Point Cloud Library (PCL)
1.7.0
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00001 /* 00002 * Software License Agreement (BSD License) 00003 * 00004 * Point Cloud Library (PCL) - www.pointclouds.org 00005 * Copyright (c) 2011, Alexandru-Eugen Ichim 00006 * Copyright (c) 2012-, Open Perception, Inc. 00007 * 00008 * All rights reserved. 00009 * 00010 * Redistribution and use in source and binary forms, with or without 00011 * modification, are permitted provided that the following conditions 00012 * are met: 00013 * 00014 * * Redistributions of source code must retain the above copyright 00015 * notice, this list of conditions and the following disclaimer. 00016 * * Redistributions in binary form must reproduce the above 00017 * copyright notice, this list of conditions and the following 00018 * disclaimer in the documentation and/or other materials provided 00019 * with the distribution. 00020 * * Neither the name of the copyright holder(s) nor the names of its 00021 * contributors may be used to endorse or promote products derived 00022 * from this software without specific prior written permission. 00023 * 00024 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 00025 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 00026 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS 00027 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE 00028 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, 00029 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, 00030 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 00031 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 00032 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT 00033 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN 00034 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 00035 * POSSIBILITY OF SUCH DAMAGE. 00036 * 00037 * $Id$ 00038 */ 00039 00040 #ifndef PCL_FEATURES_IMPL_PFHRGB_H_ 00041 #define PCL_FEATURES_IMPL_PFHRGB_H_ 00042 00043 #include <pcl/features/pfhrgb.h> 00044 00045 ////////////////////////////////////////////////////////////////////////////////////////////// 00046 template <typename PointInT, typename PointNT, typename PointOutT> bool 00047 pcl::PFHRGBEstimation<PointInT, PointNT, PointOutT>::computeRGBPairFeatures ( 00048 const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals, 00049 int p_idx, int q_idx, 00050 float &f1, float &f2, float &f3, float &f4, float &f5, float &f6, float &f7) 00051 { 00052 Eigen::Vector4i colors1 (cloud.points[p_idx].r, cloud.points[p_idx].g, cloud.points[p_idx].b, 0), 00053 colors2 (cloud.points[q_idx].r, cloud.points[q_idx].g, cloud.points[q_idx].b, 0); 00054 pcl::computeRGBPairFeatures (cloud.points[p_idx].getVector4fMap (), normals.points[p_idx].getNormalVector4fMap (), 00055 colors1, 00056 cloud.points[q_idx].getVector4fMap (), normals.points[q_idx].getNormalVector4fMap (), 00057 colors2, 00058 f1, f2, f3, f4, f5, f6, f7); 00059 return (true); 00060 } 00061 00062 ////////////////////////////////////////////////////////////////////////////////////////////// 00063 template <typename PointInT, typename PointNT, typename PointOutT> void 00064 pcl::PFHRGBEstimation<PointInT, PointNT, PointOutT>::computePointPFHRGBSignature ( 00065 const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals, 00066 const std::vector<int> &indices, int nr_split, Eigen::VectorXf &pfhrgb_histogram) 00067 { 00068 int h_index, h_p; 00069 00070 // Clear the resultant point histogram 00071 pfhrgb_histogram.setZero (); 00072 00073 // Factorization constant 00074 float hist_incr = 100.0f / static_cast<float> (indices.size () * indices.size () - 1); 00075 00076 // Iterate over all the points in the neighborhood 00077 for (size_t i_idx = 0; i_idx < indices.size (); ++i_idx) 00078 { 00079 for (size_t j_idx = 0; j_idx < indices.size (); ++j_idx) 00080 { 00081 // Avoid unnecessary returns 00082 if (i_idx == j_idx) 00083 continue; 00084 00085 // Compute the pair NNi to NNj 00086 if (!computeRGBPairFeatures (cloud, normals, indices[i_idx], indices[j_idx], 00087 pfhrgb_tuple_[0], pfhrgb_tuple_[1], pfhrgb_tuple_[2], pfhrgb_tuple_[3], 00088 pfhrgb_tuple_[4], pfhrgb_tuple_[5], pfhrgb_tuple_[6])) 00089 continue; 00090 00091 // Normalize the f1, f2, f3, f5, f6, f7 features and push them in the histogram 00092 f_index_[0] = static_cast<int> (floor (nr_split * ((pfhrgb_tuple_[0] + M_PI) * d_pi_))); 00093 if (f_index_[0] < 0) f_index_[0] = 0; 00094 if (f_index_[0] >= nr_split) f_index_[0] = nr_split - 1; 00095 00096 f_index_[1] = static_cast<int> (floor (nr_split * ((pfhrgb_tuple_[1] + 1.0) * 0.5))); 00097 if (f_index_[1] < 0) f_index_[1] = 0; 00098 if (f_index_[1] >= nr_split) f_index_[1] = nr_split - 1; 00099 00100 f_index_[2] = static_cast<int> (floor (nr_split * ((pfhrgb_tuple_[2] + 1.0) * 0.5))); 00101 if (f_index_[2] < 0) f_index_[2] = 0; 00102 if (f_index_[2] >= nr_split) f_index_[2] = nr_split - 1; 00103 00104 // color ratios are in [-1, 1] 00105 f_index_[4] = static_cast<int> (floor (nr_split * ((pfhrgb_tuple_[4] + 1.0) * 0.5))); 00106 if (f_index_[4] < 0) f_index_[4] = 0; 00107 if (f_index_[4] >= nr_split) f_index_[4] = nr_split - 1; 00108 00109 f_index_[5] = static_cast<int> (floor (nr_split * ((pfhrgb_tuple_[5] + 1.0) * 0.5))); 00110 if (f_index_[5] < 0) f_index_[5] = 0; 00111 if (f_index_[5] >= nr_split) f_index_[5] = nr_split - 1; 00112 00113 f_index_[6] = static_cast<int> (floor (nr_split * ((pfhrgb_tuple_[6] + 1.0) * 0.5))); 00114 if (f_index_[6] < 0) f_index_[6] = 0; 00115 if (f_index_[6] >= nr_split) f_index_[6] = nr_split - 1; 00116 00117 00118 // Copy into the histogram 00119 h_index = 0; 00120 h_p = 1; 00121 for (int d = 0; d < 3; ++d) 00122 { 00123 h_index += h_p * f_index_[d]; 00124 h_p *= nr_split; 00125 } 00126 pfhrgb_histogram[h_index] += hist_incr; 00127 00128 // and the colors 00129 h_index = 125; 00130 h_p = 1; 00131 for (int d = 4; d < 7; ++d) 00132 { 00133 h_index += h_p * f_index_[d]; 00134 h_p *= nr_split; 00135 } 00136 pfhrgb_histogram[h_index] += hist_incr; 00137 } 00138 } 00139 } 00140 00141 ////////////////////////////////////////////////////////////////////////////////////////////// 00142 template <typename PointInT, typename PointNT, typename PointOutT> void 00143 pcl::PFHRGBEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output) 00144 { 00145 /// nr_subdiv^3 for RGB and nr_subdiv^3 for the angular features 00146 pfhrgb_histogram_.setZero (2 * nr_subdiv_ * nr_subdiv_ * nr_subdiv_); 00147 pfhrgb_tuple_.setZero (7); 00148 00149 // Allocate enough space to hold the results 00150 // \note This resize is irrelevant for a radiusSearch (). 00151 std::vector<int> nn_indices (k_); 00152 std::vector<float> nn_dists (k_); 00153 00154 // Iterating over the entire index vector 00155 for (size_t idx = 0; idx < indices_->size (); ++idx) 00156 { 00157 this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists); 00158 00159 // Estimate the PFH signature at each patch 00160 computePointPFHRGBSignature (*surface_, *normals_, nn_indices, nr_subdiv_, pfhrgb_histogram_); 00161 00162 // Copy into the resultant cloud 00163 for (int d = 0; d < pfhrgb_histogram_.size (); ++d) { 00164 output.points[idx].histogram[d] = pfhrgb_histogram_[d]; 00165 // PCL_INFO ("%f ", pfhrgb_histogram_[d]); 00166 } 00167 // PCL_INFO ("\n"); 00168 } 00169 } 00170 00171 #define PCL_INSTANTIATE_PFHRGBEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::PFHRGBEstimation<T,NT,OutT>; 00172 00173 #endif /* PCL_FEATURES_IMPL_PFHRGB_H_ */