167 lines
6.9 KiB
C++
167 lines
6.9 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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#pragma once
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#include <pcl/features/principal_curvatures.h>
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#include <pcl/common/point_tests.h> // for pcl::isFinite
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT> void
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pcl::PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT>::computePointPrincipalCurvatures (
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const pcl::PointCloud<PointNT> &normals, int p_idx, const pcl::Indices &indices,
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float &pcx, float &pcy, float &pcz, float &pc1, float &pc2)
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{
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EIGEN_ALIGN16 Eigen::Matrix3f I = Eigen::Matrix3f::Identity ();
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Eigen::Vector3f n_idx (normals[p_idx].normal[0], normals[p_idx].normal[1], normals[p_idx].normal[2]);
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EIGEN_ALIGN16 Eigen::Matrix3f M = I - n_idx * n_idx.transpose (); // projection matrix (into tangent plane)
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// Project normals into the tangent plane
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Eigen::Vector3f normal;
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projected_normals_.resize (indices.size ());
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xyz_centroid_.setZero ();
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for (std::size_t idx = 0; idx < indices.size(); ++idx)
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{
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normal[0] = normals[indices[idx]].normal[0];
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normal[1] = normals[indices[idx]].normal[1];
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normal[2] = normals[indices[idx]].normal[2];
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projected_normals_[idx] = M * normal;
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xyz_centroid_ += projected_normals_[idx];
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}
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// Estimate the XYZ centroid
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xyz_centroid_ /= static_cast<float> (indices.size ());
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// Initialize to 0
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covariance_matrix_.setZero ();
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// For each point in the cloud
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for (std::size_t idx = 0; idx < indices.size (); ++idx)
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{
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demean_ = projected_normals_[idx] - xyz_centroid_;
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double demean_xy = demean_[0] * demean_[1];
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double demean_xz = demean_[0] * demean_[2];
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double demean_yz = demean_[1] * demean_[2];
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covariance_matrix_(0, 0) += demean_[0] * demean_[0];
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covariance_matrix_(0, 1) += static_cast<float> (demean_xy);
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covariance_matrix_(0, 2) += static_cast<float> (demean_xz);
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covariance_matrix_(1, 0) += static_cast<float> (demean_xy);
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covariance_matrix_(1, 1) += demean_[1] * demean_[1];
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covariance_matrix_(1, 2) += static_cast<float> (demean_yz);
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covariance_matrix_(2, 0) += static_cast<float> (demean_xz);
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covariance_matrix_(2, 1) += static_cast<float> (demean_yz);
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covariance_matrix_(2, 2) += demean_[2] * demean_[2];
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}
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// Extract the eigenvalues and eigenvectors
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pcl::eigen33 (covariance_matrix_, eigenvalues_);
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pcl::computeCorrespondingEigenVector (covariance_matrix_, eigenvalues_ [2], eigenvector_);
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pcx = eigenvector_ [0];
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pcy = eigenvector_ [1];
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pcz = eigenvector_ [2];
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float indices_size = 1.0f / static_cast<float> (indices.size ());
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pc1 = eigenvalues_ [2] * indices_size;
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pc2 = eigenvalues_ [1] * indices_size;
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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::PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
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{
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// Allocate enough space to hold the results
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// \note This resize is irrelevant for a radiusSearch ().
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pcl::Indices nn_indices (k_);
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std::vector<float> nn_dists (k_);
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output.is_dense = true;
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// Save a few cycles by not checking every point for NaN/Inf values if the cloud is set to dense
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if (input_->is_dense)
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{
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// Iterating over the entire index vector
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for (std::size_t idx = 0; idx < indices_->size (); ++idx)
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{
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if (this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
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{
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output[idx].principal_curvature[0] = output[idx].principal_curvature[1] = output[idx].principal_curvature[2] =
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output[idx].pc1 = output[idx].pc2 = std::numeric_limits<float>::quiet_NaN ();
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output.is_dense = false;
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continue;
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}
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// Estimate the principal curvatures at each patch
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computePointPrincipalCurvatures (*normals_, (*indices_)[idx], nn_indices,
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output[idx].principal_curvature[0], output[idx].principal_curvature[1], output[idx].principal_curvature[2],
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output[idx].pc1, output[idx].pc2);
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}
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}
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else
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{
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// Iterating over the entire index vector
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for (std::size_t idx = 0; idx < indices_->size (); ++idx)
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{
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if (!isFinite ((*input_)[(*indices_)[idx]]) ||
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this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
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{
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output[idx].principal_curvature[0] = output[idx].principal_curvature[1] = output[idx].principal_curvature[2] =
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output[idx].pc1 = output[idx].pc2 = std::numeric_limits<float>::quiet_NaN ();
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output.is_dense = false;
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continue;
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}
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// Estimate the principal curvatures at each patch
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computePointPrincipalCurvatures (*normals_, (*indices_)[idx], nn_indices,
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output[idx].principal_curvature[0], output[idx].principal_curvature[1], output[idx].principal_curvature[2],
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output[idx].pc1, output[idx].pc2);
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}
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}
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}
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#define PCL_INSTANTIATE_PrincipalCurvaturesEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::PrincipalCurvaturesEstimation<T,NT,OutT>;
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