예제 #1
0
파일: world_model.hpp 프로젝트: 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 ());
	
  }
}
예제 #2
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);
}
int main(int argc, char** argv)
{
    if( argc < 2 )
    {
        printPrompt( argv[0] );
        return -1;
    }

    initModule_nonfree();

    // Get Input Data
    ifstream file(argv[1]);
    if ( !file.is_open() )
        return false;
    
    string str;
    
        // Image Name
    getline( file, str ); getline( file, str );
    string image_name = str;
        // Cloud Name
    getline( file, str ); getline( file, str );
    string cloud_name = str;
        // width of images to be created.
    getline( file, str ); getline( file, str );
    int w = atoi(str.c_str());
        // height of images to be created
    getline( file, str ); getline( file, str );
    int h = atoi(str.c_str());
        // resolution of voxel grids
    getline( file, str ); getline( file, str );
    float r = atof(str.c_str());
        // f (distance from pinhole)
    getline( file, str ); getline( file, str );
    float f = atof(str.c_str());
        // thetax (initial rotation about X Axis of map)
    getline( file, str ); getline( file, str );
    float thetaX = atof(str.c_str());
        // thetay (initial rotation about Y Axis of map)
    getline( file, str ); getline( file, str );
    float thetaY = atof(str.c_str());
        // number of points to go to
    getline( file, str ); getline( file, str );
    float nop = atoi(str.c_str());
        // Number of divisions
    getline( file, str ); getline( file, str );
    float divs = atoi(str.c_str());
        // Number of images to return
    getline( file, str ); getline( file, str );
    int numtoreturn = atoi(str.c_str());    
        // Should we load or create photos?
    getline( file, str ); getline( file, str );
    string lorc =str.c_str();
        // Directory to look for photos
    getline( file, str ); getline( file, str );
    string dir =str.c_str();
        // Directory to look for kp and descriptors
    getline( file, str ); getline( file, str );
    string kdir =str.c_str();
        // save photos?
    getline( file, str ); getline( file, str );
    string savePhotos =str.c_str();
    
    file.close();
    // Done Getting Input Data

    map<vector<float>, Mat> imagemap;
    map<vector<float>, Mat> surfmap;
    map<vector<float>, Mat> siftmap;
    map<vector<float>, Mat> orbmap;
    map<vector<float>, Mat> fastmap;
    imagemap.clear();

    vector<KeyPoint> SurfKeypoints;
    vector<KeyPoint> SiftKeypoints;
    vector<KeyPoint> OrbKeypoints;
    vector<KeyPoint> FastKeypoints;
    Mat SurfDescriptors;
    Mat SiftDescriptors;
    Mat OrbDescriptors;
    Mat FastDescriptors;

    int minHessian = 300;

    SurfFeatureDetector SurfDetector (minHessian);
    SiftFeatureDetector SiftDetector (minHessian);
    OrbFeatureDetector OrbDetector (minHessian);
    FastFeatureDetector FastDetector (minHessian);


    SurfDescriptorExtractor SurfExtractor;
    SiftDescriptorExtractor SiftExtractor;
    OrbDescriptorExtractor OrbExtractor;

    if ( !fs::exists( dir ) || lorc == "c" )
    { // Load Point Cloud and render images
        PointCloud<PT>::Ptr cloud (new pcl::PointCloud<PT>);
        io::loadPCDFile<PT>(cloud_name, *cloud);

        Eigen::Affine3f tf = Eigen::Affine3f::Identity();
        tf.rotate (Eigen::AngleAxisf (thetaX, Eigen::Vector3f::UnitX()));
        pcl::transformPointCloud (*cloud, *cloud, tf);
        tf = Eigen::Affine3f::Identity();
        tf.rotate (Eigen::AngleAxisf (thetaY, Eigen::Vector3f::UnitY()));
        pcl::transformPointCloud (*cloud, *cloud, tf);

        // Create images from point cloud
        imagemap = render::createImages(cloud, nop, w, h, r, f);

        if (savePhotos == "y")
        {
            for (map<vector<float>, Mat>::iterator i = imagemap.begin(); i != imagemap.end(); ++i)
            {
                // Create image name and storagename
                string imfn = dir + "/";
                string kpfn = kdir + "/";
                for (int j = 0; j < i->first.size(); j++)
                {
                    imfn += boost::to_string(i->first[j]) + " ";
                    kpfn += boost::to_string(i->first[j]) + " ";
                }
                imfn += ".jpg";
                imwrite(imfn, i->second);

                // Detect keypoints, add to keypoint map. Same with descriptors

                SurfDetector.detect(i->second, SurfKeypoints);
                SiftDetector.detect(i->second, SiftKeypoints);
                OrbDetector.detect(i->second, OrbKeypoints);
                FastDetector.detect(i->second, FastKeypoints);

                SurfExtractor.compute(i->second, SurfKeypoints, SurfDescriptors);
                SiftExtractor.compute(i->second, SiftKeypoints, SiftDescriptors);
                OrbExtractor.compute(i->second, OrbKeypoints, OrbDescriptors);
                SiftExtractor.compute(i->second, FastKeypoints, FastDescriptors);

                // Store KP and Descriptors in yaml file.

                kpfn += ".yml";
                FileStorage store(kpfn, cv::FileStorage::WRITE);
                write(store,"SurfKeypoints",SurfKeypoints);
                write(store,"SiftKeypoints",SiftKeypoints);
                write(store,"OrbKeypoints", OrbKeypoints);
                write(store,"FastKeypoints",FastKeypoints);
                write(store,"SurfDescriptors",SurfDescriptors);
                write(store,"SiftDescriptors",SiftDescriptors);
                write(store,"OrbDescriptors", OrbDescriptors);
                write(store,"FastDescriptors",FastDescriptors);
                store.release();

                surfmap[i->first] = SurfDescriptors;
                siftmap[i->first] = SiftDescriptors;
                orbmap[i->first]  = OrbDescriptors;
                fastmap[i->first] = FastDescriptors;
            }
        }
    } 
    else 
    { // load images from the folder dir
        // First look into the folder to get a list of filenames
        vector<fs::path> ret;
        const char * pstr = dir.c_str();
        fs::path p(pstr);
        get_all(pstr, ret);

        for (int i = 0; i < ret.size(); i++)
        {
            // Load Image via filename
            string fn = ret[i].string();
            istringstream iss(fn);
            vector<string> tokens;
            copy(istream_iterator<string>(iss), istream_iterator<string>(), back_inserter<vector<string> >(tokens));

            // Construct ID from filename
            vector<float> ID;
            for (int i = 0; i < 6; i++) // 6 because there are three location floats and three direction floats
                ID.push_back(::atof(tokens[i].c_str()));
            string imfn = dir + "/" + fn;

            // Read image and add to imagemap.
            Mat m = imread(imfn);
            imagemap[ID] = m;

            // Create Filename for loading Keypoints and descriptors
            string kpfn = kdir + "/";
            for (int j = 0; j < ID.size(); j++)
            {
                kpfn += boost::to_string(ID[j]) + " ";
            }

            kpfn = kpfn+ ".yml";
            
            // Create filestorage item to read from and add to map.
            FileStorage store(kpfn, cv::FileStorage::READ);

            FileNode n1 = store["SurfKeypoints"];
            read(n1,SurfKeypoints);
            FileNode n2 = store["SiftKeypoints"];
            read(n2,SiftKeypoints);
            FileNode n3 = store["OrbKeypoints"];
            read(n3,OrbKeypoints);
            FileNode n4 = store["FastKeypoints"];
            read(n4,FastKeypoints);
            FileNode n5 = store["SurfDescriptors"];
            read(n5,SurfDescriptors);
            FileNode n6 = store["SiftDescriptors"];
            read(n6,SiftDescriptors);
            FileNode n7 = store["OrbDescriptors"];
            read(n7,OrbDescriptors);
            FileNode n8 = store["FastDescriptors"];
            read(n8,FastDescriptors);

            store.release();

            surfmap[ID] = SurfDescriptors;
            siftmap[ID] = SiftDescriptors;
            orbmap[ID]  = OrbDescriptors;
            fastmap[ID] = FastDescriptors;
        }
    }

    TickMeter tm;
    tm.reset();
    cout << "<\n  Analyzing Images ..." << endl;

    // We have a bunch of images, now we compute their grayscale and black and white.
    map<vector<float>, Mat> gsmap;
    map<vector<float>, Mat> bwmap;
    for (map<vector<float>, Mat>::iterator i = imagemap.begin(); i != imagemap.end(); ++i)
    {
        vector<float> ID = i->first;
        Mat Image = i-> second;
        GaussianBlur( Image, Image, Size(5,5), 0, 0, BORDER_DEFAULT );


        gsmap[ID] = averageImage::getPixSumFromImage(Image, divs);
        bwmap[ID] = averageImage::aboveBelow(gsmap[ID]);
    }
    Mat image = imread(image_name);
    Mat gsimage = averageImage::getPixSumFromImage(image, divs);
    Mat bwimage = averageImage::aboveBelow(gsimage);

    // cout << gsimage <<endl;
    imwrite("GS.png", gsimage);
    namedWindow("GSIMAGE (Line 319)");
    imshow("GSIMAGE (Line 319)", gsimage);
    waitKey(0);

    vector<KeyPoint> imgSurfKeypoints;
    vector<KeyPoint> imgSiftKeypoints;
    vector<KeyPoint> imgOrbKeypoints;
    vector<KeyPoint> imgFastKeypoints;
    Mat imgSurfDescriptors;
    Mat imgSiftDescriptors;
    Mat imgOrbDescriptors;
    Mat imgFastDescriptors;

    SurfDetector.detect(image, imgSurfKeypoints);
    SiftDetector.detect(image, imgSiftKeypoints);
    OrbDetector.detect(image, imgOrbKeypoints);
    FastDetector.detect(image, imgFastKeypoints);

    SurfExtractor.compute(image, imgSurfKeypoints, imgSurfDescriptors);
    SiftExtractor.compute(image, imgSiftKeypoints, imgSiftDescriptors);
    OrbExtractor.compute(image, imgOrbKeypoints, imgOrbDescriptors);
    SiftExtractor.compute(image, imgFastKeypoints, imgFastDescriptors);


    tm.start();

    cout << ">\n<\n  Comparing Images ..." << endl;

    // We have their features, now compare them!
    map<vector<float>, float> gssim; // Gray Scale Similarity
    map<vector<float>, float> bwsim; // Above Below Similarity
    map<vector<float>, float> surfsim;
    map<vector<float>, float> siftsim;
    map<vector<float>, float> orbsim;
    map<vector<float>, float> fastsim;

    for (map<vector<float>, Mat>::iterator i = gsmap.begin(); i != gsmap.end(); ++i)
    {
        vector<float> ID = i->first;
        gssim[ID] = similarities::getSimilarity(i->second, gsimage);
        bwsim[ID] = similarities::getSimilarity(bwmap[ID], bwimage); 
        surfsim[ID] = similarities::compareDescriptors(surfmap[ID], imgSurfDescriptors);
        siftsim[ID] = similarities::compareDescriptors(siftmap[ID], imgSiftDescriptors);
        orbsim[ID] = 0;//similarities::compareDescriptors(orbmap[ID], imgOrbDescriptors);
        fastsim[ID] = 0;//similarities::compareDescriptors(fastmap[ID], imgFastDescriptors);
    }

    map<vector<float>, int> top;

    bool gotone = false;
    typedef map<vector<float>, int>::iterator iter;

    // Choose the best ones!
    for (map<vector<float>, Mat>::iterator i = imagemap.begin(); i != imagemap.end(); ++i)
    {
        vector<float> ID = i->first;

        int sim = /* gssim[ID] + 0.5*bwsim[ID] + */ 5*surfsim[ID] + 0.3*siftsim[ID] + orbsim[ID] + fastsim[ID];

        // cout << surfsim[ID] << "\t";
        // cout << siftsim[ID] << "\t";
        // cout << orbsim[ID] << "\t";
        // cout << fastsim[ID] << endl;

        if (!gotone)
        {
            top[ID] = sim;
            gotone = true;
        }

        iter it = top.begin();
        iter end = top.end();
        int max_value = it->second;
        vector<float> max_ID = it->first;
        for( ; it != end; ++it) 
        {
            int current = it->second;
            if(current > max_value) 
            {
                max_value = it->second;
                max_ID = it->first;
            }
        }
        // cout << "Sim: " << sim << "\tmax_value: " << max_value << endl;
        if (top.size() < numtoreturn)
            top[ID] = sim;
        else
        {
            if (sim < max_value)
            {
                top[ID] = sim;
                top.erase(max_ID);
            }
        }
    }
    tm.stop();
        double s = tm.getTimeSec();


    cout << ">\n<\n  Writing top " << numtoreturn << " images ..." << endl;

    int count = 1;
    namedWindow("Image");
    namedWindow("Match");
    namedWindow("ImageBW");
    namedWindow("MatchBW");
    namedWindow("ImageGS");
    namedWindow("MatchGS");

    imshow("Image", image);
    imshow("ImageBW", bwimage);
    imshow("ImageGS", gsimage);


    vector<KeyPoint> currentPoints;

    for (iter i = top.begin(); i != top.end(); ++i)
    {
        vector<float> ID = i->first;

        cout << "  Score: "<< i->second << "\tGrayScale: " << gssim[ID] << "\tBW: " << bwsim[ID] << "  \tSURF: " << surfsim[ID] << "\tSIFT: " << siftsim[ID] << endl;
        string fn = "Sim_" + boost::to_string(count) + "_" + boost::to_string(i->second) + ".png";
        imwrite(fn, imagemap[ID]);
        count++;

        normalize(bwmap[ID], bwmap[ID], 0, 255, NORM_MINMAX, CV_64F);
        normalize(gsmap[ID], gsmap[ID], 0, 255, NORM_MINMAX, CV_64F);

        imshow("Match", imagemap[ID]);
        imshow("MatchBW", bwmap[ID]);
        imshow("MatchGS", gsmap[ID]);


        waitKey(0);

    }

    cout << ">\nComparisons took " << s << " seconds for " << imagemap.size() << " images (" 
        << (int) imagemap.size()/s << " images per second)." << endl;

return 0;
}
예제 #4
0
void simulate_callback (const pcl::visualization::KeyboardEvent &event,
                        void* viewer_void)
{
  boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer = *static_cast<boost::shared_ptr<pcl::visualization::PCLVisualizer> *> (viewer_void);
  // I choose v for virtual as s for simulate is takwen
  if (event.getKeySym () == "v" && event.keyDown ())
  {
    std::cout << "v was pressed => simulate cloud" << std::endl;

    std::vector<pcl::visualization::Camera> cams;
    viewer->getCameras(cams);
    
    if (cams.size() !=1){
      std::cout << "n cams not 1 exiting\n"; // for now in case ...
     return; 
    }
   // cout << "n cams: " << cams.size() << "\n";
    pcl::visualization::Camera cam = cams[0];
    


	      
	      Eigen::Affine3f pose;
	      pose = viewer->getViewerPose() ;
	      
	      
     std::cout << cam.pos[0] << " " 
               << cam.pos[1] << " " 
               << cam.pos[2] << " p\n";	      
	      
	     Eigen::Matrix3f m;
	     m =pose.rotation();
	     //All axies use right hand rule. x=red axis, y=green axis, z=blue axis z direction is point into the screen. z \ \ \ -----------> x | | | | | | y 
	      
 cout << pose(0,0)  << " " << pose(0,1) << " " << pose(0,2)  << " " << pose(0,3) << " x0\n";
 cout << pose(1,0) << " " << pose(1,1) << " " << pose(1,2) << " " << pose(1,3) << " x1\n";
  cout << pose(2,0) << " " << pose(2,1)  << " " << pose(2,2) << " " << pose(2,3)<< "x2\n";

  double yaw,pitch, roll;
  wRo_to_euler(m,yaw,pitch,roll);
  
 printf("RPY: %f %f %f\n", roll*180/M_PI,pitch*180/M_PI,yaw*180/M_PI);    

// matrix->GetElement(1,0);
  
 cout << m(0,0)  << " " << m(0,1) << " " << m(0,2)  << " "  << " x0\n";
 cout << m(1,0) << " " << m(1,1) << " " << m(1,2) << " "  << " x1\n";
  cout << m(2,0) << " " << m(2,1)  << " " << m(2,2) << " "<< "x2\n\n";
  
  Eigen::Quaternionf rf;
  rf = Eigen::Quaternionf(m);
  
  
   Eigen::Quaterniond r(rf.w(),rf.x(),rf.y(),rf.z());
  
   Eigen::Isometry3d init_pose;
   init_pose.setIdentity();
   init_pose.translation() << cam.pos[0], cam.pos[1], cam.pos[2];  
   //Eigen::Quaterniond m = euler_to_quat(-1.54, 0, 0);
   init_pose.rotate(r);  
//   
  
    std::stringstream ss;
  print_Isometry3d(init_pose,ss);
  std::cout << "init_pose: " << ss.str() << "\n";
  
  
  
  
  viewer->addCoordinateSystem (1.0,pose);
  
  
  
    double tic = getTime();
    std::stringstream ss2;
    ss2.precision(20);
    ss2 << "simulated_pcl_" << tic << ".pcd";
    capture(init_pose,ss2.str());
    cout << (getTime() -tic) << " sec\n";  
  
  
  
  // these three variables determine the position and orientation of
    // the camera.
	      
	      
//     double lookat[3]; - focal location
//     double eye[3]; - my location
//     double up[3]; - updirection
     
	      
	      
//    std::cout << view[0]  << "," << view[1]  << "," << view[2] 
    
//    cameras.back ().view[2] = renderer->GetActiveCamera ()->GetOrientationWXYZ()[2];    
    
//ViewTransform->GetOrientationWXYZ();    
    
  //  Superclass::OnKeyUp ();
    
//       vtkSmartPointer<vtkCamera> cam = event.Interactor->GetRenderWindow ()->GetRenderers ()->GetFirstRenderer ()->GetActiveCamera ();
//       double clip[2], focal[3], pos[3], view[3];
//       cam->GetClippingRange (clip);
/*      cam->GetFocalPoint (focal);
      cam->GetPosition (pos);
      cam->GetViewUp (view);
      int *win_pos = Interactor->GetRenderWindow ()->GetPosition ();
      int *win_size = Interactor->GetRenderWindow ()->GetSize ();
      std::cerr << clip[0]  << "," << clip[1]  << "/" << focal[0] << "," << focal[1] << "," << focal[2] << "/" <<
                   pos[0]   << "," << pos[1]   << "," << pos[2]   << "/" << view[0]  << "," << view[1]  << "," << view[2] << "/" <<
                   cam->GetViewAngle () / 180.0 * M_PI  << "/" << win_size[0] << "," << win_size[1] << "/" << win_pos[0] << "," << win_pos[1]
                << endl;    
  */  
  }
}
예제 #5
0
void Terrain::Draw(int type, const Camera& camera, const Light& light){


	//Get new position of the cube and update the model view matrix
    Eigen::Affine3f wMo;//object to world matrix
    Eigen::Affine3f cMw;
    Eigen::Affine3f proj;

    glUseProgram(m_shader);
#ifdef __APPLE__
    glBindVertexArrayAPPLE(m_vertexArrayObject); 
#else
	glBindVertexArray(m_vertexArrayObject);
#endif
    
    glBindBuffer(GL_ARRAY_BUFFER, m_vertexBufferObject);//use as current buffer
    
    GLint world2camera = glGetUniformLocation(m_shader, "cMw"); 
	GLint projection = glGetUniformLocation(m_shader, "proj");
    GLint kAmbient = glGetUniformLocation(m_shader,"kAmbient");
    GLint kDiffuse = glGetUniformLocation(m_shader,"kDiffuse");
    GLint kSpecular = glGetUniformLocation(m_shader,"kSpecular");
    GLint shininess = glGetUniformLocation(m_shader,"shininess");
    GLint camera_position = glGetUniformLocation(m_shader, "cameraPosition");
    GLint light_position = glGetUniformLocation(m_shader, "lightPosition");
    
    //generate the Angel::Angel::Angel::matrixes
    proj = Util::Perspective( camera.m_fovy, camera.m_aspect, camera.m_znear, camera.m_zfar );
	cMw = camera.m_cMw;//LookAt(camera.position,camera.lookat, camera.up );
    
    Eigen::Vector4f v4color(0.55,0.25,0.08,1.0);
    Eigen::Vector4f Ambient;
    Ambient = 0.3*v4color;
    Eigen::Vector4f Diffuse;
    Diffuse = 0.5*v4color;
    Eigen::Vector4f Specular(0.3,0.3,0.3,1.0);
    
    glUniformMatrix4fv( world2camera, 1, GL_FALSE, cMw.data() );
    glUniformMatrix4fv( projection, 1, GL_FALSE, proj.data() );
    
    glUniform4fv(kAmbient, 1, Ambient.data());
    glUniform4fv(kDiffuse, 1, Diffuse.data()); 
    glUniform4fv(kSpecular, 1, Specular.data());
    glUniform4fv(camera_position, 1, camera.m_position.data());
    glUniform4fv(light_position, 1, light.m_position.data());
    glUniform1f(shininess, 10);

    switch (type) {
        case DRAW_MESH:
            glUniform1i(glGetUniformLocation(m_shader, "renderType"), 1);
            glDrawArrays(GL_LINES, 0, m_NTrianglePoints);
            break;
        case DRAW_PHONG:
            glUniform1i(glGetUniformLocation(m_shader, "renderType"), 2);
            glDrawArrays(GL_TRIANGLES, 0, m_NTrianglePoints);
            break;
    }

	//draw the obstacles
	for(int i = 0; i < m_obstacles.size(); i++)
	{
		m_obstacles[i]->Draw(type,camera, light);
	}

	for(int i = 0; i < m_foods.size(); i++)
	{
		m_foods[i]->Draw(type,camera, light);
	}

	//draw the obstacles
	for(int i = 0; i < m_surface_objects.size(); i++)
	{
		m_surface_objects[i]->Draw(type,camera, light);
	}

}
void keypoint_map::optimize() {

	g2o::SparseOptimizer optimizer;
	optimizer.setVerbose(true);
	g2o::BlockSolver_6_3::LinearSolverType * linearSolver;

	linearSolver = new g2o::LinearSolverCholmod<
			g2o::BlockSolver_6_3::PoseMatrixType>();

	g2o::BlockSolver_6_3 * solver_ptr = new g2o::BlockSolver_6_3(linearSolver);
	g2o::OptimizationAlgorithmLevenberg* solver =
			new g2o::OptimizationAlgorithmLevenberg(solver_ptr);
	optimizer.setAlgorithm(solver);

	double focal_length = intrinsics[0];
	Eigen::Vector2d principal_point(intrinsics[2], intrinsics[3]);

	g2o::CameraParameters * cam_params = new g2o::CameraParameters(focal_length,
			principal_point, 0.);
	cam_params->setId(0);

	if (!optimizer.addParameter(cam_params)) {
		assert(false);
	}

	std::cerr << camera_positions.size() << " " << keypoints3d.size() << " "
			<< observations.size() << std::endl;

	int vertex_id = 0, point_id = 0;

	for (size_t i = 0; i < camera_positions.size(); i++) {
		Eigen::Affine3f cam_world = camera_positions[i].inverse();
		Eigen::Vector3d trans(cam_world.translation().cast<double>());
		Eigen::Quaterniond q(cam_world.rotation().cast<double>());

		g2o::SE3Quat pose(q, trans);
		g2o::VertexSE3Expmap * v_se3 = new g2o::VertexSE3Expmap();
		v_se3->setId(vertex_id);
		if (i < 1) {
			v_se3->setFixed(true);
		}
		v_se3->setEstimate(pose);
		optimizer.addVertex(v_se3);
		vertex_id++;
	}

	for (size_t i = 0; i < keypoints3d.size(); i++) {
		g2o::VertexSBAPointXYZ * v_p = new g2o::VertexSBAPointXYZ();
		v_p->setId(vertex_id + point_id);
		v_p->setMarginalized(true);
		v_p->setEstimate(keypoints3d[i].getVector3fMap().cast<double>());
		optimizer.addVertex(v_p);
		point_id++;
	}

	for (size_t i = 0; i < observations.size(); i++) {
		g2o::EdgeProjectXYZ2UV * e = new g2o::EdgeProjectXYZ2UV();
		e->setVertex(0,
				dynamic_cast<g2o::OptimizableGraph::Vertex*>(optimizer.vertices().find(
						vertex_id + observations[i].point_id)->second));
		e->setVertex(1,
				dynamic_cast<g2o::OptimizableGraph::Vertex*>(optimizer.vertices().find(
						observations[i].cam_id)->second));
		e->setMeasurement(observations[i].coord.cast<double>());
		e->information() = Eigen::Matrix2d::Identity();

		g2o::RobustKernelHuber* rk = new g2o::RobustKernelHuber;
		e->setRobustKernel(rk);

		e->setParameterId(0, 0);
		optimizer.addEdge(e);

	}

	//optimizer.save("debug.txt");

	optimizer.initializeOptimization();
	optimizer.setVerbose(true);

	std::cout << std::endl;
	std::cout << "Performing full BA:" << std::endl;
	optimizer.optimize(1);
	std::cout << std::endl;

	for (int i = 0; i < vertex_id; i++) {
		g2o::HyperGraph::VertexIDMap::iterator v_it = optimizer.vertices().find(
				i);
		if (v_it == optimizer.vertices().end()) {
			std::cerr << "Vertex " << i << " not in graph!" << std::endl;
			exit(-1);
		}

		g2o::VertexSE3Expmap * v_c =
				dynamic_cast<g2o::VertexSE3Expmap *>(v_it->second);
		if (v_c == 0) {
			std::cerr << "Vertex " << i << "is not a VertexSE3Expmap!"
					<< std::endl;
			exit(-1);
		}

		Eigen::Affine3f pos;
		pos.fromPositionOrientationScale(
				v_c->estimate().translation().cast<float>(),
				v_c->estimate().rotation().cast<float>(),
				Eigen::Vector3f(1, 1, 1));
		camera_positions[i] = pos.inverse();
	}

	for (int i = 0; i < point_id; i++) {
		g2o::HyperGraph::VertexIDMap::iterator v_it = optimizer.vertices().find(
				vertex_id + i);
		if (v_it == optimizer.vertices().end()) {
			std::cerr << "Vertex " << vertex_id + i << " not in graph!"
					<< std::endl;
			exit(-1);
		}

		g2o::VertexSBAPointXYZ * v_p =
				dynamic_cast<g2o::VertexSBAPointXYZ *>(v_it->second);
		if (v_p == 0) {
			std::cerr << "Vertex " << vertex_id + i
					<< "is not a VertexSE3Expmap!" << std::endl;
			exit(-1);
		}

		keypoints3d[i].getVector3fMap() = v_p->estimate().cast<float>();
	}

}
void jsk_pcl_ros::DepthImageCreator::publish_points(const sensor_msgs::CameraInfoConstPtr& info,
                                                    const sensor_msgs::PointCloud2ConstPtr& pcloud2) {
  JSK_ROS_DEBUG("DepthImageCreator::publish_points");
  if (!pcloud2)  return;
  bool proc_cloud = true, proc_image = true, proc_disp = true;
  if ( pub_cloud_.getNumSubscribers()==0 ) {
    proc_cloud = false;
  }
  if ( pub_image_.getNumSubscribers()==0 ) {
    proc_image = false;
  }
  if ( pub_disp_image_.getNumSubscribers()==0 ) {
    proc_disp = false;
  }
  if( !proc_cloud && !proc_image && !proc_disp) return;

  int width = info->width;
  int height = info->height;
  float fx = info->P[0];
  float cx = info->P[2];
  float tx = info->P[3];
  float fy = info->P[5];
  float cy = info->P[6];

  Eigen::Affine3f sensorPose;
  {
    tf::StampedTransform transform;
    if(use_fixed_transform) {
      transform = fixed_transform;
    } else {
      try {
        tf_listener_->waitForTransform(pcloud2->header.frame_id,
                                       info->header.frame_id,
                                       info->header.stamp,
                                       ros::Duration(0.001));
        tf_listener_->lookupTransform(pcloud2->header.frame_id,
                                      info->header.frame_id,
                                      info->header.stamp, transform);
      }
      catch ( std::runtime_error e ) {
        JSK_ROS_ERROR("%s",e.what());
        return;
      }
    }
    tf::Vector3 p = transform.getOrigin();
    tf::Quaternion q = transform.getRotation();
    sensorPose = (Eigen::Affine3f)Eigen::Translation3f(p.getX(), p.getY(), p.getZ());
    Eigen::Quaternion<float> rot(q.getW(), q.getX(), q.getY(), q.getZ());
    sensorPose = sensorPose * rot;

    if (tx != 0.0) {
      Eigen::Affine3f trans = (Eigen::Affine3f)Eigen::Translation3f(-tx/fx , 0, 0);
      sensorPose = sensorPose * trans;
    }
#if 0 // debug print
    JSK_ROS_INFO("%f %f %f %f %f %f %f %f %f, %f %f %f",
             sensorPose(0,0), sensorPose(0,1), sensorPose(0,2),
             sensorPose(1,0), sensorPose(1,1), sensorPose(1,2),
             sensorPose(2,0), sensorPose(2,1), sensorPose(2,2),
             sensorPose(0,3), sensorPose(1,3), sensorPose(2,3));
#endif
  }

  PointCloud pointCloud;
  pcl::RangeImagePlanar rangeImageP;
  {
    // code here is dirty, some bag is in RangeImagePlanar
    PointCloud tpc;
    pcl::fromROSMsg(*pcloud2, tpc);

    Eigen::Affine3f inv;
#if ( PCL_MAJOR_VERSION >= 1 && PCL_MINOR_VERSION >= 5 )
    inv = sensorPose.inverse();
    pcl::transformPointCloud< Point > (tpc, pointCloud, inv);
#else
    pcl::getInverse(sensorPose, inv);
    pcl::getTransformedPointCloud<PointCloud> (tpc, inv, pointCloud);
#endif

    Eigen::Affine3f dummytrans;
    dummytrans.setIdentity();
    rangeImageP.createFromPointCloudWithFixedSize (pointCloud,
                                                   width/scale_depth, height/scale_depth,
                                                   cx/scale_depth, cy/scale_depth,
                                                   fx/scale_depth, fy/scale_depth,
                                                   dummytrans); //sensorPose);
  }

  cv::Mat mat(rangeImageP.height, rangeImageP.width, CV_32FC1);
  float *tmpf = (float *)mat.ptr();
  for(unsigned int i = 0; i < rangeImageP.height*rangeImageP.width; i++) {
    tmpf[i] = rangeImageP.points[i].z;
  }

  if(scale_depth != 1.0) {
    cv::Mat tmpmat(info->height, info->width, CV_32FC1);
    cv::resize(mat, tmpmat, cv::Size(info->width, info->height)); // LINEAR
    //cv::resize(mat, tmpmat, cv::Size(info->width, info->height), 0.0, 0.0, cv::INTER_NEAREST);
    mat = tmpmat;
  }

  if (proc_image) {
    sensor_msgs::Image pubimg;
    pubimg.header = info->header;
    pubimg.width = info->width;
    pubimg.height = info->height;
    pubimg.encoding = "32FC1";
    pubimg.step = sizeof(float)*info->width;
    pubimg.data.resize(sizeof(float)*info->width*info->height);

    // publish image
    memcpy(&(pubimg.data[0]), mat.ptr(), sizeof(float)*info->height*info->width);
    pub_image_.publish(boost::make_shared<sensor_msgs::Image>(pubimg));
  }

  if(proc_cloud || proc_disp) {
    // publish point cloud
    pcl::RangeImagePlanar rangeImagePP;
    rangeImagePP.setDepthImage ((float *)mat.ptr(),
                                width, height,
                                cx, cy, fx, fy);
#if PCL_MAJOR_VERSION == 1 && PCL_MINOR_VERSION >= 7
    rangeImagePP.header = pcl_conversions::toPCL(info->header);
#else
    rangeImagePP.header = info->header;
#endif
    if(proc_cloud) {
      pub_cloud_.publish(boost::make_shared<pcl::PointCloud<pcl::PointWithRange > >
                         ( (pcl::PointCloud<pcl::PointWithRange>)rangeImagePP) );
    }

    if(proc_disp) {
      stereo_msgs::DisparityImage disp;
#if PCL_MAJOR_VERSION == 1 && PCL_MINOR_VERSION >= 7
      disp.header = pcl_conversions::fromPCL(rangeImagePP.header);
#else
      disp.header = rangeImagePP.header;
#endif
      disp.image.encoding  = sensor_msgs::image_encodings::TYPE_32FC1;
      disp.image.height    = rangeImagePP.height;
      disp.image.width     = rangeImagePP.width;
      disp.image.step      = disp.image.width * sizeof(float);
      disp.f = fx; disp.T = 0.075;
      disp.min_disparity = 0;
      disp.max_disparity = disp.T * disp.f / 0.3;
      disp.delta_d = 0.125;

      disp.image.data.resize (disp.image.height * disp.image.step);
      float *data = reinterpret_cast<float*> (&disp.image.data[0]);

      float normalization_factor = disp.f * disp.T;
      for (int y = 0; y < (int)rangeImagePP.height; y++ ) {
        for (int x = 0; x < (int)rangeImagePP.width; x++ ) {
          pcl::PointWithRange p = rangeImagePP.getPoint(x,y);
          data[y*disp.image.width+x] = normalization_factor / p.z;
        }
      }
      pub_disp_image_.publish(boost::make_shared<stereo_msgs::DisparityImage> (disp));
    }
  }
}
예제 #8
0
	void cloud_cb(
			const typename pcl::PointCloud<pcl::PointXYZRGB>::ConstPtr& cloud) {

		iter++;
		if(iter != skip) return;
		iter = 0;

		pcl::PointCloud<pcl::PointXYZRGB> cloud_transformed,
				cloud_aligned, cloud_filtered;

		Eigen::Affine3f view_transform;
		view_transform.matrix() << 0, 0, 1, 0, -1, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 1;

		Eigen::AngleAxis<float> rot(tilt * M_PI / 180,
				Eigen::Vector3f(0, 1, 0));

		view_transform.prerotate(rot);

		pcl::transformPointCloud(*cloud, cloud_transformed, view_transform);

		pcl::ModelCoefficients::Ptr coefficients(
				new pcl::ModelCoefficients);
		pcl::PointIndices::Ptr inliers(new pcl::PointIndices);

		pcl::SACSegmentation<pcl::PointXYZRGB> seg;

		seg.setOptimizeCoefficients(true);

		seg.setModelType(pcl::SACMODEL_PLANE);
		seg.setMethodType(pcl::SAC_RANSAC);
		seg.setDistanceThreshold(0.05);
		seg.setProbability(0.99);

		seg.setInputCloud(cloud_transformed.makeShared());
		seg.segment(*inliers, *coefficients);

		pcl::ExtractIndices<pcl::PointXYZRGB> extract;

		extract.setInputCloud(cloud_transformed.makeShared());
		extract.setIndices(inliers);
		extract.setNegative(true);
		extract.filter(cloud_transformed);

		std::cout << "Z vector: " << coefficients->values[0] << " "
				<< coefficients->values[1] << " " << coefficients->values[2]
				<< " " << coefficients->values[3] << std::endl;

		Eigen::Vector3f z_current(coefficients->values[0],
				coefficients->values[1], coefficients->values[2]);
		Eigen::Vector3f y(0, 1, 0);

		Eigen::Affine3f rotation;
		rotation = pcl::getTransFromUnitVectorsZY(z_current, y);
		rotation.translate(Eigen::Vector3f(0, 0, coefficients->values[3]));

		pcl::transformPointCloud(cloud_transformed, cloud_aligned, rotation);

		Eigen::Affine3f res = (rotation * view_transform);

		cloud_aligned.sensor_origin_ = res * Eigen::Vector4f(0, 0, 0, 1);
		cloud_aligned.sensor_orientation_ = res.rotate(
				Eigen::AngleAxisf(M_PI, Eigen::Vector3f(0, 0, 1))).rotation();

		seg.setInputCloud(cloud_aligned.makeShared());
		seg.segment(*inliers, *coefficients);

		std::cout << "Z vector: " << coefficients->values[0] << " "
				<< coefficients->values[1] << " " << coefficients->values[2]
				<< " " << coefficients->values[3] << std::endl;

		pub.publish(cloud_aligned);



	}
예제 #9
0
void IterativeClosestPoint::execute() {
    float error = std::numeric_limits<float>::max(), previousError;
    uint iterations = 0;

    // Get access to the two point sets
    PointSetAccess::pointer accessFixedSet = ((PointSet::pointer)getStaticInputData<PointSet>(0))->getAccess(ACCESS_READ);
    PointSetAccess::pointer accessMovingSet = ((PointSet::pointer)getStaticInputData<PointSet>(1))->getAccess(ACCESS_READ);

    // Get transformations of point sets
    AffineTransformation::pointer fixedPointTransform2 = SceneGraph::getAffineTransformationFromData(getStaticInputData<PointSet>(0));
    Eigen::Affine3f fixedPointTransform;
    fixedPointTransform.matrix() = fixedPointTransform2->matrix();
    AffineTransformation::pointer initialMovingTransform2 = SceneGraph::getAffineTransformationFromData(getStaticInputData<PointSet>(1));
    Eigen::Affine3f initialMovingTransform;
    initialMovingTransform.matrix() = initialMovingTransform2->matrix();

    // These matrices are Nx3
    MatrixXf fixedPoints = accessFixedSet->getPointSetAsMatrix();
    MatrixXf movingPoints = accessMovingSet->getPointSetAsMatrix();

    Eigen::Affine3f currentTransformation = Eigen::Affine3f::Identity();

    // Want to choose the smallest one as moving
    bool invertTransform = false;
    if(false && fixedPoints.cols() < movingPoints.cols()) {
        reportInfo() << "switching fixed and moving" << Reporter::end;
        // Switch fixed and moving
        MatrixXf temp = fixedPoints;
        fixedPoints = movingPoints;
        movingPoints = temp;
        invertTransform = true;

        // Apply initial transformations
        //currentTransformation = fixedPointTransform.getTransform();
        movingPoints = fixedPointTransform*movingPoints.colwise().homogeneous();
        fixedPoints = initialMovingTransform*fixedPoints.colwise().homogeneous();
    } else {
        // Apply initial transformations
        //currentTransformation = initialMovingTransform.getTransform();
        movingPoints = initialMovingTransform*movingPoints.colwise().homogeneous();
        fixedPoints = fixedPointTransform*fixedPoints.colwise().homogeneous();
    }
    do {
        previousError = error;
        MatrixXf movedPoints = currentTransformation*(movingPoints.colwise().homogeneous());

        // Match closest points using current transformation
        MatrixXf rearrangedFixedPoints = rearrangeMatrixToClosestPoints(
                fixedPoints, movedPoints);

        // Get centroids
        Vector3f centroidFixed = getCentroid(rearrangedFixedPoints);
        //reportInfo() << "Centroid fixed: " << Reporter::end;
        //reportInfo() << centroidFixed << Reporter::end;
        Vector3f centroidMoving = getCentroid(movedPoints);
        //reportInfo() << "Centroid moving: " << Reporter::end;
        //reportInfo() << centroidMoving << Reporter::end;

        Eigen::Transform<float, 3, Eigen::Affine> updateTransform = Eigen::Transform<float, 3, Eigen::Affine>::Identity();

        if(mTransformationType == IterativeClosestPoint::RIGID) {
            // Create correlation matrix H of the deviations from centroid
            MatrixXf H = (movedPoints.colwise() - centroidMoving)*
                    (rearrangedFixedPoints.colwise() - centroidFixed).transpose();

            // Do SVD on H
            Eigen::JacobiSVD<Eigen::MatrixXf> svd(H, Eigen::ComputeFullU | Eigen::ComputeFullV);

            // Estimate rotation as R=V*U.transpose()
            MatrixXf temp = svd.matrixV()*svd.matrixU().transpose();
            Matrix3f d = Matrix3f::Identity();
            d(2,2) = sign(temp.determinant());
            Matrix3f R = svd.matrixV()*d*svd.matrixU().transpose();

            // Estimate translation
            Vector3f T = centroidFixed - R*centroidMoving;

            updateTransform.linear() = R;
            updateTransform.translation() = T;
        } else {
            // Only translation
            Vector3f T = centroidFixed - centroidMoving;
            updateTransform.translation() = T;
        }

        // Update current transformation
        currentTransformation = updateTransform*currentTransformation;

        // Calculate RMS error
        MatrixXf distance = rearrangedFixedPoints - currentTransformation*(movingPoints.colwise().homogeneous());
        error = 0;
        for(uint i = 0; i < distance.cols(); i++) {
            error += pow(distance.col(i).norm(),2);
        }
        error = sqrt(error / distance.cols());

        iterations++;
        reportInfo() << "Error: " << error << Reporter::end;
        // To continue, change in error has to be above min error change and nr of iterations less than max iterations
    } while(previousError-error > mMinErrorChange && iterations < mMaxIterations);


    if(invertTransform){
        currentTransformation = currentTransformation.inverse();
    }

    mError = error;
    mTransformation->matrix() = currentTransformation.matrix();
}
예제 #10
0
파일: sim_viewer.cpp 프로젝트: hobu/pcl
void simulate_callback (const pcl::visualization::KeyboardEvent &event,
                        void* viewer_void)
{
  pcl::visualization::PCLVisualizer::Ptr viewer = *static_cast<pcl::visualization::PCLVisualizer::Ptr *> (viewer_void);
  // I choose v for virtual as s for simulate is takwen
  if (event.getKeySym () == "v" && event.keyDown ())
  {
    std::cout << "v was pressed => simulate cloud" << std::endl;

    std::vector<pcl::visualization::Camera> cams;
    viewer->getCameras(cams);
    
    if (cams.size() !=1){
      std::cout << "n cams not 1 exiting\n"; // for now in case ...
     return; 
    }
   // cout << "n cams: " << cams.size() << "\n";
    pcl::visualization::Camera cam = cams[0];
    


	      
	      Eigen::Affine3f pose;
	      pose = viewer->getViewerPose() ;
	      
	      
     std::cout << cam.pos[0] << " " 
               << cam.pos[1] << " " 
               << cam.pos[2] << " p\n";	      
	      
	     Eigen::Matrix3f m;
	     m =pose.rotation();
	     //All axies use right hand rule. x=red axis, y=green axis, z=blue axis z direction is point into the screen. z \ \ \ -----------> x | | | | | | y 
	      
 cout << pose(0,0)  << " " << pose(0,1) << " " << pose(0,2)  << " " << pose(0,3) << " x0\n";
 cout << pose(1,0) << " " << pose(1,1) << " " << pose(1,2) << " " << pose(1,3) << " x1\n";
  cout << pose(2,0) << " " << pose(2,1)  << " " << pose(2,2) << " " << pose(2,3)<< "x2\n";

  double yaw,pitch, roll;
  wRo_to_euler(m,yaw,pitch,roll);
  
 printf("RPY: %f %f %f\n", roll*180/M_PI,pitch*180/M_PI,yaw*180/M_PI);    

// matrix->GetElement(1,0);
  
 cout << m(0,0)  << " " << m(0,1) << " " << m(0,2)  << " "  << " x0\n";
 cout << m(1,0) << " " << m(1,1) << " " << m(1,2) << " "  << " x1\n";
  cout << m(2,0) << " " << m(2,1)  << " " << m(2,2) << " "<< "x2\n\n";
  
  Eigen::Quaternionf rf;
  rf = Eigen::Quaternionf(m);
  
  
   Eigen::Quaterniond r(rf.w(),rf.x(),rf.y(),rf.z());
  
   Eigen::Isometry3d init_pose;
   init_pose.setIdentity();
   init_pose.translation() << cam.pos[0], cam.pos[1], cam.pos[2];  
   //Eigen::Quaterniond m = euler_to_quat(-1.54, 0, 0);
   init_pose.rotate(r);  
//   
  
    std::stringstream ss;
  print_Isometry3d(init_pose,ss);
  std::cout << "init_pose: " << ss.str() << "\n";
  
  
  
  
  viewer->addCoordinateSystem (1.0,pose,"reference");
  
  
  
    double tic = getTime();
    std::stringstream ss2;
    ss2.precision(20);
    ss2 << "simulated_pcl_" << tic << ".pcd";
    capture(init_pose);
    cout << (getTime() -tic) << " sec\n";  
  }
}
void ShapeVisualization::setShapePosition(const visualization_msgs::InteractiveMarkerFeedbackConstPtr& feedback)//,const cob_3d_mapping_msgs::Shape& shape)
{

  cob_3d_mapping_msgs::ShapeArray map_msg;
  map_msg.header.frame_id = frame_id_;
  map_msg.header.stamp = ros::Time::now();

  int shape_id,index;
  index=-1;
  stringstream name(feedback->marker_name);

  Eigen::Quaternionf quat;

  Eigen::Matrix3f rotationMat;
  Eigen::MatrixXf rotationMatInit;

  Eigen::Vector3f normalVec;
  Eigen::Vector3f normalVecNew;
  Eigen::Vector3f newCentroid;
  Eigen::Matrix4f transSecondStep;


  if (feedback->marker_name != "Text"){
    name >> shape_id ;
    cob_3d_mapping::Polygon p;

    for(unsigned int i=0;i<sha.shapes.size();++i)
    {
    	if (sha.shapes[i].id == shape_id)
	{
		index = i;
	}
    }
    // temporary fix.
    //do nothing if index of shape is not found
    // this is not supposed to occur , but apparently it does
    if(index==-1){
    ROS_WARN("shape not in map array");
    return;
	}

    cob_3d_mapping::fromROSMsg (sha.shapes.at(index), p);

    if (feedback->event_type == 2 && feedback->menu_entry_id == 5){
      quatInit.x() = (float)feedback->pose.orientation.x ;           //normalized
      quatInit.y() = (float)feedback->pose.orientation.y ;
      quatInit.z() = (float)feedback->pose.orientation.z ;
      quatInit.w() = (float)feedback->pose.orientation.w ;

      oldCentroid (0) = (float)feedback->pose.position.x ;
      oldCentroid (1) = (float)feedback->pose.position.y ;
      oldCentroid (2) = (float)feedback->pose.position.z ;

      quatInit.normalize() ;

      rotationMatInit = quatInit.toRotationMatrix() ;

      transInit.block(0,0,3,3) << rotationMatInit ;
      transInit.col(3).head(3) << oldCentroid(0) , oldCentroid(1), oldCentroid(2) ;
      transInit.row(3) << 0,0,0,1 ;

      transInitInv = transInit.inverse() ;
      Eigen::Affine3f affineInitFinal (transInitInv) ;
      affineInit = affineInitFinal ;

      std::cout << "transInit : " << "\n"    << affineInitFinal.matrix() << "\n" ;
    }

    if (feedback->event_type == 5){
      /* the name of the marker is arrows_shape_.id, we need to erase the "arrows_" part */
      //string strName(feedback->marker_name);
      //strName.erase(strName.begin(),strName.begin()+7);
//      stringstream name(strName);
	stringstream name(feedback->marker_name);

      /* the name of the marker is arrows_shape_.id, we need to erase the "arrows_" part */
      //      int test ;
      //      string strName(feedback->marker_name);
      //      strName.erase(strName.begin(),strName.begin()+7);
      //      stringstream nameTest(strName);
      //      std::cout << "nameTest : " << nameTest.str() << "\n" ;
      //      nameTest >> test ;
      //      std::cout << "test : " << test << "\n" ;

      //      name >> shape_id ;
      cob_3d_mapping::Polygon p;
      //      for(size_t i=0;i<sha.shapes.size();++i)
      //      {
      //        if (sha.shapes[i].id == shape_id)
      //        {
      //          index = i;
      //        }
      //      }
      std::cout << "index is : " << index << "\n" ;
      cob_3d_mapping::fromROSMsg (sha.shapes.at(index), p);

      quat.x() = (float)feedback->pose.orientation.x ;           //normalized
      quat.y() = (float)feedback->pose.orientation.y ;
      quat.z() = (float)feedback->pose.orientation.z ;
      quat.w() = (float)feedback->pose.orientation.w ;

      quat.normalize() ;

      rotationMat = quat.toRotationMatrix() ;

      normalVec << sha.shapes.at(index).params[0],                   //normalized
          sha.shapes.at(index).params[1],
          sha.shapes.at(index).params[2];

      newCentroid << (float)feedback->pose.position.x ,
          (float)feedback->pose.position.y ,
          (float)feedback->pose.position.z ;


      transSecondStep.block(0,0,3,3) << rotationMat ;
      transSecondStep.col(3).head(3) << newCentroid(0) , newCentroid(1), newCentroid(2) ;
      transSecondStep.row(3) << 0,0,0,1 ;

      Eigen::Affine3f affineSecondStep(transSecondStep) ;

      std::cout << "transfrom : " << "\n"    << affineSecondStep.matrix() << "\n" ;

      Eigen::Affine3f affineFinal(affineSecondStep*affineInit) ;
      Eigen::Matrix4f matFinal = (transSecondStep*transInitInv) ;

      normalVecNew    = (matFinal.block(0,0,3,3))* normalVec;
      //      newCentroid  = transFinal *OldCentroid ;


      sha.shapes.at(index).centroid.x = newCentroid(0) ;
      sha.shapes.at(index).centroid.y = newCentroid(1) ;
      sha.shapes.at(index).centroid.z = newCentroid(2) ;


      sha.shapes.at(index).params[0] = normalVecNew(0) ;
      sha.shapes.at(index).params[1] = normalVecNew(1) ;
      sha.shapes.at(index).params[2] = normalVecNew(2) ;


      std::cout << "transfromFinal : " << "\n"    << affineFinal.matrix() << "\n" ;

      pcl::PointCloud<pcl::PointXYZ> pc;
      pcl::PointXYZ pt;
      sensor_msgs::PointCloud2 pc2;

      for(unsigned int j=0; j<p.contours.size(); j++)
      {
        for(unsigned int k=0; k<p.contours[j].size(); k++)
        {
          p.contours[j][k] = affineFinal * p.contours[j][k];
          pt.x = p.contours[j][k][0] ;
          pt.y = p.contours[j][k][1] ;
          pt.z = p.contours[j][k][2] ;
          pc.push_back(pt) ;
        }
      }

      pcl::toROSMsg (pc, pc2);
      sha.shapes.at(index).points.clear() ;
      sha.shapes.at(index).points.push_back (pc2);

      // uncomment when using test_shape_array

      //      for(unsigned int i=0;i<sha.shapes.size();i++){
      //        map_msg.header = sha.shapes.at(i).header ;
      //        map_msg.shapes.push_back(sha.shapes.at(i)) ;
      //      }
      //      shape_pub_.publish(map_msg);

      // end uncomment

      modified_shapes_.shapes.push_back(sha.shapes.at(index));
    }
  }
  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;
      }
    }
  }
예제 #13
0
파일: crop_box.cpp 프로젝트: 5irius/pcl
void
pcl::CropBox<pcl::PCLPointCloud2>::applyFilter (PCLPointCloud2 &output)
{
  // Resize output cloud to sample size
  output.data.resize (input_->data.size ());
  removed_indices_->resize (input_->data.size ());

  // Copy the common fields
  output.fields = input_->fields;
  output.is_bigendian = input_->is_bigendian;
  output.row_step = input_->row_step;
  output.point_step = input_->point_step;
  output.height = 1;

  int indices_count = 0;
  int removed_indices_count = 0;

  Eigen::Affine3f transform = Eigen::Affine3f::Identity();
  Eigen::Affine3f inverse_transform = Eigen::Affine3f::Identity();

  if (rotation_ != Eigen::Vector3f::Zero ())
  {
    pcl::getTransformation (0, 0, 0,
                            rotation_ (0), rotation_ (1), rotation_ (2),
                            transform);
    inverse_transform = transform.inverse();
  }

  //PointXYZ local_pt;
  Eigen::Vector3f local_pt (Eigen::Vector3f::Zero ());

  for (size_t index = 0; index < indices_->size (); ++index)
  {
    // Get local point
    int point_offset = ((*indices_)[index] * input_->point_step);
    int offset = point_offset + input_->fields[x_idx_].offset;
    memcpy (&local_pt, &input_->data[offset], sizeof (float)*3);

    // Check if the point is invalid
    if (!pcl_isfinite (local_pt.x ()) ||
        !pcl_isfinite (local_pt.y ()) ||
        !pcl_isfinite (local_pt.z ()))
      continue;

    // Transform point to world space
    if (!(transform_.matrix().isIdentity()))
      local_pt = transform_ * local_pt;

    if (translation_ != Eigen::Vector3f::Zero ())
    {
      local_pt.x () = local_pt.x () - translation_ (0);
      local_pt.y () = local_pt.y () - translation_ (1);
      local_pt.z () = local_pt.z () - translation_ (2);
    }

    // Transform point to local space of crop box
    if (!(inverse_transform.matrix ().isIdentity ()))
      local_pt = inverse_transform * local_pt;

    // If outside the cropbox
    if ( (local_pt.x () < min_pt_[0] || local_pt.y () < min_pt_[1] || local_pt.z () < min_pt_[2]) ||
         (local_pt.x () > max_pt_[0] || local_pt.y () > max_pt_[1] || local_pt.z () > max_pt_[2]))
    {
      if (negative_)
      {
        memcpy (&output.data[indices_count++ * output.point_step],
                &input_->data[index * output.point_step], output.point_step);
      }
      else if (extract_removed_indices_)
      {
        (*removed_indices_)[removed_indices_count++] = static_cast<int> (index);
      }
    }
    // If inside the cropbox
    else
    {
      if (negative_ && extract_removed_indices_)
      {
        (*removed_indices_)[removed_indices_count++] = static_cast<int> (index);
      }
      else if (!negative_) {
        memcpy (&output.data[indices_count++ * output.point_step],
                &input_->data[index * output.point_step], output.point_step);
      }
    }
  }
  output.width = indices_count;
  output.row_step = output.point_step * output.width;
  output.data.resize (output.width * output.height * output.point_step);

  removed_indices_->resize (removed_indices_count);
}
예제 #14
0
파일: crop_box.cpp 프로젝트: 5irius/pcl
void
pcl::CropBox<pcl::PCLPointCloud2>::applyFilter (std::vector<int> &indices)
{
  indices.resize (input_->width * input_->height);
  removed_indices_->resize (input_->width * input_->height);

  int indices_count = 0;
  int removed_indices_count = 0;

  Eigen::Affine3f transform = Eigen::Affine3f::Identity();
  Eigen::Affine3f inverse_transform = Eigen::Affine3f::Identity();

  if (rotation_ != Eigen::Vector3f::Zero ())
  {
    pcl::getTransformation (0, 0, 0,
                            rotation_ (0), rotation_ (1), rotation_ (2),
                            transform);
    inverse_transform = transform.inverse();
  }

  //PointXYZ local_pt;
  Eigen::Vector3f local_pt (Eigen::Vector3f::Zero ());

  for (size_t index = 0; index < indices_->size (); index++)
  {
    // Get local point
    int point_offset = ((*indices_)[index] * input_->point_step);
    int offset = point_offset + input_->fields[x_idx_].offset;
    memcpy (&local_pt, &input_->data[offset], sizeof (float)*3);

    // Transform point to world space
    if (!(transform_.matrix().isIdentity()))
      local_pt = transform_ * local_pt;

    if (translation_ != Eigen::Vector3f::Zero ())
    {
      local_pt.x () -= translation_ (0);
      local_pt.y () -= translation_ (1);
      local_pt.z () -= translation_ (2);
    }

    // Transform point to local space of crop box
    if (!(inverse_transform.matrix().isIdentity()))
      local_pt = inverse_transform * local_pt;

    // If outside the cropbox
    if ( (local_pt.x () < min_pt_[0] || local_pt.y () < min_pt_[1] || local_pt.z () < min_pt_[2]) ||
         (local_pt.x () > max_pt_[0] || local_pt.y () > max_pt_[1] || local_pt.z () > max_pt_[2]))
    {
      if (negative_)
      {
        indices[indices_count++] = (*indices_)[index];
      }
      else if (extract_removed_indices_)
      {
        (*removed_indices_)[removed_indices_count++] = static_cast<int> (index);
      }
    }
    // If inside the cropbox
    else
    {
      if (negative_ && extract_removed_indices_)
      {
        (*removed_indices_)[removed_indices_count++] = static_cast<int> (index);
      }
      else if (!negative_) {
        indices[indices_count++] = (*indices_)[index];
      }
    }
  }

  indices.resize (indices_count);
  removed_indices_->resize (removed_indices_count);
}
예제 #15
0
 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_++;
 }
예제 #16
0
Eigen::Affine3f OpenNiInterfacePlain::getCloudPose() {
    Eigen::Affine3f mid;
    mid.setIdentity();
    return mid;
}
예제 #17
0
int main(void)
{
	std::cout << "Starting engine" << std::endl;

	pastry::initialize();

	auto dr = std::make_shared<pastry::deferred::DeferredRenderer>();
	pastry::scene_add(dr);

	// scene (need this first!)
	auto scene = std::make_shared<pastry::deferred::Scene>();
	dr->setScene(scene);

	// skybox
	{
		auto go = pastry::deferred::FactorSkyBox();
		go->skybox->setTexture("assets/stormydays_cm.jpg");
		go->skybox->setGeometry("assets/sphere_4.obj");
		scene->setMainSkybox(go);
	}

	// camera
	{
		auto go = pastry::deferred::FactorCamera();
		go->camera->setProjection(90.0f, 1.0f, 100.0f);
		go->camera->setView({4,14,6},{2,3,0},{0,0,-1});
		scene->setMainCamera(go);
	}

	constexpr float SPACE = 3.0f;

	// geometry
	{
		constexpr int R = 3;
		for(int x=-R; x<=+R; x++) {
			for(int y=-R; y<=+R; y++) {
				auto go = pastry::deferred::FactorGeometry();
				go->geometry->load("assets/suzanne.obj");
				Eigen::Affine3f pose = Eigen::Translation3f(Eigen::Vector3f(SPACE*x,SPACE*y,0))
					* Eigen::AngleAxisf(0.0f,Eigen::Vector3f{0,0,1});
				go->geometry->setPose(pose.matrix());
				float p = static_cast<float>(x+R)/static_cast<float>(2*R);
				go->geometry->setRoughness(0.2f + 0.8f*p);
				scene->add(go);				
			}
		}
	}

	// lights
	// {
	// 	auto light = std::make_shared<pastry::deferred::PointLight>();
	// 	light->setLightPosition({+3,3,4});
	// 	light->setLightColor(20.0f*Eigen::Vector3f{1,1,1});
	// 	scene->add(light);
	// }
	// {
	// 	auto light = std::make_shared<pastry::deferred::PointLight>();
	// 	light->setLightPosition({-4,1,4});
	// 	light->setLightColor(20.0f*Eigen::Vector3f{1,0.5,0.5});
	// 	scene->add(light);
	// }
	{
		auto go = pastry::deferred::FactorEnvironmentLight();
		scene->add(go);
	}
	{
		constexpr int R = 3;
		for(int x=-R; x<=+R; x++) {
			for(int y=-R; y<=+R; y++) {
				auto go = pastry::deferred::FactorPointLight();
				((pastry::deferred::PointLight*)(go->light.get()))->setLightPosition({SPACE*x,SPACE*y,1.5});
				((pastry::deferred::PointLight*)(go->light.get()))->setLightColor(35.0f*HSL(std::atan2(y,x)/6.2831853f,0.5f,0.5f));
				((pastry::deferred::PointLight*)(go->light.get()))->setLightFalloff(0.05f);
				scene->add(go);
			}
		}
	}

	std::cout << "Running main loop" << std::endl;

	pastry::run();

	std::cout << "Graceful quit" << std::endl;

	return 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++;
}
void keypoint_map::align_z_axis() {

	pcl::PointCloud<pcl::PointXYZ>::Ptr point_cloud(
			new pcl::PointCloud<pcl::PointXYZ>);

	for (int v = 0; v < depth_imgs[0].rows; v++) {
		for (int u = 0; u < depth_imgs[0].cols; u++) {
			if (depth_imgs[0].at<unsigned short>(v, u) != 0) {
				pcl::PointXYZ p;

				p.z = depth_imgs[0].at<unsigned short>(v, u) / 1000.0f;
				p.x = (u - intrinsics[2]) * p.z / intrinsics[0];
				p.y = (v - intrinsics[3]) * p.z / intrinsics[1];

				p.getVector4fMap() = camera_positions[0] * p.getVector4fMap();

				point_cloud->push_back(p);

			}
		}
	}

	pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients);
	pcl::PointIndices::Ptr inliers(new pcl::PointIndices);
	// Create the segmentation object
	pcl::SACSegmentation<pcl::PointXYZ> seg;
	// Optional
	seg.setOptimizeCoefficients(true);
	// Mandatory
	seg.setModelType(pcl::SACMODEL_PLANE);
	seg.setMethodType(pcl::SAC_RANSAC);
	seg.setDistanceThreshold(0.005);
	seg.setProbability(0.99);

	seg.setInputCloud(point_cloud);
	seg.segment(*inliers, *coefficients);

	std::cerr << "Model coefficients: " << coefficients->values[0] << " "
			<< coefficients->values[1] << " " << coefficients->values[2] << " "
			<< coefficients->values[3] << " Num inliers "
			<< inliers->indices.size() << std::endl;

	Eigen::Affine3f transform = Eigen::Affine3f::Identity();
	if (coefficients->values[2] > 0) {
		transform.matrix().coeffRef(0, 2) = coefficients->values[0];
		transform.matrix().coeffRef(1, 2) = coefficients->values[1];
		transform.matrix().coeffRef(2, 2) = coefficients->values[2];
	} else {
		transform.matrix().coeffRef(0, 2) = -coefficients->values[0];
		transform.matrix().coeffRef(1, 2) = -coefficients->values[1];
		transform.matrix().coeffRef(2, 2) = -coefficients->values[2];
	}

	transform.matrix().col(0).head<3>() =
			transform.matrix().col(1).head<3>().cross(
					transform.matrix().col(2).head<3>());
	transform.matrix().col(1).head<3>() =
			transform.matrix().col(2).head<3>().cross(
					transform.matrix().col(0).head<3>());

	transform = transform.inverse();

	transform.matrix().coeffRef(2, 3) = coefficients->values[3];

	pcl::transformPointCloud(keypoints3d, keypoints3d, transform);

	for (size_t i = 0; i < camera_positions.size(); i++) {
		camera_positions[i] = transform * camera_positions[i];
	}

}
예제 #20
0
bool AirwaysFilter::execute()
{
	std::cout << "EXECUTING AIRWAYS FILTER" << std::endl;
    ImagePtr input = this->getCopiedInputImage();
    	if (!input)
    		return false;
    mInputImage = input;

	std::string filename = (patientService()->getActivePatientFolder()+"/"+input->getFilename()).toStdString();

	try {
		// Import image data from disk
		fast::ImageFileImporter::pointer importer = fast::ImageFileImporter::New();
		importer->setFilename(filename);

	    // Need to know the data type
	    importer->update();
	    fast::Image::pointer image = importer->getOutputData<fast::Image>();
	    std::cout << "IMAGE LOADED" << std::endl;

	    // Set up algorithm
		fast::TubeSegmentationAndCenterlineExtraction::pointer tubeExtraction = fast::TubeSegmentationAndCenterlineExtraction::New();
	    tubeExtraction->setInputConnection(importer->getOutputPort());
	    tubeExtraction->extractDarkTubes();
	    if(getCroppingOption(mOptions)->getValue())
			tubeExtraction->enableAutomaticCropping(true);
	    // Set min and max intensity based on HU unit scale
	    if(image->getDataType() == fast::TYPE_UINT16) {
	        tubeExtraction->setMinimumIntensity(0);
	        tubeExtraction->setMaximumIntensity(1124);
	    } else {
	        tubeExtraction->setMinimumIntensity(-1024);
	        tubeExtraction->setMaximumIntensity(100);
	    }
	    tubeExtraction->setMinimumRadius(0.5);
	    tubeExtraction->setMaximumRadius(50);
	    tubeExtraction->setSensitivity(getSensitivityOption(mOptions)->getValue());
	    // TODO set blur amount..

	    // Convert fast segmentation data to VTK data which CX can use
        vtkSmartPointer<fast::VTKImageExporter> vtkExporter = fast::VTKImageExporter::New();
	    vtkExporter->setInputConnection(tubeExtraction->getSegmentationOutputPort());
	    vtkExporter->Update();
	    mSegmentationOutput = vtkExporter->GetOutput();
	    std::cout << "FINISHED AIRWAY SEGMENTATION" << std::endl;

	    // Get output segmentation data
	    fast::Segmentation::pointer segmentation = tubeExtraction->getOutputData<fast::Segmentation>(0);

	    // Get the transformation of the segmentation
	    Eigen::Affine3f T = fast::SceneGraph::getEigenAffineTransformationFromData(segmentation);
	    mTransformation.matrix() = T.matrix().cast<double>(); // cast to double

	    // Get centerline
	    vtkSmartPointer<fast::VTKLineSetExporter> vtkCenterlineExporter = fast::VTKLineSetExporter::New();
	    vtkCenterlineExporter->setInputConnection(tubeExtraction->getCenterlineOutputPort());
	    mCenterlineOutput = vtkCenterlineExporter->GetOutput();
	    vtkCenterlineExporter->Update();

	} catch(fast::Exception& e) {
		std::string error = e.what();
		reportError("fast::Exception: "+qstring_cast(error));

		return false;
	} catch(cl::Error& e) {
		reportError("cl::Error:"+qstring_cast(e.what()));

		return false;
	} catch (std::exception& e){
		reportError("std::exception:"+qstring_cast(e.what()));

		return false;
	} catch (...){
		reportError("Airway segmentation algorithm threw a unknown exception.");

		return false;
	}
 	return true;
}
예제 #21
0
TEST (PCL, GSHOTWithRTransNoised)
{
  PointCloud<PointNormal>::Ptr cloud_nr (new PointCloud<PointNormal> ());
  PointCloud<PointNormal>::Ptr cloud_rot (new PointCloud<PointNormal> ());
  PointCloud<PointNormal>::Ptr cloud_trans (new PointCloud<PointNormal> ());
  PointCloud<PointNormal>::Ptr cloud_rot_trans (new PointCloud<PointNormal> ());
  PointCloud<PointXYZ>::Ptr cloud_noise (new PointCloud<PointXYZ> ());

  Eigen::Affine3f rot = Eigen::Affine3f::Identity ();
  float rot_x = static_cast <float> (rand ()) / static_cast <float> (RAND_MAX);
  float rot_y = static_cast <float> (rand ()) / static_cast <float> (RAND_MAX);
  float rot_z = static_cast <float> (rand ()) / static_cast <float> (RAND_MAX);
  rot.prerotate (Eigen::AngleAxisf (rot_x * M_PI, Eigen::Vector3f::UnitX ()));
  rot.prerotate (Eigen::AngleAxisf (rot_y * M_PI, Eigen::Vector3f::UnitY ()));
  rot.prerotate (Eigen::AngleAxisf (rot_z * M_PI, Eigen::Vector3f::UnitZ ()));
  //std::cout << "rot = (" << (rot_x * M_PI) << ", " << (rot_y * M_PI) << ", " << (rot_z * M_PI) << ")" << std::endl;

  Eigen::Affine3f trans = Eigen::Affine3f::Identity ();
  float HI = 5.0f;
  float LO = -HI;
  float trans_x = LO + static_cast<float> (rand ()) / (static_cast<float> (RAND_MAX / (HI - LO)));
  float trans_y = LO + static_cast<float> (rand ()) / (static_cast<float> (RAND_MAX / (HI - LO)));
  float trans_z = LO + static_cast<float> (rand ()) / (static_cast<float> (RAND_MAX / (HI - LO)));
  //std::cout << "trans = (" << trans_x << ", " << trans_y << ", " << trans_z << ")" << std::endl;
  trans.translate (Eigen::Vector3f (trans_x, trans_y, trans_z));

  // Estimate normals first
  float mr = 0.002;
  NormalEstimation<PointXYZ, pcl::Normal> n;
  PointCloud<Normal>::Ptr normals1 (new PointCloud<Normal> ());
  n.setViewPoint (0.0, 0.0, 1.0);
  n.setInputCloud (cloud.makeShared ());
  n.setRadiusSearch (20 * mr);
  n.compute (*normals1);

  pcl::concatenateFields<PointXYZ, Normal, PointNormal> (cloud, *normals1, *cloud_nr);
  pcl::transformPointCloudWithNormals<PointNormal, float> (*cloud_nr, *cloud_rot, rot);
  pcl::transformPointCloudWithNormals<PointNormal, float> (*cloud_nr, *cloud_trans, trans);
  pcl::transformPointCloudWithNormals<PointNormal, float> (*cloud_rot, *cloud_rot_trans, trans);

  add_gaussian_noise (cloud.makeShared (), cloud_noise, 0.005);

  PointCloud<Normal>::Ptr normals_noise (new PointCloud<Normal> ());
  n.setInputCloud (cloud_noise);
  n.compute (*normals_noise);

  PointCloud<Normal>::Ptr normals2 (new PointCloud<Normal> ());
  n.setInputCloud (cloud2.makeShared ());
  n.compute (*normals2);

  PointCloud<Normal>::Ptr normals3 (new PointCloud<Normal> ());
  n.setInputCloud (cloud3.makeShared ());
  n.compute (*normals3);

  // Objects
  // Descriptors for ground truth (using SHOT)
  PointCloud<SHOT352>::Ptr desc01 (new PointCloud<SHOT352> ());
  PointCloud<SHOT352>::Ptr desc02 (new PointCloud<SHOT352> ());
  PointCloud<SHOT352>::Ptr desc03 (new PointCloud<SHOT352> ());
  PointCloud<SHOT352>::Ptr desc04 (new PointCloud<SHOT352> ());
  // Descriptors for test GSHOT
  PointCloud<SHOT352>::Ptr desc1 (new PointCloud<SHOT352> ());
  PointCloud<SHOT352>::Ptr desc2 (new PointCloud<SHOT352> ());
  PointCloud<SHOT352>::Ptr desc3 (new PointCloud<SHOT352> ());
  PointCloud<SHOT352>::Ptr desc4 (new PointCloud<SHOT352> ());
  PointCloud<SHOT352>::Ptr desc5 (new PointCloud<SHOT352> ());
  PointCloud<SHOT352>::Ptr desc6 (new PointCloud<SHOT352> ());
  PointCloud<SHOT352>::Ptr desc7 (new PointCloud<SHOT352> ());

  // SHOT352 (global)
  GSHOTEstimation<PointNormal, PointNormal, SHOT352> gshot1;
  gshot1.setInputNormals (cloud_nr);
  gshot1.setInputCloud (cloud_nr);
  gshot1.compute (*desc1);
  // Eigen::Vector4f center_desc1 = gshot.getCentralPoint ();

  gshot1.setInputNormals (cloud_rot);
  gshot1.setInputCloud (cloud_rot);
  gshot1.compute (*desc2);
  // Eigen::Vector4f center_desc2 = gshot.getCentralPoint ();

  gshot1.setInputNormals (cloud_trans);
  gshot1.setInputCloud (cloud_trans);
  gshot1.compute (*desc3);
  // Eigen::Vector4f center_desc3 = gshot.getCentralPoint ();

  gshot1.setInputNormals (cloud_rot_trans);
  gshot1.setInputCloud (cloud_rot_trans);
  gshot1.compute (*desc4);
  // Eigen::Vector4f center_desc4 = gshot.getCentralPoint ();

  GSHOTEstimation<PointXYZ, Normal, SHOT352> gshot2;
  gshot2.setInputNormals (normals1);
  gshot2.setInputCloud (cloud_noise);
  gshot2.compute (*desc5);

  gshot2.setInputNormals (normals2);
  gshot2.setInputCloud (cloud2.makeShared ());
  gshot2.compute (*desc6);

  gshot2.setInputNormals (normals3);
  gshot2.setInputCloud (cloud3.makeShared ());
  gshot2.compute (*desc7);

  // Eigen::Vector3f distance_desc = (center_desc3.head<3> () - center_desc1.head<3> ());
  // std::cout << "dist of desc0 and desc3 -> (" << distance_desc[0] << ", " << distance_desc[1] << ", " << distance_desc[2] << ")\n";

  // SHOT352 (local)
  GSHOTEstimation<PointNormal, PointNormal, SHOT352> shot;
  shot.setInputNormals (cloud_nr);
  shot.setInputCloud (ground_truth.makeShared());
  shot.setSearchSurface (cloud_nr);
  shot.setRadiusSearch (radius_local_shot);
  shot.compute (*desc01);

  shot.setInputNormals (cloud_rot);
  shot.setInputCloud (ground_truth.makeShared());
  shot.setSearchSurface (cloud_rot);
  shot.setRadiusSearch (radius_local_shot);
  shot.compute (*desc02);

  shot.setInputNormals (cloud_trans);
  shot.setInputCloud (ground_truth.makeShared());
  shot.setSearchSurface (cloud_trans);
  shot.setRadiusSearch (radius_local_shot);
  shot.compute (*desc03);

  shot.setInputNormals (cloud_rot_trans);
  shot.setInputCloud (ground_truth.makeShared());
  shot.setSearchSurface (cloud_rot_trans);
  shot.setRadiusSearch (radius_local_shot);
  shot.compute (*desc04);

  // CHECK GSHOT
  checkDesc(*desc01, *desc1);
  checkDesc(*desc02, *desc2);
  checkDesc(*desc03, *desc3);
  checkDesc(*desc04, *desc4);

  std::vector<float> d0, d1, d2, d3, d4, d5, d6;
  for(int i = 0; i < 352; ++i)
  {
    d0.push_back(desc1->points[0].descriptor[i]);
    d1.push_back(desc2->points[0].descriptor[i]);
    d2.push_back(desc3->points[0].descriptor[i]);
    d3.push_back(desc4->points[0].descriptor[i]);
    d4.push_back(desc5->points[0].descriptor[i]);
    d5.push_back(desc6->points[0].descriptor[i]);
    d6.push_back(desc7->points[0].descriptor[i]);
  }

  float dist_0 = pcl::selectNorm< std::vector<float> > (d0, d0, 352, pcl::HIK) ;
  float dist_1 = pcl::selectNorm< std::vector<float> > (d0, d1, 352, pcl::HIK) ;
  float dist_2 = pcl::selectNorm< std::vector<float> > (d0, d2, 352, pcl::HIK) ;
  float dist_3 = pcl::selectNorm< std::vector<float> > (d0, d3, 352, pcl::HIK) ;
  float dist_4 = pcl::selectNorm< std::vector<float> > (d0, d4, 352, pcl::HIK) ;
  float dist_5 = pcl::selectNorm< std::vector<float> > (d0, d5, 352, pcl::HIK) ;
  float dist_6 = pcl::selectNorm< std::vector<float> > (d0, d6, 352, pcl::HIK) ;
  
  std::cout << ">> Itself[HIK]:      " << dist_0 << std::endl
            << ">> Rotation[HIK]:    " << dist_1 << std::endl
            << ">> Translate[HIK]:   " << dist_2 << std::endl
            << ">> Rot+Trans[HIK]    " << dist_3 << std::endl
            << ">> GaussNoise[HIK]:  " << dist_4 << std::endl
            << ">> bun03[HIK]:       " << dist_5 << std::endl
            << ">> milk[HIK]:        " << dist_6 << std::endl;

  float high_barrier = dist_0 * 0.999f;
  float noise_barrier = dist_0 * 0.75f;
  float cut_barrier = dist_0 * 0.20f;
  float low_barrier = dist_0 * 0.02f;

  EXPECT_GT (dist_1, high_barrier);
  EXPECT_GT (dist_2, high_barrier);
  //EXPECT_GT (dist_3, high_barrier);
  EXPECT_GT (dist_4, noise_barrier);
  EXPECT_GT (dist_5, cut_barrier);
  EXPECT_LT (dist_6, low_barrier);
}
template<typename Point> void
TableObjectCluster<Point>::calculateBoundingBox(
  const PointCloudPtr& cloud,
  const pcl::PointIndices& indices,
  const Eigen::Vector3f& plane_normal,
  const Eigen::Vector3f& plane_point,
  Eigen::Vector3f& position,
  Eigen::Quaternion<float>& orientation,
  Eigen::Vector3f& size)
{
  // transform to table coordinate frame and project points on X-Y, save max height
  Eigen::Affine3f tf;
  pcl::getTransformationFromTwoUnitVectorsAndOrigin(plane_normal.unitOrthogonal(), plane_normal, plane_point, tf);
  pcl::PointCloud<pcl::PointXYZ>::Ptr pc2d(new pcl::PointCloud<pcl::PointXYZ>);
  float height = 0.0;
  for(std::vector<int>::const_iterator it=indices.indices.begin(); it != indices.indices.end(); ++it)
  {
    Eigen::Vector3f tmp = tf * (*cloud)[*it].getVector3fMap();
    height = std::max<float>(height, fabs(tmp(2)));
    pc2d->push_back(pcl::PointXYZ(tmp(0),tmp(1),0.0));
  }

  // create convex hull of projected points
  #ifdef PCL_MINOR_VERSION >= 6
  pcl::PointCloud<pcl::PointXYZ>::Ptr conv_hull(new pcl::PointCloud<pcl::PointXYZ>);
  pcl::ConvexHull<pcl::PointXYZ> chull;
  chull.setDimension(2);
  chull.setInputCloud(pc2d);
  chull.reconstruct(*conv_hull);
  #endif

  /*for(int i=0; i<conv_hull->size(); ++i)
    std::cout << (*conv_hull)[i].x << "," << (*conv_hull)[i].y << std::endl;*/

  // find the minimal bounding rectangle in 2D and table coordinates
  Eigen::Vector2f p1, p2, p3;
  cob_3d_mapping::MinimalRectangle2D mr2d;
  mr2d.setConvexHull(conv_hull->points);
  mr2d.rotatingCalipers(p2, p1, p3);
  /*std::cout << "BB: \n" << p1[0] << "," << p1[1] <<"\n"
            << p2[0] << "," << p2[1] <<"\n"
            << p3[0] << "," << p3[1] <<"\n ---" << std::endl;*/

  // compute center of rectangle
  position[0] = 0.5f*(p1[0] + p3[0]);
  position[1] = 0.5f*(p1[1] + p3[1]);
  position[2] = 0.0f;
  // transform back
  Eigen::Affine3f inv_tf = tf.inverse();
  position = inv_tf * position;
  // set size of bounding box
  float norm_1 = (p3-p2).norm();
  float norm_2 = (p1-p2).norm();
  // BoundingBox coordinates: X:= main direction, Z:= table normal
  Eigen::Vector3f direction; // y direction
  if (norm_1 < norm_2)
  {
    direction = Eigen::Vector3f(p3[0]-p2[0], p3[1]-p2[1], 0) / (norm_1);
    size[0] = norm_2 * 0.5f;
    size[1] = norm_1 * 0.5f;
  }
  else
  {
    direction = Eigen::Vector3f(p1[0]-p2[0], p1[1]-p2[1], 0) / (norm_2);
    size[0] = norm_1 * 0.5f;
    size[1] = norm_2 * 0.5f;
  }
  size[2] = -height;


  direction = inv_tf.rotation() * direction;
  orientation = pcl::getTransformationFromTwoUnitVectors(direction, plane_normal).rotation(); // (y, z-direction)

  return;
  Eigen::Matrix3f M = Eigen::Matrix3f::Identity() - plane_normal * plane_normal.transpose();
  Eigen::Vector3f xn = M * Eigen::Vector3f::UnitX(); // X-axis project on normal
  Eigen::Vector3f xxn = M * direction;
  float cos_phi = acos(xn.normalized().dot(xxn.normalized())); // angle between xn and main direction
  cos_phi = cos(0.5f * cos_phi);
  float sin_phi = sqrt(1.0f-cos_phi*cos_phi);
  //orientation.w() = cos_phi;
  //orientation.x() = sin_phi * plane_normal(0);
  //orientation.y() = sin_phi * plane_normal(1);
  //orientation.z() = sin_phi * plane_normal(2);
}
예제 #23
0
파일: icp.cpp 프로젝트: zengzhen/ros_zhen
int
 main (int argc, char** argv)
{
    // Get input object and scene
    if (argc < 2)
    {
        pcl::console::print_error ("Syntax is: %s cloud1.pcd (cloud2.pcd)\n", argv[0]);
        return (1);
    }
    
    pcl::PointCloud<pcl::PointXYZRGBA>::Ptr cloud_in (new pcl::PointCloud<pcl::PointXYZRGBA>);
    pcl::PointCloud<pcl::PointXYZRGBA>::Ptr cloud_out (new pcl::PointCloud<pcl::PointXYZRGBA>);

    // Load object and scene
    pcl::console::print_highlight ("Loading point clouds...\n");
    if(argc<3)
    {
        if (pcl::io::loadPCDFile<pcl::PointXYZRGBA> (argv[1], *cloud_in) < 0)
            pcl::console::print_error ("Error loading first file!\n");
        *cloud_out = *cloud_in;
        
        //transform cloud
        Eigen::Affine3f transformation;
        transformation.setIdentity();
        transformation.translate(Eigen::Vector3f(0.3,0.02,-0.1));
        float roll, pitch, yaw;
        roll = 0.02; pitch = 1.2; yaw = 0;
        Eigen::AngleAxisf rollAngle(roll, Eigen::Vector3f::UnitX());
        Eigen::AngleAxisf pitchAngle(pitch, Eigen::Vector3f::UnitY());
        Eigen::AngleAxisf yawAngle(yaw, Eigen::Vector3f::UnitZ());
        Eigen::Quaternion<float> q = rollAngle*pitchAngle*yawAngle;
        transformation.rotate(q);
        
        pcl::transformPointCloud<pcl::PointXYZRGBA>(*cloud_in, *cloud_out, transformation);
        std::cout << "Transformed " << cloud_in->points.size () << " data points:"
            << std::endl;
    }else{
       if (pcl::io::loadPCDFile<pcl::PointXYZRGBA> (argv[1], *cloud_in) < 0 ||
        pcl::io::loadPCDFile<pcl::PointXYZRGBA> (argv[2], *cloud_out) < 0)
        {
            pcl::console::print_error ("Error loading files!\n");
            return (1);
        } 
    }
    
    // Fill in the CloudIn data
//     cloud_in->width    = 100;
//     cloud_in->height   = 1;
//     cloud_in->is_dense = false;
//     cloud_in->points.resize (cloud_in->width * cloud_in->height);
//     for (size_t i = 0; i < cloud_in->points.size (); ++i)
//     {
//         cloud_in->points[i].x = 1024 * rand () / (RAND_MAX + 1.0f);
//         cloud_in->points[i].y = 1024 * rand () / (RAND_MAX + 1.0f);
//         cloud_in->points[i].z = 1024 * rand () / (RAND_MAX + 1.0f);
//     }

    std::cout << "size:" << cloud_out->points.size() << std::endl;
      
    {
        pcl::ScopeTime("icp proces");
        
        pcl::IterativeClosestPoint<pcl::PointXYZRGBA, pcl::PointXYZRGBA> icp;
        icp.setInputSource(cloud_in);
        icp.setInputTarget(cloud_out);
        pcl::PointCloud<pcl::PointXYZRGBA> Final;
        icp.setMaximumIterations(1000000);
        icp.setRANSACOutlierRejectionThreshold(0.01);
        icp.align(Final);
        std::cout << "has converged:" << icp.hasConverged() << " score: " <<
        icp.getFitnessScore() << std::endl;
        std::cout << icp.getFinalTransformation() << std::endl;
        
        //translation, rotation
        Eigen::Matrix4f icp_transformation=icp.getFinalTransformation();
        Eigen::Matrix3f icp_rotation = icp_transformation.block<3,3>(0,0);
        Eigen::Vector3f euler = icp_rotation.eulerAngles(0,1,2);
        std::cout << "rotation: " << euler.transpose() << std::endl;
        std::cout << "translation:" << icp_transformation.block<3,1>(0,3).transpose() << std::endl;
    }
  

 return (0);
}
/* ---[ */
int
main (int argc, char** argv)
{
  srand (static_cast<unsigned int> (time (0)));

  print_info ("The viewer window provides interactive commands; for help, press 'h' or 'H' from within the window.\n");

  if (argc < 2)
  {
    printHelp (argc, argv);
    return (-1);
  }

  bool debug = false;
  pcl::console::parse_argument (argc, argv, "-debug", debug);
  if (debug)
    pcl::console::setVerbosityLevel (pcl::console::L_DEBUG);

  // Parse the command line arguments for .pcd files
  std::vector<int> p_file_indices   = pcl::console::parse_file_extension_argument (argc, argv, ".pcd");
  std::vector<int> vtk_file_indices = pcl::console::parse_file_extension_argument (argc, argv, ".vtk");

  if (p_file_indices.size () == 0 && vtk_file_indices.size () == 0)
  {
    print_error ("No .PCD or .VTK file given. Nothing to visualize.\n");
    return (-1);
  }

  // Command line parsing
  double bcolor[3] = {0, 0, 0};
  pcl::console::parse_3x_arguments (argc, argv, "-bc", bcolor[0], bcolor[1], bcolor[2]);

  std::vector<double> fcolor_r, fcolor_b, fcolor_g;
  bool fcolorparam = pcl::console::parse_multiple_3x_arguments (argc, argv, "-fc", fcolor_r, fcolor_g, fcolor_b);

  std::vector<double> pose_x, pose_y, pose_z, pose_roll, pose_pitch, pose_yaw;
  bool poseparam = pcl::console::parse_multiple_3x_arguments (argc, argv, "-position", pose_x, pose_y, pose_z);
  poseparam &= pcl::console::parse_multiple_3x_arguments (argc, argv, "-orientation", pose_roll, pose_pitch, pose_yaw);

  std::vector<int> psize;
  pcl::console::parse_multiple_arguments (argc, argv, "-ps", psize);

  std::vector<double> opaque;
  pcl::console::parse_multiple_arguments (argc, argv, "-opaque", opaque);

  std::vector<std::string> shadings;
  pcl::console::parse_multiple_arguments (argc, argv, "-shading", shadings);

  int mview = 0;
  pcl::console::parse_argument (argc, argv, "-multiview", mview);

  int normals = 0;
  pcl::console::parse_argument (argc, argv, "-normals", normals);
  float normals_scale = NORMALS_SCALE;
  pcl::console::parse_argument (argc, argv, "-normals_scale", normals_scale);

  int pc = 0;
  pcl::console::parse_argument (argc, argv, "-pc", pc);
  float pc_scale = PC_SCALE;
  pcl::console::parse_argument (argc, argv, "-pc_scale", pc_scale);

  bool use_vbos = false;
  pcl::console::parse_argument (argc, argv, "-vbo_rendering", use_vbos);
  if (use_vbos) 
    print_highlight ("Vertex Buffer Object (VBO) visualization enabled.\n");

  bool use_pp   = pcl::console::find_switch (argc, argv, "-use_point_picking");
  if (use_pp) 
    print_highlight ("Point picking enabled.\n");

  bool use_optimal_l_colors = pcl::console::find_switch (argc, argv, "-optimal_label_colors");
  if (use_optimal_l_colors)
    print_highlight ("Optimal glasbey colors are being assigned to existing labels.\nNote: No static mapping between label ids and colors\n");

  // If VBOs are not enabled, then try to use immediate rendering
  bool use_immediate_rendering = false;
  if (!use_vbos)
  {
    pcl::console::parse_argument (argc, argv, "-immediate_rendering", use_immediate_rendering);
    if (use_immediate_rendering) 
      print_highlight ("Using immediate mode rendering.\n");
  }

  // Multiview enabled?
  int y_s = 0, x_s = 0;
  double x_step = 0, y_step = 0;
  if (mview)
  {
    print_highlight ("Multi-viewport rendering enabled.\n");

    y_s = static_cast<int>(floor (sqrt (static_cast<float>(p_file_indices.size () + vtk_file_indices.size ()))));
    x_s = y_s + static_cast<int>(ceil (double (p_file_indices.size () + vtk_file_indices.size ()) / double (y_s) - y_s));

    if (p_file_indices.size () != 0)
    {
      print_highlight ("Preparing to load "); print_value ("%d", p_file_indices.size ()); print_info (" pcd files.\n");
    }

    if (vtk_file_indices.size () != 0)
    {
      print_highlight ("Preparing to load "); print_value ("%d", vtk_file_indices.size ()); print_info (" vtk files.\n");
    }

    x_step = static_cast<double>(1.0 / static_cast<double>(x_s));
    y_step = static_cast<double>(1.0 / static_cast<double>(y_s));
    print_value ("%d", x_s);    print_info ("x"); print_value ("%d", y_s);
    print_info (" / ");      print_value ("%f", x_step); print_info ("x"); print_value ("%f", y_step);
    print_info (")\n");
  }

  // Fix invalid multiple arguments
  if (psize.size () != p_file_indices.size () && psize.size () > 0)
    for (size_t i = psize.size (); i < p_file_indices.size (); ++i)
      psize.push_back (1);
  if (opaque.size () != p_file_indices.size () && opaque.size () > 0)
    for (size_t i = opaque.size (); i < p_file_indices.size (); ++i)
      opaque.push_back (1.0);

  if (shadings.size () != p_file_indices.size () && shadings.size () > 0)
    for (size_t i = shadings.size (); i < p_file_indices.size (); ++i)
      shadings.push_back ("flat");

  // Create the PCLVisualizer object
#if VTK_MAJOR_VERSION==6 || (VTK_MAJOR_VERSION==5 && VTK_MINOR_VERSION>6)
  boost::shared_ptr<pcl::visualization::PCLPlotter> ph;
#endif  
  // Using min_p, max_p to set the global Y min/max range for the histogram
  float min_p = FLT_MAX; float max_p = -FLT_MAX;

  int k = 0, l = 0, viewport = 0;
  // Load the data files
  pcl::PCDReader pcd;
  pcl::console::TicToc tt;
  ColorHandlerPtr color_handler;
  GeometryHandlerPtr geometry_handler;

  // Go through VTK files
  for (size_t i = 0; i < vtk_file_indices.size (); ++i)
  {
    // Load file
    tt.tic ();
    print_highlight (stderr, "Loading "); print_value (stderr, "%s ", argv[vtk_file_indices.at (i)]);
    vtkPolyDataReader* reader = vtkPolyDataReader::New ();
    reader->SetFileName (argv[vtk_file_indices.at (i)]);
    reader->Update ();
    vtkSmartPointer<vtkPolyData> polydata = reader->GetOutput ();
    if (!polydata)
      return (-1);
    print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", polydata->GetNumberOfPoints ()); print_info (" points]\n");

    // Create the PCLVisualizer object here on the first encountered XYZ file
    if (!p)
      p.reset (new pcl::visualization::PCLVisualizer (argc, argv, "PCD viewer"));

    // Multiview enabled?
    if (mview)
    {
      p->createViewPort (k * x_step, l * y_step, (k + 1) * x_step, (l + 1) * y_step, viewport);
      k++;
      if (k >= x_s)
      {
        k = 0;
        l++;
      }
    }

    // Add as actor
    std::stringstream cloud_name ("vtk-");
    cloud_name << argv[vtk_file_indices.at (i)] << "-" << i;
    p->addModelFromPolyData (polydata, cloud_name.str (), viewport);

    // Change the shape rendered color
    if (fcolorparam && fcolor_r.size () > i && fcolor_g.size () > i && fcolor_b.size () > i)
      p->setShapeRenderingProperties (pcl::visualization::PCL_VISUALIZER_COLOR, fcolor_r[i], fcolor_g[i], fcolor_b[i], cloud_name.str ());

    // Change the shape rendered point size
    if (psize.size () > 0)
      p->setShapeRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, psize.at (i), cloud_name.str ());

    // Change the shape rendered opacity
    if (opaque.size () > 0)
      p->setShapeRenderingProperties (pcl::visualization::PCL_VISUALIZER_OPACITY, opaque.at (i), cloud_name.str ());

    // Change the shape rendered shading
    if (shadings.size () > 0)
    {
      if (shadings[i] == "flat")
      {
        print_highlight (stderr, "Setting shading property for %s to FLAT.\n", argv[vtk_file_indices.at (i)]);
        p->setShapeRenderingProperties (pcl::visualization::PCL_VISUALIZER_SHADING, pcl::visualization::PCL_VISUALIZER_SHADING_FLAT, cloud_name.str ());
      }
      else if (shadings[i] == "gouraud")
      {
        print_highlight (stderr, "Setting shading property for %s to GOURAUD.\n", argv[vtk_file_indices.at (i)]);
        p->setShapeRenderingProperties (pcl::visualization::PCL_VISUALIZER_SHADING, pcl::visualization::PCL_VISUALIZER_SHADING_GOURAUD, cloud_name.str ());
      }
      else if (shadings[i] == "phong")
      {
        print_highlight (stderr, "Setting shading property for %s to PHONG.\n", argv[vtk_file_indices.at (i)]);
        p->setShapeRenderingProperties (pcl::visualization::PCL_VISUALIZER_SHADING, pcl::visualization::PCL_VISUALIZER_SHADING_PHONG, cloud_name.str ());
      }
    }
  }

  pcl::PCLPointCloud2::Ptr cloud;
  // Go through PCD files
  for (size_t i = 0; i < p_file_indices.size (); ++i)
  {
    tt.tic ();
    cloud.reset (new pcl::PCLPointCloud2);
    Eigen::Vector4f origin;
    Eigen::Quaternionf orientation;
    int version;

    print_highlight (stderr, "Loading "); print_value (stderr, "%s ", argv[p_file_indices.at (i)]);

    if (pcd.read (argv[p_file_indices.at (i)], *cloud, origin, orientation, version) < 0)
      return (-1);

    // Calculate transform if available.
    if (pose_x.size () > i && pose_y.size () > i && pose_z.size () > i &&
        pose_roll.size () > i && pose_pitch.size () > i && pose_yaw.size () > i)
    {
      Eigen::Affine3f pose =
        Eigen::Translation3f (Eigen::Vector3f (pose_x[i], pose_y[i], pose_z[i])) *
        Eigen::AngleAxisf (pose_yaw[i],   Eigen::Vector3f::UnitZ ()) *
        Eigen::AngleAxisf (pose_pitch[i], Eigen::Vector3f::UnitY ()) *
        Eigen::AngleAxisf (pose_roll[i],  Eigen::Vector3f::UnitX ());
      orientation = pose.rotation () * orientation;
      origin.block<3, 1> (0, 0) = (pose * Eigen::Translation3f (origin.block<3, 1> (0, 0))).translation ();
    }

    std::stringstream cloud_name;

    // ---[ Special check for 1-point multi-dimension histograms
    if (cloud->fields.size () == 1 && isMultiDimensionalFeatureField (cloud->fields[0]))
    {
      cloud_name << argv[p_file_indices.at (i)];

#if VTK_MAJOR_VERSION==6 || (VTK_MAJOR_VERSION==5 && VTK_MINOR_VERSION>6)
      if (!ph)
        ph.reset (new pcl::visualization::PCLPlotter);
#endif

      pcl::getMinMax (*cloud, 0, cloud->fields[0].name, min_p, max_p);
#if VTK_MAJOR_VERSION==6 || (VTK_MAJOR_VERSION==5 && VTK_MINOR_VERSION>6)
      ph->addFeatureHistogram (*cloud, cloud->fields[0].name, cloud_name.str ());
#endif
      print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", cloud->fields[0].count); print_info (" points]\n");
      continue;
    }

    // ---[ Special check for 2D images
    if (cloud->fields.size () == 1 && isOnly2DImage (cloud->fields[0]))
    {
      print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%u", cloud->width * cloud->height); print_info (" points]\n");
      print_info ("Available dimensions: "); print_value ("%s\n", pcl::getFieldsList (*cloud).c_str ());
      
      std::stringstream name;
      name << "PCD Viewer :: " << argv[p_file_indices.at (i)];
      pcl::visualization::ImageViewer::Ptr img (new pcl::visualization::ImageViewer (name.str ()));
      pcl::PointCloud<pcl::RGB> rgb_cloud;
      pcl::fromPCLPointCloud2 (*cloud, rgb_cloud);

      img->addRGBImage (rgb_cloud);
      imgs.push_back (img);

      continue;
    }

    cloud_name << argv[p_file_indices.at (i)] << "-" << i;

    // Create the PCLVisualizer object here on the first encountered XYZ file
    if (!p)
    {
      p.reset (new pcl::visualization::PCLVisualizer (argc, argv, "PCD viewer"));
      if (use_pp)   // Only enable the point picking callback if the command line parameter is enabled
        p->registerPointPickingCallback (&pp_callback, static_cast<void*> (&cloud));

      // Set whether or not we should be using the vtkVertexBufferObjectMapper
      p->setUseVbos (use_vbos);

      if (!p->cameraParamsSet () && !p->cameraFileLoaded ())
      {
        Eigen::Matrix3f rotation;
        rotation = orientation;
        p->setCameraPosition (origin [0]                  , origin [1]                  , origin [2],
                              origin [0] + rotation (0, 2), origin [1] + rotation (1, 2), origin [2] + rotation (2, 2),
                                           rotation (0, 1),              rotation (1, 1),              rotation (2, 1));
      }
    }

    // Multiview enabled?
    if (mview)
    {
      p->createViewPort (k * x_step, l * y_step, (k + 1) * x_step, (l + 1) * y_step, viewport);
      k++;
      if (k >= x_s)
      {
        k = 0;
        l++;
      }
    }

    if (cloud->width * cloud->height == 0)
    {
      print_error ("[error: no points found!]\n");
      return (-1);
    }

    // If no color was given, get random colors
    if (fcolorparam)
    {
      if (fcolor_r.size () > i && fcolor_g.size () > i && fcolor_b.size () > i)
        color_handler.reset (new pcl::visualization::PointCloudColorHandlerCustom<pcl::PCLPointCloud2> (cloud, fcolor_r[i], fcolor_g[i], fcolor_b[i]));
      else
        color_handler.reset (new pcl::visualization::PointCloudColorHandlerRandom<pcl::PCLPointCloud2> (cloud));
    }
    else
      color_handler.reset (new pcl::visualization::PointCloudColorHandlerRandom<pcl::PCLPointCloud2> (cloud));

    // Add the dataset with a XYZ and a random handler
    geometry_handler.reset (new pcl::visualization::PointCloudGeometryHandlerXYZ<pcl::PCLPointCloud2> (cloud));
    // Add the cloud to the renderer
    //p->addPointCloud<pcl::PointXYZ> (cloud_xyz, geometry_handler, color_handler, cloud_name.str (), viewport);
    p->addPointCloud (cloud, geometry_handler, color_handler, origin, orientation, cloud_name.str (), viewport);


    if (mview)
      // Add text with file name
      p->addText (argv[p_file_indices.at (i)], 5, 5, 10, 1.0, 1.0, 1.0, "text_" + std::string (argv[p_file_indices.at (i)]), viewport);

    // If normal lines are enabled
    if (normals != 0)
    {
      int normal_idx = pcl::getFieldIndex (*cloud, "normal_x");
      if (normal_idx == -1)
      {
        print_error ("Normal information requested but not available.\n");
        continue;
        //return (-1);
      }
      //
      // Convert from blob to pcl::PointCloud
      pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_xyz (new pcl::PointCloud<pcl::PointXYZ>);
      pcl::fromPCLPointCloud2 (*cloud, *cloud_xyz);
      cloud_xyz->sensor_origin_ = origin;
      cloud_xyz->sensor_orientation_ = orientation;

      pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);
      pcl::fromPCLPointCloud2 (*cloud, *cloud_normals);
      std::stringstream cloud_name_normals;
      cloud_name_normals << argv[p_file_indices.at (i)] << "-" << i << "-normals";
      p->addPointCloudNormals<pcl::PointXYZ, pcl::Normal> (cloud_xyz, cloud_normals, normals, normals_scale, cloud_name_normals.str (), viewport);
    }
	/*
    // If principal curvature lines are enabled
    if (pc != 0)
    {
      if (normals == 0)
        normals = pc;

      int normal_idx = pcl::getFieldIndex (*cloud, "normal_x");
      if (normal_idx == -1)
      {
        print_error ("Normal information requested but not available.\n");
        continue;
        //return (-1);
      }
      int pc_idx = pcl::getFieldIndex (*cloud, "principal_curvature_x");
      if (pc_idx == -1)
      {
        print_error ("Principal Curvature information requested but not available.\n");
        continue;
        //return (-1);
      }
      //
      // Convert from blob to pcl::PointCloud
      pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_xyz (new pcl::PointCloud<pcl::PointXYZ>);
      pcl::fromPCLPointCloud2 (*cloud, *cloud_xyz);
      cloud_xyz->sensor_origin_ = origin;
      cloud_xyz->sensor_orientation_ = orientation;
      pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);
      pcl::fromPCLPointCloud2 (*cloud, *cloud_normals);
      pcl::PointCloud<pcl::PrincipalCurvatures>::Ptr cloud_pc (new pcl::PointCloud<pcl::PrincipalCurvatures>);
      pcl::fromPCLPointCloud2 (*cloud, *cloud_pc);
      std::stringstream cloud_name_normals_pc;
      cloud_name_normals_pc << argv[p_file_indices.at (i)] << "-" << i << "-normals";
      int factor = (std::min)(normals, pc);
      p->addPointCloudNormals<pcl::PointXYZ, pcl::Normal> (cloud_xyz, cloud_normals, factor, normals_scale, cloud_name_normals_pc.str (), viewport);
      p->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_COLOR, 1.0, 0.0, 0.0, cloud_name_normals_pc.str ());
      p->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_LINE_WIDTH, 3, cloud_name_normals_pc.str ());
      cloud_name_normals_pc << "-pc";
      p->addPointCloudPrincipalCurvatures<pcl::PointXYZ, pcl::Normal> (cloud_xyz, cloud_normals, cloud_pc, factor, pc_scale, cloud_name_normals_pc.str (), viewport);
      p->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_LINE_WIDTH, 3, cloud_name_normals_pc.str ());
    }
	*/
    // Add every dimension as a possible color
    if (!fcolorparam)
    {
      int rgb_idx = 0;
      int label_idx = 0;
      for (size_t f = 0; f < cloud->fields.size (); ++f)
      {
        if (cloud->fields[f].name == "rgb" || cloud->fields[f].name == "rgba")
        {
          rgb_idx = f + 1;
          color_handler.reset (new pcl::visualization::PointCloudColorHandlerRGBField<pcl::PCLPointCloud2> (cloud));
        }
        //else if (cloud->fields[f].name == "label")
        //{
         // label_idx = f + 1;
          //color_handler.reset (new pcl::visualization::PointCloudColorHandlerLabelField<pcl::PCLPointCloud2> (cloud, !use_optimal_l_colors));
        //}
        else
        {
          if (!isValidFieldName (cloud->fields[f].name))
            continue;
          color_handler.reset (new pcl::visualization::PointCloudColorHandlerGenericField<pcl::PCLPointCloud2> (cloud, cloud->fields[f].name));
        }
        // Add the cloud to the renderer
        //p->addPointCloud<pcl::PointXYZ> (cloud_xyz, color_handler, cloud_name.str (), viewport);
        p->addPointCloud (cloud, color_handler, origin, orientation, cloud_name.str (), viewport);
      }
      // Set RGB color handler or label handler as default
      p->updateColorHandlerIndex (cloud_name.str (), (rgb_idx ? rgb_idx : label_idx));
    }

    // Additionally, add normals as a handler
    geometry_handler.reset (new pcl::visualization::PointCloudGeometryHandlerSurfaceNormal<pcl::PCLPointCloud2> (cloud));
    if (geometry_handler->isCapable ())
      //p->addPointCloud<pcl::PointXYZ> (cloud_xyz, geometry_handler, cloud_name.str (), viewport);
      p->addPointCloud (cloud, geometry_handler, origin, orientation, cloud_name.str (), viewport);

    if (use_immediate_rendering)
      // Set immediate mode rendering ON
      p->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_IMMEDIATE_RENDERING, 1.0, cloud_name.str ());

    // Change the cloud rendered point size
    if (psize.size () > 0)
      p->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, psize.at (i), cloud_name.str ());

    // Change the cloud rendered opacity
    if (opaque.size () > 0)
      p->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_OPACITY, opaque.at (i), cloud_name.str ());

    // Reset camera viewpoint to center of cloud if camera parameters were not passed manually and this is the first loaded cloud
    if (i == 0 && !p->cameraParamsSet () && !p->cameraFileLoaded ())
    {
      p->resetCameraViewpoint (cloud_name.str ());
      p->resetCamera ();
    }

    print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%u", cloud->width * cloud->height); print_info (" points]\n");
    print_info ("Available dimensions: "); print_value ("%s\n", pcl::getFieldsList (*cloud).c_str ());
    if (p->cameraFileLoaded ())
      print_info ("Camera parameters restored from %s.\n", p->getCameraFile ().c_str ());
  }

  if (!mview && p)
  {
    std::string str;
    if (!p_file_indices.empty ())
      str = std::string (argv[p_file_indices.at (0)]);
    else if (!vtk_file_indices.empty ())
      str = std::string (argv[vtk_file_indices.at (0)]);

    for (size_t i = 1; i < p_file_indices.size (); ++i)
      str += ", " + std::string (argv[p_file_indices.at (i)]);

    for (size_t i = 1; i < vtk_file_indices.size (); ++i)
      str += ", " + std::string (argv[vtk_file_indices.at (i)]);

    p->addText (str, 5, 5, 10, 1.0, 1.0, 1.0, "text_allnames");
  }

  if (p)
    p->setBackgroundColor (bcolor[0], bcolor[1], bcolor[2]);
  // Read axes settings
  double axes  = 0.0;
  pcl::console::parse_argument (argc, argv, "-ax", axes);
  if (axes != 0.0 && p)
  {
    float ax_x = 0.0, ax_y = 0.0, ax_z = 0.0;
    pcl::console::parse_3x_arguments (argc, argv, "-ax_pos", ax_x, ax_y, ax_z);
    // Draw XYZ axes if command-line enabled
    p->addCoordinateSystem (axes, ax_x, ax_y, ax_z, "global");
  }

  // Clean up the memory used by the binary blob
  // Note: avoid resetting the cloud, otherwise the PointPicking callback will fail
  if (!use_pp)   // Only enable the point picking callback if the command line parameter is enabled
  {
    cloud.reset ();
    xyzcloud.reset ();
  }

  // If we have been given images, create our own loop so that we can spin each individually
  if (!imgs.empty ())
  {
    bool stopped = false;
    do
    {
#if VTK_MAJOR_VERSION==6 || (VTK_MAJOR_VERSION==5 && VTK_MINOR_VERSION>6)
      if (ph) ph->spinOnce ();
#endif

      for (int i = 0; i < int (imgs.size ()); ++i)
      {
        if (imgs[i]->wasStopped ())
        {
          stopped = true;
          break;
        }
        imgs[i]->spinOnce ();
      }
        
      if (p)
      {
        if (p->wasStopped ())
        {
          stopped = true;
          break;
        }
        p->spinOnce ();
      }
      boost::this_thread::sleep (boost::posix_time::microseconds (100));
    }
    while (!stopped);
  }
  else
  {
    // If no images, continue
#if VTK_MAJOR_VERSION==6 || (VTK_MAJOR_VERSION==5 && VTK_MINOR_VERSION>6)
    if (ph)
    {
      //print_highlight ("Setting the global Y range for all histograms to: "); print_value ("%f -> %f\n", min_p, max_p);
      //ph->setGlobalYRange (min_p, max_p);
      //ph->updateWindowPositions ();
      if (p)
        p->spin ();
      else
        ph->spin ();
    }
    else
#endif
      if (p)
        p->spin ();
  }
}
예제 #25
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 ;
    }
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_);
		}
	}
}
예제 #27
0
	void setMatrix(const Eigen::Affine3f& matrix)
	{
		setMatrix((float*)matrix.data());
	}
예제 #28
0
NORI_NAMESPACE_BEGIN

NoriObject *loadFromXML(const std::string &filename) {
    /* Load the XML file using 'pugi' (a tiny self-contained XML parser implemented in C++) */
    pugi::xml_document doc;
    pugi::xml_parse_result result = doc.load_file(filename.c_str());

    /* Helper function: map a position offset in bytes to a more readable row/column value */
    auto offset = [&](ptrdiff_t pos) -> std::string {
        std::fstream is(filename);
        char buffer[1024];
        int line = 0, linestart = 0, offset = 0;
        while (is.good()) {
            is.read(buffer, sizeof(buffer));
            for (int i = 0; i < is.gcount(); ++i) {
                if (buffer[i] == '\n') {
                    if (offset + i >= pos)
                        return tfm::format("row %i, col %i", line + 1, pos - linestart);
                    ++line;
                    linestart = offset + i;
                }
            }
            offset += (int) is.gcount();
        }
        return "byte offset " + std::to_string(pos);
    };

    if (!result) /* There was a parser / file IO error */
        throw NoriException("Error while parsing \"%s\": %s (at %s)", filename, result.description(), offset(result.offset));

    /* Set of supported XML tags */
    enum ETag {
        /* Object classes */
        EScene                = NoriObject::EScene,
        EMesh                 = NoriObject::EMesh,
        EBSDF                 = NoriObject::EBSDF,
        ETEXTURE              = NoriObject::ETEXTURE,
        EPERLIN               = NoriObject::EPERLIN,
        EMIXTEXTURE           = NoriObject::EMIXTEXTURE,
        EMIXBUMPMAP           = NoriObject::EMIXBUMPMAP,
        EBUMPMAP              = NoriObject::EBUMPMAP,
        EPhaseFunction        = NoriObject::EPhaseFunction,
        EEmitter              = NoriObject::EEmitter,
        EMedium               = NoriObject::EMedium,
        EVolume               = NoriObject::EVolume,
        ECamera               = NoriObject::ECamera,
        EIntegrator           = NoriObject::EIntegrator,
        ESampler              = NoriObject::ESampler,
        ETest                 = NoriObject::ETest,
        EReconstructionFilter = NoriObject::EReconstructionFilter,

        /* Properties */
        EBoolean = NoriObject::EClassTypeCount,
        EInteger,
        EFloat,
        EString,
        EPoint,
        EVector,
        EColor,
        ETransform,
        ETranslate,
        EMatrix,
        ERotate,
        EScale,
        ELookAt,

        EInvalid
    };

    /* Create a mapping from tag names to tag IDs */
    std::map<std::string, ETag> tags;
    tags["scene"]      = EScene;
    tags["mesh"]       = EMesh;
    tags["bsdf"]       = EBSDF;
    tags["texture"]    = ETEXTURE;
    tags["bumpmap"]    = EBUMPMAP;
    tags["perlin"]     = EPERLIN;
    tags["mixTexture"] = EMIXTEXTURE;
    tags["mixBumpmap"] = EMIXBUMPMAP;
    tags["bumpmap"]    = EBUMPMAP;
    tags["emitter"]    = EEmitter;
    tags["camera"]     = ECamera;
    tags["medium"]     = EMedium;
    tags["volume"]     = EVolume;
    tags["phase"]      = EPhaseFunction;
    tags["integrator"] = EIntegrator;
    tags["sampler"]    = ESampler;
    tags["rfilter"]    = EReconstructionFilter;
    tags["test"]       = ETest;
    tags["boolean"]    = EBoolean;
    tags["integer"]    = EInteger;
    tags["float"]      = EFloat;
    tags["string"]     = EString;
    tags["point"]      = EPoint;
    tags["vector"]     = EVector;
    tags["color"]      = EColor;
    tags["transform"]  = ETransform;
    tags["translate"]  = ETranslate;
    tags["matrix"]     = EMatrix;
    tags["rotate"]     = ERotate;
    tags["scale"]      = EScale;
    tags["lookat"]     = ELookAt;

    /* Helper function to check if attributes are fully specified */
    auto check_attributes = [&](const pugi::xml_node &node, std::set<std::string> attrs) {
        for (auto attr : node.attributes()) {
            auto it = attrs.find(attr.name());
            if (it == attrs.end())
                throw NoriException("Error while parsing \"%s\": unexpected attribute \"%s\" in \"%s\" at %s",
                                    filename, attr.name(), node.name(), offset(node.offset_debug()));
            attrs.erase(it);
        }
        if (!attrs.empty())
            throw NoriException("Error while parsing \"%s\": missing attribute \"%s\" in \"%s\" at %s",
                                filename, *attrs.begin(), node.name(), offset(node.offset_debug()));
    };

    Eigen::Affine3f transform;

    /* Helper function to parse a Nori XML node (recursive) */
    std::function<NoriObject *(pugi::xml_node &, PropertyList &, int)> parseTag = [&](
    pugi::xml_node &node, PropertyList &list, int parentTag) -> NoriObject * {
        /* Skip over comments */
        if (node.type() == pugi::node_comment || node.type() == pugi::node_declaration)
            return nullptr;

        if (node.type() != pugi::node_element)
            throw NoriException(
                "Error while parsing \"%s\": unexpected content at %s",
                filename, offset(node.offset_debug()));

        /* Look up the name of the current element */
        auto it = tags.find(node.name());
        if (it == tags.end())
            throw NoriException("Error while parsing \"%s\": unexpected tag \"%s\" at %s",
            filename, node.name(), offset(node.offset_debug()));
        int tag = it->second;

        /* Perform some safety checks to make sure that the XML tree really makes sense */
        bool hasParent            = parentTag != EInvalid;
        bool parentIsObject       = hasParent && parentTag < NoriObject::EClassTypeCount;
        bool currentIsObject      = tag < NoriObject::EClassTypeCount;
        bool parentIsTransform    = parentTag == ETransform;
        bool currentIsTransformOp = tag == ETranslate || tag == ERotate || tag == EScale || tag == ELookAt || tag == EMatrix;

        if (!hasParent && !currentIsObject)
            throw NoriException("Error while parsing \"%s\": root element \"%s\" must be a Nori object (at %s)",
            filename, node.name(), offset(node.offset_debug()));

        if (parentIsTransform != currentIsTransformOp)
            throw NoriException("Error while parsing \"%s\": transform nodes "
            "can only contain transform operations (at %s)",
            filename,  offset(node.offset_debug()));

        if (hasParent && !parentIsObject && !(parentIsTransform && currentIsTransformOp))
            throw NoriException("Error while parsing \"%s\": node \"%s\" requires a Nori object as parent (at %s)",
            filename, node.name(), offset(node.offset_debug()));

        if (tag == EScene)
            node.append_attribute("type") = "scene";
        else if (tag == ETransform)
            transform.setIdentity();

        PropertyList propList;
        std::vector<NoriObject *> children;
        for (pugi::xml_node &ch: node.children()) {
            NoriObject *child = parseTag(ch, propList, tag);
            if (child)
                children.push_back(child);
        }

        NoriObject *result = nullptr;
        try {
            if (currentIsObject) {
                check_attributes(node, { "type" });

                /* This is an object, first instantiate it */
                result = NoriObjectFactory::createInstance(
                             node.attribute("type").value(),
                             propList
                         );

                if (result->getClassType() != (int) tag) {
                    throw NoriException(
                        "Unexpectedly constructed an object "
                        "of type <%s> (expected type <%s>): %s",
                        NoriObject::classTypeName(result->getClassType()),
                        NoriObject::classTypeName((NoriObject::EClassType) tag),
                        result->toString());
                }

                /* Add all children */
                for (auto ch: children) {
                    result->addChild(ch);
                    ch->setParent(result);
                }

                /* Activate / configure the object */
                result->activate();
            } else {
                /* This is a property */
                switch (tag) {
                case EString: {
                    check_attributes(node, { "name", "value" });
                    list.setString(node.attribute("name").value(), node.attribute("value").value());
                }
                break;
                case EFloat: {
                    check_attributes(node, { "name", "value" });
                    list.setFloat(node.attribute("name").value(), toFloat(node.attribute("value").value()));
                }
                break;
                case EInteger: {
                    check_attributes(node, { "name", "value" });
                    list.setInteger(node.attribute("name").value(), toInt(node.attribute("value").value()));
                }
                break;
                case EBoolean: {
                    check_attributes(node, { "name", "value" });
                    list.setBoolean(node.attribute("name").value(), toBool(node.attribute("value").value()));
                }
                break;
                case EPoint: {
                    check_attributes(node, { "name", "value" });
                    list.setPoint(node.attribute("name").value(), Point3f(toVector3f(node.attribute("value").value())));
                }
                break;
                case EVector: {
                    check_attributes(node, { "name", "value" });
                    list.setVector(node.attribute("name").value(), Vector3f(toVector3f(node.attribute("value").value())));
                }
                break;
                case EColor: {
                    check_attributes(node, { "name", "value" });
                    list.setColor(node.attribute("name").value(), Color3f(toVector3f(node.attribute("value").value()).array()));
                }
                break;
                case ETransform: {
                    check_attributes(node, { "name" });
                    list.setTransform(node.attribute("name").value(), transform.matrix());
                }
                break;
                case ETranslate: {
                    check_attributes(node, { "value" });
                    Eigen::Vector3f v = toVector3f(node.attribute("value").value());
                    transform = Eigen::Translation<float, 3>(v.x(), v.y(), v.z()) * transform;
                }
                break;
                case EMatrix: {
                    check_attributes(node, { "value" });
                    std::vector<std::string> tokens = tokenize(node.attribute("value").value());
                    if (tokens.size() != 16)
                        throw NoriException("Expected 16 values");
                    Eigen::Matrix4f matrix;
                    for (int i=0; i<4; ++i)
                        for (int j=0; j<4; ++j)
                            matrix(i, j) = toFloat(tokens[i*4+j]);
                    transform = Eigen::Affine3f(matrix) * transform;
                }
                break;
                case EScale: {
                    check_attributes(node, { "value" });
                    Eigen::Vector3f v = toVector3f(node.attribute("value").value());
                    transform = Eigen::DiagonalMatrix<float, 3>(v) * transform;
                }
                break;
                case ERotate: {
                    check_attributes(node, { "angle", "axis" });
                    float angle = degToRad(toFloat(node.attribute("angle").value()));
                    Eigen::Vector3f axis = toVector3f(node.attribute("axis").value());
                    transform = Eigen::AngleAxis<float>(angle, axis) * transform;
                }
                break;
                case ELookAt: {
                    check_attributes(node, { "origin", "target", "up" });
                    Eigen::Vector3f origin = toVector3f(node.attribute("origin").value());
                    Eigen::Vector3f target = toVector3f(node.attribute("target").value());
                    Eigen::Vector3f up = toVector3f(node.attribute("up").value());

                    Vector3f dir = (target - origin).normalized();
                    Vector3f left = up.normalized().cross(dir);
                    Vector3f newUp = dir.cross(left);

                    Eigen::Matrix4f trafo;
                    trafo << left, newUp, dir, origin,
                          0, 0, 0, 1;

                    transform = Eigen::Affine3f(trafo) * transform;
                }
                break;

                default:
                    throw NoriException("Unhandled element \"%s\"", node.name());
                };
            }
        } catch (const NoriException &e) {
            throw NoriException("Error while parsing \"%s\": %s (at %s)", filename,
                                e.what(), offset(node.offset_debug()));
        }

        return result;
    };

    PropertyList list;
    return parseTag(*doc.begin(), list, EInvalid);
}
예제 #29
0
Eigen::Affine3f createTransMatrix( float tx, float ty, float tz ) {
  Eigen::Affine3f m = Eigen::Affine3f::Identity();
  m.translation() = Eigen::Vector3f( tx, ty, tz );
  
  return m;
}
  /**
   * @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;
  }