162 lines
6.4 KiB
C++
162 lines
6.4 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) 2011, Alexandru-Eugen Ichim
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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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#ifndef PCL_FEATURES_IMPL_PFHRGB_H_
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#define PCL_FEATURES_IMPL_PFHRGB_H_
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#include <pcl/features/pfhrgb.h>
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT> bool
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pcl::PFHRGBEstimation<PointInT, PointNT, PointOutT>::computeRGBPairFeatures (
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const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
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int p_idx, int q_idx,
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float &f1, float &f2, float &f3, float &f4, float &f5, float &f6, float &f7)
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{
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Eigen::Vector4i colors1 (cloud[p_idx].r, cloud[p_idx].g, cloud[p_idx].b, 0),
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colors2 (cloud[q_idx].r, cloud[q_idx].g, cloud[q_idx].b, 0);
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pcl::computeRGBPairFeatures (cloud[p_idx].getVector4fMap (), normals[p_idx].getNormalVector4fMap (),
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colors1,
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cloud[q_idx].getVector4fMap (), normals[q_idx].getNormalVector4fMap (),
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colors2,
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f1, f2, f3, f4, f5, f6, f7);
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return (true);
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT> void
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pcl::PFHRGBEstimation<PointInT, PointNT, PointOutT>::computePointPFHRGBSignature (
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const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
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const pcl::Indices &indices, int nr_split, Eigen::VectorXf &pfhrgb_histogram)
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{
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int h_index, h_p;
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// Clear the resultant point histogram
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pfhrgb_histogram.setZero ();
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// Factorization constant
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float hist_incr = 100.0f / static_cast<float> (indices.size () * (indices.size () - 1) / 2);
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// Iterate over all the points in the neighborhood
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for (const auto& index_i: indices)
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{
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for (const auto& index_j: indices)
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{
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// Avoid unnecessary returns
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if (index_i == index_j)
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continue;
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// Compute the pair NNi to NNj
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if (!computeRGBPairFeatures (cloud, normals, index_i, index_j,
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pfhrgb_tuple_[0], pfhrgb_tuple_[1], pfhrgb_tuple_[2], pfhrgb_tuple_[3],
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pfhrgb_tuple_[4], pfhrgb_tuple_[5], pfhrgb_tuple_[6]))
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continue;
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// Normalize the f1, f2, f3, f5, f6, f7 features and push them in the histogram
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f_index_[0] = static_cast<int> (std::floor (nr_split * ((pfhrgb_tuple_[0] + M_PI) * d_pi_)));
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// @TODO: confirm "not to do for i == 3"
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for (int i = 1; i < 3; ++i)
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{
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const float feature_value = nr_split * ((pfhrgb_tuple_[i] + 1.0) * 0.5);
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f_index_[i] = static_cast<int> (std::floor (feature_value));
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}
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// color ratios are in [-1, 1]
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for (int i = 4; i < 7; ++i)
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{
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const float feature_value = nr_split * ((pfhrgb_tuple_[i] + 1.0) * 0.5);
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f_index_[i] = static_cast<int> (std::floor (feature_value));
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}
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for (auto& feature: f_index_)
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{
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feature = std::min(nr_split - 1, std::max(0, feature));
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}
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// Copy into the histogram
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h_index = 0;
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h_p = 1;
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for (int d = 0; d < 3; ++d)
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{
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h_index += h_p * f_index_[d];
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h_p *= nr_split;
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}
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pfhrgb_histogram[h_index] += hist_incr;
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// and the colors
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h_index = 125;
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h_p = 1;
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for (int d = 4; d < 7; ++d)
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{
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h_index += h_p * f_index_[d];
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h_p *= nr_split;
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}
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pfhrgb_histogram[h_index] += hist_incr;
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT> void
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pcl::PFHRGBEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
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{
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/// nr_subdiv^3 for RGB and nr_subdiv^3 for the angular features
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pfhrgb_histogram_.setZero (2 * nr_subdiv_ * nr_subdiv_ * nr_subdiv_);
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pfhrgb_tuple_.setZero (7);
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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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// Iterating over the entire index vector
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for (std::size_t idx = 0; idx < indices_->size (); ++idx)
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{
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this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists);
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// Estimate the PFH signature at each patch
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computePointPFHRGBSignature (*surface_, *normals_, nn_indices, nr_subdiv_, pfhrgb_histogram_);
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std::copy_n (pfhrgb_histogram_.data (), pfhrgb_histogram_.size (),
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output[idx].histogram);
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}
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}
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#define PCL_INSTANTIATE_PFHRGBEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::PFHRGBEstimation<T,NT,OutT>;
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#endif /* PCL_FEATURES_IMPL_PFHRGB_H_ */
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