339 lines
11 KiB
C++
339 lines
11 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_FEATURE_H_
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#define PCL_FEATURES_IMPL_FEATURE_H_
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#include <pcl/search/kdtree.h> // for KdTree
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#include <pcl/search/organized.h> // for OrganizedNeighbor
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namespace pcl
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{
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inline void
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solvePlaneParameters (const Eigen::Matrix3f &covariance_matrix,
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const Eigen::Vector4f &point,
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Eigen::Vector4f &plane_parameters, float &curvature)
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{
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solvePlaneParameters (covariance_matrix, plane_parameters [0], plane_parameters [1], plane_parameters [2], curvature);
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plane_parameters[3] = 0;
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// Hessian form (D = nc . p_plane (centroid here) + p)
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plane_parameters[3] = -1 * plane_parameters.dot (point);
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}
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inline void
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solvePlaneParameters (const Eigen::Matrix3f &covariance_matrix,
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float &nx, float &ny, float &nz, float &curvature)
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{
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// Avoid getting hung on Eigen's optimizers
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// for (int i = 0; i < 9; ++i)
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// if (!std::isfinite (covariance_matrix.coeff (i)))
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// {
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// //PCL_WARN ("[pcl::solvePlaneParameters] Covariance matrix has NaN/Inf values!\n");
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// nx = ny = nz = curvature = std::numeric_limits<float>::quiet_NaN ();
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// return;
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// }
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// Extract the smallest eigenvalue and its eigenvector
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EIGEN_ALIGN16 Eigen::Vector3f::Scalar eigen_value;
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EIGEN_ALIGN16 Eigen::Vector3f eigen_vector;
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pcl::eigen33 (covariance_matrix, eigen_value, eigen_vector);
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nx = eigen_vector [0];
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ny = eigen_vector [1];
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nz = eigen_vector [2];
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// Compute the curvature surface change
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float eig_sum = covariance_matrix.coeff (0) + covariance_matrix.coeff (4) + covariance_matrix.coeff (8);
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if (eig_sum != 0)
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curvature = std::abs (eigen_value / eig_sum);
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else
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curvature = 0;
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}
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template <typename PointInT, typename PointOutT> bool
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Feature<PointInT, PointOutT>::initCompute ()
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{
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if (!PCLBase<PointInT>::initCompute ())
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{
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PCL_ERROR ("[pcl::%s::initCompute] Init failed.\n", getClassName ().c_str ());
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return (false);
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}
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// If the dataset is empty, just return
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if (input_->points.empty ())
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{
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PCL_ERROR ("[pcl::%s::compute] input_ is empty!\n", getClassName ().c_str ());
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// Cleanup
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deinitCompute ();
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return (false);
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}
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// If no search surface has been defined, use the input dataset as the search surface itself
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if (!surface_)
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{
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fake_surface_ = true;
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surface_ = input_;
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}
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// Check if a space search locator was given
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if (!tree_)
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{
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if (surface_->isOrganized () && input_->isOrganized ())
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tree_.reset (new pcl::search::OrganizedNeighbor<PointInT> ());
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else
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tree_.reset (new pcl::search::KdTree<PointInT> (false));
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}
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if (tree_->getInputCloud () != surface_) // Make sure the tree searches the surface
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tree_->setInputCloud (surface_);
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// Do a fast check to see if the search parameters are well defined
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if (search_radius_ != 0.0)
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{
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if (k_ != 0)
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{
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PCL_ERROR ("[pcl::%s::compute] ", getClassName ().c_str ());
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PCL_ERROR ("Both radius (%f) and K (%d) defined! ", search_radius_, k_);
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PCL_ERROR ("Set one of them to zero first and then re-run compute ().\n");
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// Cleanup
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deinitCompute ();
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return (false);
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}
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else // Use the radiusSearch () function
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{
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search_parameter_ = search_radius_;
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// Declare the search locator definition
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search_method_surface_ = [this] (const PointCloudIn &cloud, int index, double radius,
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pcl::Indices &k_indices, std::vector<float> &k_distances)
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{
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return tree_->radiusSearch (cloud, index, radius, k_indices, k_distances, 0);
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};
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}
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}
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else
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{
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if (k_ != 0) // Use the nearestKSearch () function
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{
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search_parameter_ = k_;
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// Declare the search locator definition
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search_method_surface_ = [this] (const PointCloudIn &cloud, int index, int k, pcl::Indices &k_indices,
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std::vector<float> &k_distances)
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{
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return tree_->nearestKSearch (cloud, index, k, k_indices, k_distances);
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};
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}
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else
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{
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PCL_ERROR ("[pcl::%s::compute] Neither radius nor K defined! ", getClassName ().c_str ());
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PCL_ERROR ("Set one of them to a positive number first and then re-run compute ().\n");
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// Cleanup
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deinitCompute ();
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return (false);
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}
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}
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return (true);
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}
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template <typename PointInT, typename PointOutT> bool
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Feature<PointInT, PointOutT>::deinitCompute ()
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{
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// Reset the surface
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if (fake_surface_)
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{
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surface_.reset ();
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fake_surface_ = false;
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}
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return (true);
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}
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template <typename PointInT, typename PointOutT> void
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Feature<PointInT, 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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if (output.size () != indices_->size ())
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output.resize (indices_->size ());
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// Check if the output will be computed for all points or only a subset
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// If the input width or height are not set, set output width as size
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if (indices_->size () != input_->points.size () || input_->width * input_->height == 0)
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{
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output.width = indices_->size ();
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output.height = 1;
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}
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else
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{
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output.width = input_->width;
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output.height = input_->height;
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}
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output.is_dense = input_->is_dense;
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// Perform the actual feature computation
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computeFeature (output);
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deinitCompute ();
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}
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template <typename PointInT, typename PointNT, typename PointOutT> bool
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FeatureFromNormals<PointInT, PointNT, PointOutT>::initCompute ()
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{
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if (!Feature<PointInT, PointOutT>::initCompute ())
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{
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PCL_ERROR ("[pcl::%s::initCompute] Init failed.\n", getClassName ().c_str ());
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return (false);
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}
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// Check if input normals are set
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if (!normals_)
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{
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PCL_ERROR ("[pcl::%s::initCompute] No input dataset containing normals was given!\n", getClassName ().c_str ());
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Feature<PointInT, PointOutT>::deinitCompute ();
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return (false);
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}
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// Check if the size of normals is the same as the size of the surface
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if (normals_->points.size () != surface_->points.size ())
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{
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PCL_ERROR ("[pcl::%s::initCompute] ", getClassName ().c_str ());
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PCL_ERROR("The number of points in the surface dataset (%zu) differs from ",
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static_cast<std::size_t>(surface_->points.size()));
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PCL_ERROR("the number of points in the dataset containing the normals (%zu)!\n",
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static_cast<std::size_t>(normals_->points.size()));
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Feature<PointInT, PointOutT>::deinitCompute ();
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return (false);
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}
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return (true);
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}
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template <typename PointInT, typename PointLT, typename PointOutT> bool
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FeatureFromLabels<PointInT, PointLT, PointOutT>::initCompute ()
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{
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if (!Feature<PointInT, PointOutT>::initCompute ())
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{
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PCL_ERROR ("[pcl::%s::initCompute] Init failed.\n", getClassName ().c_str ());
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return (false);
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}
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// Check if input normals are set
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if (!labels_)
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{
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PCL_ERROR ("[pcl::%s::initCompute] No input dataset containing labels was given!\n", getClassName ().c_str ());
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Feature<PointInT, PointOutT>::deinitCompute ();
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return (false);
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}
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// Check if the size of normals is the same as the size of the surface
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if (labels_->points.size () != surface_->points.size ())
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{
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PCL_ERROR ("[pcl::%s::initCompute] The number of points in the input dataset differs from the number of points in the dataset containing the labels!\n", getClassName ().c_str ());
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Feature<PointInT, PointOutT>::deinitCompute ();
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return (false);
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}
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return (true);
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}
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template <typename PointInT, typename PointRFT> bool
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FeatureWithLocalReferenceFrames<PointInT, PointRFT>::initLocalReferenceFrames (const std::size_t& indices_size,
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const LRFEstimationPtr& lrf_estimation)
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{
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if (frames_never_defined_)
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frames_.reset ();
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// Check if input frames are set
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if (!frames_)
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{
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if (!lrf_estimation)
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{
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PCL_ERROR ("[initLocalReferenceFrames] No input dataset containing reference frames was given!\n");
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return (false);
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} else
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{
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//PCL_WARN ("[initLocalReferenceFrames] No input dataset containing reference frames was given! Proceed using default\n");
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PointCloudLRFPtr default_frames (new PointCloudLRF());
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lrf_estimation->compute (*default_frames);
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frames_ = default_frames;
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}
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}
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// Check if the size of frames is the same as the size of the input cloud
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if (frames_->points.size () != indices_size)
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{
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if (!lrf_estimation)
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{
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PCL_ERROR ("[initLocalReferenceFrames] The number of points in the input dataset differs from the number of points in the dataset containing the reference frames!\n");
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return (false);
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} else
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{
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//PCL_WARN ("[initLocalReferenceFrames] The number of points in the input dataset differs from the number of points in the dataset containing the reference frames! Proceed using default\n");
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PointCloudLRFPtr default_frames (new PointCloudLRF());
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lrf_estimation->compute (*default_frames);
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frames_ = default_frames;
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}
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}
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return (true);
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}
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} // namespace pcl
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#endif //#ifndef PCL_FEATURES_IMPL_FEATURE_H_
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