () <= max_pts_per_cluster)
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{
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pcl::PointIndices r;
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r.indices.resize (seed_queue.size ());
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for (std::size_t j = 0; j < seed_queue.size (); ++j)
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r.indices[j] = seed_queue[j];
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std::sort (r.indices.begin (), r.indices.end ());
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r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ());
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r.header = cloud.header;
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clusters.push_back (r); // We could avoid a copy by working directly in the vector
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template<typename PointInT, typename PointNT, typename PointOutT> void
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pcl::OURCVFHEstimation<PointInT, PointNT, PointOutT>::filterNormalsWithHighCurvature (const pcl::PointCloud<PointNT> & cloud,
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pcl::Indices &indices_to_use,
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pcl::Indices &indices_out, pcl::Indices &indices_in,
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float threshold)
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{
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indices_out.resize (cloud.size ());
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indices_in.resize (cloud.size ());
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std::size_t in, out;
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in = out = 0;
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for (const auto &index : indices_to_use)
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{
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if (cloud[index].curvature > threshold)
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{
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indices_out[out] = index;
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out++;
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}
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else
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{
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indices_in[in] = index;
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in++;
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}
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}
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indices_out.resize (out);
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indices_in.resize (in);
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}
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template<typename PointInT, typename PointNT, typename PointOutT> bool
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pcl::OURCVFHEstimation<PointInT, PointNT, PointOutT>::sgurf (Eigen::Vector3f & centroid, Eigen::Vector3f & normal_centroid,
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PointInTPtr & processed, std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > & transformations,
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PointInTPtr & grid, pcl::PointIndices & indices)
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{
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Eigen::Vector3f plane_normal;
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plane_normal[0] = -centroid[0];
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plane_normal[1] = -centroid[1];
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plane_normal[2] = -centroid[2];
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Eigen::Vector3f z_vector = Eigen::Vector3f::UnitZ ();
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plane_normal.normalize ();
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Eigen::Vector3f axis = plane_normal.cross (z_vector);
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double rotation = -asin (axis.norm ());
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axis.normalize ();
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Eigen::Affine3f transformPC (Eigen::AngleAxisf (static_cast<float> (rotation), axis));
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grid->resize (processed->size ());
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for (std::size_t k = 0; k < processed->size (); k++)
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(*grid)[k].getVector4fMap () = (*processed)[k].getVector4fMap ();
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pcl::transformPointCloud (*grid, *grid, transformPC);
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Eigen::Vector4f centroid4f (centroid[0], centroid[1], centroid[2], 0);
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Eigen::Vector4f normal_centroid4f (normal_centroid[0], normal_centroid[1], normal_centroid[2], 0);
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centroid4f = transformPC * centroid4f;
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normal_centroid4f = transformPC * normal_centroid4f;
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Eigen::Vector3f centroid3f (centroid4f[0], centroid4f[1], centroid4f[2]);
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Eigen::Vector4f farthest_away;
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pcl::getMaxDistance (*grid, indices.indices, centroid4f, farthest_away);
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farthest_away[3] = 0;
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float max_dist = (farthest_away - centroid4f).norm ();
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pcl::demeanPointCloud (*grid, centroid4f, *grid);
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Eigen::Matrix4f center_mat;
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center_mat.setIdentity (4, 4);
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center_mat (0, 3) = -centroid4f[0];
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center_mat (1, 3) = -centroid4f[1];
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center_mat (2, 3) = -centroid4f[2];
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Eigen::Matrix3f scatter;
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scatter.setZero ();
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float sum_w = 0.f;
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for (const auto &index : indices.indices)
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{
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Eigen::Vector3f pvector = (*grid)[index].getVector3fMap ();
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float d_k = (pvector).norm ();
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float w = (max_dist - d_k);
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Eigen::Vector3f diff = (pvector);
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Eigen::Matrix3f mat = diff * diff.transpose ();
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scatter += mat * w;
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sum_w += w;
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}
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scatter /= sum_w;
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Eigen::JacobiSVD <Eigen::MatrixXf> svd (scatter, Eigen::ComputeFullV);
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Eigen::Vector3f evx = svd.matrixV ().col (0);
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Eigen::Vector3f evy = svd.matrixV ().col (1);
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Eigen::Vector3f evz = svd.matrixV ().col (2);
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Eigen::Vector3f evxminus = evx * -1;
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Eigen::Vector3f evyminus = evy * -1;
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Eigen::Vector3f evzminus = evz * -1;
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float s_xplus, s_xminus, s_yplus, s_yminus;
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s_xplus = s_xminus = s_yplus = s_yminus = 0.f;
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//disambiguate rf using all points
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for (const auto& point: grid->points)
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{
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Eigen::Vector3f pvector = point.getVector3fMap ();
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float dist_x, dist_y;
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dist_x = std::abs (evx.dot (pvector));
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dist_y = std::abs (evy.dot (pvector));
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if ((pvector).dot (evx) >= 0)
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s_xplus += dist_x;
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else
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s_xminus += dist_x;
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if ((pvector).dot (evy) >= 0)
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s_yplus += dist_y;
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else
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s_yminus += dist_y;
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}
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if (s_xplus < s_xminus)
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evx = evxminus;
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if (s_yplus < s_yminus)
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evy = evyminus;
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//select the axis that could be disambiguated more easily
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float fx, fy;
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float max_x = static_cast<float> (std::max (s_xplus, s_xminus));
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float min_x = static_cast<float> (std::min (s_xplus, s_xminus));
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float max_y = static_cast<float> (std::max (s_yplus, s_yminus));
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float min_y = static_cast<float> (std::min (s_yplus, s_yminus));
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fx = (min_x / max_x);
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fy = (min_y / max_y);
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Eigen::Vector3f normal3f = Eigen::Vector3f (normal_centroid4f[0], normal_centroid4f[1], normal_centroid4f[2]);
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if (normal3f.dot (evz) < 0)
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evz = evzminus;
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//if fx/y close to 1, it was hard to disambiguate
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//what if both are equally easy or difficult to disambiguate, namely fy == fx or very close
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float max_axis = std::max (fx, fy);
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float min_axis = std::min (fx, fy);
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if ((min_axis / max_axis) > axis_ratio_)
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{
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PCL_WARN ("Both axes are equally easy/difficult to disambiguate\n");
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Eigen::Vector3f evy_copy = evy;
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Eigen::Vector3f evxminus = evx * -1;
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Eigen::Vector3f evyminus = evy * -1;
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if (min_axis > min_axis_value_)
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{
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//combination of all possibilities
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evy = evx.cross (evz);
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Eigen::Matrix4f trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
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transformations.push_back (trans);
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evx = evxminus;
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evy = evx.cross (evz);
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trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
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transformations.push_back (trans);
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evx = evy_copy;
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evy = evx.cross (evz);
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trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
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transformations.push_back (trans);
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evx = evyminus;
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evy = evx.cross (evz);
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trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
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transformations.push_back (trans);
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}
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else
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{
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//1-st case (evx selected)
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evy = evx.cross (evz);
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Eigen::Matrix4f trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
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transformations.push_back (trans);
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//2-nd case (evy selected)
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evx = evy_copy;
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evy = evx.cross (evz);
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trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
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transformations.push_back (trans);
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}
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}
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else
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{
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if (fy < fx)
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{
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evx = evy;
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fx = fy;
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}
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evy = evx.cross (evz);
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Eigen::Matrix4f trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
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transformations.push_back (trans);
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}
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return true;
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template<typename PointInT, typename PointNT, typename PointOutT> void
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pcl::OURCVFHEstimation<PointInT, PointNT, PointOutT>::computeRFAndShapeDistribution (PointInTPtr & processed, PointCloudOut & output,
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std::vector<pcl::PointIndices> & cluster_indices)
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{
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PointCloudOut ourcvfh_output;
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cluster_axes_.clear ();
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cluster_axes_.resize (centroids_dominant_orientations_.size ());
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for (std::size_t i = 0; i < centroids_dominant_orientations_.size (); i++)
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{
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std::vector < Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > transformations;
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PointInTPtr grid (new pcl::PointCloud<PointInT>);
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sgurf (centroids_dominant_orientations_[i], dominant_normals_[i], processed, transformations, grid, cluster_indices[i]);
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// Make a note of how many transformations correspond to each cluster
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cluster_axes_[i] = transformations.size ();
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for (const auto &transformation : transformations)
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{
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pcl::transformPointCloud (*processed, *grid, transformation);
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transforms_.push_back (transformation);
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valid_transforms_.push_back (true);
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std::vector < Eigen::VectorXf > quadrants (8);
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int size_hists = 13;
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int num_hists = 8;
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for (int k = 0; k < num_hists; k++)
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quadrants[k].setZero (size_hists);
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Eigen::Vector4f centroid_p;
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centroid_p.setZero ();
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Eigen::Vector4f max_pt;
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pcl::getMaxDistance (*grid, centroid_p, max_pt);
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max_pt[3] = 0;
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double distance_normalization_factor = (centroid_p - max_pt).norm ();
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float hist_incr;
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if (normalize_bins_)
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hist_incr = 100.0f / static_cast<float> (grid->size () - 1);
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else
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hist_incr = 1.0f;
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float * weights = new float[num_hists];
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float sigma = 0.01f; //1cm
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float sigma_sq = sigma * sigma;
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for (const auto& point: grid->points)
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{
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Eigen::Vector4f p = point.getVector4fMap ();
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p[3] = 0.f;
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float d = p.norm ();
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//compute weight for all octants
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float wx = 1.f - std::exp (-((p[0] * p[0]) / (2.f * sigma_sq))); //how is the weight distributed among two semi-cubes
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float wy = 1.f - std::exp (-((p[1] * p[1]) / (2.f * sigma_sq)));
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float wz = 1.f - std::exp (-((p[2] * p[2]) / (2.f * sigma_sq)));
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//distribute the weights using the x-coordinate
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if (p[0] >= 0)
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{
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for (std::size_t ii = 0; ii <= 3; ii++)
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weights[ii] = 0.5f - wx * 0.5f;
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for (std::size_t ii = 4; ii <= 7; ii++)
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weights[ii] = 0.5f + wx * 0.5f;
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}
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else
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{
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for (std::size_t ii = 0; ii <= 3; ii++)
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weights[ii] = 0.5f + wx * 0.5f;
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for (std::size_t ii = 4; ii <= 7; ii++)
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weights[ii] = 0.5f - wx * 0.5f;
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}
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//distribute the weights using the y-coordinate
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if (p[1] >= 0)
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{
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for (std::size_t ii = 0; ii <= 1; ii++)
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weights[ii] *= 0.5f - wy * 0.5f;
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for (std::size_t ii = 4; ii <= 5; ii++)
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weights[ii] *= 0.5f - wy * 0.5f;
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for (std::size_t ii = 2; ii <= 3; ii++)
|
|
weights[ii] *= 0.5f + wy * 0.5f;
|
|
|
|
for (std::size_t ii = 6; ii <= 7; ii++)
|
|
weights[ii] *= 0.5f + wy * 0.5f;
|
|
}
|
|
else
|
|
{
|
|
for (std::size_t ii = 0; ii <= 1; ii++)
|
|
weights[ii] *= 0.5f + wy * 0.5f;
|
|
for (std::size_t ii = 4; ii <= 5; ii++)
|
|
weights[ii] *= 0.5f + wy * 0.5f;
|
|
|
|
for (std::size_t ii = 2; ii <= 3; ii++)
|
|
weights[ii] *= 0.5f - wy * 0.5f;
|
|
|
|
for (std::size_t ii = 6; ii <= 7; ii++)
|
|
weights[ii] *= 0.5f - wy * 0.5f;
|
|
}
|
|
|
|
//distribute the weights using the z-coordinate
|
|
if (p[2] >= 0)
|
|
{
|
|
for (std::size_t ii = 0; ii <= 7; ii += 2)
|
|
weights[ii] *= 0.5f - wz * 0.5f;
|
|
|
|
for (std::size_t ii = 1; ii <= 7; ii += 2)
|
|
weights[ii] *= 0.5f + wz * 0.5f;
|
|
|
|
}
|
|
else
|
|
{
|
|
for (std::size_t ii = 0; ii <= 7; ii += 2)
|
|
weights[ii] *= 0.5f + wz * 0.5f;
|
|
|
|
for (std::size_t ii = 1; ii <= 7; ii += 2)
|
|
weights[ii] *= 0.5f - wz * 0.5f;
|
|
}
|
|
|
|
int h_index = (d <= 0) ? 0 : std::ceil (size_hists * (d / distance_normalization_factor)) - 1;
|
|
/* from http://www.pcl-users.org/OUR-CVFH-problem-td4028436.html
|
|
h_index will be 13 when d is computed on the farthest away point.
|
|
|
|
adding the following after computing h_index fixes the problem:
|
|
*/
|
|
if(h_index > 12)
|
|
h_index = 12;
|
|
for (int j = 0; j < num_hists; j++)
|
|
quadrants[j][h_index] += hist_incr * weights[j];
|
|
|
|
}
|
|
|
|
//copy to the cvfh signature
|
|
PointCloudOut vfh_signature;
|
|
vfh_signature.resize (1);
|
|
vfh_signature.width = vfh_signature.height = 1;
|
|
for (int d = 0; d < 308; ++d)
|
|
vfh_signature[0].histogram[d] = output[i].histogram[d];
|
|
|
|
int pos = 45 * 3;
|
|
for (int k = 0; k < num_hists; k++)
|
|
{
|
|
for (int ii = 0; ii < size_hists; ii++, pos++)
|
|
{
|
|
vfh_signature[0].histogram[pos] = quadrants[k][ii];
|
|
}
|
|
}
|
|
|
|
ourcvfh_output.push_back (vfh_signature[0]);
|
|
ourcvfh_output.width = ourcvfh_output.size ();
|
|
delete[] weights;
|
|
}
|
|
}
|
|
|
|
if (!ourcvfh_output.empty ())
|
|
{
|
|
ourcvfh_output.height = 1;
|
|
}
|
|
output = ourcvfh_output;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////////////////////////
|
|
template<typename PointInT, typename PointNT, typename PointOutT> void
|
|
pcl::OURCVFHEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
|
|
{
|
|
if (refine_clusters_ <= 0.f)
|
|
refine_clusters_ = 1.f;
|
|
|
|
// Check if input was set
|
|
if (!normals_)
|
|
{
|
|
PCL_ERROR ("[pcl::%s::computeFeature] No input dataset containing normals was given!\n", getClassName ().c_str ());
|
|
output.width = output.height = 0;
|
|
output.clear ();
|
|
return;
|
|
}
|
|
if (normals_->size () != surface_->size ())
|
|
{
|
|
PCL_ERROR ("[pcl::%s::computeFeature] The number of points in the input dataset differs from the number of points in the dataset containing the normals!\n", getClassName ().c_str ());
|
|
output.width = output.height = 0;
|
|
output.clear ();
|
|
return;
|
|
}
|
|
|
|
centroids_dominant_orientations_.clear ();
|
|
clusters_.clear ();
|
|
transforms_.clear ();
|
|
dominant_normals_.clear ();
|
|
|
|
// ---[ Step 0: remove normals with high curvature
|
|
pcl::Indices indices_out;
|
|
pcl::Indices indices_in;
|
|
filterNormalsWithHighCurvature (*normals_, *indices_, indices_out, indices_in, curv_threshold_);
|
|
|
|
pcl::PointCloud<pcl::PointNormal>::Ptr normals_filtered_cloud (new pcl::PointCloud<pcl::PointNormal> ());
|
|
normals_filtered_cloud->width = indices_in.size ();
|
|
normals_filtered_cloud->height = 1;
|
|
normals_filtered_cloud->points.resize (normals_filtered_cloud->width);
|
|
|
|
pcl::Indices indices_from_nfc_to_indices;
|
|
indices_from_nfc_to_indices.resize (indices_in.size ());
|
|
|
|
for (std::size_t i = 0; i < indices_in.size (); ++i)
|
|
{
|
|
(*normals_filtered_cloud)[i].x = (*surface_)[indices_in[i]].x;
|
|
(*normals_filtered_cloud)[i].y = (*surface_)[indices_in[i]].y;
|
|
(*normals_filtered_cloud)[i].z = (*surface_)[indices_in[i]].z;
|
|
//(*normals_filtered_cloud)[i].getNormalVector4fMap() = (*normals_)[indices_in[i]].getNormalVector4fMap();
|
|
indices_from_nfc_to_indices[i] = indices_in[i];
|
|
}
|
|
|
|
std::vector<pcl::PointIndices> clusters;
|
|
|
|
if (normals_filtered_cloud->size () >= min_points_)
|
|
{
|
|
//recompute normals and use them for clustering
|
|
{
|
|
KdTreePtr normals_tree_filtered (new pcl::search::KdTree<pcl::PointNormal> (false));
|
|
normals_tree_filtered->setInputCloud (normals_filtered_cloud);
|
|
pcl::NormalEstimation<PointNormal, PointNormal> n3d;
|
|
n3d.setRadiusSearch (radius_normals_);
|
|
n3d.setSearchMethod (normals_tree_filtered);
|
|
n3d.setInputCloud (normals_filtered_cloud);
|
|
n3d.compute (*normals_filtered_cloud);
|
|
}
|
|
|
|
KdTreePtr normals_tree (new pcl::search::KdTree<pcl::PointNormal> (false));
|
|
normals_tree->setInputCloud (normals_filtered_cloud);
|
|
|
|
extractEuclideanClustersSmooth (*normals_filtered_cloud, *normals_filtered_cloud, cluster_tolerance_, normals_tree, clusters,
|
|
eps_angle_threshold_, static_cast<unsigned int> (min_points_));
|
|
|
|
std::vector<pcl::PointIndices> clusters_filtered;
|
|
int cluster_filtered_idx = 0;
|
|
for (const auto &cluster : clusters)
|
|
{
|
|
|
|
pcl::PointIndices pi;
|
|
pcl::PointIndices pi_cvfh;
|
|
pcl::PointIndices pi_filtered;
|
|
|
|
clusters_.push_back (pi);
|
|
clusters_filtered.push_back (pi_filtered);
|
|
|
|
Eigen::Vector4f avg_normal = Eigen::Vector4f::Zero ();
|
|
Eigen::Vector4f avg_centroid = Eigen::Vector4f::Zero ();
|
|
|
|
for (const auto &index : cluster.indices)
|
|
{
|
|
avg_normal += (*normals_filtered_cloud)[index].getNormalVector4fMap ();
|
|
avg_centroid += (*normals_filtered_cloud)[index].getVector4fMap ();
|
|
}
|
|
|
|
avg_normal /= static_cast<float> (cluster.indices.size ());
|
|
avg_centroid /= static_cast<float> (cluster.indices.size ());
|
|
avg_normal.normalize ();
|
|
|
|
Eigen::Vector3f avg_norm (avg_normal[0], avg_normal[1], avg_normal[2]);
|
|
Eigen::Vector3f avg_dominant_centroid (avg_centroid[0], avg_centroid[1], avg_centroid[2]);
|
|
|
|
for (const auto &index : cluster.indices)
|
|
{
|
|
//decide if normal should be added
|
|
double dot_p = avg_normal.dot ((*normals_filtered_cloud)[index].getNormalVector4fMap ());
|
|
if (std::abs (std::acos (dot_p)) < (eps_angle_threshold_ * refine_clusters_))
|
|
{
|
|
clusters_[cluster_filtered_idx].indices.push_back (indices_from_nfc_to_indices[index]);
|
|
clusters_filtered[cluster_filtered_idx].indices.push_back (index);
|
|
}
|
|
}
|
|
|
|
//remove last cluster if no points found...
|
|
if (clusters_[cluster_filtered_idx].indices.empty ())
|
|
{
|
|
clusters_.pop_back ();
|
|
clusters_filtered.pop_back ();
|
|
}
|
|
else
|
|
cluster_filtered_idx++;
|
|
}
|
|
|
|
clusters = clusters_filtered;
|
|
|
|
}
|
|
|
|
pcl::VFHEstimation<PointInT, PointNT, pcl::VFHSignature308> vfh;
|
|
vfh.setInputCloud (surface_);
|
|
vfh.setInputNormals (normals_);
|
|
vfh.setIndices (indices_);
|
|
vfh.setSearchMethod (this->tree_);
|
|
vfh.setUseGivenNormal (true);
|
|
vfh.setUseGivenCentroid (true);
|
|
vfh.setNormalizeBins (normalize_bins_);
|
|
output.height = 1;
|
|
|
|
// ---[ Step 1b : check if any dominant cluster was found
|
|
if (!clusters.empty ())
|
|
{ // ---[ Step 1b.1 : If yes, compute CVFH using the cluster information
|
|
for (const auto &cluster : clusters) //for each cluster
|
|
{
|
|
Eigen::Vector4f avg_normal = Eigen::Vector4f::Zero ();
|
|
Eigen::Vector4f avg_centroid = Eigen::Vector4f::Zero ();
|
|
|
|
for (const auto &index : cluster.indices)
|
|
{
|
|
avg_normal += (*normals_filtered_cloud)[index].getNormalVector4fMap ();
|
|
avg_centroid += (*normals_filtered_cloud)[index].getVector4fMap ();
|
|
}
|
|
|
|
avg_normal /= static_cast<float> (cluster.indices.size ());
|
|
avg_centroid /= static_cast<float> (cluster.indices.size ());
|
|
avg_normal.normalize ();
|
|
|
|
//append normal and centroid for the clusters
|
|
dominant_normals_.emplace_back (avg_normal[0], avg_normal[1], avg_normal[2]);
|
|
centroids_dominant_orientations_.emplace_back (avg_centroid[0], avg_centroid[1], avg_centroid[2]);
|
|
}
|
|
|
|
//compute modified VFH for all dominant clusters and add them to the list!
|
|
output.resize (dominant_normals_.size ());
|
|
output.width = dominant_normals_.size ();
|
|
|
|
for (std::size_t i = 0; i < dominant_normals_.size (); ++i)
|
|
{
|
|
//configure VFH computation for CVFH
|
|
vfh.setNormalToUse (dominant_normals_[i]);
|
|
vfh.setCentroidToUse (centroids_dominant_orientations_[i]);
|
|
pcl::PointCloud<pcl::VFHSignature308> vfh_signature;
|
|
vfh.compute (vfh_signature);
|
|
output[i] = vfh_signature[0];
|
|
}
|
|
|
|
//finish filling the descriptor with the shape distribution
|
|
PointInTPtr cloud_input (new pcl::PointCloud<PointInT>);
|
|
pcl::copyPointCloud (*surface_, *indices_, *cloud_input);
|
|
computeRFAndShapeDistribution (cloud_input, output, clusters_); //this will set transforms_
|
|
}
|
|
else
|
|
{ // ---[ Step 1b.1 : If no, compute a VFH using all the object points
|
|
|
|
PCL_WARN("No clusters were found in the surface... using VFH...\n");
|
|
Eigen::Vector4f avg_centroid;
|
|
pcl::compute3DCentroid (*surface_, avg_centroid);
|
|
Eigen::Vector3f cloud_centroid (avg_centroid[0], avg_centroid[1], avg_centroid[2]);
|
|
centroids_dominant_orientations_.push_back (cloud_centroid);
|
|
|
|
//configure VFH computation using all object points
|
|
vfh.setCentroidToUse (cloud_centroid);
|
|
vfh.setUseGivenNormal (false);
|
|
|
|
pcl::PointCloud<pcl::VFHSignature308> vfh_signature;
|
|
vfh.compute (vfh_signature);
|
|
|
|
output.resize (1);
|
|
output.width = 1;
|
|
|
|
output[0] = vfh_signature[0];
|
|
Eigen::Matrix4f id = Eigen::Matrix4f::Identity ();
|
|
transforms_.push_back (id);
|
|
valid_transforms_.push_back (false);
|
|
}
|
|
}
|
|
|
|
#define PCL_INSTANTIATE_OURCVFHEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::OURCVFHEstimation<T,NT,OutT>;
|
|
|
|
#endif // PCL_FEATURES_IMPL_OURCVFH_H_
|