202 lines
8.1 KiB
C++
202 lines
8.1 KiB
C++
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/*
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* Software License Agreement (BSD License)
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*
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* Copyright (c) 2011, Willow Garage, Inc.
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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_SEGMENTATION_IMPL_EXTRACT_LABELED_CLUSTERS_H_
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#define PCL_SEGMENTATION_IMPL_EXTRACT_LABELED_CLUSTERS_H_
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#include <pcl/segmentation/extract_labeled_clusters.h>
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointT>
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void
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pcl::extractLabeledEuclideanClusters(
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const PointCloud<PointT>& cloud,
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const typename search::Search<PointT>::Ptr& tree,
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float tolerance,
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std::vector<std::vector<PointIndices>>& labeled_clusters,
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unsigned int min_pts_per_cluster,
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unsigned int max_pts_per_cluster,
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unsigned int)
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{
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pcl::extractLabeledEuclideanClusters<PointT>(cloud,
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tree,
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tolerance,
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labeled_clusters,
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min_pts_per_cluster,
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max_pts_per_cluster);
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}
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template <typename PointT>
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void
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pcl::extractLabeledEuclideanClusters(
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const PointCloud<PointT>& cloud,
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const typename search::Search<PointT>::Ptr& tree,
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float tolerance,
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std::vector<std::vector<PointIndices>>& labeled_clusters,
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unsigned int min_pts_per_cluster,
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unsigned int max_pts_per_cluster)
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{
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if (tree->getInputCloud()->size() != cloud.size()) {
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PCL_ERROR("[pcl::extractLabeledEuclideanClusters] Tree built for a different point "
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"cloud dataset (%lu) than the input cloud (%lu)!\n",
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tree->getInputCloud()->size(),
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cloud.size());
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return;
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}
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// Create a bool vector of processed point indices, and initialize it to false
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std::vector<bool> processed(cloud.size(), false);
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Indices nn_indices;
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std::vector<float> nn_distances;
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// Process all points in the indices vector
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for (index_t i = 0; i < static_cast<index_t>(cloud.size()); ++i) {
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if (processed[i])
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continue;
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Indices seed_queue;
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int sq_idx = 0;
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seed_queue.push_back(i);
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processed[i] = true;
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while (sq_idx < static_cast<int>(seed_queue.size())) {
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// Search for sq_idx
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int ret = tree->radiusSearch(seed_queue[sq_idx],
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tolerance,
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nn_indices,
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nn_distances,
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std::numeric_limits<int>::max());
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if (ret == -1)
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PCL_ERROR("radiusSearch on tree came back with error -1");
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if (!ret) {
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sq_idx++;
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continue;
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}
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for (std::size_t j = 1; j < nn_indices.size();
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++j) // nn_indices[0] should be sq_idx
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{
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if (processed[nn_indices[j]]) // Has this point been processed before ?
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continue;
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if (cloud[i].label == cloud[nn_indices[j]].label) {
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// Perform a simple Euclidean clustering
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seed_queue.push_back(nn_indices[j]);
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processed[nn_indices[j]] = true;
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}
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}
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sq_idx++;
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}
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// If this queue is satisfactory, add to the clusters
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if (seed_queue.size() >= min_pts_per_cluster &&
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seed_queue.size() <= max_pts_per_cluster) {
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pcl::PointIndices r;
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r.indices.resize(seed_queue.size());
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for (std::size_t j = 0; j < seed_queue.size(); ++j)
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r.indices[j] = seed_queue[j];
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std::sort(r.indices.begin(), r.indices.end());
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r.indices.erase(std::unique(r.indices.begin(), r.indices.end()), r.indices.end());
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r.header = cloud.header;
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labeled_clusters[cloud[i].label].push_back(
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r); // We could avoid a copy by working directly in the vector
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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 PointT>
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void
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pcl::LabeledEuclideanClusterExtraction<PointT>::extract(
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std::vector<std::vector<PointIndices>>& labeled_clusters)
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{
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if (!initCompute() || (input_ && input_->empty()) ||
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(indices_ && indices_->empty())) {
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labeled_clusters.clear();
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return;
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}
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// Initialize the spatial locator
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if (!tree_) {
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if (input_->isOrganized())
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tree_.reset(new pcl::search::OrganizedNeighbor<PointT>());
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else
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tree_.reset(new pcl::search::KdTree<PointT>(false));
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}
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// Send the input dataset to the spatial locator
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tree_->setInputCloud(input_);
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extractLabeledEuclideanClusters(*input_,
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tree_,
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static_cast<float>(cluster_tolerance_),
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labeled_clusters,
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min_pts_per_cluster_,
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max_pts_per_cluster_);
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// Sort the clusters based on their size (largest one first)
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for (auto& labeled_cluster : labeled_clusters)
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std::sort(labeled_cluster.rbegin(), labeled_cluster.rend(), comparePointClusters);
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deinitCompute();
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}
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#define PCL_INSTANTIATE_LabeledEuclideanClusterExtraction(T) \
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template class PCL_EXPORTS pcl::LabeledEuclideanClusterExtraction<T>;
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#define PCL_INSTANTIATE_extractLabeledEuclideanClusters_deprecated(T) \
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template void PCL_EXPORTS pcl::extractLabeledEuclideanClusters<T>( \
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const pcl::PointCloud<T>&, \
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const typename pcl::search::Search<T>::Ptr&, \
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float, \
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std::vector<std::vector<pcl::PointIndices>>&, \
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unsigned int, \
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unsigned int, \
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unsigned int);
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#define PCL_INSTANTIATE_extractLabeledEuclideanClusters(T) \
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template void PCL_EXPORTS pcl::extractLabeledEuclideanClusters<T>( \
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const pcl::PointCloud<T>&, \
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const typename pcl::search::Search<T>::Ptr&, \
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float, \
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std::vector<std::vector<pcl::PointIndices>>&, \
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unsigned int, \
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unsigned int);
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#endif // PCL_EXTRACT_CLUSTERS_IMPL_H_
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