本文整理汇总了C++中pointcloudt::Ptr::reset方法的典型用法代码示例。如果您正苦于以下问题：C++ Ptr::reset方法的具体用法？C++ Ptr::reset怎么用？C++ Ptr::reset使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pointcloudt::Ptr的用法示例。
在下文中一共展示了Ptr::reset方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: main
int main(int argc, char** argv)
{
if (argc != 3)
{
printUsage(argv);
return -1;
}
std::string filename_in = argv[1];
std::string filename_out = argv[2];
// read in
printf("Reading cloud\n");
PointCloudT::Ptr cloud;
cloud.reset(new rgbdtools::PointCloudT());
pcl::PCDReader reader;
reader.read(filename_in, *cloud);
alignGlobalCloud(cloud);
return 0;
}示例2: main
//.........这里部分代码省略.........
// Get the points associated with the planar surface
extract.filter (*cloud_plane);
// Remove the planar inliers, extract the rest
extract.setNegative (true);
extract.filter (*cloud_f);
*cloud_filtered = *cloud_f;
}
////////////////////
// euclidean clustering
////////////////////
// Creating the KdTree object for the search method of the extraction
eucl_tree->setInputCloud (cloud_filtered);
ec.setInputCloud (cloud_filtered);
ec.extract (cluster_indices);
for (std::vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin (); it != cluster_indices.end (); ++it)
{
//PointCloudT::Ptr cloud_cluster (new PointCloudT);
cloud_cluster = boost::shared_ptr<PointCloudT>(new PointCloudT);
for (std::vector<int>::const_iterator pit = it->indices.begin (); pit != it->indices.end (); pit++)
cloud_cluster->points.push_back (cloud_filtered->points [*pit]);
cloud_cluster->width = cloud_cluster->points.size ();
cloud_cluster->height = 1;
cloud_cluster->is_dense = true;
extracted_clusters.push_back(cloud_cluster);
}
cloud_cluster.reset();
////////////////////
// vfh estimate
////////////////////
for (int z=0; z<extracted_clusters.size(); ++z)
{
vfhs = boost::shared_ptr<pcl::PointCloud<pcl::VFHSignature308> > (new pcl::PointCloud<pcl::VFHSignature308>);
normals = boost::shared_ptr<pcl::PointCloud<pcl::Normal> > (new pcl::PointCloud<pcl::Normal>);
ne.setInputCloud (extracted_clusters.at(z));
ne.compute (*normals);
vfh.setInputCloud (extracted_clusters.at(z));
vfh.setInputNormals (normals);
vfh.compute (*vfhs);
computed_vfhs.push_back(vfhs);
}
vfhs.reset();
normals.reset();
////////////////////
// nearest neighbour
////////////////////
std::vector<vfh_model> models;
flann::Matrix<int> k_indices;
flann::Matrix<float> k_distances;
flann::Matrix<float> data;
loadFileList (models, training_data_list_file_name);