EuclideanClusterExtraction in real-time with Rviz with publishing cluster centroid

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Hello i am trying to work on UAV localization with ti_mmwave radars and my goal right now is to cluster the pointcloud in RVIZ i did it with using the nodelet but as i am not good with cpp, i wanted to use a normal node and change the current euclideancluster program with real-time one on rviz and publish the centroid while also drawing bounding boxes on the clusters but i couldn't i found many exemples out there in the internet but they are not complete and it's kinda hard to make them work by myself

so i am asking for your help guys and thank you so much in advance i would appreciate if anyone could help me write such program i will provide the exemples i found

this is an exemple of using EuclideanClusterExtraction With rviz

    sensor_msgs::PointCloud2::Ptr clusters (new sensor_msgs::PointCloud2);
pcl::PointCloud<pcl::PointXYZ>::Ptr input_cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr clustered_cloud (new pcl::PointCloud<pcl::PointXYZ>);
    /* Creating the KdTree from input point cloud*/
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);
tree->setInputCloud (input_cloud);

/* Here we are creating a vector of PointIndices, which contains the actual index
 * information in a vector<int>. The indices of each detected cluster are saved here.
 * Cluster_indices is a vector containing one instance of PointIndices for each detected 
 * cluster. Cluster_indices[0] contain all indices of the first cluster in input point cloud.
 */
std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
ec.setClusterTolerance (0.06); 
ec.setMinClusterSize (30);
ec.setMaxClusterSize (600);
ec.setSearchMethod (tree);
ec.setInputCloud (input_cloud);
/* Extract the clusters out of pc and save indices in cluster_indices.*/
ec.extract (cluster_indices);

/* To separate each cluster out of the vector<PointIndices> we have to 
 * iterate through cluster_indices, create a new PointCloud for each 
 * entry and write all points of the current cluster in the PointCloud. 
 */

std::vector<pcl::PointIndices>::const_iterator it;
std::vector<int>::const_iterator pit;
for(it = cluster_indices.begin(); it != cluster_indices.end(); ++it) {
        pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster (new pcl::PointCloud<pcl::PointXYZ>);
        for(pit = it->indices.begin(); pit != it->indices.end(); pit++) {
        //push_back: add a point to the end of the existing vector
                cloud_cluster->points.push_back(input_cloud->points[*pit]); 
        }

        //Merge current clusters to whole point cloud
    *clustered_cloud += *cloud_cluster;

  }

pcl::toROSMsg (*clustered_cloud , *clusters);
publish_clusters.publish (*clusters);

and here is an exemple of drawing bounding box with clusters in rviz :

visualization_msgs::Marker mark_cluster(pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_cluster, std::string ns ,int id, float r, float g, float b)
{
  Eigen::Vector4f centroid;
  Eigen::Vector4f min;
  Eigen::Vector4f max;

  pcl::compute3DCentroid (*cloud_cluster, centroid);
  pcl::getMinMax3D (*cloud_cluster, min, max);

  uint32_t shape = visualization_msgs::Marker::CUBE;
  visualization_msgs::Marker marker;
  marker.header.frame_id = cloud_cluster->header.frame_id;
  marker.header.stamp = ros::Time::now();

  marker.ns = ns;
  marker.id = id;
  marker.type = shape;
  marker.action = visualization_msgs::Marker::ADD;

  marker.pose.position.x = centroid[0];
  marker.pose.position.y = centroid[1];
  marker.pose.position.z = centroid[2];
  marker.pose.orientation.x = 0.0;
  marker.pose.orientation.y = 0.0;
  marker.pose.orientation.z = 0.0;
  marker.pose.orientation.w = 1.0;

  marker.scale.x ...