254 lines
9.5 KiB
C++
254 lines
9.5 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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* $Id$
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*
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*/
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#ifndef PCL_FEATURES_IMPL_VFH_H_
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#define PCL_FEATURES_IMPL_VFH_H_
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#include <pcl/features/vfh.h>
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#include <pcl/features/pfh_tools.h>
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#include <pcl/common/common.h>
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#include <pcl/common/centroid.h>
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//////////////////////////////////////////////////////////////////////////////////////////////
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template<typename PointInT, typename PointNT, typename PointOutT> bool
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pcl::VFHEstimation<PointInT, PointNT, PointOutT>::initCompute ()
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{
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if (input_->size () < 2 || (surface_ && surface_->size () < 2))
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{
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PCL_ERROR ("[pcl::VFHEstimation::initCompute] Input dataset must have at least 2 points!\n");
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return (false);
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}
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if (search_radius_ == 0 && k_ == 0)
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k_ = 1;
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return (Feature<PointInT, PointOutT>::initCompute ());
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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::VFHEstimation<PointInT, PointNT, PointOutT>::compute (PointCloudOut &output)
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{
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if (!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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// Copy the header
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output.header = input_->header;
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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.is_dense = input_->is_dense;
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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::VFHEstimation<PointInT, PointNT, PointOutT>::computePointSPFHSignature (const Eigen::Vector4f ¢roid_p,
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const Eigen::Vector4f ¢roid_n,
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const pcl::PointCloud<PointInT> &cloud,
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const pcl::PointCloud<PointNT> &normals,
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const pcl::Indices &indices)
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{
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Eigen::Vector4f pfh_tuple;
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// Reset the whole thing
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for (int i = 0; i < 4; ++i)
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{
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hist_f_[i].setZero (nr_bins_f_[i]);
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}
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// Get the bounding box of the current cluster
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//Eigen::Vector4f min_pt, max_pt;
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//pcl::getMinMax3D (cloud, indices, min_pt, max_pt);
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//double distance_normalization_factor = (std::max)((centroid_p - min_pt).norm (), (centroid_p - max_pt).norm ());
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//Instead of using the bounding box to normalize the VFH distance component, it is better to use the max_distance
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//from any point to centroid. VFH is invariant to rotation about the roll axis but the bounding box is not,
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//resulting in different normalization factors for point clouds that are just rotated about that axis.
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double distance_normalization_factor = 1.0;
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if (normalize_distances_)
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{
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Eigen::Vector4f max_pt;
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pcl::getMaxDistance (cloud, indices, centroid_p, max_pt);
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max_pt[3] = 0;
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distance_normalization_factor = (centroid_p - max_pt).norm ();
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}
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// Factorization constant
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float hist_incr = 1;
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if (normalize_bins_)
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hist_incr = 100.0f / static_cast<float> (indices.size () - 1);
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float hist_incr_size_component = 0;;
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if (size_component_)
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hist_incr_size_component = hist_incr;
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// Iterate over all the points in the neighborhood
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for (const auto &index : indices)
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{
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// Compute the pair P to NNi
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if (!computePairFeatures (centroid_p, centroid_n, cloud[index].getVector4fMap (),
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normals[index].getNormalVector4fMap (), pfh_tuple[0], pfh_tuple[1],
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pfh_tuple[2], pfh_tuple[3]))
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continue;
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// Normalize the f1, f2, f3, f4 features and push them in the histogram
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for (int i = 0; i < 3; ++i)
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{
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const int raw_index = static_cast<int> (std::floor (nr_bins_f_[i] * ((pfh_tuple[i] + M_PI) * d_pi_)));
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const int h_index = std::max(std::min(raw_index, nr_bins_f_[i] - 1), 0);
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hist_f_[i] (h_index) += hist_incr;
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}
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if (hist_incr_size_component)
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{
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int h_index;
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if (normalize_distances_)
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h_index = static_cast<int> (std::floor (nr_bins_f_[3] * (pfh_tuple[3] / distance_normalization_factor)));
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else
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h_index = static_cast<int> (pcl_round (pfh_tuple[3] * 100));
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h_index = std::max (std::min (h_index, nr_bins_f_[3] - 1), 0);
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hist_f_[3] (h_index) += hist_incr_size_component;
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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::VFHEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
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{
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// ---[ Step 1a : compute the centroid in XYZ space
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Eigen::Vector4f xyz_centroid (0, 0, 0, 0);
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if (use_given_centroid_)
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xyz_centroid = centroid_to_use_;
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else
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compute3DCentroid (*surface_, *indices_, xyz_centroid); // Estimate the XYZ centroid
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// ---[ Step 1b : compute the centroid in normal space
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Eigen::Vector4f normal_centroid = Eigen::Vector4f::Zero ();
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// If the data is dense, we don't need to check for NaN
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if (use_given_normal_)
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normal_centroid = normal_to_use_;
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else
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{
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std::size_t cp = 0;
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if (normals_->is_dense)
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{
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for (const auto& index: *indices_)
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{
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normal_centroid.noalias () += (*normals_)[index].getNormalVector4fMap ();
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}
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cp = indices_->size();
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}
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// NaN or Inf values could exist => check for them
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else
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{
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for (const auto& index: *indices_)
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{
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if (!std::isfinite ((*normals_)[index].normal[0]) ||
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!std::isfinite ((*normals_)[index].normal[1]) ||
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!std::isfinite ((*normals_)[index].normal[2]))
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continue;
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normal_centroid.noalias () += (*normals_)[index].getNormalVector4fMap ();
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cp++;
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}
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}
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normal_centroid /= static_cast<float> (cp);
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}
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// Compute the direction of view from the viewpoint to the centroid
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Eigen::Vector4f viewpoint (vpx_, vpy_, vpz_, 0);
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Eigen::Vector4f d_vp_p = viewpoint - xyz_centroid;
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d_vp_p.normalize ();
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// Estimate the SPFH at nn_indices[0] using the entire cloud
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computePointSPFHSignature (xyz_centroid, normal_centroid, *surface_, *normals_, *indices_);
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// ---[ Step 2 : obtain the viewpoint component
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hist_vp_.setZero (nr_bins_vp_);
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float hist_incr = 1.0;
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if (normalize_bins_)
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hist_incr = 100.0 / static_cast<double> (indices_->size ());
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for (const auto& index: *indices_)
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{
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Eigen::Vector4f normal ((*normals_)[index].normal[0],
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(*normals_)[index].normal[1],
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(*normals_)[index].normal[2], 0);
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// Normalize
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double alpha = (normal.dot (d_vp_p) + 1.0) * 0.5;
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std::size_t fi = static_cast<std::size_t> (std::floor (alpha * hist_vp_.size ()));
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fi = std::max<std::size_t> (0u, fi);
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fi = std::min<std::size_t> (hist_vp_.size () - 1, fi);
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// Bin into the histogram
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hist_vp_ [fi] += hist_incr;
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}
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// We only output _1_ signature
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output.resize (1);
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output.width = 1;
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output.height = 1;
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// Estimate the FPFH at nn_indices[0] using the entire cloud and copy the resultant signature
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auto outPtr = std::begin (output[0].histogram);
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for (int i = 0; i < 4; ++i)
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{
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outPtr = std::copy_n (hist_f_[i].data (), hist_f_[i].size (), outPtr);
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}
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outPtr = std::copy_n (hist_vp_.data (), hist_vp_.size (), outPtr);
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}
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#define PCL_INSTANTIATE_VFHEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::VFHEstimation<T,NT,OutT>;
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#endif // PCL_FEATURES_IMPL_VFH_H_
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