/* * Software License Agreement (BSD License) * * Copyright (c) 2012-, Open Perception, Inc. * All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of the copyright holder(s) nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. * * * */ #pragma once #include #include #undef Success namespace pcl { namespace on_nurbs { enum { NORTH = 1, NORTHEAST = 2, EAST = 3, SOUTHEAST = 4, SOUTH = 5, SOUTHWEST = 6, WEST = 7, NORTHWEST = 8 }; /** \brief Some useful tools for initialization, point search, ... */ class NurbsTools { public: // static std::list // getClosestPoints (const Eigen::Vector2d &p, const vector_vec2d &data, unsigned s); /** \brief Get the closest point with respect to 'point' * \param[in] point The point to which the closest point is searched for. * \param[in] data Vector containing the set of points for searching. */ static unsigned getClosestPoint (const Eigen::Vector2d &point, const vector_vec2d &data); /** \brief Get the closest point with respect to 'point' * \param[in] point The point to which the closest point is searched for. * \param[in] data Vector containing the set of points for searching. */ static unsigned getClosestPoint (const Eigen::Vector3d &point, const vector_vec3d &data); /** \brief Get the closest point with respect to 'point' in Non-Euclidean metric * \brief Related paper: TODO * \param[in] point The point to which the closest point is searched for. * \param[in] dir The direction defining 'inside' and 'outside' * \param[in] data Vector containing the set of points for searching. * \param[out] idxcp Closest point with respect to Euclidean metric. */ static unsigned getClosestPoint (const Eigen::Vector2d &point, const Eigen::Vector2d &dir, const vector_vec2d &data, unsigned &idxcp); /** \brief Compute the mean of a set of points * \param[in] data Set of points. */ static Eigen::Vector3d computeMean (const vector_vec3d &data); /** \brief Compute the mean of a set of points * \param[in] data Set of points. */ static Eigen::Vector2d computeMean (const vector_vec2d &data); /** \brief Compute the variance of a set of points * \param[in] data Set of points */ static Eigen::Vector3d computeVariance (const Eigen::Vector3d &mean, const vector_vec3d &data); /** \brief Compute the variance of a set of points * \param[in] data Set of points */ static Eigen::Vector2d computeVariance (const Eigen::Vector2d &mean, const vector_vec2d &data); /** compute bounding box of curve control points */ static void computeBoundingBox (const ON_NurbsCurve &nurbs, Eigen::Vector3d &_min, Eigen::Vector3d &_max); static void computeBoundingBox (const ON_NurbsSurface &nurbs, Eigen::Vector3d &_min, Eigen::Vector3d &_max); static double computeRScale (const Eigen::Vector3d &_min, const Eigen::Vector3d &_max); /** \brief PCA - principal-component-analysis * \param[in] data Set of points. * \param[out] mean The mean of the set of points. * \param[out] eigenvectors Matrix containing column-wise the eigenvectors of the set of points. * \param[out] eigenvalues The eigenvalues of the set of points with respect to the eigenvectors. */ static void pca (const vector_vec3d &data, Eigen::Vector3d &mean, Eigen::Matrix3d &eigenvectors, Eigen::Vector3d &eigenvalues); /** \brief PCA - principal-component-analysis * \param[in] data Set of points. * \param[out] mean The mean of the set of points. * \param[out] eigenvectors Matrix containing column-wise the eigenvectors of the set of points. * \param[out] eigenvalues The eigenvalues of the set of points with respect to the eigenvectors. */ static void pca (const vector_vec2d &data, Eigen::Vector2d &mean, Eigen::Matrix2d &eigenvectors, Eigen::Vector2d &eigenvalues); /** \brief Downsample data points to a certain size. * \param[in] data1 The original set of points. * \param[out] data2 The downsampled set of points of size 'size'. * \param[in] size The desired size of the resulting set of points. */ static void downsample_random (const vector_vec3d &data1, vector_vec3d &data2, unsigned size); /** \brief Downsample data points to a certain size. * \param[in/out] data1 The set of points for downsampling; * will be replaced by the resulting set of points of size 'size'. * \param[in] size The desired size of the resulting set of points. */ static void downsample_random (vector_vec3d &data1, unsigned size); }; } }