175 lines
7.2 KiB
C++
175 lines
7.2 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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#ifndef PCL_FEATURES_IMPL_INTENSITY_SPIN_H_
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#define PCL_FEATURES_IMPL_INTENSITY_SPIN_H_
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#include <pcl/features/intensity_spin.h>
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointOutT> void
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pcl::IntensitySpinEstimation<PointInT, PointOutT>::computeIntensitySpinImage (
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const PointCloudIn &cloud, float radius, float sigma,
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int k,
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const pcl::Indices &indices,
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const std::vector<float> &squared_distances,
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Eigen::MatrixXf &intensity_spin_image)
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{
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// Determine the number of bins to use based on the size of intensity_spin_image
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int nr_distance_bins = static_cast<int> (intensity_spin_image.cols ());
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int nr_intensity_bins = static_cast<int> (intensity_spin_image.rows ());
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// Find the min and max intensity values in the given neighborhood
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float min_intensity = std::numeric_limits<float>::max ();
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float max_intensity = -std::numeric_limits<float>::max ();
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for (int idx = 0; idx < k; ++idx)
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{
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min_intensity = (std::min) (min_intensity, cloud[indices[idx]].intensity);
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max_intensity = (std::max) (max_intensity, cloud[indices[idx]].intensity);
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}
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float constant = 1.0f / (2.0f * sigma_ * sigma_);
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// Compute the intensity spin image
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intensity_spin_image.setZero ();
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for (int idx = 0; idx < k; ++idx)
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{
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// Normalize distance and intensity values to: 0.0 <= d,i < nr_distance_bins,nr_intensity_bins
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const float eps = std::numeric_limits<float>::epsilon ();
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float d = static_cast<float> (nr_distance_bins) * std::sqrt (squared_distances[idx]) / (radius + eps);
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float i = static_cast<float> (nr_intensity_bins) *
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(cloud[indices[idx]].intensity - min_intensity) / (max_intensity - min_intensity + eps);
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if (sigma == 0)
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{
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// If sigma is zero, update the histogram with no smoothing kernel
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int d_idx = static_cast<int> (d);
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int i_idx = static_cast<int> (i);
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intensity_spin_image (i_idx, d_idx) += 1;
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}
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else
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{
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// Compute the bin indices that need to be updated (+/- 3 standard deviations)
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int d_idx_min = (std::max)(static_cast<int> (std::floor (d - 3*sigma)), 0);
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int d_idx_max = (std::min)(static_cast<int> (std::ceil (d + 3*sigma)), nr_distance_bins - 1);
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int i_idx_min = (std::max)(static_cast<int> (std::floor (i - 3*sigma)), 0);
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int i_idx_max = (std::min)(static_cast<int> (std::ceil (i + 3*sigma)), nr_intensity_bins - 1);
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// Update the appropriate bins of the histogram
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for (int i_idx = i_idx_min; i_idx <= i_idx_max; ++i_idx)
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{
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for (int d_idx = d_idx_min; d_idx <= d_idx_max; ++d_idx)
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{
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// Compute a "soft" update weight based on the distance between the point and the bin
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float w = std::exp (-powf (d - static_cast<float> (d_idx), 2.0f) * constant - powf (i - static_cast<float> (i_idx), 2.0f) * constant);
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intensity_spin_image (i_idx, d_idx) += w;
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}
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}
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointOutT> void
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pcl::IntensitySpinEstimation<PointInT, PointOutT>::computeFeature (PointCloudOut &output)
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{
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// Make sure a search radius is set
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if (search_radius_ == 0.0)
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{
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PCL_ERROR ("[pcl::%s::computeFeature] The search radius must be set before computing the feature!\n",
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getClassName ().c_str ());
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output.width = output.height = 0;
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output.clear ();
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return;
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}
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// Make sure the spin image has valid dimensions
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if (nr_intensity_bins_ <= 0)
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{
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PCL_ERROR ("[pcl::%s::computeFeature] The number of intensity bins must be greater than zero!\n",
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getClassName ().c_str ());
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output.width = output.height = 0;
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output.clear ();
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return;
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}
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if (nr_distance_bins_ <= 0)
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{
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PCL_ERROR ("[pcl::%s::computeFeature] The number of distance bins must be greater than zero!\n",
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getClassName ().c_str ());
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output.width = output.height = 0;
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output.clear ();
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return;
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}
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Eigen::MatrixXf intensity_spin_image (nr_intensity_bins_, nr_distance_bins_);
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// Allocate enough space to hold the radiusSearch results
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pcl::Indices nn_indices (surface_->size ());
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std::vector<float> nn_dist_sqr (surface_->size ());
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output.is_dense = true;
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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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// Find neighbors within the search radius
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// TODO: do we want to use searchForNeigbors instead?
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int k = tree_->radiusSearch ((*indices_)[idx], search_radius_, nn_indices, nn_dist_sqr);
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if (k == 0)
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{
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for (int bin = 0; bin < nr_intensity_bins_ * nr_distance_bins_; ++bin)
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output[idx].histogram[bin] = 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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// Compute the intensity spin image
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computeIntensitySpinImage (*surface_, static_cast<float> (search_radius_), sigma_, k, nn_indices, nn_dist_sqr, intensity_spin_image);
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// Copy into the resultant cloud
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std::size_t bin = 0;
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for (Eigen::Index bin_j = 0; bin_j < intensity_spin_image.cols (); ++bin_j)
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for (Eigen::Index bin_i = 0; bin_i < intensity_spin_image.rows (); ++bin_i)
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output[idx].histogram[bin++] = intensity_spin_image (bin_i, bin_j);
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}
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}
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#define PCL_INSTANTIATE_IntensitySpinEstimation(T,NT) template class PCL_EXPORTS pcl::IntensitySpinEstimation<T,NT>;
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#endif // PCL_FEATURES_IMPL_INTENSITY_SPIN_H_
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