144 lines
5.0 KiB
C++
144 lines
5.0 KiB
C++
/*
|
|
* Software License Agreement (BSD License)
|
|
*
|
|
* Point Cloud Library (PCL) - www.pointclouds.org
|
|
* Copyright (c) 2010-2011, Willow Garage, Inc.
|
|
*
|
|
* All rights reserved.
|
|
*
|
|
* Redistribution and use in source and binary forms, with or without
|
|
* modification, are permitted provided that the following conditions
|
|
* are met:
|
|
*
|
|
* * Redistributions of source code must retain the above copyright
|
|
* notice, this list of conditions and the following disclaimer.
|
|
* * Redistributions in binary form must reproduce the above
|
|
* copyright notice, this list of conditions and the following
|
|
* disclaimer in the documentation and/or other materials provided
|
|
* with the distribution.
|
|
* * Neither the name of Willow Garage, Inc. nor the names of its
|
|
* contributors may be used to endorse or promote products derived
|
|
* from this software without specific prior written permission.
|
|
*
|
|
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
|
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
|
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
|
|
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
|
|
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
|
|
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
|
|
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
|
|
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
|
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
|
|
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
|
|
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
|
* POSSIBILITY OF SUCH DAMAGE.
|
|
*/
|
|
|
|
#pragma once
|
|
#include <pcl/recognition/hv/greedy_verification.h>
|
|
|
|
template<typename ModelT, typename SceneT>
|
|
void
|
|
pcl::GreedyVerification<ModelT, SceneT>::initialize ()
|
|
{
|
|
//clear stuff
|
|
recognition_models_.clear ();
|
|
points_explained_by_rm_.clear ();
|
|
|
|
// initialize mask...
|
|
mask_.resize (visible_models_.size ());
|
|
for (std::size_t i = 0; i < visible_models_.size (); i++)
|
|
mask_[i] = false;
|
|
|
|
// initialize explained_by_RM
|
|
points_explained_by_rm_.resize (scene_cloud_downsampled_->size ());
|
|
|
|
// initialize model
|
|
for (std::size_t m = 0; m < visible_models_.size (); m++)
|
|
{
|
|
RecognitionModelPtr recog_model (new RecognitionModel);
|
|
// voxelize model cloud
|
|
recog_model->cloud_.reset (new pcl::PointCloud<ModelT>);
|
|
recog_model->id_ = static_cast<int> (m);
|
|
|
|
pcl::VoxelGrid<ModelT> voxel_grid;
|
|
voxel_grid.setInputCloud (visible_models_[m]);
|
|
voxel_grid.setLeafSize (resolution_, resolution_, resolution_);
|
|
voxel_grid.filter (*(recog_model->cloud_));
|
|
|
|
std::vector<int> explained_indices;
|
|
std::vector<int> outliers;
|
|
pcl::Indices nn_indices;
|
|
std::vector<float> nn_distances;
|
|
|
|
for (std::size_t i = 0; i < recog_model->cloud_->size (); i++)
|
|
{
|
|
if (!scene_downsampled_tree_->radiusSearch ((*recog_model->cloud_)[i], inliers_threshold_, nn_indices, nn_distances,
|
|
std::numeric_limits<int>::max ()))
|
|
{
|
|
outliers.push_back (static_cast<int> (i));
|
|
}
|
|
else
|
|
{
|
|
for (std::size_t k = 0; k < nn_distances.size (); k++)
|
|
{
|
|
explained_indices.push_back (nn_indices[k]); //nn_indices[k] points to the scene
|
|
}
|
|
}
|
|
}
|
|
|
|
std::sort (explained_indices.begin (), explained_indices.end ());
|
|
explained_indices.erase (std::unique (explained_indices.begin (), explained_indices.end ()), explained_indices.end ());
|
|
|
|
recog_model->bad_information_ = static_cast<int> (outliers.size ());
|
|
recog_model->explained_ = explained_indices;
|
|
recog_model->good_information_ = static_cast<int> (explained_indices.size ());
|
|
recog_model->regularizer_ = regularizer_;
|
|
|
|
recognition_models_.push_back (recog_model);
|
|
|
|
for (const int &explained_index : explained_indices)
|
|
{
|
|
points_explained_by_rm_[explained_index].push_back (recog_model);
|
|
}
|
|
}
|
|
|
|
sortModels ();
|
|
}
|
|
|
|
template<typename ModelT, typename SceneT>
|
|
void
|
|
pcl::GreedyVerification<ModelT, SceneT>::verify ()
|
|
{
|
|
initialize ();
|
|
|
|
std::vector<bool> best_solution_;
|
|
best_solution_.resize (recognition_models_.size ());
|
|
|
|
for (std::size_t i = 0; i < recognition_models_.size (); i++)
|
|
{
|
|
if (static_cast<float> (recognition_models_[i]->good_information_) > (regularizer_
|
|
* static_cast<float> (recognition_models_[i]->bad_information_)))
|
|
{
|
|
best_solution_[i] = true;
|
|
updateGoodInformation (static_cast<int> (i));
|
|
}
|
|
else
|
|
best_solution_[i] = false;
|
|
}
|
|
|
|
for (std::size_t i = 0; i < best_solution_.size (); i++)
|
|
{
|
|
if (best_solution_[i])
|
|
{
|
|
mask_[indices_models_[i].index_] = true;
|
|
}
|
|
else
|
|
{
|
|
mask_[indices_models_[i].index_] = false;
|
|
}
|
|
}
|
|
}
|
|
|
|
#define PCL_INSTANTIATE_GreedyVerification(T1,T2) template class PCL_EXPORTS pcl::GreedyVerification<T1,T2>;
|