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#include <pcl/ModelCoefficients.h>
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/features/normal_3d.h>
#include <pcl/kdtree/kdtree.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/segmentation/extract_clusters.h>
// The GPU specific stuff here
#include <pcl/gpu/octree/octree.hpp>
#include <pcl/gpu/containers/device_array.hpp>
#include <pcl/gpu/segmentation/gpu_extract_clusters.h>
#include <pcl/gpu/segmentation/impl/gpu_extract_clusters.hpp>
#include <time.h>
int
main (int argc, char** argv)
{
// Read in the cloud data
pcl::PCDReader reader;
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PCDWriter writer;
reader.read (argv[1], *cloud_filtered);
pcl::Indices unused;
pcl::removeNaNFromPointCloud(*cloud_filtered, *cloud_filtered, unused);
/////////////////////////////////////////////
/// CPU VERSION
/////////////////////////////////////////////
std::cout << "INFO: PointCloud_filtered still has " << cloud_filtered->size() << " Points " << std::endl;
clock_t tStart = clock();
// Creating the KdTree object for the search method of the extraction
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);
tree->setInputCloud (cloud_filtered);
std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
ec.setClusterTolerance (0.02); // 2cm
ec.setMinClusterSize (100);
ec.setMaxClusterSize (25000);
ec.setSearchMethod (tree);
ec.setInputCloud( cloud_filtered);
ec.extract (cluster_indices);
printf("CPU Time taken: %.2fs\n", (double)(clock() - tStart)/CLOCKS_PER_SEC);
int j = 0;
for (const pcl::PointIndices& cluster: cluster_indices)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster (new pcl::PointCloud<pcl::PointXYZ>);
for (const auto& index : (cluster.indices))
cloud_cluster->push_back ((*cloud_filtered)[index]); //*
cloud_cluster->width = cloud_cluster->size ();
cloud_cluster->height = 1;
cloud_cluster->is_dense = true;
std::cout << "PointCloud representing the Cluster: " << cloud_cluster->size () << " data points." << std::endl;
std::stringstream ss;
ss << "cloud_cluster_" << j << ".pcd";
writer.write<pcl::PointXYZ> (ss.str (), *cloud_cluster, false); //*
j++;
}
/////////////////////////////////////////////
/// GPU VERSION
/////////////////////////////////////////////
std::cout << "INFO: starting with the GPU version" << std::endl;
tStart = clock();
pcl::gpu::Octree::PointCloud cloud_device;
cloud_device.upload(cloud_filtered->points);
pcl::gpu::Octree::Ptr octree_device (new pcl::gpu::Octree);
octree_device->setCloud(cloud_device);
octree_device->build();
std::vector<pcl::PointIndices> cluster_indices_gpu;
pcl::gpu::EuclideanClusterExtraction<pcl::PointXYZ> gec;
gec.setClusterTolerance (0.02); // 2cm
gec.setMinClusterSize (100);
gec.setMaxClusterSize (25000);
gec.setSearchMethod (octree_device);
gec.setHostCloud( cloud_filtered);
gec.extract (cluster_indices_gpu);
// octree_device.clear();
printf("GPU Time taken: %.2fs\n", (double)(clock() - tStart)/CLOCKS_PER_SEC);
std::cout << "INFO: stopped with the GPU version" << std::endl;
j = 0;
for (const pcl::PointIndices& cluster : cluster_indices_gpu)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster_gpu (new pcl::PointCloud<pcl::PointXYZ>);
for (const auto& index : (cluster.indices))
cloud_cluster_gpu->push_back ((*cloud_filtered)[index]); //*
cloud_cluster_gpu->width = cloud_cluster_gpu->size ();
cloud_cluster_gpu->height = 1;
cloud_cluster_gpu->is_dense = true;
std::cout << "PointCloud representing the Cluster: " << cloud_cluster_gpu->size () << " data points." << std::endl;
std::stringstream ss;
ss << "gpu_cloud_cluster_" << j << ".pcd";
writer.write<pcl::PointXYZ> (ss.str (), *cloud_cluster_gpu, false); //*
j++;
}
return (0);
}
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