예제 #1
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  /** \brief Computes the gradient parameters of the patch (patch gradient components dx_ dy_, Hessian H_, Shi-Tomasi Score s_, Eigenvalues of the Hessian e0_ and e1_).
   *         The expanded patch patchWithBorder_ must be set.
   *         Sets validGradientParameters_ afterwards to true.
   */
  void computeGradientParameters() const{
    if(!validGradientParameters_){
      H_.setZero();
      const int refStep = patchSize+2;
      float* it_dx = dx_;
      float* it_dy = dy_;
      const float* it;
      Eigen::Vector3f J;
      J.setZero();
      for(int y=0; y<patchSize; ++y){
        it = patchWithBorder_ + (y+1)*refStep + 1;
        for(int x=0; x<patchSize; ++x, ++it, ++it_dx, ++it_dy){
          J[0] = 0.5 * (it[1] - it[-1]);
          J[1] = 0.5 * (it[refStep] - it[-refStep]);
          J[2] = 1;
          *it_dx = J[0];
          *it_dy = J[1];
          H_ += J*J.transpose();
        }
      }
      const float dXX = H_(0,0)/(patchSize*patchSize);
      const float dYY = H_(1,1)/(patchSize*patchSize);
      const float dXY = H_(0,1)/(patchSize*patchSize);

      e0_ = 0.5 * (dXX + dYY - sqrtf((dXX + dYY) * (dXX + dYY) - 4 * (dXX * dYY - dXY * dXY)));
      e1_ = 0.5 * (dXX + dYY + sqrtf((dXX + dYY) * (dXX + dYY) - 4 * (dXX * dYY - dXY * dXY)));
      s_ = e0_;
      validGradientParameters_ = true;
    }
  }
예제 #2
0
  void computeCovarianceMatrix(PointCloudRGB cloud, std::vector<int> indices, Eigen::Vector4f centroid, Eigen::Matrix3f &covariance_matrix)
  {
    // ROS_INFO("Inside computeCovarianceMatrix() ");
    // Initialize to 0
    covariance_matrix.setZero ();
    
    if (indices.empty ())
      return;
  
    // If the data is dense, we don't need to check for NaN
    if (cloud.is_dense)
      {
	// For each point in the cloud
	for (size_t i = 0; i < indices.size (); ++i)
	  {
	    //Eigen::Vector4f ptx = cloud.points[indices[i]].getVector4fMap ();
	    ///std::cout << "Index : "<< i <<" : "<<ptx<<std::endl;
	    Eigen::Vector4f pt = cloud.points[indices[i]].getVector4fMap () - centroid;
	    
	    covariance_matrix (0, 0) += pt.x () * pt.x ();
	    covariance_matrix (0, 1) += pt.x () * pt.y ();
	    covariance_matrix (0, 2) += pt.x () * pt.z ();
	    covariance_matrix (1, 1) += pt.y () * pt.y ();
	    covariance_matrix (1, 2) += pt.y () * pt.z ();	
	    covariance_matrix (2, 2) += pt.z () * pt.z ();
	  }

      }
    // NaN or Inf values could exist => check for them
    else
      {
	//std::cout<<"Cloud is not dense "<<std::endl;
	// For each point in the cloud
	for (size_t i = 0; i < indices.size (); ++i)
	  {
	    // Check if the point is invalid
	    if (!pcl_isfinite (cloud.points[indices[i]].x) || 
		!pcl_isfinite (cloud.points[indices[i]].y) || 
		!pcl_isfinite (cloud.points[indices[i]].z))
	      continue;
	    
	    // Eigen::Vector4f ptx = cloud.points[indices[i]].getVector4fMap ();
	    // std::cout << "Index : "<< i <<" : "<<ptx<<std::endl;
	    Eigen::Vector4f pt = cloud.points[indices[i]].getVector4fMap () - centroid;
	    
	    covariance_matrix (0, 0) += pt.x () * pt.x ();
	    covariance_matrix (0, 1) += pt.x () * pt.y ();
	    covariance_matrix (0, 2) += pt.x () * pt.z ();
	    covariance_matrix (1, 1) += pt.y () * pt.y ();
	    covariance_matrix (1, 2) += pt.y () * pt.z ();	
	    covariance_matrix (2, 2) += pt.z () * pt.z ();
	    covariance_matrix (1, 1) += pt.y () * pt.y ();
	  }
      }
    covariance_matrix (1, 0) = covariance_matrix (0, 1);
    covariance_matrix (2, 0) = covariance_matrix (0, 2);
    covariance_matrix (2, 1) = covariance_matrix (1, 2);
    
    covariance_matrix /= indices.size();
  }
예제 #3
0
파일: feature.hpp 프로젝트: jonaswitt/nestk
template <typename PointT> inline void
pcl::computeCovarianceMatrix (const pcl::PointCloud<PointT> &cloud, 
                              const std::vector<int> &indices,
                              const Eigen::Vector4f &centroid, 
                              Eigen::Matrix3f &covariance_matrix)
{
  // Initialize to 0
  covariance_matrix.setZero ();

  if (indices.empty ())
    return;
  // If the data is dense, we don't need to check for NaN
  if (cloud.is_dense)
  {
    // For each point in the cloud
    for (size_t i = 0; i < indices.size (); ++i)
    {
      Eigen::Vector4f pt = cloud.points[indices[i]].getVector4fMap () - centroid;

      covariance_matrix (1, 1) += pt.y () * pt.y ();
      covariance_matrix (1, 2) += pt.y () * pt.z ();

      covariance_matrix (2, 2) += pt.z () * pt.z ();

      pt *= pt.x ();
      covariance_matrix (0, 0) += pt.x ();
      covariance_matrix (0, 1) += pt.y ();
      covariance_matrix (0, 2) += pt.z ();
    }
  }
  // NaN or Inf values could exist => check for them
  else
  {
    // For each point in the cloud
    for (size_t i = 0; i < indices.size (); ++i)
    {
      // Check if the point is invalid
      if (!pcl_isfinite (cloud.points[indices[i]].x) || 
          !pcl_isfinite (cloud.points[indices[i]].y) || 
          !pcl_isfinite (cloud.points[indices[i]].z))
        continue;

      Eigen::Vector4f pt = cloud.points[indices[i]].getVector4fMap () - centroid;

      covariance_matrix (1, 1) += pt.y () * pt.y ();
      covariance_matrix (1, 2) += pt.y () * pt.z ();

      covariance_matrix (2, 2) += pt.z () * pt.z ();

      pt *= pt.x ();
      covariance_matrix (0, 0) += pt.x ();
      covariance_matrix (0, 1) += pt.y ();
      covariance_matrix (0, 2) += pt.z ();
    }
  }
  covariance_matrix (1, 0) = covariance_matrix (0, 1);
  covariance_matrix (2, 0) = covariance_matrix (0, 2);
  covariance_matrix (2, 1) = covariance_matrix (1, 2);
}
void PointCloudProcessing::getRTGeometricLinearSystem(PointCloudC::Ptr corrRef,
                                                      PointCloudC::Ptr corrNew,
                                                      Eigen::Matrix4f &transMat)
{
  // build linear system Ax = b;
  int rows = 3*corrRef->points.size();
  int cols = 6;
  Eigen::MatrixXf A(rows, cols); A.setZero();
  Eigen::MatrixXf b(rows, 1); b.setZero();
  for(int i=0; i<corrRef->points.size(); i++)
    {
      float X1 = corrRef->points.at(i).x;
      float Y1 = corrRef->points.at(i).y;
      float Z1 = corrRef->points.at(i).z;
      float X0 = corrNew->points.at(i).x;
      float Y0 = corrNew->points.at(i).y;
      float Z0 = corrNew->points.at(i).z;
      A(3*i,   0) = 0;      A(3*i,   1) = Z0+Z1;  A(3*i,   2) = -Y0-Y1; A(3*i,   3) = 1;
      A(3*i+1, 0) = -Z0-Z1; A(3*i+1, 1) = 0;      A(3*i+1, 2) = X0+X1;  A(3*i+1, 4) = 1;
      A(3*i+2, 0) = Y0+Y1;  A(3*i+1, 1) = -X0-X1; A(3*i+2, 2) = 0;      A(3*i+2, 5) = 1;

      b(3*i,   0) = X1-X0;
      b(3*i+1, 0) = Y1-Y0;
      b(3*i+2, 0) = Z1-Z0;
    }
  //    std::cout<<"A = "<<A<<std::endl;
  //    std::cout<<"b = "<<b<<std::endl;
  Eigen::MatrixXf solveX(rows, 1); solveX.setZero();
  solveX = A.jacobiSvd(Eigen::ComputeThinU | Eigen::ComputeThinV).solve(b);
  //    std::cout<<"solveX: "<<solveX<<"\n";

  // solveX = [Rx, Ry, Rz, T'x, T'y, T'z];
  Eigen::Matrix3f I_Vx; I_Vx.setOnes();
  I_Vx(0,0) = 1;          I_Vx(0,1) = solveX(2);  I_Vx(0,2) = -solveX(1);
  I_Vx(1,0) = -solveX(2); I_Vx(1,1) = 1;          I_Vx(1,2) = solveX(0);
  I_Vx(2,0) = solveX(1);  I_Vx(2,1) = -solveX(0); I_Vx(2,2) = 1;
  Eigen::Vector3f Tx; Tx.setZero();
  Tx(0) = solveX(3); Tx(1) = solveX(4); Tx(2) = solveX(5);
  Eigen::Vector3f tx; tx.setZero();
  tx = I_Vx.inverse()*Tx;
  transMat(0,3) = tx(0); transMat(1,3) = tx(1); transMat(2,3) = tx(2);

  Eigen::Matrix3f IplusVx; IplusVx.setOnes();
  IplusVx(0,0) = 1;          IplusVx(0,1) = -solveX(2);  IplusVx(0,2) = solveX(1);
  IplusVx(1,0) = solveX(2);  IplusVx(1,1) = 1;           IplusVx(1,2) = -solveX(0);
  IplusVx(2,0) = -solveX(1); IplusVx(2,1) = solveX(0);   IplusVx(2,2) = 1;
  Eigen::Matrix3f Ro; Ro.setZero();
  Ro = I_Vx.inverse()*IplusVx;
  transMat(0,0) = Ro(0,0); transMat(0,1) = Ro(0,1); transMat(0,2) = Ro(0,2); transMat(0,3) = tx(0);
  transMat(1,0) = Ro(1,0); transMat(1,1) = Ro(1,1); transMat(1,2) = Ro(1,2); transMat(1,3) = tx(1);
  transMat(2,0) = Ro(2,0); transMat(2,1) = Ro(2,1); transMat(2,2) = Ro(2,2); transMat(2,3) = tx(2);
  //    std::cout<<"transMat Geometric: "<<transMat<<"\n";
}
void btl::image::CTrackerSimpleFreak::track( const Eigen::Matrix3f& eimHomoInit_,
											 boost::shared_ptr<cv::gpu::GpuMat> acvgmShrPtrPyrBW_[4],
											 const cv::Mat& cvmMaskCurr_,
											 Eigen::Matrix3f* peimHomo_ ){
												 
	//from coarse to fine
	*peimHomo_ =  eimHomoInit_;
	cv::gpu::GpuMat cvgmMaskPrev; cvgmMaskPrev.upload(_cvmMaskPrev);
	cv::gpu::GpuMat cvgmMaskCurr; cvgmMaskCurr.upload(cvmMaskCurr_);
	cv::gpu::GpuMat cvgmBuffer;
	for (unsigned int uLevel = _uPyrHeight-1; uLevel >= -1; uLevel--){
		for (int n = 0; n < 5; n++){ //iterations
			btl::device::cudaFullFrame( *_acgvmShrPtrPyrBWPrev[uLevel], cvgmMaskPrev, *acvgmShrPtrPyrBW_[uLevel], cvgmMaskCurr, peimHomo_->data(), &cvgmBuffer);
			//solve out the delta homography
			Eigen::Matrix3f eimDeltaHomo; eimDeltaHomo.setZero();
			extractHomography(cvgmBuffer,&eimDeltaHomo);
			*peimHomo_ += eimDeltaHomo; 
		}//iterations
	}//for pyramid
	return;
}
예제 #6
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 /** \brief Computes and sets the multilevel Shi-Tomasi Score \ref s_, considering a defined pyramid level interval.
  *
  * @param l1 - Start level (l1<l2)
  * @param l2 - End level (l1<l2)
  */
 void computeMultilevelShiTomasiScore(const int l1 = 0, const int l2 = nLevels_-1) const{
   H_.setZero();
   int count = 0;
   for(int i=l1;i<=l2;i++){
     if(isValidPatch_[i]){
       H_ += pow(0.25,i)*patches_[i].getHessian();
       count++;
     }
   }
   if(count > 0){
     const float dXX = H_(0,0)/(count*patchSize*patchSize);
     const float dYY = H_(1,1)/(count*patchSize*patchSize);
     const float dXY = H_(0,1)/(count*patchSize*patchSize);
     e0_ = 0.5 * (dXX + dYY - sqrtf((dXX + dYY) * (dXX + dYY) - 4 * (dXX * dYY - dXY * dXY)));
     e1_ = 0.5 * (dXX + dYY + sqrtf((dXX + dYY) * (dXX + dYY) - 4 * (dXX * dYY - dXY * dXY)));
     s_ = e0_;
   } else {
     e0_ = 0;
     e1_ = 0;
     s_ = -1;
   }
 }
예제 #7
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void computeSensorOffsetAndK(Eigen::Isometry3f &sensorOffset, Eigen::Matrix3f &cameraMatrix, ParameterCamera *cameraParam, int reduction) {
  sensorOffset = Isometry3f::Identity();
  cameraMatrix.setZero();
      
  int cmax = 4;
  int rmax = 3;
  for (int c=0; c<cmax; c++){
    for (int r=0; r<rmax; r++){
      sensorOffset.matrix()(r, c) = cameraParam->offset()(r, c);
      if (c<3)
	cameraMatrix(r,c) = cameraParam->Kcam()(r, c);
    }
  }
  sensorOffset.translation() = Vector3f(0.15f, 0.0f, 0.05f);
  Quaternionf quat = Quaternionf(0.5, -0.5, 0.5, -0.5);
  sensorOffset.linear() = quat.toRotationMatrix();
  sensorOffset.matrix().block<1, 4>(3, 0) << 0.0f, 0.0f, 0.0f, 1.0f;
	
  float scale = 1./reduction;
  cameraMatrix *= scale;
  cameraMatrix(2,2) = 1;
}
예제 #8
0
/**
 * ComputeCovarianceMatrix
 */
void ZAdaptiveNormals::computeCovarianceMatrix (const v4r::DataMatrix2D<Eigen::Vector3f> &cloud, const std::vector<int> &indices, const Eigen::Vector3f &mean, Eigen::Matrix3f &cov)
{
  Eigen::Vector3f pt;
  cov.setZero ();

  for (unsigned i = 0; i < indices.size (); ++i)
  {
    pt = cloud.data[indices[i]] - mean;

    cov(1,1) += pt[1] * pt[1];
    cov(1,2) += pt[1] * pt[2];
    cov(2,2) += pt[2] * pt[2];

    pt *= pt[0];
    cov(0,0) += pt[0];
    cov(0,1) += pt[1];
    cov(0,2) += pt[2];
  }

  cov(1,0) = cov(0,1);
  cov(2,0) = cov(0,2);
  cov(2,1) = cov(1,2);
}
예제 #9
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/**
 * computeCovarianceMatrix
 */
void PlaneEstimationRANSAC::computeCovarianceMatrix (const std::vector<Eigen::Vector3f> &pts, const std::vector<int> &indices, const Eigen::Vector3f &mean, Eigen::Matrix3f &cov)
{
  Eigen::Vector3f pt;
  cov.setZero ();

  for (unsigned i = 0; i < indices.size (); ++i)
  {
    pt = pts[indices[i]] - mean;

    cov(1,1) += pt[1] * pt[1];
    cov(1,2) += pt[1] * pt[2];

    cov(2,2) += pt[2] * pt[2];

    pt *= pt[0];
    cov(0,0) += pt[0];
    cov(0,1) += pt[1];
    cov(0,2) += pt[2];
  }

  cov(1,0) = cov(0,1);
  cov(2,0) = cov(0,2);
  cov(2,1) = cov(1,2);
}
예제 #10
0
  void ViewerState::load(const std::string& filename){
    clear();
    listWidget->clear();
    graph->clear();
    graph->load(filename.c_str());

    vector<int> vertexIds(graph->vertices().size());
    int k=0;
    for (OptimizableGraph::VertexIDMap::iterator it = graph->vertices().begin(); it != graph->vertices().end(); ++it) {
      vertexIds[k++] = (it->first);
    }

    sort(vertexIds.begin(), vertexIds.end());

    listAssociations.clear();
    size_t maxCount = 20000;
    for(size_t i = 0; i < vertexIds.size() &&  i< maxCount; ++i) {
      OptimizableGraph::Vertex* _v = graph->vertex(vertexIds[i]);
      g2o::VertexSE3* v = dynamic_cast<g2o::VertexSE3*>(_v);
      if (! v)
	continue;
      OptimizableGraph::Data* d = v->userData();
      while(d) {
	RGBDData* rgbdData = dynamic_cast<RGBDData*>(d);
	if (!rgbdData){
	  d=d->next();
	  continue;
	}
	// retrieve from the rgb data the index of the parameter
	int paramIndex = rgbdData->paramIndex();
	// retrieve from the graph the parameter given the index  
	g2o::Parameter* _cameraParam = graph->parameter(paramIndex);
	// attempt a cast to a parameter camera  
	ParameterCamera* cameraParam = dynamic_cast<ParameterCamera*>(_cameraParam);
	if (! cameraParam){
	  cerr << "shall thou be damned forever" << endl;
	  return;
	}
	// yayyy we got the parameter
	Eigen::Matrix3f cameraMatrix;
	Eigen::Isometry3f sensorOffset;
	cameraMatrix.setZero();
      
	int cmax = 4;
	int rmax = 3;
	for (int c=0; c<cmax; c++){
	  for (int r=0; r<rmax; r++){
	    sensorOffset.matrix()(r,c)= cameraParam->offset()(r, c);
	    if (c<3)
	      cameraMatrix(r,c) = cameraParam->Kcam()(r, c);
	  }
	}
	char buf[1024];
	sprintf(buf,"%d",v->id());
	QString listItem(buf);
	listAssociations.push_back(rgbdData);
	listWidget->addItem(listItem);
	d=d->next();
      }
    }

  }
void
v4r::MultiPlaneSegmentation<PointT>::segment(bool force_unorganized)
{
  models_.clear();

  if(input_->isOrganized() && !force_unorganized)
  {
    pcl::PointCloud<pcl::Normal>::Ptr normal_cloud (new pcl::PointCloud<pcl::Normal>);
    if(!normals_set_)
    {
        pcl::IntegralImageNormalEstimation<PointT, pcl::Normal> ne;
        ne.setNormalEstimationMethod (ne.COVARIANCE_MATRIX);
        ne.setMaxDepthChangeFactor (0.02f);
        ne.setNormalSmoothingSize (20.0f);
        ne.setBorderPolicy (pcl::IntegralImageNormalEstimation<PointT, pcl::Normal>::BORDER_POLICY_IGNORE);
        ne.setInputCloud (input_);
        ne.compute (*normal_cloud);
    }
    else
    {
        normal_cloud = normal_cloud_;
    }

    pcl::OrganizedMultiPlaneSegmentation<PointT, pcl::Normal, pcl::Label> mps;
    mps.setMinInliers (min_plane_inliers_);
    mps.setAngularThreshold (0.017453 * 2.f); // 2 degrees
    mps.setDistanceThreshold (0.01); // 1cm
    mps.setMaximumCurvature(0.002);
    mps.setInputNormals (normal_cloud);
    mps.setInputCloud (input_);

    std::vector<pcl::PlanarRegion<PointT>, Eigen::aligned_allocator<pcl::PlanarRegion<PointT> > > regions;
    std::vector<pcl::ModelCoefficients> model_coefficients;
    std::vector<pcl::PointIndices> inlier_indices;
    pcl::PointCloud<pcl::Label>::Ptr labels (new pcl::PointCloud<pcl::Label>);
    std::vector<pcl::PointIndices> label_indices;
    std::vector<pcl::PointIndices> boundary_indices;

    typename pcl::PlaneRefinementComparator<PointT, pcl::Normal, pcl::Label>::Ptr ref_comp (
                                                                                             new pcl::PlaneRefinementComparator<PointT,
                                                                                                 pcl::Normal, pcl::Label> ());
    ref_comp->setDistanceThreshold (0.01f, false);
    ref_comp->setAngularThreshold (0.017453 * 2.f);
    mps.setRefinementComparator (ref_comp);
    mps.segmentAndRefine (regions, model_coefficients, inlier_indices, labels, label_indices, boundary_indices);
    //mps.segment (model_coefficients, inlier_indices);

    //std::cout << model_coefficients.size() << std::endl;

    if(merge_planes_)
    {
      //sort planes by size
      //check if the first plane can be merged against the others, if yes, define a new plane combining both and add it to the cue

      typedef boost::adjacency_matrix<boost::undirectedS, int> GraphPlane;
      GraphPlane mergeable_planes (model_coefficients.size ());
      for(size_t i=0; i < model_coefficients.size(); i++)
      {

        Eigen::Vector3f plane_i = Eigen::Vector3f (model_coefficients[i].values[0], model_coefficients[i].values[1],
                                                   model_coefficients[i].values[2]);

        plane_i.normalize();
        for(size_t j=(i+1); j < model_coefficients.size(); j++)
        {
          Eigen::Vector3f plane_j = Eigen::Vector3f (model_coefficients[j].values[0], model_coefficients[j].values[1],
                                                     model_coefficients[j].values[2]);

          plane_j.normalize();

          //std::cout << "dot product:" << plane_i.dot(plane_j) << " diff:" << std::abs(model_coefficients[i].values[3] - model_coefficients[j].values[3]) << std::endl;
          if(plane_i.dot(plane_j) > 0.95)
          {
            if(std::abs(model_coefficients[i].values[3] - model_coefficients[j].values[3]) < 0.015)
            {
              boost::add_edge (static_cast<int>(i), static_cast<int>(j), mergeable_planes);
            }
          }
        }
      }

      boost::vector_property_map<int> components (boost::num_vertices (mergeable_planes));
      int n_cc = static_cast<int> (boost::connected_components (mergeable_planes, &components[0]));

      std::vector<int> cc_sizes;
      std::vector< std::vector<int> > cc_to_model_coeff;
      cc_sizes.resize (n_cc, 0);
      cc_to_model_coeff.resize(n_cc);

      for (size_t i = 0; i < model_coefficients.size (); i++)
      {
        cc_sizes[components[i]]++;
        cc_to_model_coeff[components[i]].push_back(i);
      }

      std::vector<pcl::ModelCoefficients> new_model_coefficients;
      std::vector<pcl::PointIndices> new_inlier_indices;

      for(size_t i=0; i < cc_sizes.size(); i++)
      {
        if(cc_sizes[i] < 2)
        {
          new_model_coefficients.push_back(model_coefficients[cc_to_model_coeff[i][0]]);
          new_inlier_indices.push_back(inlier_indices[cc_to_model_coeff[i][0]]);
          continue;
        }

        //std::cout << "going to merge CC:" << cc_sizes[i] << std::endl;
        pcl::ModelCoefficients model_coeff;
        model_coeff.values.resize(4);

        for(size_t k=0; k < 4; k++)
          model_coeff.values[k] = 0.f;

        pcl::PointIndices merged_indices;
        for(size_t j=0; j < cc_to_model_coeff[i].size(); j++)
        {
          for(size_t k=0; k < 4; k++)
            model_coeff.values[k] += model_coefficients[cc_to_model_coeff[i][j]].values[k];

          merged_indices.indices.insert(merged_indices.indices.end(), inlier_indices[cc_to_model_coeff[i][j]].indices.begin(),
                                                                      inlier_indices[cc_to_model_coeff[i][j]].indices.end());
        }

        for(size_t k=0; k < 4; k++)
          model_coeff.values[k] /= static_cast<float>(cc_to_model_coeff[i].size());

        new_model_coefficients.push_back(model_coeff);
        new_inlier_indices.push_back(merged_indices);
      }

      model_coefficients = new_model_coefficients;
      inlier_indices = new_inlier_indices;
    }

    for(size_t i=0; i < model_coefficients.size(); i++)
    {
      PlaneModel<PointT> pm;
      pm.coefficients_ = model_coefficients[i];
      pm.cloud_ = input_;
      pm.inliers_ = inlier_indices[i];

      //recompute coefficients based on distance to camera and normal?
      Eigen::Vector4f centroid;
      pcl::compute3DCentroid(*pm.cloud_, pm.inliers_, centroid);
      Eigen::Vector3f c(centroid[0],centroid[1],centroid[2]);

      Eigen::MatrixXf M_w(inlier_indices[i].indices.size(), 3);

      float sum_w = 0.f;
      for(size_t k=0; k < inlier_indices[i].indices.size(); k++)
      {
          const int &idx = inlier_indices[i].indices[k];
          float d_c = (pm.cloud_->points[idx].getVector3fMap()).norm();
          float w_k = std::max(1.f - std::abs(1.f - d_c), 0.f);
          //w_k = 1.f;
          M_w.row(k) = w_k * (pm.cloud_->points[idx].getVector3fMap() - c);
          sum_w += w_k;
      }

      Eigen::Matrix3f scatter;
      scatter.setZero ();
      scatter = M_w.transpose() * M_w;

      Eigen::JacobiSVD<Eigen::MatrixXf> svd(scatter, Eigen::ComputeFullV);
      //std::cout << svd.matrixV() << std::endl;

      Eigen::Vector3f n = svd.matrixV().col(2);
      //flip normal if required
      if(n.dot(c*-1) < 0)
          n = n * -1.f;

      float d = n.dot(c) * -1.f;
      //std::cout << "normal:" << n << std::endl;
      //std::cout << "d:" << d << std::endl;

      pm.coefficients_.values[0] = n[0];
      pm.coefficients_.values[1] = n[1];
      pm.coefficients_.values[2] = n[2];
      pm.coefficients_.values[3] = d;

      pcl::PointIndices clean_inlier_indices;
      float dist_threshold_ = 0.01f;

      for(size_t k=0; k < inlier_indices[i].indices.size(); k++)
      {
          const int &idx = inlier_indices[i].indices[k];
          Eigen::Vector3f p = pm.cloud_->points[idx].getVector3fMap();
          float val = n.dot(p) + d;

          if(std::abs(val) <= dist_threshold_)
              clean_inlier_indices.indices.push_back( idx );
      }

      pm.inliers_ = clean_inlier_indices;
      models_.push_back(pm);
    }
  }
  else
  {
    // Create the filtering object: downsample the dataset using a leaf size of 1cm
    pcl::VoxelGrid<PointT> vg;
    PointTCloudPtr cloud_filtered (new PointTCloud);
    vg.setInputCloud (input_);
    float leaf_size_ = 0.005f;
    float dist_threshold_ = 0.01f;
    vg.setLeafSize (leaf_size_, leaf_size_, leaf_size_);
    vg.filter (*cloud_filtered);

    std::vector<bool> pixel_has_not_been_labelled( cloud_filtered->points.size(), true );

    // Create the segmentation object for the planar model and set all the parameters
    pcl::SACSegmentation<PointT> seg;
    pcl::ModelCoefficients coefficients;
    seg.setOptimizeCoefficients (true);
    seg.setModelType (pcl::SACMODEL_PLANE);
    seg.setMethodType (pcl::SAC_RANSAC);
    seg.setMaxIterations (1000);
    seg.setDistanceThreshold (dist_threshold_);

    PointTCloudPtr cloud_filtered_leftover (new PointTCloud (*cloud_filtered));

    while (1)
    {
      // Segment the largest planar component from the remaining cloud
      seg.setInputCloud (cloud_filtered_leftover);
      pcl::PointIndices inliers_in_leftover;
      seg.segment (inliers_in_leftover, coefficients);

      std::cout << "inliers in left over: " << inliers_in_leftover.indices.size() << " of " << cloud_filtered_leftover->points.size() << std::endl;

      if ( (int)inliers_in_leftover.indices.size () < min_plane_inliers_) // Could not estimate a(nother) planar model big enough for the given cloud.
        break;


      // make indices correspond to complete downsampled cloud
      pcl::PointIndices indices_in_original_cloud;
      size_t current_index_in_leftover = 0;
      size_t px=0;
      indices_in_original_cloud.indices.resize(inliers_in_leftover.indices.size());
      for(size_t inl_id=0; inl_id < inliers_in_leftover.indices.size(); inl_id++) {    // assumes indices are sorted in ascending order
          bool found = false;
          do {
              if( pixel_has_not_been_labelled[px] ) {
                  if( current_index_in_leftover == inliers_in_leftover.indices [inl_id] ) {
                      indices_in_original_cloud.indices[ inl_id ] = px;
                      found = true;
                  }
                  current_index_in_leftover++;
              }
              px++;
          } while( !found );
      }

      for(size_t i=0; i<indices_in_original_cloud.indices.size(); i++)
          pixel_has_not_been_labelled[ indices_in_original_cloud.indices[i] ] = false;


      //save coefficients
        PlaneModel<PointT> pm;
        pm.coefficients_ = coefficients;
        pm.cloud_ = cloud_filtered;
        pm.inliers_ = indices_in_original_cloud;
        models_.push_back(pm);

        pcl::copyPointCloud(*cloud_filtered, pixel_has_not_been_labelled, *cloud_filtered_leftover);
    }

    std::cout << "Number of planes found:" << models_.size() << "organized:" << static_cast<int>(input_->isOrganized() && !force_unorganized) << std::endl;
  }
}
예제 #12
0
파일: our_cvfh.hpp 프로젝트: 5irius/pcl
template<typename PointInT, typename PointNT, typename PointOutT> bool
pcl::OURCVFHEstimation<PointInT, PointNT, PointOutT>::sgurf (Eigen::Vector3f & centroid, Eigen::Vector3f & normal_centroid,
                                                               PointInTPtr & processed, std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > & transformations,
                                                               PointInTPtr & grid, pcl::PointIndices & indices)
{

  Eigen::Vector3f plane_normal;
  plane_normal[0] = -centroid[0];
  plane_normal[1] = -centroid[1];
  plane_normal[2] = -centroid[2];
  Eigen::Vector3f z_vector = Eigen::Vector3f::UnitZ ();
  plane_normal.normalize ();
  Eigen::Vector3f axis = plane_normal.cross (z_vector);
  double rotation = -asin (axis.norm ());
  axis.normalize ();

  Eigen::Affine3f transformPC (Eigen::AngleAxisf (static_cast<float> (rotation), axis));

  grid->points.resize (processed->points.size ());
  for (size_t k = 0; k < processed->points.size (); k++)
    grid->points[k].getVector4fMap () = processed->points[k].getVector4fMap ();

  pcl::transformPointCloud (*grid, *grid, transformPC);

  Eigen::Vector4f centroid4f (centroid[0], centroid[1], centroid[2], 0);
  Eigen::Vector4f normal_centroid4f (normal_centroid[0], normal_centroid[1], normal_centroid[2], 0);

  centroid4f = transformPC * centroid4f;
  normal_centroid4f = transformPC * normal_centroid4f;

  Eigen::Vector3f centroid3f (centroid4f[0], centroid4f[1], centroid4f[2]);

  Eigen::Vector4f farthest_away;
  pcl::getMaxDistance (*grid, indices.indices, centroid4f, farthest_away);
  farthest_away[3] = 0;

  float max_dist = (farthest_away - centroid4f).norm ();

  pcl::demeanPointCloud (*grid, centroid4f, *grid);

  Eigen::Matrix4f center_mat;
  center_mat.setIdentity (4, 4);
  center_mat (0, 3) = -centroid4f[0];
  center_mat (1, 3) = -centroid4f[1];
  center_mat (2, 3) = -centroid4f[2];

  Eigen::Matrix3f scatter;
  scatter.setZero ();
  float sum_w = 0.f;

  //for (int k = 0; k < static_cast<intgrid->points[k].getVector3fMap ();> (grid->points.size ()); k++)
  for (int k = 0; k < static_cast<int> (indices.indices.size ()); k++)
  {
    Eigen::Vector3f pvector = grid->points[indices.indices[k]].getVector3fMap ();
    float d_k = (pvector).norm ();
    float w = (max_dist - d_k);
    Eigen::Vector3f diff = (pvector);
    Eigen::Matrix3f mat = diff * diff.transpose ();
    scatter = scatter + mat * w;
    sum_w += w;
  }

  scatter /= sum_w;

  Eigen::JacobiSVD <Eigen::MatrixXf> svd (scatter, Eigen::ComputeFullV);
  Eigen::Vector3f evx = svd.matrixV ().col (0);
  Eigen::Vector3f evy = svd.matrixV ().col (1);
  Eigen::Vector3f evz = svd.matrixV ().col (2);
  Eigen::Vector3f evxminus = evx * -1;
  Eigen::Vector3f evyminus = evy * -1;
  Eigen::Vector3f evzminus = evz * -1;

  float s_xplus, s_xminus, s_yplus, s_yminus;
  s_xplus = s_xminus = s_yplus = s_yminus = 0.f;

  //disambiguate rf using all points
  for (int k = 0; k < static_cast<int> (grid->points.size ()); k++)
  {
    Eigen::Vector3f pvector = grid->points[k].getVector3fMap ();
    float dist_x, dist_y;
    dist_x = std::abs (evx.dot (pvector));
    dist_y = std::abs (evy.dot (pvector));

    if ((pvector).dot (evx) >= 0)
      s_xplus += dist_x;
    else
      s_xminus += dist_x;

    if ((pvector).dot (evy) >= 0)
      s_yplus += dist_y;
    else
      s_yminus += dist_y;

  }

  if (s_xplus < s_xminus)
    evx = evxminus;

  if (s_yplus < s_yminus)
    evy = evyminus;

  //select the axis that could be disambiguated more easily
  float fx, fy;
  float max_x = static_cast<float> (std::max (s_xplus, s_xminus));
  float min_x = static_cast<float> (std::min (s_xplus, s_xminus));
  float max_y = static_cast<float> (std::max (s_yplus, s_yminus));
  float min_y = static_cast<float> (std::min (s_yplus, s_yminus));

  fx = (min_x / max_x);
  fy = (min_y / max_y);

  Eigen::Vector3f normal3f = Eigen::Vector3f (normal_centroid4f[0], normal_centroid4f[1], normal_centroid4f[2]);
  if (normal3f.dot (evz) < 0)
    evz = evzminus;

  //if fx/y close to 1, it was hard to disambiguate
  //what if both are equally easy or difficult to disambiguate, namely fy == fx or very close

  float max_axis = std::max (fx, fy);
  float min_axis = std::min (fx, fy);

  if ((min_axis / max_axis) > axis_ratio_)
  {
    PCL_WARN("Both axis are equally easy/difficult to disambiguate\n");

    Eigen::Vector3f evy_copy = evy;
    Eigen::Vector3f evxminus = evx * -1;
    Eigen::Vector3f evyminus = evy * -1;

    if (min_axis > min_axis_value_)
    {
      //combination of all possibilities
      evy = evx.cross (evz);
      Eigen::Matrix4f trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
      transformations.push_back (trans);

      evx = evxminus;
      evy = evx.cross (evz);
      trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
      transformations.push_back (trans);

      evx = evy_copy;
      evy = evx.cross (evz);
      trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
      transformations.push_back (trans);

      evx = evyminus;
      evy = evx.cross (evz);
      trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
      transformations.push_back (trans);

    }
    else
    {
      //1-st case (evx selected)
      evy = evx.cross (evz);
      Eigen::Matrix4f trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
      transformations.push_back (trans);

      //2-nd case (evy selected)
      evx = evy_copy;
      evy = evx.cross (evz);
      trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
      transformations.push_back (trans);
    }
  }
  else
  {
    if (fy < fx)
    {
      evx = evy;
      fx = fy;
    }

    evy = evx.cross (evz);
    Eigen::Matrix4f trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
    transformations.push_back (trans);

  }

  return true;
}
예제 #13
0
파일: centroid.hpp 프로젝트: 9gel/hellopcl
template <typename PointT> inline unsigned
pcl::computeCovarianceMatrix (const pcl::PointCloud<PointT> &cloud,
                              const Eigen::Vector4f &centroid,
                              Eigen::Matrix3f &covariance_matrix)
{
  if (cloud.points.empty ())
    return (0);

  // Initialize to 0
  covariance_matrix.setZero ();

  unsigned point_count;
  // If the data is dense, we don't need to check for NaN
  if (cloud.is_dense)
  {
    point_count = static_cast<unsigned> (cloud.size ());
    // For each point in the cloud
    for (size_t i = 0; i < point_count; ++i)
    {
      Eigen::Vector4f pt = cloud [i].getVector4fMap () - centroid;

      covariance_matrix (1, 1) += pt.y () * pt.y ();
      covariance_matrix (1, 2) += pt.y () * pt.z ();

      covariance_matrix (2, 2) += pt.z () * pt.z ();

      pt *= pt.x ();
      covariance_matrix (0, 0) += pt.x ();
      covariance_matrix (0, 1) += pt.y ();
      covariance_matrix (0, 2) += pt.z ();
    }
  }
  // NaN or Inf values could exist => check for them
  else
  {
    point_count = 0;
    // For each point in the cloud
    for (size_t i = 0; i < cloud.size (); ++i)
    {
      // Check if the point is invalid
      if (!isFinite (cloud [i]))
        continue;

      Eigen::Vector4f pt = cloud [i].getVector4fMap () - centroid;

      covariance_matrix (1, 1) += pt.y () * pt.y ();
      covariance_matrix (1, 2) += pt.y () * pt.z ();

      covariance_matrix (2, 2) += pt.z () * pt.z ();

      pt *= pt.x ();
      covariance_matrix (0, 0) += pt.x ();
      covariance_matrix (0, 1) += pt.y ();
      covariance_matrix (0, 2) += pt.z ();
      ++point_count;
    }
  }
  covariance_matrix (1, 0) = covariance_matrix (0, 1);
  covariance_matrix (2, 0) = covariance_matrix (0, 2);
  covariance_matrix (2, 1) = covariance_matrix (1, 2);

  return (point_count);
}