本文整理汇总了C++中eigen::Vector4f::topRows方法的典型用法代码示例。如果您正苦于以下问题：C++ Vector4f::topRows方法的具体用法？C++ Vector4f::topRows怎么用？C++ Vector4f::topRows使用的例子？那么恭喜您, 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eigen::Vector4f的用法示例。
在下文中一共展示了Vector4f::topRows方法的1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: cloud
template<template<class > class Distance, typename PointT, typename FeatureT> bool
GlobalNNPipelineROS<Distance,PointT,FeatureT>::classifyROS (classifier_srv_definitions::classify::Request & req, classifier_srv_definitions::classify::Response & response)
{
typename pcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PointT>());
pcl::fromROSMsg(req.cloud, *cloud);
this->input_ = cloud;
pcl::PointCloud<pcl::PointXYZRGB>::Ptr pClusteredPCl (new pcl::PointCloud<pcl::PointXYZRGB>());
pcl::copyPointCloud(*cloud, *pClusteredPCl);
//clear all data from previous visualization steps and publish empty markers/point cloud
for (size_t i=0; i < markerArray_.markers.size(); i++)
{
markerArray_.markers[i].action = visualization_msgs::Marker::DELETE;
}
vis_pub_.publish( markerArray_ );
sensor_msgs::PointCloud2 scenePc2;
vis_pc_pub_.publish(scenePc2);
markerArray_.markers.clear();
for(size_t cluster_id=0; cluster_id < req.clusters_indices.size(); cluster_id++)
{
std::vector<int> cluster_indices_int;
Eigen::Vector4f centroid;
Eigen::Matrix3f covariance_matrix;
const float r = std::rand() % 255;
const float g = std::rand() % 255;
const float b = std::rand() % 255;
this->indices_.resize(req.clusters_indices[cluster_id].data.size());
for(size_t kk=0; kk < req.clusters_indices[cluster_id].data.size(); kk++)
{
this->indices_[kk] = static_cast<int>(req.clusters_indices[cluster_id].data[kk]);
pClusteredPCl->at(req.clusters_indices[cluster_id].data[kk]).r = 0.8*r;
pClusteredPCl->at(req.clusters_indices[cluster_id].data[kk]).g = 0.8*g;
pClusteredPCl->at(req.clusters_indices[cluster_id].data[kk]).b = 0.8*b;
}
this->classify ();
std::cout << "for cluster " << cluster_id << " with size " << cluster_indices_int.size() << ", I have following hypotheses: " << std::endl;
object_perception_msgs::classification class_tmp;
for(size_t kk=0; kk < this->categories_.size(); kk++)
{
std::cout << this->categories_[kk] << " with confidence " << this->confidences_[kk] << std::endl;
std_msgs::String str_tmp;
str_tmp.data = this->categories_[kk];
class_tmp.class_type.push_back(str_tmp);
class_tmp.confidence.push_back( this->confidences_[kk] );
}
response.class_results.push_back(class_tmp);
typename pcl::PointCloud<PointT>::Ptr pClusterPCl_transformed (new pcl::PointCloud<PointT>());
pcl::computeMeanAndCovarianceMatrix(*this->input_, cluster_indices_int, covariance_matrix, centroid);
Eigen::Matrix3f eigvects;
Eigen::Vector3f eigvals;
pcl::eigen33(covariance_matrix, eigvects, eigvals);
Eigen::Vector3f centroid_transformed = eigvects.transpose() * centroid.topRows(3);
Eigen::Matrix4f transformation_matrix = Eigen::Matrix4f::Zero(4,4);
transformation_matrix.block<3,3>(0,0) = eigvects.transpose();
transformation_matrix.block<3,1>(0,3) = -centroid_transformed;
transformation_matrix(3,3) = 1;
pcl::transformPointCloud(*this->input_, cluster_indices_int, *pClusterPCl_transformed, transformation_matrix);
//pcl::transformPointCloud(*frame_, cluster_indices_int, *frame_eigencoordinates_, eigvects);
PointT min_pt, max_pt;
pcl::getMinMax3D(*pClusterPCl_transformed, min_pt, max_pt);
std::cout << "Elongations along eigenvectors: " << max_pt.x - min_pt.x << ", " << max_pt.y - min_pt.y
<< ", " << max_pt.z - min_pt.z << std::endl;
geometry_msgs::Point32 centroid_ros;
centroid_ros.x = centroid[0];
centroid_ros.y = centroid[1];
centroid_ros.z = centroid[2];
response.centroid.push_back(centroid_ros);
// calculating the bounding box of the cluster
Eigen::Vector4f min;
Eigen::Vector4f max;
pcl::getMinMax3D (*this->input_, cluster_indices_int, min, max);
object_perception_msgs::BBox bbox;
geometry_msgs::Point32 pt;
pt.x = min[0]; pt.y = min[1]; pt.z = min[2]; bbox.point.push_back(pt);
pt.x = min[0]; pt.y = min[1]; pt.z = max[2]; bbox.point.push_back(pt);
pt.x = min[0]; pt.y = max[1]; pt.z = min[2]; bbox.point.push_back(pt);
pt.x = min[0]; pt.y = max[1]; pt.z = max[2]; bbox.point.push_back(pt);
pt.x = max[0]; pt.y = min[1]; pt.z = min[2]; bbox.point.push_back(pt);
pt.x = max[0]; pt.y = min[1]; pt.z = max[2]; bbox.point.push_back(pt);
pt.x = max[0]; pt.y = max[1]; pt.z = min[2]; bbox.point.push_back(pt);
pt.x = max[0]; pt.y = max[1]; pt.z = max[2]; bbox.point.push_back(pt);
response.bbox.push_back(bbox);
// getting the point cloud of the cluster
typename pcl::PointCloud<PointT>::Ptr cluster (new pcl::PointCloud<PointT>);
pcl::copyPointCloud(*this->input_, cluster_indices_int, *cluster);
//.........这里部分代码省略.........