148 lines
6.3 KiB
C
148 lines
6.3 KiB
C
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/*
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* Software License Agreement (BSD License)
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*
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* Copyright (c) 2012-, Open Perception, Inc.
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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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*
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*
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*/
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#pragma once
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#include <pcl/surface/on_nurbs/nurbs_data.h>
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#include <pcl/surface/3rdparty/opennurbs/opennurbs.h>
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#undef Success
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namespace pcl
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{
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namespace on_nurbs
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{
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enum
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{
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NORTH = 1, NORTHEAST = 2, EAST = 3, SOUTHEAST = 4, SOUTH = 5, SOUTHWEST = 6, WEST = 7, NORTHWEST = 8
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};
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/** \brief Some useful tools for initialization, point search, ... */
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class NurbsTools
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{
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public:
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// static std::list<unsigned>
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// getClosestPoints (const Eigen::Vector2d &p, const vector_vec2d &data, unsigned s);
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/** \brief Get the closest point with respect to 'point'
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* \param[in] point The point to which the closest point is searched for.
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* \param[in] data Vector containing the set of points for searching. */
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static unsigned
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getClosestPoint (const Eigen::Vector2d &point, const vector_vec2d &data);
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/** \brief Get the closest point with respect to 'point'
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* \param[in] point The point to which the closest point is searched for.
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* \param[in] data Vector containing the set of points for searching. */
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static unsigned
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getClosestPoint (const Eigen::Vector3d &point, const vector_vec3d &data);
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/** \brief Get the closest point with respect to 'point' in Non-Euclidean metric
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* \brief Related paper: TODO
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* \param[in] point The point to which the closest point is searched for.
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* \param[in] dir The direction defining 'inside' and 'outside'
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* \param[in] data Vector containing the set of points for searching.
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* \param[out] idxcp Closest point with respect to Euclidean metric. */
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static unsigned
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getClosestPoint (const Eigen::Vector2d &point, const Eigen::Vector2d &dir, const vector_vec2d &data,
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unsigned &idxcp);
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/** \brief Compute the mean of a set of points
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* \param[in] data Set of points. */
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static Eigen::Vector3d
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computeMean (const vector_vec3d &data);
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/** \brief Compute the mean of a set of points
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* \param[in] data Set of points. */
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static Eigen::Vector2d
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computeMean (const vector_vec2d &data);
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/** \brief Compute the variance of a set of points
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* \param[in] data Set of points */
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static Eigen::Vector3d
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computeVariance (const Eigen::Vector3d &mean, const vector_vec3d &data);
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/** \brief Compute the variance of a set of points
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* \param[in] data Set of points */
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static Eigen::Vector2d
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computeVariance (const Eigen::Vector2d &mean, const vector_vec2d &data);
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/** compute bounding box of curve control points */
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static void
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computeBoundingBox (const ON_NurbsCurve &nurbs, Eigen::Vector3d &_min, Eigen::Vector3d &_max);
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static void
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computeBoundingBox (const ON_NurbsSurface &nurbs, Eigen::Vector3d &_min, Eigen::Vector3d &_max);
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static double
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computeRScale (const Eigen::Vector3d &_min, const Eigen::Vector3d &_max);
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/** \brief PCA - principal-component-analysis
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* \param[in] data Set of points.
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* \param[out] mean The mean of the set of points.
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* \param[out] eigenvectors Matrix containing column-wise the eigenvectors of the set of points.
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* \param[out] eigenvalues The eigenvalues of the set of points with respect to the eigenvectors. */
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static void
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pca (const vector_vec3d &data, Eigen::Vector3d &mean, Eigen::Matrix3d &eigenvectors,
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Eigen::Vector3d &eigenvalues);
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/** \brief PCA - principal-component-analysis
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* \param[in] data Set of points.
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* \param[out] mean The mean of the set of points.
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* \param[out] eigenvectors Matrix containing column-wise the eigenvectors of the set of points.
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* \param[out] eigenvalues The eigenvalues of the set of points with respect to the eigenvectors. */
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static void
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pca (const vector_vec2d &data, Eigen::Vector2d &mean, Eigen::Matrix2d &eigenvectors,
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Eigen::Vector2d &eigenvalues);
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/** \brief Downsample data points to a certain size.
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* \param[in] data1 The original set of points.
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* \param[out] data2 The downsampled set of points of size 'size'.
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* \param[in] size The desired size of the resulting set of points. */
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static void
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downsample_random (const vector_vec3d &data1, vector_vec3d &data2, unsigned size);
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/** \brief Downsample data points to a certain size.
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* \param[in/out] data1 The set of points for downsampling;
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* will be replaced by the resulting set of points of size 'size'.
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* \param[in] size The desired size of the resulting set of points. */
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static void
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downsample_random (vector_vec3d &data1, unsigned size);
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};
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}
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}
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