327 lines
12 KiB
C++
327 lines
12 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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*
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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 Willow Garage, Inc. 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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#ifndef PCL_SIFT_KEYPOINT_IMPL_H_
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#define PCL_SIFT_KEYPOINT_IMPL_H_
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#include <pcl/keypoints/sift_keypoint.h>
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#include <pcl/common/io.h>
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#include <pcl/filters/voxel_grid.h>
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//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointOutT> void
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pcl::SIFTKeypoint<PointInT, PointOutT>::setScales (float min_scale, int nr_octaves, int nr_scales_per_octave)
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{
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min_scale_ = min_scale;
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nr_octaves_ = nr_octaves;
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nr_scales_per_octave_ = nr_scales_per_octave;
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}
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//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointOutT> void
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pcl::SIFTKeypoint<PointInT, PointOutT>::setMinimumContrast (float min_contrast)
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{
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min_contrast_ = min_contrast;
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}
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//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointOutT> bool
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pcl::SIFTKeypoint<PointInT, PointOutT>::initCompute ()
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{
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if (min_scale_ <= 0)
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{
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PCL_ERROR ("[pcl::%s::initCompute] : Minimum scale (%f) must be strict positive!\n",
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name_.c_str (), min_scale_);
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return (false);
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}
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if (nr_octaves_ < 1)
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{
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PCL_ERROR ("[pcl::%s::initCompute] : Number of octaves (%d) must be at least 1!\n",
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name_.c_str (), nr_octaves_);
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return (false);
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}
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if (nr_scales_per_octave_ < 1)
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{
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PCL_ERROR ("[pcl::%s::initCompute] : Number of scales per octave (%d) must be at least 1!\n",
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name_.c_str (), nr_scales_per_octave_);
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return (false);
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}
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if (min_contrast_ < 0)
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{
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PCL_ERROR ("[pcl::%s::initCompute] : Minimum contrast (%f) must be non-negative!\n",
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name_.c_str (), min_contrast_);
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return (false);
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}
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this->setKSearch (1);
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tree_.reset (new pcl::search::KdTree<PointInT> (true));
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return (true);
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}
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//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointOutT> void
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pcl::SIFTKeypoint<PointInT, PointOutT>::detectKeypoints (PointCloudOut &output)
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{
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if (surface_ && surface_ != input_)
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{
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PCL_WARN ("[pcl::%s::detectKeypoints] : ", name_.c_str ());
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PCL_WARN ("A search surface has been set by setSearchSurface, but this SIFT keypoint detection algorithm does ");
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PCL_WARN ("not support search surfaces other than the input cloud. ");
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PCL_WARN ("The cloud provided in setInputCloud is being used instead.\n");
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}
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// Check if the output has a "scale" field
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scale_idx_ = pcl::getFieldIndex<PointOutT> ("scale", out_fields_);
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// Make sure the output cloud is empty
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output.clear ();
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// Create a local copy of the input cloud that will be resized for each octave
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typename pcl::PointCloud<PointInT>::Ptr cloud (new pcl::PointCloud<PointInT> (*input_));
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VoxelGrid<PointInT> voxel_grid;
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// Search for keypoints at each octave
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float scale = min_scale_;
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for (int i_octave = 0; i_octave < nr_octaves_; ++i_octave)
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{
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// Downsample the point cloud
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const float s = 1.0f * scale; // note: this can be adjusted
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voxel_grid.setLeafSize (s, s, s);
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voxel_grid.setInputCloud (cloud);
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typename pcl::PointCloud<PointInT>::Ptr temp (new pcl::PointCloud<PointInT>);
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voxel_grid.filter (*temp);
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cloud = temp;
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// Make sure the downsampled cloud still has enough points
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const std::size_t min_nr_points = 25;
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if (cloud->size () < min_nr_points)
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break;
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// Update the KdTree with the downsampled points
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tree_->setInputCloud (cloud);
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// Detect keypoints for the current scale
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detectKeypointsForOctave (*cloud, *tree_, scale, nr_scales_per_octave_, output);
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// Increase the scale by another octave
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scale *= 2;
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}
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// Set final properties
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output.height = 1;
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output.width = output.size ();
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output.header = input_->header;
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output.sensor_origin_ = input_->sensor_origin_;
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output.sensor_orientation_ = input_->sensor_orientation_;
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}
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//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointOutT> void
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pcl::SIFTKeypoint<PointInT, PointOutT>::detectKeypointsForOctave (
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const PointCloudIn &input, KdTree &tree, float base_scale, int nr_scales_per_octave,
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PointCloudOut &output)
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{
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// Compute the difference of Gaussians (DoG) scale space
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std::vector<float> scales (nr_scales_per_octave + 3);
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for (int i_scale = 0; i_scale <= nr_scales_per_octave + 2; ++i_scale)
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{
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scales[i_scale] = base_scale * powf (2.0f, (1.0f * static_cast<float> (i_scale) - 1.0f) / static_cast<float> (nr_scales_per_octave));
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}
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Eigen::MatrixXf diff_of_gauss;
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computeScaleSpace (input, tree, scales, diff_of_gauss);
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// Find extrema in the DoG scale space
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pcl::Indices extrema_indices;
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std::vector<int> extrema_scales;
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findScaleSpaceExtrema (input, tree, diff_of_gauss, extrema_indices, extrema_scales);
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output.reserve (output.size () + extrema_indices.size ());
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// Save scale?
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if (scale_idx_ != -1)
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{
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// Add keypoints to output
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for (std::size_t i_keypoint = 0; i_keypoint < extrema_indices.size (); ++i_keypoint)
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{
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PointOutT keypoint;
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const auto &keypoint_index = extrema_indices[i_keypoint];
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keypoint.x = input[keypoint_index].x;
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keypoint.y = input[keypoint_index].y;
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keypoint.z = input[keypoint_index].z;
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memcpy (reinterpret_cast<char*> (&keypoint) + out_fields_[scale_idx_].offset,
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&scales[extrema_scales[i_keypoint]], sizeof (float));
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output.push_back (keypoint);
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}
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}
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else
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{
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// Add keypoints to output
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for (const auto &keypoint_index : extrema_indices)
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{
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PointOutT keypoint;
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keypoint.x = input[keypoint_index].x;
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keypoint.y = input[keypoint_index].y;
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keypoint.z = input[keypoint_index].z;
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output.push_back (keypoint);
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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>
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void pcl::SIFTKeypoint<PointInT, PointOutT>::computeScaleSpace (
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const PointCloudIn &input, KdTree &tree, const std::vector<float> &scales,
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Eigen::MatrixXf &diff_of_gauss)
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{
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diff_of_gauss.resize (input.size (), scales.size () - 1);
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// For efficiency, we will only filter over points within 3 standard deviations
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const float max_radius = 3.0f * scales.back ();
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for (int i_point = 0; i_point < static_cast<int> (input.size ()); ++i_point)
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{
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pcl::Indices nn_indices;
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std::vector<float> nn_dist;
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tree.radiusSearch (i_point, max_radius, nn_indices, nn_dist); // *
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// * note: at this stage of the algorithm, we must find all points within a radius defined by the maximum scale,
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// regardless of the configurable search method specified by the user, so we directly employ tree.radiusSearch
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// here instead of using searchForNeighbors.
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// For each scale, compute the Gaussian "filter response" at the current point
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float filter_response = 0.0f;
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for (std::size_t i_scale = 0; i_scale < scales.size (); ++i_scale)
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{
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float sigma_sqr = powf (scales[i_scale], 2.0f);
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float numerator = 0.0f;
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float denominator = 0.0f;
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for (std::size_t i_neighbor = 0; i_neighbor < nn_indices.size (); ++i_neighbor)
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{
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const float &value = getFieldValue_ (input[nn_indices[i_neighbor]]);
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const float &dist_sqr = nn_dist[i_neighbor];
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if (dist_sqr <= 9*sigma_sqr)
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{
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float w = std::exp (-0.5f * dist_sqr / sigma_sqr);
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numerator += value * w;
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denominator += w;
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}
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else break; // i.e. if dist > 3 standard deviations, then terminate early
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}
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float previous_filter_response = filter_response;
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filter_response = numerator / denominator;
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// Compute the difference between adjacent scales
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if (i_scale > 0)
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diff_of_gauss (i_point, i_scale - 1) = filter_response - previous_filter_response;
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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::SIFTKeypoint<PointInT, PointOutT>::findScaleSpaceExtrema (
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const PointCloudIn &input, KdTree &tree, const Eigen::MatrixXf &diff_of_gauss,
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pcl::Indices &extrema_indices, std::vector<int> &extrema_scales)
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{
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const int k = 25;
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pcl::Indices nn_indices (k);
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std::vector<float> nn_dist (k);
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const int nr_scales = static_cast<int> (diff_of_gauss.cols ());
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std::vector<float> min_val (nr_scales), max_val (nr_scales);
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for (int i_point = 0; i_point < static_cast<int> (input.size ()); ++i_point)
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{
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// Define the local neighborhood around the current point
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const std::size_t nr_nn = tree.nearestKSearch (i_point, k, nn_indices, nn_dist); //*
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// * note: the neighborhood for finding local extrema is best defined as a small fixed-k neighborhood, regardless of
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// the configurable search method specified by the user, so we directly employ tree.nearestKSearch here instead
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// of using searchForNeighbors
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// At each scale, find the extreme values of the DoG within the current neighborhood
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for (int i_scale = 0; i_scale < nr_scales; ++i_scale)
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{
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min_val[i_scale] = std::numeric_limits<float>::max ();
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max_val[i_scale] = -std::numeric_limits<float>::max ();
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for (std::size_t i_neighbor = 0; i_neighbor < nr_nn; ++i_neighbor)
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{
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const float &d = diff_of_gauss (nn_indices[i_neighbor], i_scale);
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min_val[i_scale] = (std::min) (min_val[i_scale], d);
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max_val[i_scale] = (std::max) (max_val[i_scale], d);
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}
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}
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// If the current point is an extreme value with high enough contrast, add it as a keypoint
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for (int i_scale = 1; i_scale < nr_scales - 1; ++i_scale)
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{
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const float &val = diff_of_gauss (i_point, i_scale);
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// Does the point have sufficient contrast?
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if (std::abs (val) >= min_contrast_)
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{
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// Is it a local minimum?
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if ((val == min_val[i_scale]) &&
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(val < min_val[i_scale - 1]) &&
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(val < min_val[i_scale + 1]))
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{
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extrema_indices.push_back (i_point);
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extrema_scales.push_back (i_scale);
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}
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// Is it a local maximum?
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else if ((val == max_val[i_scale]) &&
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(val > max_val[i_scale - 1]) &&
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(val > max_val[i_scale + 1]))
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{
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extrema_indices.push_back (i_point);
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extrema_scales.push_back (i_scale);
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}
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}
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}
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}
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}
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#define PCL_INSTANTIATE_SIFTKeypoint(T,U) template class PCL_EXPORTS pcl::SIFTKeypoint<T,U>;
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#endif // #ifndef PCL_SIFT_KEYPOINT_IMPL_H_
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