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gpu_extract_labeled_clusters.hpp
/*
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* Point Cloud Library (PCL) - www.pointclouds.org
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* $Id:$
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#pragma once
#include <pcl/gpu/segmentation/gpu_extract_labeled_clusters.h>
template <typename PointT>
void
pcl::gpu::extractLabeledEuclideanClusters(
const typename pcl::PointCloud<PointT>::Ptr& host_cloud_,
const pcl::gpu::Octree::Ptr& tree,
float tolerance,
std::vector<PointIndices>& clusters,
unsigned int min_pts_per_cluster,
unsigned int max_pts_per_cluster)
{
// Create a bool vector of processed point indices, and initialize it to false
// cloud is a DeviceArray<PointType>
std::vector<bool> processed(host_cloud_->size(), false);
int max_answers;
if (max_pts_per_cluster > host_cloud_->size())
max_answers = static_cast<int>(host_cloud_->size());
else
max_answers = max_pts_per_cluster;
// to store the current cluster
pcl::PointIndices r;
// Process all points in the cloud
for (std::size_t i = 0; i < host_cloud_->size(); ++i) {
// if we already processed this point continue with the next one
if (processed[i])
continue;
// now we will process this point
processed[i] = true;
// Create the query queue on the device, point based not indices
pcl::gpu::Octree::Queries queries_device;
// Create the query queue on the host
pcl::PointCloud<pcl::PointXYZ>::VectorType queries_host;
// Buffer in a new PointXYZ type
PointT t = (*host_cloud_)[i];
PointXYZ p;
p.x = t.x;
p.y = t.y;
p.z = t.z;
// Push the starting point in the vector
queries_host.push_back(p);
// Clear vector
r.indices.clear();
// Push the starting point in
r.indices.push_back(static_cast<int>(i));
unsigned int found_points = static_cast<unsigned int>(queries_host.size());
unsigned int previous_found_points = 0;
pcl::gpu::NeighborIndices result_device;
// once the area stop growing, stop also iterating.
while (previous_found_points < found_points) {
// Move queries to GPU
queries_device.upload(queries_host);
// Execute search
tree->radiusSearch(queries_device, tolerance, max_answers, result_device);
// Store the previously found number of points
previous_found_points = found_points;
// Host buffer for results
std::vector<int> sizes, data;
// Copy results from GPU to Host
result_device.sizes.download(sizes);
result_device.data.download(data);
for (std::size_t qp = 0; qp < sizes.size(); qp++) {
for (int qp_r = 0; qp_r < sizes[qp]; qp_r++) {
if (processed[data[qp_r + qp * max_answers]])
continue;
// Only add if label matches the original label
if ((*host_cloud_)[i].label ==
(*host_cloud_)[data[qp_r + qp * max_answers]].label) {
processed[data[qp_r + qp * max_answers]] = true;
PointT t_l = (*host_cloud_)[data[qp_r + qp * max_answers]];
PointXYZ p_l;
p_l.x = t_l.x;
p_l.y = t_l.y;
p_l.z = t_l.z;
queries_host.push_back(p_l);
found_points++;
r.indices.push_back(data[qp_r + qp * max_answers]);
}
}
}
}
// If this queue is satisfactory, add to the clusters
if (found_points >= min_pts_per_cluster && found_points <= max_pts_per_cluster) {
std::sort(r.indices.begin(), r.indices.end());
// @todo: check if the following is actually still needed
// r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()),
// r.indices.end ());
r.header = host_cloud_->header;
clusters.push_back(r); // We could avoid a copy by working directly in the vector
}
}
}
template <typename PointT>
void
pcl::gpu::EuclideanLabeledClusterExtraction<PointT>::extract(
std::vector<PointIndices>& clusters)
{
// Initialize the GPU search tree
if (!tree_) {
tree_.reset(new pcl::gpu::Octree());
///@todo what do we do if input isn't a PointXYZ cloud?
tree_->setCloud(input_);
}
if (!tree_->isBuilt()) {
tree_->build();
}
/*
if(tree_->cloud_.size() != host_cloud.size ())
{
PCL_ERROR("[pcl::gpu::EuclideanClusterExtraction] size of host cloud and device
cloud don't match!\n"); return;
}
*/
// Extract the actual clusters
extractLabeledEuclideanClusters<PointT>(host_cloud_,
tree_,
cluster_tolerance_,
clusters,
min_pts_per_cluster_,
max_pts_per_cluster_);
// Sort the clusters based on their size (largest one first)
std::sort(clusters.rbegin(), clusters.rend(), compareLabeledPointClusters);
}
#define PCL_INSTANTIATE_extractLabeledEuclideanClusters(T) \
template void PCL_EXPORTS pcl::gpu::extractLabeledEuclideanClusters<T>( \
const typename pcl::PointCloud<T>::Ptr&, \
const pcl::gpu::Octree::Ptr&, \
float, \
std::vector<PointIndices>&, \
unsigned int, \
unsigned int);
#define PCL_INSTANTIATE_EuclideanLabeledClusterExtraction(T) \
template class PCL_EXPORTS pcl::gpu::EuclideanLabeledClusterExtraction<T>;
pcl::PointCloud::push_back
void push_back(const PointT &pt)
Insert a new point in the cloud, at the end of the container.
Definition point_cloud.h:663
pcl::PointCloud::header
pcl::PCLHeader header
The point cloud header.
Definition point_cloud.h:392
pcl::PointCloud::size
std::size_t size() const
Definition point_cloud.h:443
pcl::PointCloud::VectorType
std::vector< PointT, Eigen::aligned_allocator< PointT > > VectorType
Definition point_cloud.h:411
pcl::PointCloud::Ptr
shared_ptr< PointCloud< PointT > > Ptr
Definition point_cloud.h:413
pcl::gpu::DeviceArray< PointType >
pcl::gpu::DeviceArray::upload
void upload(const T *host_ptr, std::size_t size)
Uploads data to internal buffer in GPU memory.
Definition device_array.hpp:94
pcl::gpu::DeviceArray::download
void download(T *host_ptr) const
Downloads data from internal buffer to CPU memory.
Definition device_array.hpp:112
pcl::gpu::EuclideanLabeledClusterExtraction::extract
void extract(std::vector< PointIndices > &clusters)
extract clusters of a PointCloud given by <setInputCloud(), setIndices()>
Definition gpu_extract_labeled_clusters.hpp:152
pcl::gpu::Octree
Octree implementation on GPU.
Definition octree.hpp:59
pcl::gpu::Octree::Ptr
shared_ptr< Octree > Ptr
Types.
Definition octree.hpp:69
pcl::gpu::compareLabeledPointClusters
bool compareLabeledPointClusters(const pcl::PointIndices &a, const pcl::PointIndices &b)
Sort clusters method (for std::sort).
Definition gpu_extract_labeled_clusters.h:209
pcl::gpu::extractLabeledEuclideanClusters
void extractLabeledEuclideanClusters(const typename pcl::PointCloud< PointT >::Ptr &host_cloud_, const pcl::gpu::Octree::Ptr &tree, float tolerance, std::vector< PointIndices > &clusters, unsigned int min_pts_per_cluster, unsigned int max_pts_per_cluster)
Definition gpu_extract_labeled_clusters.hpp:45
pcl::PointIndices
Definition PointIndices.h:12
pcl::PointIndices::header
::pcl::PCLHeader header
Definition PointIndices.h:18
pcl::PointIndices::indices
Indices indices
Definition PointIndices.h:20
pcl::PointXYZ
A point structure representing Euclidean xyz coordinates.
Definition point_types.hpp:322
pcl::PointXYZRGB
A point structure representing Euclidean xyz coordinates, and the RGB color.
Definition point_types.hpp:594
pcl::gpu::NeighborIndices
Definition device_format.hpp:47
pcl::gpu::NeighborIndices::sizes
DeviceArray< int > sizes
Definition device_format.hpp:49
pcl::gpu::NeighborIndices::data
DeviceArray< int > data
Definition device_format.hpp:48