240 lines
8.0 KiB
C++
240 lines
8.0 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/intensity_gradient.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, typename IntensitySelectorT> void
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pcl::IntensityGradientEstimation <PointInT, PointNT, PointOutT, IntensitySelectorT>::computePointIntensityGradient (
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const pcl::PointCloud <PointInT> &cloud, const pcl::Indices &indices,
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const Eigen::Vector3f &point, float mean_intensity, const Eigen::Vector3f &normal, Eigen::Vector3f &gradient)
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{
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if (indices.size () < 3)
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{
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gradient[0] = gradient[1] = gradient[2] = std::numeric_limits<float>::quiet_NaN ();
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return;
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}
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Eigen::Matrix3f A = Eigen::Matrix3f::Zero ();
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Eigen::Vector3f b = Eigen::Vector3f::Zero ();
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for (const auto &nn_index : indices)
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{
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PointInT p = cloud[nn_index];
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if (!std::isfinite (p.x) ||
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!std::isfinite (p.y) ||
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!std::isfinite (p.z) ||
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!std::isfinite (intensity_ (p)))
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continue;
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p.x -= point[0];
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p.y -= point[1];
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p.z -= point[2];
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intensity_.demean (p, mean_intensity);
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A (0, 0) += p.x * p.x;
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A (0, 1) += p.x * p.y;
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A (0, 2) += p.x * p.z;
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A (1, 1) += p.y * p.y;
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A (1, 2) += p.y * p.z;
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A (2, 2) += p.z * p.z;
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b[0] += p.x * intensity_ (p);
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b[1] += p.y * intensity_ (p);
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b[2] += p.z * intensity_ (p);
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}
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// Fill in the lower triangle of A
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A (1, 0) = A (0, 1);
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A (2, 0) = A (0, 2);
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A (2, 1) = A (1, 2);
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// Eigen::Vector3f x = A.colPivHouseholderQr ().solve (b);
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Eigen::Vector3f eigen_values;
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Eigen::Matrix3f eigen_vectors;
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eigen33 (A, eigen_vectors, eigen_values);
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b = eigen_vectors.transpose () * b;
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if ( eigen_values (0) != 0)
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b (0) /= eigen_values (0);
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else
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b (0) = 0;
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if ( eigen_values (1) != 0)
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b (1) /= eigen_values (1);
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else
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b (1) = 0;
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if ( eigen_values (2) != 0)
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b (2) /= eigen_values (2);
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else
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b (2) = 0;
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Eigen::Vector3f x = eigen_vectors * b;
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// if (A.col (0).squaredNorm () != 0)
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// x [0] /= A.col (0).squaredNorm ();
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// b -= x [0] * A.col (0);
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//
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//
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// if (A.col (1).squaredNorm () != 0)
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// x [1] /= A.col (1).squaredNorm ();
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// b -= x[1] * A.col (1);
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//
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// x [2] = b.dot (A.col (2));
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// if (A.col (2).squaredNorm () != 0)
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// x[2] /= A.col (2).squaredNorm ();
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// // Fit a hyperplane to the data
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//
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// std::cout << A << "\n*\n" << bb << "\n=\n" << x << "\nvs.\n" << x2 << "\n\n";
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// std::cout << A * x << "\nvs.\n" << A * x2 << "\n\n------\n";
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// Project the gradient vector, x, onto the tangent plane
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gradient = (Eigen::Matrix3f::Identity () - normal*normal.transpose ()) * x;
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT, typename IntensitySelectorT> void
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pcl::IntensityGradientEstimation<PointInT, PointNT, PointOutT, IntensitySelectorT>::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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// If the data is dense, we don't need to check for NaN
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if (surface_->is_dense)
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{
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#pragma omp parallel for \
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default(none) \
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shared(output) \
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firstprivate(nn_indices, nn_dists) \
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num_threads(threads_)
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// Iterating over the entire index vector
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for (std::ptrdiff_t idx = 0; idx < static_cast<std::ptrdiff_t> (indices_->size ()); ++idx)
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{
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PointOutT &p_out = output[idx];
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if (!this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists))
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{
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p_out.gradient[0] = p_out.gradient[1] = p_out.gradient[2] = 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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Eigen::Vector3f centroid;
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float mean_intensity = 0;
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// Initialize to 0
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centroid.setZero ();
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for (const auto &nn_index : nn_indices)
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{
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centroid += (*surface_)[nn_index].getVector3fMap ();
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mean_intensity += intensity_ ((*surface_)[nn_index]);
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}
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centroid /= static_cast<float> (nn_indices.size ());
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mean_intensity /= static_cast<float> (nn_indices.size ());
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Eigen::Vector3f normal = Eigen::Vector3f::Map ((*normals_)[(*indices_) [idx]].normal);
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Eigen::Vector3f gradient;
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computePointIntensityGradient (*surface_, nn_indices, centroid, mean_intensity, normal, gradient);
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p_out.gradient[0] = gradient[0];
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p_out.gradient[1] = gradient[1];
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p_out.gradient[2] = gradient[2];
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}
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}
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else
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{
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#pragma omp parallel for \
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default(none) \
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shared(output) \
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firstprivate(nn_indices, nn_dists) \
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num_threads(threads_)
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// Iterating over the entire index vector
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for (std::ptrdiff_t idx = 0; idx < static_cast<std::ptrdiff_t> (indices_->size ()); ++idx)
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{
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PointOutT &p_out = output[idx];
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if (!isFinite ((*surface_) [(*indices_)[idx]]) ||
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!this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists))
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{
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p_out.gradient[0] = p_out.gradient[1] = p_out.gradient[2] = 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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Eigen::Vector3f centroid;
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float mean_intensity = 0;
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// Initialize to 0
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centroid.setZero ();
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unsigned cp = 0;
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for (const auto &nn_index : nn_indices)
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{
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// Check if the point is invalid
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if (!isFinite ((*surface_) [nn_index]))
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continue;
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centroid += surface_->points [nn_index].getVector3fMap ();
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mean_intensity += intensity_ (surface_->points [nn_index]);
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++cp;
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}
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centroid /= static_cast<float> (cp);
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mean_intensity /= static_cast<float> (cp);
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Eigen::Vector3f normal = Eigen::Vector3f::Map ((*normals_)[(*indices_) [idx]].normal);
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Eigen::Vector3f gradient;
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computePointIntensityGradient (*surface_, nn_indices, centroid, mean_intensity, normal, gradient);
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p_out.gradient[0] = gradient[0];
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p_out.gradient[1] = gradient[1];
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p_out.gradient[2] = gradient[2];
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}
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}
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}
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#define PCL_INSTANTIATE_IntensityGradientEstimation(InT,NT,OutT) template class PCL_EXPORTS pcl::IntensityGradientEstimation<InT,NT,OutT>;
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