/home/laradmin/lar/utils/process_target/src/classify_features.cpp

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#include "ros/ros.h"
#include "std_msgs/String.h"
#include "geometry_msgs/PoseWithCovariance.h"
#include "tf/transform_listener.h"
#include "mtt/TargetList.h"
#include <visualization_msgs/Marker.h>
#include <visualization_msgs/MarkerArray.h>
#include "opencv/cv.h"
#include "opencv/ml.h"
#include <vector>
#include <iostream>

// using namespace std;
// using namespace cv;

CvBoost adaboost;
CvMat* sample_to_classify = 0;
CvMat* weak_responses = 0;

// static ros::Time start_time;
ros::Time time_last_msg(0);
ros::Duration time_elapsed;
ros::Duration duration_btw_msg;
ros::Publisher nfeatures_pub;
ros::Publisher marker_pub;

tf::TransformListener *p_listener;
tf::StampedTransform transform;

geometry_msgs::Twist twist;
geometry_msgs::Twist features;
geometry_msgs::PoseWithCovariance nfeatures;
visualization_msgs::MarkerArray ma;

std::list<geometry_msgs::PoseWithCovariance> matlab_list;

double target_x, target_y;
double target_vel, target_theta;
double robot_x, robot_y;
double robot_vel, robot_theta;
double position_diff, heading_diff;
double angle_to_robot;
double velocity_diff;
double lateral_disp;
double label;
uint badLeader = 0;
uint target_id = 0;
uint single_target = 0;
uint counter = 0;

void drawPose(const geometry_msgs::Pose& input, uint target, double classification)
{
  
  visualization_msgs::Marker marker;
  
  marker.header.frame_id = "/map";
  marker.header.stamp = ros::Time::now();

  marker.ns = "quality";
  marker.id = target;

  marker.type = visualization_msgs::Marker::CYLINDER;
  marker.action = visualization_msgs::Marker::ADD;

  // Set the pose of the marker.  This is a full 6DOF pose relative to the frame/time specified in the header
  marker.pose.position.x = input.position.x;
  marker.pose.position.y = input.position.y;
  marker.pose.position.z = 0;
  marker.pose.orientation.x = 0.0;
  marker.pose.orientation.y = 0.0;
  marker.pose.orientation.z = 0.0;
  marker.pose.orientation.w = 1.0;

  // Set the scale of the marker
  marker.scale.x = 0.5;
  marker.scale.y = 0.5;
  marker.scale.z = 0.5;
 
  //good or bad leader
  if(classification == 1){
    marker.color.r = 0.0f;
    marker.color.g = 1.0f;
  }
  else if (classification == 2){
    marker.color.r = 1.0f;
    marker.color.g = 0.0f;
  }
      
  marker.color.b = 0.0f;
  marker.color.a = 1.0;

  marker.lifetime = ros::Duration(1.0);
  
//   ROS_INFO("created marker, pushing back");
  ma.markers.push_back(marker);
//   ROS_INFO("created marker end");
  // Publish the marker
//   marker_pub.publish(marker);
}

void targetsCallback(const mtt::TargetList& list)
{
  static ros::Time start_time = ros::Time::now();
  time_elapsed = ros::Time::now() - start_time;
  matlab_list.clear();
  
  //use transformations to extract robot features
//   try{
//     p_listener->lookupTransform("/map", "/robot_0/base_link", ros::Time(0), transform);
//     p_listener->lookupTwist("/map", "/robot_0/base_link", ros::Time(0), ros::Duration(0.5), twist);
//   }
  try{
    p_listener->lookupTransform("/map", "/base_link", ros::Time(0), transform);
    p_listener->lookupTwist("/map", "/base_link", ros::Time(0), ros::Duration(0.5), twist);
  }
  catch (tf::TransformException ex){
    ROS_ERROR("%s",ex.what());
  }

  robot_x = transform.getOrigin().x();
  robot_y = transform.getOrigin().y();
  robot_theta = tf::getYaw(transform.getRotation());
  robot_vel = sqrt(pow(twist.linear.x, 2) + pow(twist.linear.y, 2));
  
  //sweeps target list and extract features
  for(uint i = 0; i < list.Targets.size(); i++){
    target_id = list.Targets[i].id;
    target_x = list.Targets[i].pose.position.x;
    target_y = list.Targets[i].pose.position.y;
    target_theta = tf::getYaw(list.Targets[i].pose.orientation);
    target_vel = sqrt(pow(list.Targets[i].velocity.linear.x,2)+
                      pow(list.Targets[i].velocity.linear.y,2));
    position_diff = sqrt(pow(robot_x - target_x,2)+
                        pow(robot_y - target_y,2));
    heading_diff = robot_theta - target_theta;
    angle_to_robot = -robot_theta + atan2(target_y - robot_y, 
                                        target_x - robot_x );
    velocity_diff = robot_vel - target_vel;
    lateral_disp = sin(angle_to_robot)*position_diff;
      
    
//     if(position_diff < 6.0 && target_vel > 0.5){
      sample_to_classify->data.fl[1] = target_vel;
      sample_to_classify->data.fl[2] = lateral_disp;
      sample_to_classify->data.fl[3] = heading_diff;
      sample_to_classify->data.fl[4] = angle_to_robot;
      sample_to_classify->data.fl[5] = position_diff;
      
//       ROS_INFO("classifying     ");
      label = adaboost.predict(sample_to_classify, 0, weak_responses );
//       ROS_INFO("label:%f, now will publish",label);
      drawPose(list.Targets[i].pose, target_id, label);
//     }
  }
      
  marker_pub.publish(ma);
  ma.markers.clear();
}



int main(int argc, char **argv)
{
  ros::init(argc, argv, "process_target");
  ros::NodeHandle n;
  
  tf::TransformListener listener;
  p_listener=&listener;

//   adaboost.load("trained_boost.xml");
  std::cout << "loaded file: " << argv[1] << std::endl;
  adaboost.load(argv[1]);
  sample_to_classify = cvCreateMat( 1, 6, CV_32F );
  weak_responses = cvCreateMat( 1, adaboost.get_weak_predictors()->total, CV_32F );
    
  ros::Subscriber sub = n.subscribe("/targets", 1000, targetsCallback);
//   ros::Subscriber sub_tag = n.subscribe("/timetag", 1000, tagCallback);
//   ros::Subscriber sub_draw = n.subscribe("/pose_to_draw", 1000, drawCallback);
  nfeatures_pub = n.advertise<geometry_msgs::PoseWithCovariance>("/example_topic", 1000);
  marker_pub = n.advertise<visualization_msgs::MarkerArray>("/leader_quality", 1000);

  ros::spin();

  return 0;
}

---

process_target
Author(s): Procopio Stein
autogenerated on Wed Jul 23 04:35:17 2014