target.cpp

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#include <mtt/target.h>

ostream& operator<<(ostream& o,Target& t)
{
        o<<"target: "<<t._id;
        o<<" (#"<<t._uid<<")";
        o<<" cluster: "<<t._cluster;
        o<<" hypothesis: "<<t._hypothesis;
        o<<" miss associations: "<<t._missed_associations;
        o<<" measurements size: "<<t._assigned_measurements.size();
        return o;
}

void Target::incrementMissedAssociations(int inc)
{
        stt=10;
        
        _missed_associations+=inc;
}
                
void Target::decrementMissedAssociations(int dec)
{
        if(_missed_associations>0)
                _missed_associations-=dec;
}

void Target::zeroMissedAssociations(void)
{
        _missed_associations=0;
}
                
double Target::getDistance(MeasurementPtr& mes,int algorithm)
{
        //calculate the Mahalanobis between the node and the cluster
        Vector2d measurement;
        Vector2d mean;
        Matrix2d covariance;
//      measurement(0)=mes->centroid.x;
//      measurement(1)=mes->centroid.y;
        
        measurement(0)=mes->state[0];
        measurement(1)=mes->state[1];
        
        mean(0)=_xp(0);
        mean(1)=_xp(1);
        
        covariance(0,0)=_P(0,0);
        covariance(0,1)=_P(0,1);
        
        covariance(1,0)=_P(1,0);
        covariance(1,1)=_P(1,1);

        if(algorithm==0)//Mahalanobis
        {
                double val=mahalanobis(measurement,mean,covariance);
//              cout<<"Dist calc, t: "<<_id<<" m: "<<mes->id<<" mah: "<<val<<endl;
                return val;
        }
        else if(algorithm==1)//Bivariate pdf
        {
                double val=biVariatePDF(measurement,mean,covariance);
//              cout<<"Dist calc, t: "<<_id<<" m: "<<mes->id<<" biv: "<<val<<endl;
                return val;
        }
        else if(algorithm==2)//Euclidean
        {
                double val=sqrt(pow(measurement(0)-mean(0),2)+pow(measurement(1)-mean(1),2));
                return val;
        }
        
        return -1;
}

Target::Target()
{
        _hypothesis = -1;
        _cluster = -1;
//      _id = -1;
        _uid = _euid++;
        _missed_associations = -1;
}
                
Target::~Target()
{
//      cout<<"deleted: "<<*this<<endl;
//      _assigned_measurements.clear();
//      _past_states.clear();
}
                
Target::Target(TargetPtr prev_target)
{
        //No association
        *this=*prev_target;
        
        //Copy estimator state
        initEstimator(prev_target->_x,prev_target->_P);
        
//      kVector xx = _estimator.getX();
//      kMatrix PP = _estimator.calculateP();
//      cout<<"XX: "<<xx<<endl;
//      cout<<"PP: "<<PP<<endl;
        
        assert(is_finite(prev_target->_x));
        assert(is_finite(prev_target->_P));
        
        _id=prev_target->_id;
        _uid = _euid++;
        
        Vector3d z;
        
        z(0)=prev_target->_xp(0);
        z(1)=prev_target->_xp(1);
        z(2)=prev_target->_xp(3);
        
        incrementMissedAssociations();
        
        _estimator.miss_associations++;
        _estimator.life_time++;
        
        _assigned_measurements.clear();
        
//                      cout<<"step in no association"<<endl;
//                      
        stepEstimator(z);
//                      cout<<"step done"<<endl;
        
        if(_past_states.size()>500)
                _past_states.erase(_past_states.begin());
                
        _past_states.push_back(_x);//Make a log of all past positions
        
//      if(!is_finite(_xp))
//      {
//              cout<<"_x: "<<_x<<endl;
//              cout<<"p _x: "<<prev_target->_x<<endl;
//              cout<<"z: "<<z<<endl;
//              cout<<"miss association"<<endl;
//      }

        if(!is_finite(_xp))
        {
                const char COL_RESET[] = "\x1b[0m";
                const char RED[]       = "\x1b[31m";
                cout << RED << "Error!!, Major" << COL_RESET << endl;
                cout<<"Bad failed target: "<<*this<<endl;
        }
        
//      assert(is_finite(_xp));
//      assert(is_finite(_P));
}
                
Target::Target(TargetPtr prev_target,MeasurementPtr measurement)
{
//      cout<<"NORMAL ASSOCIATION"<<endl;
        //Good association
        
        *this=*prev_target;
        
        initEstimator(prev_target->_x,prev_target->_P);
        
        _estimator.miss_associations=0;
        _estimator.life_time++;
        
        _id=prev_target->_id;
        _uid = _euid++;
        
        
        _assigned_measurements.clear();
        _assigned_measurements.push_back(measurement);
        
        zeroMissedAssociations();
        
        assert(is_finite(measurement->state));
        
//      cout<<"step in normal"<<endl;
        stepEstimator(measurement->state);
        
        if(_past_states.size()>500)
                _past_states.erase(_past_states.begin());
        
        _past_states.push_back(_x);//Make a log of all past positions
        
        if(!is_finite(_xp))
                cout<<"Bad normal target: "<<*this<<endl;
}
                
Target::Target(MeasurementPtr measurement)
{
        //New target
        Vector3d z = measurement->state;
//      Vector3d
        
        assert(is_finite(measurement->state));
        
//      cout<<"here"<<endl;
        initEstimator(z);
//      cout<<"t"<<endl;
        
        _past_states.clear();//New target
        _past_states.push_back(_x);//Make a log of all past positions
        
        _hypothesis = 0;
        _cluster = 0;
        _missed_associations = 0;
        
        _estimator.miss_associations=0;
        _estimator.life_time=0;
        
        if(!is_finite(_xp))
                cout<<"Bad new target: "<<*this<<endl;
        
        _assigned_measurements.clear();
        _assigned_measurements.push_back(measurement);
        
//      cout<<"new target"<<endl;
//      assert(is_finite(_xp));
//      assert(is_finite(_P));
        
//      _id=_uid++;

        _ntotal++;
        _id = _ntotal;//To regular use
//      _id = measurement->id;//Just with matlab ground truth data, the measurement id IS the ground truth.
        //We initialize each target with the correct id and check at each iteration if the association was good
        _uid = _euid++;
        
        
        _exuid = measurement->id;
        
//      cout<<"tc: "<<_id<<" uid: "<<_uid<<endl;
        cout<<"||||||||!!!!!!!!"<<endl;
        cout<<"_ntotal: "<<_ntotal<<endl;
}

double Target::getDistanceToPredictedPosition(TargetPtr& target,int algorithm)
{
        //calculate the Mahalanobis between the current target and the new target
        Vector2d predicted_position;
        Vector2d mean;
        Matrix2d covariance;
        
        predicted_position(0)=target->_xp(0);
        predicted_position(1)=target->_xp(1);
        
        mean(0)=_xp(0);
        mean(1)=_xp(1);
        
        covariance(0,0)=_P(0,0);
        covariance(0,1)=_P(0,1);
        
        covariance(1,0)=_P(1,0);
        covariance(1,1)=_P(1,1);

        if(algorithm==0)
        {
                double val=mahalanobis(predicted_position,mean,covariance);
//              cout<<"Dist calc, t: "<<_id<<" m: "<<mes->id<<" mah: "<<val<<endl;
                return val;
        }
        else
        {
                double val=biVariatePDF(predicted_position,mean,covariance);
//              cout<<"Dist calc, t: "<<_id<<" m: "<<mes->id<<" biv: "<<val<<endl;
                return val;
        }
}
                
void Target::cleanTargetAssociations(void)
{
        _assigned_measurements.clear();
}

bool compareTargetsById(TargetPtr t1,TargetPtr t2)
{
        return t1->_id<t2->_id;
}

bool compareTargetsByAux1(TargetPtr t1,TargetPtr t2)
{
        return t1->_aux1<t2->_aux1;
}

bool compareTargetsByAux1Descending(TargetPtr t1,TargetPtr t2)
{
        return t1->_aux1>t2->_aux1;
}