コード例 #1
0
ファイル: worlddownloadutils.cpp プロジェクト: CURG/ros_kinfu
  Eigen::Affine3f WorldDownloadManager::toEigenAffine(const T1 & linear,const T2 & translation)
{
  Eigen::Matrix3f l;
  Eigen::Vector3f t;

  for (uint r = 0; r < 3; r++)
    for (uint c = 0; c < 3; c++)
      l(r,c) = linear[r * 3 + c];

  for (uint r = 0; r < 3; r++)
    t[r] = translation[r];

  Eigen::Affine3f result;
  result.linear() = l;
  result.translation() = t;
  return result;
}
コード例 #2
0
ファイル: SLTrackerWorker.cpp プロジェクト: 1125lbs/slstudio
void SLTrackerWorker::trackPointCloud(PointCloudConstPtr pointCloud){

    // Recursively call self until latest event is hit
    busy = true;
    QCoreApplication::sendPostedEvents(this, QEvent::MetaCall);
    bool result = busy;
    busy = false;
    if(!result){
        std::cerr << "SLTrackerWorker: dropped point cloud!" << std::endl;
        return;
    }

    if(!referenceSet){
        tracker->setReference(pointCloud);
        referenceSet = true;
        return;
    }

    performanceTime.start();

    Eigen::Affine3f T;
    bool converged;
    float RMS;
    tracker->determineTransformation(pointCloud, T, converged, RMS);

    // Emit result
    if(converged)
        emit newPoseEstimate(T);

//    std::cout << "Pose: " << T.matrix() << std::endl;

    std::cout << "Tracker: " << performanceTime.elapsed() << "ms" << std::endl;

    if(writeToDisk){
        Eigen::Vector3f t(T.translation());
        Eigen::Quaternionf q(T.rotation());

        (*ofStream) << trackingTime.elapsed() << ",";
        (*ofStream) << t.x() << "," << t.y() << "," << t.z() << ",";
        (*ofStream) << q.w() << "," << q.x() << "," << q.y() << "," << q.z() << "," << RMS;
        (*ofStream) << std::endl;
    }


}
コード例 #3
0
ファイル: tracking.cpp プロジェクト: hommeabeil/perception3d
void setTrackerTarget(){
    initTracking();
    Eigen::Vector4f c;
    Eigen::Affine3f trans = Eigen::Affine3f::Identity ();
    pcl::compute3DCentroid<PointT>(*object_to_track,c);
    trans.translation().matrix() = Eigen::Vector3f(c[0],c[1],c[2]);
    tracker_->setTrans (trans);

    pcl::PointCloud<PointT>::Ptr tmp_pc(new pcl::PointCloud<PointT>);
    pcl::transformPointCloud<PointT> (*object_to_track, *tmp_pc, trans.inverse());

    tracker_->setReferenceCloud(tmp_pc);
    tracker_->setInputCloud(cloud);

    tracked_object_centroid->clear();
    tracked_object_centroid->push_back(PointT(c[0],c[1],c[2]));

}
コード例 #4
0
ファイル: AmPclUtil.cpp プロジェクト: Hirucon/KinfuSuperRes
            void
            printPose( Eigen::Affine3f const& pose )
            {
                // debug
                std::cout << pose.linear() << std::endl <<
                             pose.translation().transpose() << std::endl;

                float alpha = atan2(  pose.linear()(1,0), pose.linear()(0,0) );
                float beta  = atan2( -pose.linear()(2,0),
                                     sqrt( pose.linear()(2,1) * pose.linear()(2,1) +
                                           pose.linear()(2,2) * pose.linear()(2,2)  )
                                     );
                float gamma = atan2(  pose.linear()(2,1), pose.linear()(2,2) );

                std::cout << "alpha: " << alpha << " " << alpha * 180.f / M_PI << std::endl;
                std::cout << "beta: "  << beta  << " " << beta  * 180.f / M_PI << std::endl;
                std::cout << "gamma: " << gamma << " " << gamma * 180.f / M_PI << std::endl;
            }
コード例 #5
0
transformation objectModelSV::modelToScene(const pcl::PointNormal & pointModel, const Eigen::Affine3f & transformSceneToGlobal, const float alpha)
{
	Eigen::Vector3f modelPoint=pointModel.getVector3fMap();
	Eigen::Vector3f modelNormal=pointModel.getNormalVector3fMap ();

	// Get transformation from model local frame to global frame
	Eigen::Vector3f cross=modelNormal.cross (Eigen::Vector3f::UnitX ()).normalized ();
	Eigen::AngleAxisf rotationModelToGlobal(acosf (modelNormal.dot (Eigen::Vector3f::UnitX ())), cross);

	if (isnan(cross[0]))
	{
		rotationModelToGlobal=Eigen::AngleAxisf(0.0,Eigen::Vector3f::UnitX ());
	}		
	//std::cout<< "ola:" <<acosf (modelNormal.dot (Eigen::Vector3f::UnitX ()))<<std::endl;
	//std::cout <<"X:"<< Eigen::Translation3f( rotationModelToGlobal * ((-1) * modelPoint)).x() << std::endl;
	//std::cout <<"Y:"<< Eigen::Translation3f( rotationModelToGlobal * ((-1) * modelPoint)).y() << std::endl;
	//std::cout <<"Z:"<< Eigen::Translation3f( rotationModelToGlobal * ((-1) * modelPoint)).z() << std::endl;

    Eigen::Affine3f transformModelToGlobal = Eigen::Translation3f( rotationModelToGlobal * ((-1) * modelPoint)) * rotationModelToGlobal;

	// Get transformation from model local frame to scene local frame
    Eigen::Affine3f completeTransform = transformSceneToGlobal.inverse () * Eigen::AngleAxisf(alpha, Eigen::Vector3f::UnitX ()) * transformModelToGlobal;

	//std::cout << Eigen::AngleAxisf(alpha, Eigen::Vector3f::UnitX ()).matrix() << std::endl;

	Eigen::Quaternion<float> rotationQ=Eigen::Quaternion<float>(completeTransform.rotation());

	// if object is symmetric remove yaw rotation (assume symmetry around z axis)
	if(symmetric)
	{
		Eigen::Vector3f eulerAngles;
		// primeiro [0] -> rot. around x (roll) [1] -> rot. around y (pitch) [2] -> rot. around z (yaw)
		quaternionToEuler(rotationQ, eulerAngles[0], eulerAngles[1], eulerAngles[2]);
		//pcl::getEulerAngles(completeTransform,eulerAngles[0], eulerAngles[1], eulerAngles[2]);
		//eulerAngles[2]=0.0;
		eulerToQuaternion(rotationQ, eulerAngles[0], eulerAngles[1], eulerAngles[2]);
		//quaternionToEuler(rotationQ, eulerAngles[2], eulerAngles[1], eulerAngles[2]);
		//std::cout << "EULER ANGLES: " << eulerAngles << std::endl;
	}


	//std::cout << "rotation: " << rotationQ << std::endl;
	return transformation(rotationQ, Eigen::Translation3f(completeTransform.translation()) );
}
コード例 #6
0
ファイル: tracking_sample.cpp プロジェクト: SunBlack/pcl
//Draw model reference point cloud
void
drawResult (pcl::visualization::PCLVisualizer& viz)
{
  ParticleXYZRPY result = tracker_->getResult ();
  Eigen::Affine3f transformation = tracker_->toEigenMatrix (result);

  //move close to camera a little for better visualization
  transformation.translation () += Eigen::Vector3f (0.0f, 0.0f, -0.005f);
  CloudPtr result_cloud (new Cloud ());
  pcl::transformPointCloud<RefPointType> (*(tracker_->getReferenceCloud ()), *result_cloud, transformation);

  //Draw blue model reference point cloud
  {
    pcl::visualization::PointCloudColorHandlerCustom<RefPointType> blue_color (result_cloud, 0, 0, 255);

    if (!viz.updatePointCloud (result_cloud, blue_color, "resultcloud"))
      viz.addPointCloud (result_cloud, blue_color, "resultcloud");
  }
}
コード例 #7
0
 bool TransitionLimitXYZRPY::check(FootstepState::Ptr from,
                                   FootstepState::Ptr to) const
 {
   // from * trans = to
   const Eigen::Affine3f diff = to->getPose() * from->getPose().inverse();
   const Eigen::Vector3f diff_pos(diff.translation());
   if (std::abs(diff_pos[0]) < x_max_ &&
       std::abs(diff_pos[1]) < y_max_ &&
       std::abs(diff_pos[2]) < z_max_) {
     float roll, pitch, yaw;
     pcl::getEulerAngles(diff, roll, pitch, yaw);
     return (std::abs(roll) < roll_max_ &&
             std::abs(pitch) < pitch_max_ &&
             std::abs(yaw) < yaw_max_);
   }
   else {
     return false;
   }
 }
コード例 #8
0
geometry_msgs::Pose transformEigenAffine3fToPose(Eigen::Affine3f e) {
    Eigen::Vector3f Oe;
    Eigen::Matrix3f Re;
    geometry_msgs::Pose pose;
    Oe = e.translation();
    Re = e.linear();

    Eigen::Quaternionf q(Re); // convert rotation matrix Re to a quaternion, q
    pose.position.x = Oe(0);
    pose.position.y = Oe(1);
    pose.position.z = Oe(2);

    pose.orientation.x = q.x();
    pose.orientation.y = q.y();
    pose.orientation.z = q.z();
    pose.orientation.w = q.w();

    return pose;
}
コード例 #9
0
ファイル: glwidget.cpp プロジェクト: brunoferraz/mscProject
void GLWidget::paintGL()
{
    makeCurrent();

    glClearColor(0.7, 0.7, 0.7, 1.0);
    glClear(GL_COLOR_BUFFER_BIT|GL_DEPTH_BUFFER_BIT);

    glEnable(GL_DEPTH_TEST);
    glEnable(GL_BLEND);

    if(Interface::showBackgroundImage)
    {
//        renderTexture.imageAlpha = Interface::alphaBackgroundImage;
//        renderTexture.renderTexture(*mTextManagerObj.getBaseTexture(), Eigen::Vector2i(this->width(), this->height()));
    }


//    phong.render(*mTextManagerObj.getMesh(), *currentCamera, light_trackball);

//      multi.render(mTextManagerObj, *currentCamera, light_trackball);
      multiMask.useWeights = Interface::useWeights;
      multiMask.render(mTextManagerObj, *currentCamera, light_trackball);
//    depthMap.render(*mTextManagerObj.getMesh(), calibrationCamera, light_trackball);

    if(Interface::camera == Interface::CAMERA_TYPES::FREE)
    {
//        camera.render();
        if(Interface::ShowCameras){
            for(int i =0 ; i < mTextManagerObj.getNumPhotos(); i++)
            {
                mTextManagerObj.changePhotoReferenceTo(i);
                camRep.resetModelMatrix();
                Eigen::Affine3f m = (*(mTextManagerObj.getViewMatrix())).inverse();
                m.translation() -= mTextManagerObj.getMesh()->getCentroid();
                m.translation() *= mTextManagerObj.getMesh()->getScale();
                m.scale (0.08);
                camRep.setModelMatrix(m);
                camRep.render(*currentCamera, light_trackball);
            }
            mTextManagerObj.changePhotoReferenceTo(0);
        }
    }
}
コード例 #10
0
int main(int argc, char** argv) {
    ros::init(argc, argv, "object_finder_node"); // name this node 

    ROS_INFO("instantiating the object finder action server: ");

    ObjectFinder object_finder_as; // create an instance of the class "ObjectFinder"
    tf::TransformListener tfListener;
    ROS_INFO("listening for kinect-to-base transform:");
    tf::StampedTransform stf_kinect_wrt_base;
    bool tferr = true;
    ROS_INFO("waiting for tf between kinect_pc_frame and base_link...");
    while (tferr) {
        tferr = false;
        try {
            //The direction of the transform returned will be from the target_frame to the source_frame. 
            //Which if applied to data, will transform data in the source_frame into the target_frame. 
            //See tf/CoordinateFrameConventions#Transform_Direction
            tfListener.lookupTransform("base_link", "kinect_pc_frame", ros::Time(0), stf_kinect_wrt_base);
        } catch (tf::TransformException &exception) {
            ROS_WARN("%s; retrying...", exception.what());
            tferr = true;
            ros::Duration(0.5).sleep(); // sleep for half a second
            ros::spinOnce();
        }
    }
    ROS_INFO("kinect to base_link tf is good");
    object_finder_as.xformUtils_.printStampedTf(stf_kinect_wrt_base);
    tf::Transform tf_kinect_wrt_base = object_finder_as.xformUtils_.get_tf_from_stamped_tf(stf_kinect_wrt_base);
    g_affine_kinect_wrt_base = object_finder_as.xformUtils_.transformTFToAffine3f(tf_kinect_wrt_base);
    cout << "affine rotation: " << endl;
    cout << g_affine_kinect_wrt_base.linear() << endl;
    cout << "affine offset: " << g_affine_kinect_wrt_base.translation().transpose() << endl;

    ROS_INFO("going into spin");
    // from here, all the work is done in the action server, with the interesting stuff done within "executeCB()"
    while (ros::ok()) {
        ros::spinOnce(); //normally, can simply do: ros::spin();  
        ros::Duration(0.1).sleep();
    }

    return 0;
}
コード例 #11
0
void ViewController::launch(FileData* game, Eigen::Vector3f center)
{
	if(game->getType() != GAME)
	{
		LOG(LogError) << "tried to launch something that isn't a game";
		return;
	}

	Eigen::Affine3f origCamera = mCamera;
	origCamera.translation() = -mCurrentView->getPosition();

	center += mCurrentView->getPosition();
	stopAnimation(1); // make sure the fade in isn't still playing
	mLockInput = true;

	if(Settings::getInstance()->getString("TransitionStyle") == "fade")
	{
		// fade out, launch game, fade back in
		auto fadeFunc = [this](float t) {
			//t -= 1;
			//mFadeOpacity = lerp<float>(0.0f, 1.0f, t*t*t + 1);
			mFadeOpacity = lerp<float>(0.0f, 1.0f, t);
		};
		setAnimation(new LambdaAnimation(fadeFunc, 800), 0, [this, game, fadeFunc]
		{
			game->getSystem()->launchGame(mWindow, game);
			mLockInput = false;
			setAnimation(new LambdaAnimation(fadeFunc, 800), 0, nullptr, true);
			this->onFileChanged(game, FILE_METADATA_CHANGED);
		});
	}else{
		// move camera to zoom in on center + fade out, launch game, come back in
		setAnimation(new LaunchAnimation(mCamera, mFadeOpacity, center, 1500), 0, [this, origCamera, center, game] 
		{
			game->getSystem()->launchGame(mWindow, game);
			mCamera = origCamera;
			mLockInput = false;
			setAnimation(new LaunchAnimation(mCamera, mFadeOpacity, center, 600), 0, nullptr, true);
			this->onFileChanged(game, FILE_METADATA_CHANGED);
		});
	}
}
コード例 #12
0
gsl_vector* VelStereoMatcher::stereoToVec(const StereoProperties stereo)
{
  Eigen::Affine3f tform = stereo.getLeftCamera().tform;
  Eigen::Matrix4f intrinsics = stereo.getLeftCamera().intrinsics.matrix();

  Eigen::Vector3f tran;
  tran = tform.translation();
  float x = tran[0];
  float y = tran[1];
  float z = tran[2];

  Eigen::Matrix3f mat = tform.rotation();
  Eigen::AngleAxisf axis;
  axis = mat;
  float ax = axis.axis()[0] * axis.angle();
  float ay = axis.axis()[1] * axis.angle();
  float az = axis.axis()[2] * axis.angle();

  float fx = intrinsics(0,0);
  float fy = intrinsics(1,1);
  float cx = intrinsics(0,2);
  float cy = intrinsics(1,2);

  float baseline = stereo.baseline;

  gsl_vector* vec = gsl_vector_alloc(11);
  gsl_vector_set(vec, 0, x);
  gsl_vector_set(vec, 1, y);
  gsl_vector_set(vec, 2, z);
  gsl_vector_set(vec, 3, ax);
  gsl_vector_set(vec, 4, ay);
  gsl_vector_set(vec, 5, az);

  gsl_vector_set(vec, 6, fx);
  gsl_vector_set(vec, 7, fy);
  gsl_vector_set(vec, 8, cx);
  gsl_vector_set(vec, 9, cy);
  gsl_vector_set(vec, 10, baseline);

  return vec;

}
コード例 #13
0
ファイル: SLTrackerDialog.cpp プロジェクト: uboot/slstudio
void SLTrackerDialog::showPoseEstimate(const Eigen::Affine3f & T){

    if(ui->poseTab->isVisible()){
        ui->poseWidget->showPoseEstimate(T);
    } else if(ui->traceTab->isVisible()){
        Eigen::Vector3f t(T.translation());
        Eigen::Quaternionf q(T.rotation());

        ui->translationTraceWidget->addMeasurement("tx", t(0));
        ui->translationTraceWidget->addMeasurement("ty", t(1));
        ui->translationTraceWidget->addMeasurement("tz", t(2));
        ui->translationTraceWidget->draw();

        ui->rotationTraceWidget->addMeasurement("qw", q.w());
        ui->rotationTraceWidget->addMeasurement("qx", q.x());
        ui->rotationTraceWidget->addMeasurement("qy", q.y());
        ui->rotationTraceWidget->addMeasurement("qz", q.z());
        ui->rotationTraceWidget->draw();
    }
}
コード例 #14
0
ファイル: openni_tracking.cpp プロジェクト: 5irius/pcl
  void
  drawResult (pcl::visualization::PCLVisualizer& viz)
  {
    ParticleXYZRPY result = tracker_->getResult ();
    Eigen::Affine3f transformation = tracker_->toEigenMatrix (result);
    // move a little bit for better visualization
    transformation.translation () += Eigen::Vector3f (0.0f, 0.0f, -0.005f);
    RefCloudPtr result_cloud (new RefCloud ());

    if (!visualize_non_downsample_)
      pcl::transformPointCloud<RefPointType> (*(tracker_->getReferenceCloud ()), *result_cloud, transformation);
    else
      pcl::transformPointCloud<RefPointType> (*reference_, *result_cloud, transformation);

    {
      pcl::visualization::PointCloudColorHandlerCustom<RefPointType> red_color (result_cloud, 0, 0, 255);
      if (!viz.updatePointCloud (result_cloud, red_color, "resultcloud"))
        viz.addPointCloud (result_cloud, red_color, "resultcloud");
    }
    
  }
コード例 #15
0
	void CGLUtil::getRTFromWorld2CamCV(Eigen::Matrix3f* pRw_, Eigen::Vector3f* pTw_) {
		//only the matrix in the top of the modelview matrix stack works
		Eigen::Affine3f M;
		glGetFloatv(GL_MODELVIEW_MATRIX,M.matrix().data());

		Eigen::Matrix3f S;
		*pTw_ = M.translation();
		*pRw_ = M.linear();
		//M.computeRotationScaling(pRw_,&S);
		//*pTw_ = (*pTw_)/S(0,0);
		//negate row no. 1 and 2, to switch from GL to CV convention
		for (int r = 1; r < 3; r++){
			for	(int c = 0; c < 3; c++){
				(*pRw_)(r,c) = -(*pRw_)(r,c);
			}
			(*pTw_)(r) = -(*pTw_)(r); 
		}
		//PRINT(S);
		//PRINT(*pRw_);
		//PRINT(*pTw_);
		return;
	}
コード例 #16
0
ファイル: mathutils.cpp プロジェクト: aurelw/beholder
void affine3fToTransRotVec(const Eigen::Affine3f &aff,
        cv::Mat &tvec, cv::Mat &rvec)
{   
    /* decompose matrix */
    Eigen::Vector3f pos = aff.translation();

    Eigen::AngleAxisf rot(aff.rotation());
    Eigen::Vector3f rotVec = rot.axis();
    float angle = rot.angle();
    rotVec *= angle;

    /* copy translation vector */
    tvec = cv::Mat::zeros(3, 1, CV_32F);
    tvec.at<float>(0,0) = pos[0];
    tvec.at<float>(1,0) = pos[1];
    tvec.at<float>(2,0) = pos[2];

    /* copy axis angle rotation */
    rvec = cv::Mat::zeros(3, 1, CV_32F);
    rvec.at<float>(0,0) = rotVec[0];
    rvec.at<float>(1,0) = rotVec[1];
    rvec.at<float>(2,0) = rotVec[2];
}
コード例 #17
0
  void
  ParticleFilterTracking::resetTrackingTargetModel(const pcl::PointCloud<pcl::PointXYZRGBA>::ConstPtr &new_target_cloud)
  {
    //prepare the model of tracker's target
    Eigen::Vector4f c;
    Eigen::Affine3f trans = Eigen::Affine3f::Identity ();
    pcl::PointCloud<pcl::PointXYZRGBA>::Ptr transed_ref (new pcl::PointCloud<pcl::PointXYZRGBA>);
    pcl::PointCloud<pcl::PointXYZRGBA>::Ptr transed_ref_downsampled (new pcl::PointCloud<pcl::PointXYZRGBA>);

    pcl::compute3DCentroid (*new_target_cloud, c);
    trans.translation ().matrix () = Eigen::Vector3f (c[0], c[1], c[2]);
    pcl::transformPointCloud(*new_target_cloud, *transed_ref, trans.inverse());
    gridSampleApprox (transed_ref, *transed_ref_downsampled, downsampling_grid_size_);
    //set reference model and trans
    {
      boost::mutex::scoped_lock lock(mtx_);
      tracker_->setReferenceCloud (transed_ref_downsampled);
      tracker_->setTrans (trans);
      tracker_->resetTracking();
    }
    //Reset target Model
    ROS_INFO("RESET TARGET MODEL");
  }
コード例 #18
0
ファイル: bb_viewer.cpp プロジェクト: Lab-RoCoCo/fps_mapper
Eigen::Affine3f createTransMatrix( float tx, float ty, float tz ) {
  Eigen::Affine3f m = Eigen::Affine3f::Identity();
  m.translation() = Eigen::Vector3f( tx, ty, tz );
  
  return m;
}
コード例 #19
0
void SystemView::render(const Eigen::Affine3f& parentTrans)
{
	if(size() == 0)
		return;

	Eigen::Affine3f trans = getTransform() * parentTrans;
	
	// draw the list elements (titles, backgrounds, logos)
	const float logoSizeX = logoSize().x() + LOGO_PADDING;

	int logoCount = (int)(mSize.x() / logoSizeX) + 2; // how many logos we need to draw
	int center = (int)(mCamOffset);

	if(mEntries.size() == 1)
		logoCount = 1;

	// draw background extras
	Eigen::Affine3f extrasTrans = trans;
	int extrasCenter = (int)mExtrasCamOffset;
	for(int i = extrasCenter - 1; i < extrasCenter + 2; i++)
	{
		int index = i;
		while(index < 0)
			index += mEntries.size();
		while(index >= (int)mEntries.size())
			index -= mEntries.size();

		extrasTrans.translation() = trans.translation() + Eigen::Vector3f((i - mExtrasCamOffset) * mSize.x(), 0, 0);

		Eigen::Vector2i clipRect = Eigen::Vector2i((int)((i - mExtrasCamOffset) * mSize.x()), 0);
		Renderer::pushClipRect(clipRect, mSize.cast<int>());
		mEntries.at(index).data.backgroundExtras->render(extrasTrans);
		Renderer::popClipRect();
	}

	// fade extras if necessary
	if(mExtrasFadeOpacity)
	{
		Renderer::setMatrix(trans);
		Renderer::drawRect(0.0f, 0.0f, mSize.x(), mSize.y(), 0x00000000 | (unsigned char)(mExtrasFadeOpacity * 255));
	}

	// draw logos
	float xOff = (mSize.x() - logoSize().x())/2 - (mCamOffset * logoSizeX);
	float yOff = (mSize.y() - logoSize().y())/2;

	// background behind the logos
	Renderer::setMatrix(trans);
	Renderer::drawRect(0.f, (mSize.y() - BAND_HEIGHT) / 2, mSize.x(), BAND_HEIGHT, 0xFFFFFFD8);

	Eigen::Affine3f logoTrans = trans;
	for(int i = center - logoCount/2; i < center + logoCount/2 + 1; i++)
	{
		int index = i;
		while(index < 0)
			index += mEntries.size();
		while(index >= (int)mEntries.size())
			index -= mEntries.size();

		logoTrans.translation() = trans.translation() + Eigen::Vector3f(i * logoSizeX + xOff, yOff, 0);

		if(index == mCursor) //scale our selection up
		{
			// selected
			const std::shared_ptr<GuiComponent>& comp = mEntries.at(index).data.logoSelected;
			comp->setOpacity(0xFF);
			comp->render(logoTrans);
		}else{
			// not selected
			const std::shared_ptr<GuiComponent>& comp = mEntries.at(index).data.logo;
			comp->setOpacity(0x80);
			comp->render(logoTrans);
		}
	}

	Renderer::setMatrix(trans);
	Renderer::drawRect(mSystemInfo.getPosition().x(), mSystemInfo.getPosition().y() - 1, mSize.x(), mSystemInfo.getSize().y(), 0xDDDDDD00 | (unsigned char)(mSystemInfo.getOpacity() / 255.f * 0xD8));
	mSystemInfo.render(trans);
}
コード例 #20
0
    // visualizzazione doppia
   void VisualizationUtils::vis_doppia( string name1, string name2) {

        std::stringstream firstCloud;
        firstCloud<<"./../registrazione/cloud"<<name1<<".ply";
        std::stringstream secondCloud;
        secondCloud<<"./../registrazione/cloud"<<name2<<".ply";
        cout<<firstCloud.str()<<endl;
        cout<<secondCloud.str()<<endl;

        pcl::PointCloud<POINT_TYPE>::Ptr cloud1 (new pcl::PointCloud<POINT_TYPE>);
        pcl::PointCloud<POINT_TYPE>::Ptr cloud2 (new pcl::PointCloud<POINT_TYPE>);
        pcl::io::loadPLYFile(firstCloud.str().c_str(),*cloud1);
        pcl::io::loadPLYFile(secondCloud.str().c_str(),*cloud2);

        pcl::PointCloud<POINT_TYPE>::Ptr clicked_points_app(new pcl::PointCloud<POINT_TYPE>);
        pcl::PointCloud<POINT_TYPE>::Ptr clicked_points2_app(new pcl::PointCloud<POINT_TYPE>);
        // resetto le variabili globali
        clicked_points_app->clear();
        clicked_points2_app->clear();

        points_left.clear_stack();
        points_right.clear_stack();

        color_left.restart();
        color_right.restart();

        clicked_points = clicked_points2_app;
        clicked_points2 = clicked_points2_app;

        // creo la finestra
        boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("3D Viewer"));
        global_viewer = viewer;
//        viewer->initCameraParameters();
//        viewer->setSize(1200, 650);


        // assegno la prima cloud
        viewer->createViewPort(0.0, 0.0, 0.5, 1.0, v1);
        viewer->setCameraPosition(0.0, 0.0, 0.0, 0.0, 0.0, 0.15, 0.0, 1.0, 0.0, v1);
        viewer->setBackgroundColor(0.2, 0.2, 0.2, v1); // background grigio
        viewer->addText("Prossimo colore: " + color_left.getColorName(), 10, 10, "v1_text", v1);
        pcl::visualization::PointCloudColorHandlerRGBField<POINT_TYPE> rgb(cloud1);
        viewer->addPointCloud<POINT_TYPE>(cloud1, rgb, name1, v1);
        viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, name1);

        // assegno la seconda cloud
        viewer->createViewPort(0.5, 0.0, 1.0, 1.0, v2);
        viewer->setBackgroundColor(0.3, 0.3, 0.3, v2);
        viewer->addText("Prossimo colore: " + color_right.getColorName(), 10, 10, "v2_text", v2);
        pcl::visualization::PointCloudColorHandlerRGBField<POINT_TYPE> rgb2(cloud2);
        // la traslo per poter identificare i suoi punti
        Eigen::Affine3f transform = Eigen::Affine3f::Identity();
        transform.translation() << 0.0, 0.0, TRANLSATION_Z_SECOND_CLOUD;
        //cout << "matrice applicata alla seconda cloud" << endl << transform.matrix() << endl;
        pcl::PointCloud<POINT_TYPE>::Ptr transformed_cloud2(new pcl::PointCloud<POINT_TYPE>);
        pcl::transformPointCloud(*cloud2, *transformed_cloud2, transform);
        viewer->addPointCloud<POINT_TYPE>(transformed_cloud2, rgb2, name2, v2);
        viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, name2);

        // gestione separata della telecamera
        viewer->createViewPortCamera(v2);
        viewer->setCameraPosition(0.0, 0.0, TRANLSATION_Z_SECOND_CLOUD, 0.0, 0.0, TRANLSATION_Z_SECOND_CLOUD + 0.15, 0.0, 1.0, 0.0, v2);

//        viewer->registerKeyboardCallback(keyboardEventOccurred, (void*)&viewer);
//        viewer->registerPointPickingCallback(pointPickDoubleViewEvent, (void*)&viewer);

        loop_view(viewer);

        // chiusa la view, stampo i punti catturati
        cout << "Punti selezionati:" << endl << " -> cloud sinistra:" << endl;
        //cout << endl << points_left.makeMatrix() << endl;
        points_left.print_all();
        cout << " -> cloud destra:" << endl;
        //cout << endl << points_right.makeMatrix() << endl;
        points_right.print_all();

        Eigen::MatrixXf Mx = points_left.makeMatrix();
        Eigen::MatrixXf My = points_right.makeMatrix();

        if (Mx.cols() != My.cols()) {
            cout << "Errore... Hai preso un numero diverso di punti tra destra e sinistra?" << endl;
            return ;
        }

        /*if (Mx.cols() < 3) {
            cout << "Dai, sforzati di prendere almeno 3 punti..." << endl;
            return;
        }*/

        Eigen::Matrix4f T = TransformationUtils::trovaT(Mx, My);
        ofstream outFile("/home/miky/Scrivania/trasformazione.txt");
        outFile << T;
        cout << " -> matrice di trasformazione salvata nel file trasformazione.txt" << endl;

        pcl::PointCloud<POINT_TYPE>::Ptr result(new pcl::PointCloud<POINT_TYPE>);
        pcl::transformPointCloud(*cloud1, *result, T);
        pcl::io::savePLYFileBinary("/home/miky/Scrivania/Cloud12.ply", *result);
        cout << " -> salvata la cloud traslata con nome Cloud12.ply" << endl;

        return ;
    }
コード例 #21
0
  void
  cloud_cb (const sensor_msgs::LaserScan::ConstPtr& scan)
  {
    sensor_msgs::PointCloud2 point_cloud2;
    RefCloudPtr raw_cloud(new RefCloud);

    projector_.transformLaserScanToPointCloud("base_laser_link", *scan, point_cloud2, tfListener_);
    pcl17::fromROSMsg<PointType> (point_cloud2, *raw_cloud);


    boost::mutex::scoped_lock lock (mtx_);
    cloud_pass_.reset (new Cloud);
    cloud_pass_downsampled_.reset (new Cloud);
    pcl17::ModelCoefficients::Ptr coefficients (new pcl17::ModelCoefficients ());
    pcl17::PointIndices::Ptr inliers (new pcl17::PointIndices ());
    filterPassThrough (raw_cloud, *cloud_pass_);
    if (counter_ < 10)
      {
        gridSample (cloud_pass_, *cloud_pass_downsampled_, downsampling_grid_size_);
      }
    else if (counter_ == 10)
      {
        //gridSample (cloud_pass_, *cloud_pass_downsampled_, 0.01);
        cloud_pass_downsampled_ = cloud_pass_;
        CloudPtr target_cloud;

        if (target_cloud != NULL)
          {

            RefCloudPtr nonzero_ref (new RefCloud);
            removeZeroPoints (input_model_point_cloud_, *nonzero_ref);

            //					PCL_INFO ("calculating cog\n");

            Eigen::Vector4f c;
            RefCloudPtr transed_ref (new RefCloud);
            pcl17::compute3DCentroid<RefPointType> (*nonzero_ref, c);
            Eigen::Affine3f trans = Eigen::Affine3f::Identity ();
            trans.translation ().matrix () = Eigen::Vector3f (c[0], c[1], c[2]);
            pcl17::transformPointCloud<RefPointType> (*nonzero_ref, *transed_ref, trans.inverse());
            CloudPtr transed_ref_downsampled (new Cloud);
            gridSample (transed_ref, *transed_ref_downsampled, downsampling_grid_size_);
            tracker_->setReferenceCloud (transed_ref_downsampled);
            tracker_->setTrans (trans);	
            tracker_->setMinIndices (int (input_model_point_cloud_->points.size ()) / 2);
          }
        else
          {
            //					PCL_WARN ("euclidean segmentation failed\n");
          }
      }
    else
      {

        gridSampleApprox (cloud_pass_, *cloud_pass_downsampled_, downsampling_grid_size_);
        tracking (cloud_pass_downsampled_);
      }
    
    new_cloud_ = true;
    counter_++;
  }
コード例 #22
0
  /**
   * @brief service offering table object candidates
   *
   * @param req request for objects of a class (table objects in this case)
   * @param res response of the service which is possible table object candidates
   *
   * @return true if service successful
   */
  bool
  getTablesService2 (cob_3d_mapping_msgs::GetTablesRequest &req,
                     cob_3d_mapping_msgs::GetTablesResponse &res)
  {
    ROS_INFO("table detection started....");

    cob_3d_mapping_msgs::ShapeArray sa, tables;
    if (getMapService (sa))
    {
      tables.header = sa.header;
      //test
      tables.header.frame_id = "/map" ;
      //end of test
      processMap(sa, tables);

      for (unsigned int i = 0; i < tables.shapes.size (); i++)
      {
        //Polygon poly;
        Polygon::Ptr poly_ptr(new Polygon());
        fromROSMsg(tables.shapes[i], *poly_ptr);

        cob_3d_mapping_msgs::Table table;
        Eigen::Affine3f pose;
        Eigen::Vector4f min_pt;
        Eigen::Vector4f max_pt;
        poly_ptr->computePoseAndBoundingBox(pose,min_pt, max_pt);
        table.pose.pose.position.x = pose.translation()(0); //poly_ptr->centroid[0];
        table.pose.pose.position.y = pose.translation()(1) ;//poly_ptr->centroid[1];
        table.pose.pose.position.z = pose.translation()(2) ;//poly_ptr->centroid[2];
        Eigen::Quaternionf quat(pose.rotation());
        //            ROS_WARN("poly_ptr->centroid[0]");
        //            std::cout << poly_ptr->centroid[0]<< "\n";
        //            ROS_WARN("pose.translation()");
        //            std::cout << pose.translation() << "\n" ;

        table.pose.pose.orientation.x = quat.x();
        table.pose.pose.orientation.y = quat.y();
        table.pose.pose.orientation.z = quat.z();
        table.pose.pose.orientation.w = quat.w();
        table.x_min = min_pt(0);
        table.x_max = max_pt(0);
        table.y_min = min_pt(1);
        table.y_max = max_pt(1);

        table.convex_hull.type = arm_navigation_msgs::Shape::MESH;
        for( unsigned int j=0; j<poly_ptr->contours[0].size(); j++)
        {
          geometry_msgs::Point p;
          p.x = poly_ptr->contours[0][j](0);
          p.y = poly_ptr->contours[0][j](1);
          p.z = poly_ptr->contours[0][j](2);
          table.convex_hull.vertices.push_back(p);
        }

        cob_3d_mapping_msgs::TabletopDetectionResult det;
        det.table = table;

        res.tables.push_back(det);
      }
      //        sa_pub_.publish (tables);
      publishShapeMarker (tables);
      table_ctr_ = tables.shapes.size();

      ROS_INFO("Found %d tables", table_ctr_);
      return true;
    }
    else
      return false;
  }
コード例 #23
0
ファイル: world_model.hpp プロジェクト: 2php/pcl
void 
pcl::kinfuLS::WorldModel<PointT>::getExistingData(const double previous_origin_x, const double previous_origin_y, const double previous_origin_z, const double offset_x, const double offset_y, const double offset_z, const double volume_x, const double volume_y, const double volume_z, pcl::PointCloud<PointT> &existing_slice)
{
  double newOriginX = previous_origin_x + offset_x; 
  double newOriginY = previous_origin_y + offset_y; 
  double newOriginZ = previous_origin_z + offset_z;
  double newLimitX = newOriginX + volume_x; 
  double newLimitY = newOriginY + volume_y; 
  double newLimitZ = newOriginZ + volume_z;
	
  // filter points in the space of the new cube
  PointCloudPtr newCube (new pcl::PointCloud<PointT>);
  // condition
  ConditionAndPtr range_condAND (new pcl::ConditionAnd<PointT> ());
  range_condAND->addComparison (FieldComparisonConstPtr (new pcl::FieldComparison<PointT> ("x", pcl::ComparisonOps::GE, newOriginX)));
  range_condAND->addComparison (FieldComparisonConstPtr (new pcl::FieldComparison<PointT> ("x", pcl::ComparisonOps::LT, newLimitX)));
  range_condAND->addComparison (FieldComparisonConstPtr (new pcl::FieldComparison<PointT> ("y", pcl::ComparisonOps::GE, newOriginY)));
  range_condAND->addComparison (FieldComparisonConstPtr (new pcl::FieldComparison<PointT> ("y", pcl::ComparisonOps::LT, newLimitY)));
  range_condAND->addComparison (FieldComparisonConstPtr (new pcl::FieldComparison<PointT> ("z", pcl::ComparisonOps::GE, newOriginZ)));
  range_condAND->addComparison (FieldComparisonConstPtr (new pcl::FieldComparison<PointT> ("z", pcl::ComparisonOps::LT, newLimitZ))); 
  
  // build the filter
  pcl::ConditionalRemoval<PointT> condremAND (true);
  condremAND.setCondition (range_condAND);
  condremAND.setInputCloud (world_);
  condremAND.setKeepOrganized (false);
  
  // apply filter
  condremAND.filter (*newCube);
	
  // filter points that belong to the new slice
  ConditionOrPtr range_condOR (new pcl::ConditionOr<PointT> ());
  
  if(offset_x >= 0)
	range_condOR->addComparison (FieldComparisonConstPtr (new pcl::FieldComparison<PointT> ("x", pcl::ComparisonOps::GE,  previous_origin_x + volume_x - 1.0 )));
  else
	range_condOR->addComparison (FieldComparisonConstPtr (new pcl::FieldComparison<PointT> ("x", pcl::ComparisonOps::LT,  previous_origin_x )));
	
  if(offset_y >= 0)
	range_condOR->addComparison (FieldComparisonConstPtr (new pcl::FieldComparison<PointT> ("y", pcl::ComparisonOps::GE,  previous_origin_y + volume_y - 1.0 )));
  else
	range_condOR->addComparison (FieldComparisonConstPtr (new pcl::FieldComparison<PointT> ("y", pcl::ComparisonOps::LT,  previous_origin_y )));
	
  if(offset_z >= 0)
	range_condOR->addComparison (FieldComparisonConstPtr (new pcl::FieldComparison<PointT> ("z", pcl::ComparisonOps::GE,  previous_origin_z + volume_z - 1.0 )));
  else
	range_condOR->addComparison (FieldComparisonConstPtr (new pcl::FieldComparison<PointT> ("z", pcl::ComparisonOps::LT,  previous_origin_z )));
  
  // build the filter
  pcl::ConditionalRemoval<PointT> condrem (true);
  condrem.setCondition (range_condOR);
  condrem.setInputCloud (newCube);
  condrem.setKeepOrganized (false);
  // apply filter
  condrem.filter (existing_slice);  
 
  if(existing_slice.points.size () != 0)
  {
	//transform the slice in new cube coordinates
	Eigen::Affine3f transformation; 
	transformation.translation ()[0] = newOriginX;
	transformation.translation ()[1] = newOriginY;
	transformation.translation ()[2] = newOriginZ;
		
	transformation.linear ().setIdentity ();

	transformPointCloud (existing_slice, existing_slice, transformation.inverse ());
	
  }
}
コード例 #24
0
ファイル: test_transforms.cpp プロジェクト: 4ker/pcl
TEST (PCL, Matrix4Affine3Transform)
{
  float rot_x = 2.8827f;
  float rot_y = -0.31190f;
  float rot_z = -0.93058f;
  Eigen::Affine3f affine;
  pcl::getTransformation (0, 0, 0, rot_x, rot_y, rot_z, affine);

  EXPECT_NEAR (affine (0, 0),  0.56854731f, 1e-4); EXPECT_NEAR (affine (0, 1), -0.82217032f, 1e-4); EXPECT_NEAR (affine (0, 2), -0.028107658f, 1e-4);
  EXPECT_NEAR (affine (1, 0), -0.76327348f, 1e-4); EXPECT_NEAR (affine (1, 1), -0.51445758f, 1e-4); EXPECT_NEAR (affine (1, 2), -0.39082864f, 1e-4);
  EXPECT_NEAR (affine (2, 0),  0.30686751f, 1e-4); EXPECT_NEAR (affine (2, 1),  0.24365838f, 1e-4); EXPECT_NEAR (affine (2, 2), -0.920034f, 1e-4);

  // Approximative!!! Uses SVD internally! See http://eigen.tuxfamily.org/dox/Transform_8h_source.html
  Eigen::Matrix3f rotation = affine.rotation ();

  EXPECT_NEAR (rotation (0, 0),  0.56854731f, 1e-4); EXPECT_NEAR (rotation (0, 1), -0.82217032f, 1e-4); EXPECT_NEAR (rotation (0, 2), -0.028107658f, 1e-4);
  EXPECT_NEAR (rotation (1, 0), -0.76327348f, 1e-4); EXPECT_NEAR (rotation (1, 1), -0.51445758f, 1e-4); EXPECT_NEAR (rotation (1, 2), -0.39082864f, 1e-4);
  EXPECT_NEAR (rotation (2, 0),  0.30686751f, 1e-4); EXPECT_NEAR (rotation (2, 1),  0.24365838f, 1e-4); EXPECT_NEAR (rotation (2, 2), -0.920034f, 1e-4);

  float trans_x, trans_y, trans_z;
  pcl::getTransformation (0.1f, 0.2f, 0.3f, rot_x, rot_y, rot_z, affine);
  pcl::getTranslationAndEulerAngles (affine, trans_x, trans_y, trans_z, rot_x, rot_y, rot_z);
  EXPECT_FLOAT_EQ (trans_x, 0.1f);
  EXPECT_FLOAT_EQ (trans_y, 0.2f);
  EXPECT_FLOAT_EQ (trans_z, 0.3f);
  EXPECT_FLOAT_EQ (rot_x, 2.8827f);
  EXPECT_FLOAT_EQ (rot_y, -0.31190f);
  EXPECT_FLOAT_EQ (rot_z, -0.93058f);

  Eigen::Matrix4f transformation (Eigen::Matrix4f::Identity ());
  transformation.block<3, 3> (0, 0) = affine.rotation ();
  transformation.block<3, 1> (0, 3) = affine.translation ();

  PointXYZ p (1.f, 2.f, 3.f);
  Eigen::Vector3f v3 = p.getVector3fMap ();
  Eigen::Vector4f v4 = p.getVector4fMap ();

  Eigen::Vector3f v3t (affine * v3);
  Eigen::Vector4f v4t (transformation * v4);

  PointXYZ pt = pcl::transformPoint (p, affine);

  EXPECT_NEAR (pt.x, v3t.x (), 1e-4); EXPECT_NEAR (pt.x, v4t.x (), 1e-4);
  EXPECT_NEAR (pt.y, v3t.y (), 1e-4); EXPECT_NEAR (pt.y, v4t.y (), 1e-4);
  EXPECT_NEAR (pt.z, v3t.z (), 1e-4); EXPECT_NEAR (pt.z, v4t.z (), 1e-4);

  PointNormal pn;
  pn.getVector3fMap () = p.getVector3fMap ();
  pn.getNormalVector3fMap () = Eigen::Vector3f (0.60f, 0.48f, 0.64f);
  Eigen::Vector3f n3 = pn.getNormalVector3fMap ();
  Eigen::Vector4f n4 = pn.getNormalVector4fMap ();

  Eigen::Vector3f n3t (affine.rotation() * n3);
  Eigen::Vector4f n4t (transformation * n4);

  PointNormal pnt = pcl::transformPointWithNormal (pn, affine);

  EXPECT_NEAR (pnt.x, v3t.x (), 1e-4); EXPECT_NEAR (pnt.x, v4t.x (), 1e-4);
  EXPECT_NEAR (pnt.y, v3t.y (), 1e-4); EXPECT_NEAR (pnt.y, v4t.y (), 1e-4);
  EXPECT_NEAR (pnt.z, v3t.z (), 1e-4); EXPECT_NEAR (pnt.z, v4t.z (), 1e-4);
  EXPECT_NEAR (pnt.normal_x, n3t.x (), 1e-4); EXPECT_NEAR (pnt.normal_x, n4t.x (), 1e-4);
  EXPECT_NEAR (pnt.normal_y, n3t.y (), 1e-4); EXPECT_NEAR (pnt.normal_y, n4t.y (), 1e-4);
  EXPECT_NEAR (pnt.normal_z, n3t.z (), 1e-4); EXPECT_NEAR (pnt.normal_z, n4t.z (), 1e-4);

  PointCloud<PointXYZ> c, ct;
  c.push_back (p);
  pcl::transformPointCloud (c, ct, affine);
  EXPECT_FLOAT_EQ (pt.x, ct[0].x); 
  EXPECT_FLOAT_EQ (pt.y, ct[0].y); 
  EXPECT_FLOAT_EQ (pt.z, ct[0].z); 

  pcl::transformPointCloud (c, ct, transformation);
  EXPECT_FLOAT_EQ (pt.x, ct[0].x); 
  EXPECT_FLOAT_EQ (pt.y, ct[0].y); 
  EXPECT_FLOAT_EQ (pt.z, ct[0].z); 

  affine = transformation;

  std::vector<int> indices (1); indices[0] = 0;

  pcl::transformPointCloud (c, indices, ct, affine);
  EXPECT_FLOAT_EQ (pt.x, ct[0].x); 
  EXPECT_FLOAT_EQ (pt.y, ct[0].y); 
  EXPECT_FLOAT_EQ (pt.z, ct[0].z); 

  pcl::transformPointCloud (c, indices, ct, transformation);
  EXPECT_FLOAT_EQ (pt.x, ct[0].x); 
  EXPECT_FLOAT_EQ (pt.y, ct[0].y); 
  EXPECT_FLOAT_EQ (pt.z, ct[0].z); 
}
コード例 #25
0
void cloud_cb (const sensor_msgs::PointCloud2Ptr& input)
{
	int is_save = false;
	int is_predict = true;

	// Create a container for the data.
	pcl::PointCloud<pcl::PointXYZI>::Ptr conv_input(new pcl::PointCloud<pcl::PointXYZI>());
	
	input->fields[3].name = "intensity";
	pcl::fromROSMsg(*input, *conv_input);

	// Size of humansize cloud
	int cloud_size = conv_input->points.size();
	std::cout << "cloud_size: " << cloud_size << std::endl;

	if( cloud_size <= 1 ){
		return;
	}

	pcl::PCA<pcl::PointXYZI> pca;
	pcl::PointCloud<pcl::PointXYZI>::Ptr transform_cloud_translate(new pcl::PointCloud<pcl::PointXYZI>());
	pcl::PointCloud<pcl::PointXYZI>::Ptr transform_cloud_rotate(new pcl::PointCloud<pcl::PointXYZI>());
	sensor_msgs::PointCloud2 output;
	geometry_msgs::PointStamped::Ptr output_point(new geometry_msgs::PointStamped());
	geometry_msgs::PointStamped output_point_;
	Eigen::Vector3f eigen_values;
	Eigen::Vector4f centroid;
	Eigen::Matrix3f eigen_vectors;
	Eigen::Affine3f translate = Eigen::Affine3f::Identity();
	Eigen::Affine3f rotate = Eigen::Affine3f::Identity();
	double theta;
	std::vector<double> description;
	description.push_back(cloud_size);

	pcl::PointCloud<pcl::PointNormal>::Ptr normals(new pcl::PointCloud<pcl::PointNormal>());
	pcl::NormalEstimation<pcl::PointXYZI, pcl::PointNormal> ne;
	ne.setInputCloud(conv_input);
	ne.setKSearch(24);
	ne.compute(*normals);

	pcl::FPFHEstimation<pcl::PointXYZI, pcl::PointNormal, pcl::FPFHSignature33> fpfh;
	fpfh.setInputCloud(conv_input);
	fpfh.setInputNormals(normals);

	pcl::search::KdTree<pcl::PointXYZI>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZI>());
	fpfh.setSearchMethod(tree);

	pcl::PointCloud<pcl::FPFHSignature33>::Ptr fpfhs(new pcl::PointCloud<pcl::FPFHSignature33>());
	fpfh.setRadiusSearch(0.05);
	fpfh.compute(*fpfhs);

	// for(int i=0;i<fpfhs.histogram.size();i++){
		std::cout << fpfhs->points.size();
	// }
	std::cout << std::endl;

	//PCA
	pca.setInputCloud(conv_input);
	centroid = pca.getMean();
	std::cout << "Centroid of pointcloud: " << centroid[0] << ", " << centroid[1] << ", " << centroid[2] << std::endl;
	eigen_values = pca.getEigenValues();
	std::cout << "Eigen values of PCAed pointcloud: " << eigen_values[0] << ", "  << eigen_values[1] << ", " << eigen_values[2] << std::endl;
	eigen_vectors << pca.getEigenVectors();
	std::cout << "Eigen Vectors of PCAed pointcloud:" << std::endl;
	for(int i=0;i<eigen_vectors.rows()*eigen_vectors.cols();i++){
		std::cout << eigen_vectors(i) << " ";
		if(!((i+1)%3)){
			std::cout << std::endl;
		}
	}
	std::cout << std::endl;

	//Rotation cloud from PCA data
	theta = atan2(eigen_vectors(1,0), eigen_vectors(0,0));
	std::cout << "Theta: " << theta << std::endl;
	translate.translation() << -centroid[0], -centroid[1], -centroid[2];
	pcl::transformPointCloud(*conv_input, *transform_cloud_translate, translate);

	rotate.rotate(Eigen::AngleAxisf(-theta, Eigen::Vector3f::UnitZ()));
	pcl::transformPointCloud(*transform_cloud_translate, *transform_cloud_rotate, rotate);

	pcl::toROSMsg(*transform_cloud_rotate, output);
	output.header = input -> header;
	output.header.frame_id = "map";

	pub.publish(output);

	pcl::PointXYZI searchPoint;
	searchPoint.x = 0.0;
	searchPoint.y = 0.0;
	searchPoint.z = 0.0;

	float min_distance,min_distance_tmp;
	min_distance = sqrt(
		powf( conv_input->points[0].x - searchPoint.x, 2.0f ) +
		powf( conv_input->points[0].y - searchPoint.y, 2.0f ) +
		powf( conv_input->points[0].z - searchPoint.z, 2.0f )
	);

	//brute force nearest neighbor search
	for(int i=1; i<conv_input->size(); i++){
		min_distance_tmp = sqrt(
			powf( conv_input->points[i].x - searchPoint.x, 2.0f ) +
			powf( conv_input->points[i].y - searchPoint.y, 2.0f ) +
			powf( conv_input->points[i].z - searchPoint.z, 2.0f )
		);
		if( min_distance < min_distance_tmp){
			min_distance = min_distance_tmp;
		}
	}

	std::cout << "min_distance: " << min_distance << std::endl;
	std::cout << std::endl;

	description.push_back(min_distance);

	// Calculate center of mass of humansize cloud
	Vector4f center_of_mass;
	pcl::compute3DCentroid(*transform_cloud_rotate, center_of_mass);
	MatrixXf convariance_matrix = MatrixXf::Zero(3,3);
	MatrixXf convariance_matrix_tmp = MatrixXf::Zero(3,3);
	MatrixXf moment_of_inertia_matrix_tmp = MatrixXf::Zero(3,3);
	MatrixXf moment_of_inertia_matrix = MatrixXf::Zero(3,3);
	Vector3f point_tmp;
	Vector3f point_from_center_of_mass;

	// intensity buffer
	double intensity_sum=0, intensity_ave=0, intensity_pow_sum=0, intensity_std_dev=0, max_intensity = 5000;
	std::vector<double> intensity_histgram(25);

	for(int i=0; i<cloud_size; i++){
		point_tmp << transform_cloud_rotate->points[i].x, transform_cloud_rotate->points[i].y, transform_cloud_rotate->points[i].z;
		//Calculate three dimentional convariance matrix
		point_from_center_of_mass <<
		center_of_mass[1] - point_tmp[1],
		center_of_mass[0] - point_tmp[0],
		center_of_mass[2] - point_tmp[2];

		convariance_matrix_tmp += point_tmp * point_tmp.transpose();

		//Calculate three dimentional moment of inertia matrix
		moment_of_inertia_matrix_tmp << 
		powf(point_tmp[1],2.0f)+powf(point_tmp[2],2.0f),	-point_tmp[0]*point_tmp[1],							-point_tmp[0]*point_tmp[2],
		-point_tmp[0]*point_tmp[1],							powf(point_tmp[0],2.0f)+powf(point_tmp[2],2.0f),	-point_tmp[1]*point_tmp[2],
		-point_tmp[0]*point_tmp[2],							-point_tmp[1]*point_tmp[2],							powf(point_tmp[0],2.0f)+powf(point_tmp[1],2.0f);

		moment_of_inertia_matrix = moment_of_inertia_matrix + moment_of_inertia_matrix_tmp;

		// Calculate intensity distribution
		intensity_sum += transform_cloud_rotate->points[i].intensity;
		intensity_pow_sum += powf(transform_cloud_rotate->points[i].intensity,2);
		intensity_histgram[transform_cloud_rotate->points[i].intensity / (max_intensity / intensity_histgram.size())] += 1;
		if( g_max_inten < transform_cloud_rotate->points[i].intensity ){
			g_max_inten = transform_cloud_rotate->points[i].intensity;
		}
	}
	//Calculate Convariance matrix 
	convariance_matrix = (1.0f/cloud_size) * convariance_matrix_tmp.array();	

	// Calculate intensity distribution
	intensity_ave = intensity_sum / cloud_size;
	std::cout << "max_intensity: " << g_max_inten << std::endl;
	std::cout << "intensity_ave: " << intensity_ave << std::endl;
	intensity_std_dev = sqrt(fabs(intensity_pow_sum / cloud_size - powf(intensity_ave,2)));
	std::cout << "intensity_std_dev: " << intensity_std_dev <<std::endl;
	std::cout << "intensity_histgram: ";
	description.push_back(intensity_ave);
	description.push_back(intensity_std_dev);

	for(int i=0; i<intensity_histgram.size(); i++){
		intensity_histgram[i] = intensity_histgram[i] / cloud_size;
		std::cout << intensity_histgram[i] << " ";
		description.push_back(intensity_histgram[i]);
	}
	std::cout << std::endl;
	std::cout << std::endl;

	std::cout << "3D convariance matrix:" << std::endl;
	for(int i=0;i<convariance_matrix.rows()*convariance_matrix.cols();i++){
		if(i<5 || i==6){
			description.push_back(convariance_matrix(i));
		}
		std::cout << convariance_matrix(i) << " ";
		if(!((i+1)%3)){
			std::cout << std::endl;
		}
	}
	std::cout << std::endl;

	std::cout << "moment of inertia:" << std::endl;
	for(int i=0;i<moment_of_inertia_matrix.rows()*moment_of_inertia_matrix.cols();i++){
		if(i<5 || i==6){
			description.push_back(convariance_matrix(i));
		}
		std::cout << moment_of_inertia_matrix(i) << " ";
		if(!((i+1)%3)){
			std::cout << std::endl;
		}
	}
	std::cout << std::endl;

	//Calculate Slice distribution
	pcl::PointXYZI min_pt,max_pt,sliced_min_pt,sliced_max_pt;
	pcl::getMinMax3D(*transform_cloud_rotate, min_pt, max_pt);
	int sectors = 10;
	double sector_height = (max_pt.z - min_pt.z)/sectors;

	pcl::PassThrough<pcl::PointXYZI> pass;
	std::vector<std::vector<double> > slice_dist(sectors,std::vector<double> (2));
	
	for(double sec_h = min_pt.z,i = 0; sec_h < max_pt.z - sector_height; sec_h += sector_height, i++){
		pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_sliced(new pcl::PointCloud<pcl::PointXYZI>());
		pass.setInputCloud(transform_cloud_rotate);
		pass.setFilterFieldName("z");
		pass.setFilterLimits(sec_h, sec_h + sector_height);
		pass.filter(*cloud_sliced);

		pcl::getMinMax3D(*cloud_sliced,sliced_min_pt,sliced_max_pt);

		slice_dist[i][0] = sliced_max_pt.x - sliced_min_pt.x;
		slice_dist[i][1] = sliced_max_pt.y - sliced_min_pt.y;
	}

	std::cout << "slice distribution:" << std::endl;
	for(int i=0;i<2;i++){
		for(int j=0;j<10;j++){
			description.push_back(slice_dist[j][i]);
			std::cout << slice_dist[j][i] << " ";
		}
		std::cout << std::endl;
	}
	std::cout << std::endl;

	if(is_save){
		std::ofstream ofs;
		ofs.open("description.txt", std::ios::ate | std::ios::app);
		ofs << "1 ";
		for(int i=0;i<description.size();i++){
			ofs << i+1 << ":" << description[i] << " ";
		}
		ofs << std::endl;
		ofs.close();
	}

	if(is_predict){
		FILE *fp_scale, *fp_model;
		int idx,max_index;
		std::vector<double> scale_min;
		std::vector<double> scale_max;
		double scale_min_tmp;
		double scale_max_tmp;
		double lower,upper;
		double predict;
		struct svm_model* model = svm_load_model(
			model_filename.c_str());
		struct svm_node *nodes;
		nodes = (struct svm_node *)malloc((description.size()+1)*sizeof(struct svm_node));
		fp_scale = fopen(scale_filename.c_str(),"r");
		if(fp_scale == NULL || model == NULL){
			ROS_ERROR_STREAM("Can't find model or scale data");
			return;
		}
		if(fgetc(fp_scale) != 'x'){
			return;
		}
		fscanf(fp_scale,"%lf %lf",&lower,&upper);
		
		// ROS_INFO_STREAM("lower: " << lower << "upper: "<< upper);
		while(fscanf(fp_scale,"%d %lf %lf\n",&idx,&scale_min_tmp,&scale_max_tmp) == 3){
			scale_min.push_back(scale_min_tmp);
			scale_max.push_back(scale_max_tmp);
		}
		std::cout << "scaled description :";
		int index = 0;
		for(int i=0;i<description.size();i++){
			if(i<24 || i>28){
				if(description[i] == DBL_INF || description[i] == DBL_MINF){
					description[i] = 0;
				}
				if(description[i] < scale_min[index]){
					nodes[index].index = i+1;
					nodes[index].value = lower;
				}
				else if(description[i] > scale_max[index]){
					nodes[index].index = i+1;
					nodes[index].value = upper;
				}
				else{
					nodes[index].index = i+1;
					nodes[index].value = lower + (upper-lower) * 
							(description[i]-scale_min[index])/
							(scale_max[index]-scale_min[index]);
				}
				std::cout << nodes[index].index << ":" << nodes[index].value << ",";
				index++;
			}
		}
		nodes[index].index = -1;
		std::cout << std::endl;
		predict = svm_predict(model,nodes);
		ROS_INFO_STREAM("Predict" << predict);

		if(predict == 1){
			Vector4f center_of_mass_base;
			pcl::compute3DCentroid(*conv_input, center_of_mass_base);
			output_point->point.x = center_of_mass_base[0];
			output_point->point.y = center_of_mass_base[1];
			if(std::abs(center_of_mass_base[1]) > 2.0){
				if(center_of_mass_base[1] < 0){
					output_point->point.y = center_of_mass_base[1] + 1.0;
				}
				else{
					output_point->point.y = center_of_mass_base[1] - 1.0;
				}
			}
			else{
				output_point->point.x = center_of_mass_base[0] - 2.0;
			}
			output_point->point.z = 0.0;
			ROS_INFO_STREAM("target_center :" << center_of_mass_base[1] << "," << center_of_mass_base[0] << "," << center_of_mass_base[2]);
			output_point->header.frame_id = "base_link";
			output_point->header.stamp = input->header.stamp;
			try{
				listener->transformPoint("/map", *output_point, output_point_);
			}
			catch(tf::TransformException &e){
				ROS_WARN_STREAM("tf::TransformException: " << e.what());
			}
			pub_point.publish(output_point_);
		}
	}
}
コード例 #26
0
ファイル: camera.hpp プロジェクト: LCG-UFRJ/tucano
 /**
  * @brief Returns the center of the camera in world space.
  * @return Camera center
  */
 Eigen::Vector3f getCenter (void) const
 {
     return view_matrix.linear().inverse() * (-view_matrix.translation());
 }
コード例 #27
0
void rawCloudHandler(const sensor_msgs::PointCloud2ConstPtr& laserCloud)
{
	//double timeScanCur = laserCloud->header.stamp.toSec();
	ros::Time timestamp = laserCloud->header.stamp;
	//bool returnBool;

	pcl::fromROSMsg(*laserCloud, *laserCloudIn);

	if (visualizationFlag)
	{
	viewer->removeAllPointClouds(0);
	viewer->removeAllShapes(0);
	}

	pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloudRGB(new pcl::PointCloud<pcl::PointXYZRGB>);
	pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloudLabeled(new pcl::PointCloud<pcl::PointXYZRGB>);
	pcl::PointCloud<pcl::PointXYZI>::Ptr cloudRaw(new pcl::PointCloud<pcl::PointXYZI>);
	//	std::vector<std::string> labelsC1;
	//	std::vector<std::string> labelsC2;
	//	pcl::PointCloud<SAFEFeatures>::Ptr featureCloudC1Acc(new pcl::PointCloud<SAFEFeatures>);
	//	pcl::PointCloud<SAFEFeatures>::Ptr featureCloudC2Acc(new pcl::PointCloud<SAFEFeatures>);
	std::vector<std::string> labelsC1;
	std::vector<std::string> labelsC2;
	std::vector<std::string> labelsC3;
	std::vector<std::string> labels;
	pcl::PointCloud<AllFeatures>::Ptr featureCloudAcc(new pcl::PointCloud<AllFeatures>);
	pcl::PointCloud<AllFeatures>::Ptr featureCloudC1Acc(new pcl::PointCloud<AllFeatures>);
	pcl::PointCloud<AllFeatures>::Ptr featureCloudC2Acc(new pcl::PointCloud<AllFeatures>);
	pcl::PointCloud<AllFeatures>::Ptr featureCloudC3Acc(new pcl::PointCloud<AllFeatures>);
	std::vector<Eigen::Matrix3f> confMats;
	pcl::PointCloud<pcl::PointXYZRGB>::Ptr classificationCloud(new pcl::PointCloud<pcl::PointXYZRGB>);

	std::vector<DatasetContainer> dataset;

	std::string folder = "/home/mikkel/catkin_ws/src/trainingSet/";

	pcl::copyPointCloud(*laserCloudIn, *cloudRGB);
	pcl::copyPointCloud(*laserCloudIn, *cloudRaw);

	// Single colored
	if (visualizationFlag)
		{
		pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZRGB> color1(cloudRGB, 0, 0, 255);
	viewer->addPointCloud<pcl::PointXYZRGB>(cloudRGB,color1,"cloudRGB",viewP(RawCloud));
		}
	//viewer->addText ("RawCloud", 10, 10, "vPP text", viewP(RawCloud));

	// Ground modeling
	//	pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloudStat(new pcl::PointCloud<pcl::PointXYZRGB>);
	//	pcl::PointCloud<pcl::PointXYZ>::Ptr planeDistCloud(new pcl::PointCloud<pcl::PointXYZ>);
	//	pcl::copyPointCloud(*cloudRGB,*cloudStat);
	//	pcl::copyPointCloud(*cloudRGB,*planeDistCloud);


	// Remove everything outside the two lines
	pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZI>);
	//pcl::copyPointCloud(*cloudRaw, *cloud_filtered);

	// Rotation
	float thetaZ = theta*PI/180; // The angle of rotation in radians
	Eigen::Affine3f transform_2 = Eigen::Affine3f::Identity();
	transform_2.rotate (Eigen::AngleAxisf (thetaZ, Eigen::Vector3f::UnitZ()));
	pcl::PointCloud<pcl::PointXYZ>::Ptr transformed_cloud (new pcl::PointCloud<pcl::PointXYZ> ());
	pcl::transformPointCloud (*cloudRaw, *cloud_filtered, transform_2);

	// Passthrough
	pcl::PassThrough<pcl::PointXYZI> passX;
	passX.setInputCloud (cloud_filtered);
	passX.setFilterFieldName ("x");
	passX.setFilterLimits (-0.5, 100.0);
	//pass.setFilterLimitsNegative (true);
	passX.filter (*cloud_filtered);

	pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_filteredRGB(new pcl::PointCloud<pcl::PointXYZRGB>);
	if (receivedHough)
	{
		pcl::PassThrough<pcl::PointXYZI> pass;
		pass.setInputCloud (cloud_filtered);
		pass.setFilterFieldName ("y");
		pass.setFilterLimits (r1+WALL_CLEARANCE, r2-WALL_CLEARANCE);
		//pass.setFilterLimitsNegative (true);
		pass.filter (*cloud_filtered);
	}

	pcl::copyPointCloud(*cloud_filtered, *cloud_filteredRGB);
	if (visualizationFlag)
		{
	pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZRGB> color9(cloudRGB, 0, 0, 255);
	viewer->addPointCloud<pcl::PointXYZRGB>(cloud_filteredRGB,color9,"LinesFiltered",viewP(LinesFiltered));
	viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "LinesFiltered");
	viewer->addText ("LinesFiltered", 10, 10, fontsize, 1, 1, 1, "LinesFiltered text", viewP(LinesFiltered));
		}


	// Grid Minimum
//	pcl::PointCloud<pcl::PointXYZI>::Ptr gridCloud(new pcl::PointCloud<pcl::PointXYZI>);
//	pcl::GridMinimum<pcl::PointXYZI> gridm(1.0); // Set grid resolution
//	gridm.setInputCloud(cloud_filtered);
//	gridm.filter(*gridCloud);

	//*** Transform point cloud to adjust for a Ground Plane ***//
	pcl::ModelCoefficients ground_coefficients;
	pcl::PointIndices ground_indices;
	pcl::SACSegmentation<pcl::PointXYZI> ground_finder;
	ground_finder.setOptimizeCoefficients(true);
	ground_finder.setModelType(pcl::SACMODEL_PLANE);
	ground_finder.setMethodType(pcl::SAC_RANSAC);
	ground_finder.setDistanceThreshold(0.15);
	ground_finder.setInputCloud(cloud_filtered);
	ground_finder.segment(ground_indices, ground_coefficients);

	// Convert plane normal vector from type ModelCoefficients to Vector4f
	Eigen::Vector3f np = Eigen::Vector3f(ground_coefficients.values[0],ground_coefficients.values[1],ground_coefficients.values[2]);
	// Find rotation vector, u, and angle, theta
	Eigen::Vector3f u = np.cross(Eigen::Vector3f(0,0,1));
	u.normalize();
	float theta = acos(np.dot(Eigen::Vector3f(0,0,1)));
	// Construct transformation matrix (rotation matrix from axis and angle)
	Eigen::Affine3f tf = Eigen::Affine3f::Identity();
	float d = ground_coefficients.values[3];
	tf.translation() << d*np[0], d*np[1], d*np[2];
	tf.rotate (Eigen::AngleAxisf (theta, u));
	// Execute the transformation
	pcl::PointCloud<pcl::PointXYZI>::Ptr transformedCloud (new pcl::PointCloud<pcl::PointXYZI> ());
	pcl::transformPointCloud(*cloud_filtered, *transformedCloud, tf);


	// Compute statistical moments for least significant direction in neighborhood
#if (OLD_METHOD)
	pcl::NeighborhoodFeatures<pcl::PointXYZI, AllFeatures> features;
	pcl::PointCloud<AllFeatures>::Ptr featureCloud(new pcl::PointCloud<AllFeatures>);
	features.setInputCloud(transformedCloud);
	pcl::search::KdTree<pcl::PointXYZI>::Ptr search_tree5( new pcl::search::KdTree<pcl::PointXYZI>());
	features.setSearchMethod(search_tree5);
	//principalComponentsAnalysis.setRadiusSearch(0.6);
	//features.setKSearch(30);
	features.setRadiusSearch(0.01); // This value does not do anything! Look inside "NeighborgoodFeatures.h" to see adaptive radius calculation.
	features.compute(*featureCloud);

	//	ros::Time tFeatureCalculation2 = ros::Time::now();
	//	ros::Duration tFeatureCalculation = tFeatureCalculation2-tPreprocessing2;
	//	if (executionTimes==true)
	//		ROS_INFO("Feature calculation time = %i",tFeatureCalculation.nsec);



	visualizeFeature(*cloudRGB, *featureCloud, 0, viewP(GPDistMean), "GPDistMean");
	visualizeFeature(*cloudRGB, *featureCloud, 1, viewP(GPDistMin), "GPDistMin");
	visualizeFeature(*cloudRGB, *featureCloud, 2, viewP(GPDistPoint), "GPDistPoint");
	visualizeFeature(*cloudRGB, *featureCloud, 3, viewP(GPDistVar), "GPDistVar");
	visualizeFeature(*cloudRGB, *featureCloud, 4, viewP(PCA1), "PCA1"); // 4 = PCA1
	visualizeFeature(*cloudRGB, *featureCloud, 5, viewP(PCA2), "PCA2"); // 3 = GPVar
	visualizeFeature(*cloudRGB, *featureCloud, 6, viewP(PCA3), "PCA3"); // 0 = GPMean
	visualizeFeature(*cloudRGB, *featureCloud, 7, viewP(PCANX), "PCANX");
	visualizeFeature(*cloudRGB, *featureCloud, 8, viewP(PCANY), "PCANY");
	visualizeFeature(*cloudRGB, *featureCloud, 9, viewP(PCANZ), "PCANZ"); // 9 = PCANZ
	visualizeFeature(*cloudRGB, *featureCloud, 10, viewP(PointDist), "PointDist");
	visualizeFeature(*cloudRGB, *featureCloud, 11, viewP(RSS), "RSS");
	visualizeFeature(*cloudRGB, *featureCloud, 12, viewP(Reflectance), "Reflectance");

	ROS_INFO("Computed all neighborhood features");

	std::vector<float>* centroid = new std::vector<float>();
	std::vector<float>* stds = new std::vector<float>();

	//*** Testing ***//
	if (training == false)
	{
		Eigen::Matrix3f confMat = Eigen::Matrix3f::Zero();
		if (testdata==true)
		{
			*featureCloud = *featureCloudAcc;
		}

		pcl::copyPointCloud(*cloudRGB,*classificationCloud);

		// Load feature normalization parameters
		std::ifstream input_file((folder + "centroid").c_str());
		std::istream_iterator<float> start(input_file), end;
		std::vector<float> numbers(start, end);
		std::copy(numbers.begin(), numbers.end(), std::back_inserter(*centroid));
		std::ifstream input_file2((folder + "stds").c_str());
		std::istream_iterator<float> start2(input_file2), end2;
		std::vector<float> numbers2(start2, end2);
		std::copy(numbers2.begin(), numbers2.end(), std::back_inserter(*stds));


		// Normalize features
		//		if (pcl::io::savePCDFile ("testFeaturesNonNormalized.pcd", *featureCloud) != 0)
		//			return (false);

		ros::Time tNormalization1 = ros::Time::now();
		normalizeFeatures(*featureCloud,*featureCloud, &centroid, &stds);
		ros::Time tNormalization2 = ros::Time::now();
		ros::Duration tNormalization = tNormalization2-tNormalization1;
		if (executionTimes==true)
			ROS_INFO("Normalization time = %f",(float)(tNormalization.nsec)/1000000);

		pcl::PointIndices::Ptr unclassifiedIndices (new pcl::PointIndices ());
		//		if (pcl::io::savePCDFile ("testFeatures.pcd", *featureCloud) != 0)
		//			return (false);

		CvSVM SVM;
		//int dim = sizeof(featureCloud->points[0])/sizeof(float);
		int dim = useFeatures.size();//sizeof(useFeatures)/sizeof(int);

		SVM.load((folder + "svm").c_str());
		//SVM.load((folder + "svm2014-11-06-11-22-59").c_str());
		//SVM.load((folder + "svm2014-11-07-14-00-31").c_str());
		//SVM.load((folder + "svm2014-11-07-13-16-28").c_str());

		ros::Time tClassification1 = ros::Time::now();
		//int nMissingLabels = 0;
		for (size_t i=0;i<classificationCloud->points.size();i++)
		{
			float dataSVM[dim];
			for (int d=0;d<dim;d++)
			{
				//dataSVM[d] = featureCloud->points[i].data[d];
				dataSVM[d] = featureCloud->points[i].data[useFeatures[d]];
			}
			Mat labelsMat(1, dim, CV_32FC1, dataSVM);

			float response = SVM.predict(labelsMat);

			if (response == 1.0)
				classificationCloud->points[i].b = 255;
			else if (response == 2.0)
				classificationCloud->points[i].g = 255;
			else if (response == 3.0)
				classificationCloud->points[i].r = 255;
			else
				ROS_INFO("Another class label = %f",response);

			if (testdata==true)
			{
				int groundTruth;
				if (labels[i].compare("ground") == 0)
					groundTruth = 1;
				else if (labels[i].compare("vegetation") == 0)
					groundTruth = 2;
				else if (labels[i].compare("object") == 0)
					groundTruth = 3;
				confMat(groundTruth-1,(int)response-1)++;
			}
		}
		ros::Time tClassification2 = ros::Time::now();
		ros::Duration tClassification = tClassification2-tClassification1;
		if (executionTimes==true)
			ROS_INFO("Classification time = %f",(float)(tClassification.nsec)/100000);

		//ROS_INFO("Number of missing class labels = %i", nMissingLabels);

		pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> colorC(classificationCloud);
		viewer->addPointCloud<pcl::PointXYZRGB>(classificationCloud,colorC,"Classification",viewP(Classification));
		viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "Classification");
		viewer->addText ("Classification", 10, 10, fontsize, 1, 1, 1, "Classification text", viewP(Classification));

		// Test set - calculate performance statistics
		if (testdata == true)
		{
			std::cout << confMat << std::endl;
			confMats.push_back(confMat);

			if (true)//k==files.size()-1)
			{
				ofstream myfile;
				string resultsFolder = "/home/mikkel/catkin_ws/src/Results/";
				myfile.open ((resultsFolder + "run_number_here.txt").c_str());

				// Distance threshold
				myfile << "DistanceThreshold:" << distThr << ";\n";

				// Neighborhood parameters
				myfile << "Neighborhood:" << rmin << ";" << rmindist << ";" << rmax << ";" << rmaxdist << ";\n";

				// Features
				myfile << "Features:";
				for (int d=0;d<dim;d++)
					myfile << useFeatures[d] << ";";
				myfile << "\n";

				myfile << "ConfusionMatrix:";
				for (size_t i=0;i<confMats.size();i++)
				{
					for (int r=0;r<confMats[i].rows();r++)
						for (int c=0;c<confMats[i].cols();c++)
							myfile << confMats[i](r,c) << ";";
					myfile << "\n";
				}

				myfile << "Accuracy:" << confMat.diagonal().sum()/confMat.sum() << ";\n";


				myfile.close();
			}

		}
		featureCloudAcc->clear();
		labels.clear();

#else

		for (size_t i=0;i<transformedCloud->size();i++)
		{
			pcl::PointXYZI pTmp2 = transformedCloud->points[i];
			pcl::PointXYZRGB pTmp;
			pTmp.x = pTmp2.x;
			pTmp.y = pTmp2.y;
			pTmp.z = pTmp2.z;
			pTmp.r = 255;
			pTmp.g = 255;
			if (pTmp.z > 0.1) // Object
			{
				classificationCloud->push_back(pTmp);
			}
		}

		if (visualizationFlag)
		{
			pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> colorC(classificationCloud);
			viewer->addPointCloud<pcl::PointXYZRGB>(classificationCloud,colorC,"Classification",viewP(Classification));
			viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "Classification");
			viewer->addText ("Classification", 10, 10, fontsize, 1, 1, 1, "Classification text", viewP(Classification));
		}


#endif





		// REGION GROWING
		//ROS_INFO("region growing 5");
		pcl::PointCloud <pcl::PointXYZRGB>::Ptr objectCloud(new pcl::PointCloud<pcl::PointXYZRGB>);
		//pcl::PointCloud <pcl::PointXYZRGB>::Ptr cloud (new pcl::PointCloud <pcl::PointXYZRGB>);

		for (size_t i=0;i<classificationCloud->size();i++)
		{
			pcl::PointXYZRGB pTmp = classificationCloud->points[i];
			if ((pTmp.r == 255) || (pTmp.g == 255)) // Object
			{
				objectCloud->push_back(pTmp);
			}
		}

		pcl::RegionGrowing<pcl::PointXYZRGB, pcl::Normal> reg;

		reg.setInputCloud (objectCloud);
		//reg.setIndices (indices);
		pcl::search::Search<pcl::PointXYZRGB>::Ptr tree = boost::shared_ptr<pcl::search::Search<pcl::PointXYZRGB> > (new pcl::search::KdTree<pcl::PointXYZRGB>);
		reg.setSearchMethod (tree);
		//reg.setDistanceThreshold (0.0001f);
		//reg.setResidualThreshold(0.01f);
		//ROS_INFO("res = %f",reg.getSmoothnessThreshold());
		//reg.setPointColorThreshold (6);
		//reg.setRegionColorThreshold (5);
		reg.setMinClusterSize (1);
		reg.setResidualThreshold(min_cluster_distance);
		reg.setCurvatureTestFlag(false);
		//		reg.setResidualTestFlag(true);
		//		reg.setNormalTestFlag(false);
		//		reg.setSmoothModeFlag(false);
		std::vector <pcl::PointIndices> clusters;
		reg.extract (clusters);

		//ROS_INFO("clusters = %d", clusters.size());

		//std::cout << "testefter" << std::endl;

		pcl::PointCloud <pcl::PointXYZRGB>::Ptr colored_cloud = reg.getColoredCloud ();

		pcl::PointCloud <pcl::PointXYZRGB>::Ptr clustersFilteredCloud(new pcl::PointCloud<pcl::PointXYZRGB>);
		std::vector <pcl::PointIndices> clustersFiltered;
		//pcl::PointXYZRGB min_pt, max_pt;
		Eigen::Vector4f min_pt, max_pt;
		obstacle_detection::boundingbox bbox;
		obstacle_detection::boundingboxes bboxes;
		bboxes.header.stamp = timestamp;
		bboxes.header.frame_id = "/velodyne";

		if (visualizationFlag)
			viewer->removeAllShapes(viewP(SegmentsFiltered));


		for (size_t i=0;i<clusters.size();i++)
		{
			//ROS_INFO("cluster size = %d",clusters[i].indices.size());
			if (clusters[i].indices.size() > 100)
			{
				clustersFiltered.push_back(clusters[i]);

				for (size_t pp=0;pp<clusters[i].indices.size();pp++)
				{
					clustersFilteredCloud->push_back(colored_cloud->points[clusters[i].indices[pp]]);
				}

				// Calculate bounding box
				pcl::getMinMax3D(*colored_cloud, clusters[i], min_pt, max_pt);
				bbox.start.x = min_pt[0];
				bbox.start.y = min_pt[1];
				bbox.start.z = min_pt[2];
				bbox.width.x = max_pt[0]-min_pt[0];
				bbox.width.y = max_pt[1]-min_pt[1];
				bbox.width.z = max_pt[2]-min_pt[2];
				bbox.header.stamp = timestamp;
				bbox.header.frame_id = "/velodyne";

				bboxes.boundingboxes.push_back(bbox);

				if (visualizationFlag)
					viewer->addCube (min_pt[0], max_pt[0], min_pt[1], max_pt[1], min_pt[2], max_pt[2], 1.0f, 1.0f, 1.0f, (boost::format("%04d") % i).str().c_str(), viewP(SegmentsFiltered));
			}


		}

		pubBBoxesPointer->publish(bboxes);
		//
		//		//pcl::visualization::CloudViewer viewer ("Cluster viewer");
		//		//viewer.showCloud (colored_cloud);
		//
		if (visualizationFlag)
		{
		pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> colorC2(colored_cloud);
		viewer->addPointCloud<pcl::PointXYZRGB>(colored_cloud,colorC2,"Segments",viewP(Segments));
		viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "Segments");
		viewer->addText ("Segments", 10, 10, fontsize, 1, 1, 1, "Segments text", viewP(Segments));


		pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> colorC3(clustersFilteredCloud);
		viewer->addPointCloud<pcl::PointXYZRGB>(clustersFilteredCloud,colorC3,"SegmentsFiltered",viewP(SegmentsFiltered));
		viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "SegmentsFiltered");
		viewer->addText ("Segments", 10, 10, fontsize, 1, 1, 1, "SegmentsFiltered text", viewP(SegmentsFiltered));
		}
		//
		//
		//		// BOUNDING BOXES



		//		viewer->addCube (min_pt.x, max_pt.x, min_pt.y, max_pt.y, min_pt.z, max_pt.z);





		//		std_msgs::Float64 testF;
		//		//testF.data = 10;
		//		testF.data = 10;
		//



		// Compute principal directions
		//		Eigen::Vector4f pcaCentroid;
		//		pcl::compute3DCentroid(*clustersFilteredCloud, pcaCentroid);
		//		Eigen::Matrix3f covariance;
		//		computeCovarianceMatrixNormalized(*clustersFilteredCloud, pcaCentroid, covariance);
		//		Eigen::SelfAdjointEigenSolver<Eigen::Matrix3f> eigen_solver(covariance, Eigen::ComputeEigenvectors);
		//		Eigen::Matrix3f eigenVectorsPCA = eigen_solver.eigenvectors();
		//		eigenVectorsPCA.col(2) = eigenVectorsPCA.col(0).cross(eigenVectorsPCA.col(1));
		//
		//		// Transform the original cloud to the origin where the principal components correspond to the axes.
		//		Eigen::Matrix4f projectionTransform(Eigen::Matrix4f::Identity());
		//		projectionTransform.block<3,3>(0,0) = eigenVectorsPCA.transpose();
		//		projectionTransform.block<3,1>(0,3) = -1.f * (projectionTransform.block<3,3>(0,0) * pcaCentroid.head<3>());
		//		pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloudPointsProjected (new pcl::PointCloud<pcl::PointXYZRGB>);
		//		pcl::transformPointCloud(*clustersFilteredCloud, *cloudPointsProjected, projectionTransform);
		//		// Get the minimum and maximum points of the transformed cloud.
		//		pcl::PointXYZRGB minPoint, maxPoint;
		//		pcl::getMinMax3D(*cloudPointsProjected, minPoint, maxPoint);
		//		const Eigen::Vector3f meanDiagonal = 0.5f*(maxPoint.getVector3fMap() + minPoint.getVector3fMap());
		//
		//		// Final transform
		//		const Eigen::Quaternionf bboxQuaternion(eigenVectorsPCA); //Quaternions are a way to do rotations https://www.youtube.com/watch?v=mHVwd8gYLnI
		//		const Eigen::Vector3f bboxTransform = eigenVectorsPCA * meanDiagonal + pcaCentroid.head<3>();
		//		viewer->addCube(bboxTransform, bboxQuaternion, maxPoint.x - minPoint.x, maxPoint.y - minPoint.y, maxPoint.z - minPoint.z, "bbox");


























		//pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZRGB> colorTTF1(featureCloudTest, 0, 255, 0);
		//	pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> colorTTF1(featureCloudTest);
		//	PCL_INFO("featureCloudTest size = %i",featureCloudTest->points.size());
		//	pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZRGB> colorTTF2(featureCloudTrain, 255, 0, 0);
		//	viewer->addPointCloud<pcl::PointXYZRGB>(featureCloudTest,colorTTF1,"TestTrainFeatures1",viewP(TestTrainFeatures));
		//	viewer->addPointCloud<pcl::PointXYZRGB>(featureCloudTrain,colorTTF2,"TestTrainFeatures2",viewP(TestTrainFeatures));
		//	viewer->addText ("TestTrainFeatures", 10, 10, "TestTrainFeatures text", viewP(TestTrainFeatures));

		//		sensor_msgs::PointCloud2 processedCloud;
		//		pcl::toROSMsg(*classificationCloud, processedCloud);
		//		processedCloud.header.stamp = ros::Time::now();//processedCloud->header.stamp;
		//		processedCloud.header.frame_id = "/velodyne";
		//		pubProcessedPointCloudPointer->publish(processedCloud);
		//
		//		pcl::PointCloud<pcl::PointXYZRGB>::Ptr groundCloud2D(new pcl::PointCloud<pcl::PointXYZRGB>);
		//		pcl::PointCloud<pcl::PointXYZRGB>::Ptr objectCloud2D(new pcl::PointCloud<pcl::PointXYZRGB>);
		//
		//		for (size_t i=0;i<classificationCloud->size();i++)
		//		{
		//			pcl::PointXYZRGB pTmp = classificationCloud->points[i];
		//			pTmp.z = 0; // 3D -> 2D
		//			if ((pTmp.r == 255) || (pTmp.g == 255)) // Object
		//			{
		//				objectCloud2D->push_back(pTmp);
		//			}
		//			else if (pTmp.b == 255) // Object
		//			{
		//				groundCloud2D->push_back(pTmp);
		//			}
		//		}
		//
		//		//std::vector<int> indexVector;
		//		unsigned int leafNodeCounter = 0;
		//		unsigned int voxelDensity = 0;
		//		unsigned int totalPoints = 0;
		//		//int occupancyW = OCCUPANCY_WIDTH/OCCUPANCY_GRID_RESOLUTION;
		//		//int occupancyH = OCCUPANCY_DEPTH/OCCUPANCY_GRID_RESOLUTION;
		//		std::vector<unsigned int> ocGroundGrid(OCCUPANCY_WIDTH*OCCUPANCY_DEPTH, 0);
		//		std::vector<unsigned int> ocObjectGrid(OCCUPANCY_WIDTH*OCCUPANCY_DEPTH, 0);
		//		std::vector<signed char> ocGridFinal(OCCUPANCY_WIDTH*OCCUPANCY_DEPTH,-1);
		//
		//		// Ground grid
		//		pcl::octree::OctreePointCloudDensity< pcl::PointXYZRGB > octreeGround (OCCUPANCY_GRID_RESOLUTION);
		//		octreeGround.setInputCloud (groundCloud2D);
		//		pcl::PointXYZ bot(-OCCUPANCY_DEPTH_M/2,-OCCUPANCY_WIDTH_M/2,-0.5);
		//		pcl::PointXYZ top(OCCUPANCY_DEPTH_M/2,OCCUPANCY_WIDTH_M/2,0.5);
		//		octreeGround.defineBoundingBox(bot.x, bot.y, bot.z, top.x, top.y, top.z);    // I have already calculated the BBox of my point cloud
		//		octreeGround.addPointsFromInputCloud ();
		//		pcl::octree::OctreePointCloudDensity<pcl::PointXYZRGB>::LeafNodeIterator it1;
		//		pcl::octree::OctreePointCloudDensity<pcl::PointXYZRGB>::LeafNodeIterator it1_end = octreeGround.leaf_end();
		//		int minx = 1000;
		//		int miny = 1000;
		//		int maxx = -1000;
		//		int maxy = -1000;
		//		for (it1 = octreeGround.leaf_begin(); it1 != it1_end; ++it1)
		//		{
		//			pcl::octree::OctreePointCloudDensityContainer& container = it1.getLeafContainer();
		//			voxelDensity = container.getPointCounter();
		//			Eigen::Vector3f min_pt, max_pt;
		//			octreeGround.getVoxelBounds (it1, min_pt, max_pt);
		//			//ROS_INFO("x = %f, y = %f, z = %f, x2 = %f, y2 = %f, z2 = %f",min_pt[0],min_pt[1],min_pt[2],max_pt[0],max_pt[1],max_pt[2]);
		//			totalPoints += voxelDensity;
		//			if (min_pt[0] < minx)
		//				minx = min_pt[0];
		//			if (min_pt[0] > maxx)
		//				maxx = min_pt[0];
		//			if (min_pt[1] < miny)
		//				miny = min_pt[1];
		//			if (min_pt[1] > maxy)
		//				maxy = min_pt[1];
		//
		//			int r = OCCUPANCY_DEPTH-(min_pt[0]+OCCUPANCY_DEPTH_M/2)/OCCUPANCY_GRID_RESOLUTION;
		//			int c = OCCUPANCY_WIDTH-(min_pt[1]+OCCUPANCY_WIDTH_M/2)/OCCUPANCY_GRID_RESOLUTION;
		//
		//			//			if ((r == 51) || (c==51))
		//			//				ROS_INFO("r = %i,c = %i",r,c);
		//
		//			//if (((int)min_pt[0] == -1) || ((int)min_pt[1]==-1))
		//			//ROS_INFO("min_pt[0] = %f, min_pt[1]=%f",min_pt[0],min_pt[1]);
		//			if (!((r < 0) || (c < 0) || (r > OCCUPANCY_DEPTH-1) || (c > OCCUPANCY_WIDTH-1)))
		//				ocGroundGrid[r+OCCUPANCY_DEPTH*c] = voxelDensity;
		//			//ROS_INFO("density = %i, r = %i, c=%i",voxelDensity, r, c);
		//			//ROS_INFO("x (%f) -> r (%i), y (%f) -> c (%i)",min_pt[0],r,min_pt[1],c);
		//			leafNodeCounter++;
		//		}
		//
		//
		//		// Object grid
		//		pcl::octree::OctreePointCloudDensity< pcl::PointXYZRGB > octreeObject (OCCUPANCY_GRID_RESOLUTION);
		//		octreeObject.setInputCloud (objectCloud2D);
		//		octreeObject.defineBoundingBox(bot.x, bot.y, bot.z, top.x, top.y, top.z);    // I have already calculated the BBox of my point cloud
		//		octreeObject.addPointsFromInputCloud ();
		//		pcl::octree::OctreePointCloudDensity<pcl::PointXYZRGB>::LeafNodeIterator it2;
		//		pcl::octree::OctreePointCloudDensity<pcl::PointXYZRGB>::LeafNodeIterator it2_end = octreeObject.leaf_end();
		//		minx = 1000;
		//		miny = 1000;
		//		maxx = -1000;
		//		maxy = -1000;
		//		leafNodeCounter = 0;
		//		voxelDensity = 0;
		//		totalPoints = 0;
		//		int t1 = 0;
		//		int t2 = 0;
		//		int t3 = 0;
		//		for (it2 = octreeObject.leaf_begin(); it2 != it2_end; ++it2)
		//		{
		//			pcl::octree::OctreePointCloudDensityContainer& container = it2.getLeafContainer();
		//			voxelDensity = container.getPointCounter();
		//			Eigen::Vector3f min_pt, max_pt;
		//			octreeObject.getVoxelBounds (it2, min_pt, max_pt);
		//			//ROS_INFO("x = %f, y = %f, z = %f, x2 = %f, y2 = %f, z2 = %f",min_pt[0],min_pt[1],min_pt[2],max_pt[0],max_pt[1],max_pt[2]);
		//			totalPoints += voxelDensity;
		//			if (min_pt[0] < minx)
		//				minx = min_pt[0];
		//			if (min_pt[0] > maxx)
		//				maxx = min_pt[0];
		//			if (min_pt[1] < miny)
		//				miny = min_pt[1];
		//			if (min_pt[1] > maxy)
		//				maxy = min_pt[1];
		//
		//			int r = OCCUPANCY_DEPTH-(min_pt[0]+OCCUPANCY_DEPTH_M/2)/OCCUPANCY_GRID_RESOLUTION;
		//			int c = OCCUPANCY_WIDTH-(min_pt[1]+OCCUPANCY_WIDTH_M/2)/OCCUPANCY_GRID_RESOLUTION;
		//			if (!((r < 0) || (c < 0) || (r > OCCUPANCY_DEPTH-1) || (c > OCCUPANCY_WIDTH-1)))
		//				ocObjectGrid[r+OCCUPANCY_DEPTH*c] = voxelDensity;
		//			//ROS_INFO("object points = %i -> %i: x (%f) -> r (%i), y (%f) -> c (%i)",voxelDensity,ocGridFinal[r+OCCUPANCY_DEPTH*c],min_pt[0],r,min_pt[1],c);
		//			//ocGrid[r+OCCUPANCY_DEPTH*c] = voxelDensity;
		//			//ROS_INFO("density = %i, r = %i, c=%i",voxelDensity, r, c);
		//
		//			leafNodeCounter++;
		//		}
		//
		//		for (int r=0;r<OCCUPANCY_DEPTH;r++)
		//		{
		//			for (int c=0;c<OCCUPANCY_WIDTH;c++)
		//			{
		//				if ((ocGroundGrid[r+OCCUPANCY_DEPTH*c] == 0) && (ocObjectGrid[r+OCCUPANCY_DEPTH*c] <= 1))
		//				{
		//					ocGridFinal[r+OCCUPANCY_DEPTH*c] = -1.0; // 0.5 default likelihood
		//					t1++;
		//				}
		//				else if ((ocObjectGrid[r+OCCUPANCY_DEPTH*c] == 0))// && (ocGroundGrid[r+OCCUPANCY_DEPTH*c] > 0))
		//				{
		//					ocGridFinal[r+OCCUPANCY_DEPTH*c] = OCCUPANCY_MIN_PROB;
		//					t2++;
		//				}
		//				else
		//				{
		//					ocGridFinal[r+OCCUPANCY_DEPTH*c] = 50+(OCCUPANCY_MAX_PROB-50)*ocObjectGrid[r+OCCUPANCY_DEPTH*c]/(ocObjectGrid[r+OCCUPANCY_DEPTH*c]+ocGroundGrid[r+OCCUPANCY_DEPTH*c]);
		//					t3++;
		//				}
		//			}
		//		}
		//
		//		nav_msgs::OccupancyGrid occupancyGrid;
		//		occupancyGrid.info.resolution = OCCUPANCY_GRID_RESOLUTION;
		//		occupancyGrid.header.stamp = timestamp;//ros::Time::now();
		//		occupancyGrid.header.frame_id = "/velodyne";
		//		occupancyGrid.info.width = OCCUPANCY_WIDTH;
		//		occupancyGrid.info.height = OCCUPANCY_DEPTH;
		//		//geometry_msgs::Pose lidarPose;
		//		geometry_msgs::Point lidarPoint;
		//		lidarPoint.x = 0;
		//		lidarPoint.y = 0;
		//		lidarPoint.z = 0;
		//		geometry_msgs::Quaternion lidarQuaternion;
		//		lidarQuaternion.w = 1;
		//		lidarQuaternion.x = 0;
		//		lidarQuaternion.y = 0;
		//		lidarQuaternion.z = 0;
		//		occupancyGrid.info.origin.position = lidarPoint;
		//		occupancyGrid.info.origin.orientation = lidarQuaternion;
		//		occupancyGrid.data = ocGridFinal;
		//
		//		//ROS_INFO("total points = %i, total leaves = %i",totalPoints,leafNodeCounter);
		//		//ROS_INFO("minx = %i, maxx = %i, miny = %i, maxy = %i",minx,maxx,miny,maxy);
		//		//ROS_INFO("t1 = %i, t2 = %i, t3 = %i",t1,t2,t3);
		//
		//
		//		pubOccupancyGridPointer->publish(occupancyGrid);


		//		int l = 0;
		//		while (*++itLeafs)
		//		{
		//		    //Iteratively explore only the leaf nodes..
		//		    std::vector<PointXYZ> points;
		//		    itLeafs->getData(points);
		//		    l++;
		//		}
		//
		//		ROS_INFO("l = %i",l);

		//pcl::octree::OctreePointCloudSearch<pcl::PointXYZ> octree (OCCUPANCY_GRID_RESOLUTION);

		//		sensor_msgs::PointCloud2::Ptr cloud_filtered (new sensor_msgs::PointCloud2 ());
		//		pcl::VoxelGrid<sensor_msgs::PointCloud2> sor;
		//		sor.setInputCloud (processedCloud);
		//		sor.setLeafSize (0.01f, 0.01f, 0.01f);
		//		sor.filter (*cloud_filtered);
		if (n==0)
		{
			//viewer->setCameraPosition(-20,0,10,1,0,2,1,0,2,v1);
			//viewer->setCameraPosition(-20,0,10,1,0,2,1,0,2,v2);
			//viewer->loadCameraParameters("pcl_video.cam");
		}
#if (OLD_METHOD)
	}
#endif

	if (visualizationFlag)
		viewer->spinOnce();

	// Save screenshot
//	std::stringstream tmp;
//	tmp << n;
//	viewer->saveScreenshot("/home/mikkel/images/" + (boost::format("%04d") % n).str() + ".png");
//	n++;
}
コード例 #28
0
  void PointCloudLocalization::cloudCallback(
    const sensor_msgs::PointCloud2::ConstPtr& cloud_msg)
  {
    vital_checker_->poke();
    boost::mutex::scoped_lock lock(mutex_);
    //JSK_NODELET_INFO("cloudCallback");
    latest_cloud_ = cloud_msg;
    if (localize_requested_){
      JSK_NODELET_INFO("localization is requested");
      try {
        pcl::PointCloud<pcl::PointNormal>::Ptr
          local_cloud (new pcl::PointCloud<pcl::PointNormal>);
        pcl::fromROSMsg(*latest_cloud_, *local_cloud);
        JSK_NODELET_INFO("waiting for tf transformation from %s tp %s",
                     latest_cloud_->header.frame_id.c_str(),
                     global_frame_.c_str());
        if (tf_listener_->waitForTransform(
              latest_cloud_->header.frame_id,
              global_frame_,
              latest_cloud_->header.stamp,
              ros::Duration(1.0))) {
          pcl::PointCloud<pcl::PointNormal>::Ptr
            input_cloud (new pcl::PointCloud<pcl::PointNormal>);
          if (use_normal_) {
            pcl_ros::transformPointCloudWithNormals(global_frame_,
                                                    *local_cloud,
                                                    *input_cloud,
                                                    *tf_listener_);
          }
          else {
            pcl_ros::transformPointCloud(global_frame_,
                                         *local_cloud,
                                         *input_cloud,
                                         *tf_listener_);
          }
          pcl::PointCloud<pcl::PointNormal>::Ptr
            input_downsampled_cloud (new pcl::PointCloud<pcl::PointNormal>);
          applyDownsampling(input_cloud, *input_downsampled_cloud);
          if (isFirstTime()) {
            all_cloud_ = input_downsampled_cloud;
            first_time_ = false;
          }
          else {
            // run ICP
            ros::ServiceClient client
              = pnh_->serviceClient<jsk_pcl_ros::ICPAlign>("icp_align");
            jsk_pcl_ros::ICPAlign icp_srv;

            if (clip_unseen_pointcloud_) {
              // Before running ICP, remove pointcloud where we cannot see
              // First, transform reference pointcloud, that is all_cloud_, into
              // sensor frame.
              // And after that, remove points which are x < 0.
              tf::StampedTransform global_sensor_tf_transform
                = lookupTransformWithDuration(
                  tf_listener_,
                  global_frame_,
                  sensor_frame_,
                  cloud_msg->header.stamp,
                  ros::Duration(1.0));
              Eigen::Affine3f global_sensor_transform;
              tf::transformTFToEigen(global_sensor_tf_transform,
                                     global_sensor_transform);
              pcl::PointCloud<pcl::PointNormal>::Ptr sensor_cloud
                (new pcl::PointCloud<pcl::PointNormal>);
              pcl::transformPointCloudWithNormals(
                *all_cloud_,
                *sensor_cloud,
                global_sensor_transform.inverse());
              // Remove negative-x points
              pcl::PassThrough<pcl::PointNormal> pass;
              pass.setInputCloud(sensor_cloud);
              pass.setFilterFieldName("x");
              pass.setFilterLimits(0.0, 100.0);
              pcl::PointCloud<pcl::PointNormal>::Ptr filtered_cloud
                (new pcl::PointCloud<pcl::PointNormal>);
              pass.filter(*filtered_cloud);
              JSK_NODELET_INFO("clipping: %lu -> %lu", sensor_cloud->points.size(), filtered_cloud->points.size());
              // Convert the pointcloud to global frame again
              pcl::PointCloud<pcl::PointNormal>::Ptr global_filtered_cloud
                (new pcl::PointCloud<pcl::PointNormal>);
              pcl::transformPointCloudWithNormals(
                *filtered_cloud,
                *global_filtered_cloud,
                global_sensor_transform);
              pcl::toROSMsg(*global_filtered_cloud,
                            icp_srv.request.target_cloud);
            }
            else {
              pcl::toROSMsg(*all_cloud_,
                            icp_srv.request.target_cloud);
            }
            pcl::toROSMsg(*input_downsampled_cloud,
                          icp_srv.request.reference_cloud);
            
            if (client.call(icp_srv)) {
              Eigen::Affine3f transform;
              tf::poseMsgToEigen(icp_srv.response.result.pose, transform);
              Eigen::Vector3f transform_pos(transform.translation());
              float roll, pitch, yaw;
              pcl::getEulerAngles(transform, roll, pitch, yaw);
              JSK_NODELET_INFO("aligned parameter --");
              JSK_NODELET_INFO("  - pos: [%f, %f, %f]",
                           transform_pos[0],
                           transform_pos[1],
                           transform_pos[2]);
              JSK_NODELET_INFO("  - rot: [%f, %f, %f]", roll, pitch, yaw);
              pcl::PointCloud<pcl::PointNormal>::Ptr
                transformed_input_cloud (new pcl::PointCloud<pcl::PointNormal>);
              if (use_normal_) {
                pcl::transformPointCloudWithNormals(*input_cloud,
                                                    *transformed_input_cloud,
                                                    transform);
              }
              else {
                pcl::transformPointCloud(*input_cloud,
                                         *transformed_input_cloud,
                                         transform);
              }
              pcl::PointCloud<pcl::PointNormal>::Ptr
                concatenated_cloud (new pcl::PointCloud<pcl::PointNormal>);
              *concatenated_cloud = *all_cloud_ + *transformed_input_cloud;
              // update *all_cloud
              applyDownsampling(concatenated_cloud, *all_cloud_);
              // update localize_transform_
              tf::Transform icp_transform;
              tf::transformEigenToTF(transform, icp_transform);
              {
                boost::mutex::scoped_lock tf_lock(tf_mutex_);
                localize_transform_ = localize_transform_ * icp_transform;
              }
            }
            else {
              JSK_NODELET_ERROR("Failed to call ~icp_align");
              return;
            }
          }
          localize_requested_ = false;
        }
        else {
          JSK_NODELET_WARN("No tf transformation is available");
        }
      }
      catch (tf2::ConnectivityException &e)
      {
        JSK_NODELET_ERROR("[%s] Transform error: %s", __PRETTY_FUNCTION__, e.what());
        return;
      }
      catch (tf2::InvalidArgumentException &e)
      {
        JSK_NODELET_ERROR("[%s] Transform error: %s", __PRETTY_FUNCTION__, e.what());
        return;
      }
    }
  }
コード例 #29
0
ファイル: openni_tracking.cpp プロジェクト: 5irius/pcl
 void
 cloud_cb (const CloudConstPtr &cloud)
 {
   boost::mutex::scoped_lock lock (mtx_);
   double start = pcl::getTime ();
   FPS_CALC_BEGIN;
   cloud_pass_.reset (new Cloud);
   cloud_pass_downsampled_.reset (new Cloud);
   pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients ());
   pcl::PointIndices::Ptr inliers (new pcl::PointIndices ());
   filterPassThrough (cloud, *cloud_pass_);
   if (counter_ < 10)
   {
     gridSample (cloud_pass_, *cloud_pass_downsampled_, downsampling_grid_size_);
   }
   else if (counter_ == 10)
   {
     //gridSample (cloud_pass_, *cloud_pass_downsampled_, 0.01);
     cloud_pass_downsampled_ = cloud_pass_;
     CloudPtr target_cloud;
     if (use_convex_hull_)
     {
       planeSegmentation (cloud_pass_downsampled_, *coefficients, *inliers);
       if (inliers->indices.size () > 3)
       {
         CloudPtr cloud_projected (new Cloud);
         cloud_hull_.reset (new Cloud);
         nonplane_cloud_.reset (new Cloud);
         
         planeProjection (cloud_pass_downsampled_, *cloud_projected, coefficients);
         convexHull (cloud_projected, *cloud_hull_, hull_vertices_);
         
         extractNonPlanePoints (cloud_pass_downsampled_, cloud_hull_, *nonplane_cloud_);
         target_cloud = nonplane_cloud_;
       }
       else
       {
         PCL_WARN ("cannot segment plane\n");
       }
     }
     else
     {
       PCL_WARN ("without plane segmentation\n");
       target_cloud = cloud_pass_downsampled_;
     }
     
     if (target_cloud != NULL)
     {
       PCL_INFO ("segmentation, please wait...\n");
       std::vector<pcl::PointIndices> cluster_indices;
       euclideanSegment (target_cloud, cluster_indices);
       if (cluster_indices.size () > 0)
       {
         // select the cluster to track
         CloudPtr temp_cloud (new Cloud);
         extractSegmentCluster (target_cloud, cluster_indices, 0, *temp_cloud);
         Eigen::Vector4f c;
         pcl::compute3DCentroid<RefPointType> (*temp_cloud, c);
         int segment_index = 0;
         double segment_distance = c[0] * c[0] + c[1] * c[1];
         for (size_t i = 1; i < cluster_indices.size (); i++)
         {
           temp_cloud.reset (new Cloud);
           extractSegmentCluster (target_cloud, cluster_indices, int (i), *temp_cloud);
           pcl::compute3DCentroid<RefPointType> (*temp_cloud, c);
           double distance = c[0] * c[0] + c[1] * c[1];
           if (distance < segment_distance)
           {
             segment_index = int (i);
             segment_distance = distance;
           }
         }
         
         segmented_cloud_.reset (new Cloud);
         extractSegmentCluster (target_cloud, cluster_indices, segment_index, *segmented_cloud_);
         //pcl::PointCloud<pcl::Normal>::Ptr normals (new pcl::PointCloud<pcl::Normal>);
         //normalEstimation (segmented_cloud_, *normals);
         RefCloudPtr ref_cloud (new RefCloud);
         //addNormalToCloud (segmented_cloud_, normals, *ref_cloud);
         ref_cloud = segmented_cloud_;
         RefCloudPtr nonzero_ref (new RefCloud);
         removeZeroPoints (ref_cloud, *nonzero_ref);
         
         PCL_INFO ("calculating cog\n");
         
         RefCloudPtr transed_ref (new RefCloud);
         pcl::compute3DCentroid<RefPointType> (*nonzero_ref, c);
         Eigen::Affine3f trans = Eigen::Affine3f::Identity ();
         trans.translation ().matrix () = Eigen::Vector3f (c[0], c[1], c[2]);
         //pcl::transformPointCloudWithNormals<RefPointType> (*ref_cloud, *transed_ref, trans.inverse());
         pcl::transformPointCloud<RefPointType> (*nonzero_ref, *transed_ref, trans.inverse());
         CloudPtr transed_ref_downsampled (new Cloud);
         gridSample (transed_ref, *transed_ref_downsampled, downsampling_grid_size_);
         tracker_->setReferenceCloud (transed_ref_downsampled);
         tracker_->setTrans (trans);
         reference_ = transed_ref;
         tracker_->setMinIndices (int (ref_cloud->points.size ()) / 2);
       }
       else
       {
         PCL_WARN ("euclidean segmentation failed\n");
       }
     }
   }
   else
   {
     //normals_.reset (new pcl::PointCloud<pcl::Normal>);
     //normalEstimation (cloud_pass_downsampled_, *normals_);
     //RefCloudPtr tracking_cloud (new RefCloud ());
     //addNormalToCloud (cloud_pass_downsampled_, normals_, *tracking_cloud);
     //tracking_cloud = cloud_pass_downsampled_;
     
     //*cloud_pass_downsampled_ = *cloud_pass_;
     //cloud_pass_downsampled_ = cloud_pass_;
     gridSampleApprox (cloud_pass_, *cloud_pass_downsampled_, downsampling_grid_size_);
     tracking (cloud_pass_downsampled_);
   }
   
   new_cloud_ = true;
   double end = pcl::getTime ();
   computation_time_ = end - start;
   FPS_CALC_END("computation");
   counter_++;
 }
コード例 #30
0
ファイル: camera.hpp プロジェクト: LCG-UFRJ/tucano
 /**
  * @brief Returns the translation part of the view matrix as a vector.
  * @return The translation part of the view matrix.
  */
 Eigen::Vector3f getTranslationMatrix (void) const
 {
     return view_matrix.translation();
 }