Inheritance diagram for lar_cluster3d::VoronoiPathFinder:

Public Member Functions
	VoronoiPathFinder (const fhicl::ParameterSet &)
	Constructor. More...

	~VoronoiPathFinder ()
	Destructor. More...

void	configure (fhicl::ParameterSet const &pset) override

void	ModifyClusters (reco::ClusterParametersList &) const override
	Scan an input collection of clusters and modify those according to the specific implementing algorithm. More...

float	getTimeToExecute () const override
	If monitoring, recover the time to execute a particular function. More...

virtual void	configure (const fhicl::ParameterSet &)=0
	Interface for configuring the particular algorithm tool. More...

Private Types
using	Point = std::tuple< float, float, const reco::ClusterHit3D * >

using	PointList = std::list< Point >

using	MinMaxPoints = std::pair< Point, Point >

using	MinMaxPointPair = std::pair< MinMaxPoints, MinMaxPoints >

Private Member Functions
reco::ClusterParametersList::iterator	breakIntoTinyBits (reco::ClusterParameters &cluster, reco::ClusterParametersList::iterator positionItr, reco::ClusterParametersList &outputClusterList, int level=0) const
	Use PCA to try to find path in cluster. More...

float	closestApproach (const Eigen::Vector3f &, const Eigen::Vector3f &, const Eigen::Vector3f &, const Eigen::Vector3f &, Eigen::Vector3f &, Eigen::Vector3f &) const

void	buildConvexHull (reco::ClusterParameters &clusterParameters, int level=0) const

void	buildVoronoiDiagram (reco::ClusterParameters &clusterParameters, int level=0) const

Private Attributes
bool	m_enableMonitoring
	Data members to follow. More...

size_t	m_minTinyClusterSize
	Minimum size for a "tiny" cluster. More...

float	m_timeToProcess

geo::Geometry *	m_geometry

std::unique_ptr< lar_cluster3d::IClusterAlg >	m_clusterAlg
	Algorithm to do 3D space point clustering. More...

PrincipalComponentsAlg	m_pcaAlg

Detailed Description

Definition at line 44 of file VoronoiPathFinder_tool.cc.

Member Typedef Documentation

using lar_cluster3d::VoronoiPathFinder::MinMaxPointPair = std::pair<MinMaxPoints,MinMaxPoints>

private

Definition at line 97 of file VoronoiPathFinder_tool.cc.

using lar_cluster3d::VoronoiPathFinder::MinMaxPoints = std::pair<Point,Point>

private

Definition at line 96 of file VoronoiPathFinder_tool.cc.

using lar_cluster3d::VoronoiPathFinder::Point = std::tuple<float,float,const reco::ClusterHit3D*>

private

Definition at line 94 of file VoronoiPathFinder_tool.cc.

using lar_cluster3d::VoronoiPathFinder::PointList = std::list<Point>

private

Definition at line 95 of file VoronoiPathFinder_tool.cc.

Constructor & Destructor Documentation

lar_cluster3d::VoronoiPathFinder::VoronoiPathFinder ( const fhicl::ParameterSet & )

explicit

Constructor.

Parameters

pset

Definition at line 114 of file VoronoiPathFinder_tool.cc.

References configure().

                                                                   :
     m_pcaAlg(pset.get<fhicl::ParameterSet>("PrincipalComponentsAlg"))
 {
     this->configure(pset);
 }

lar_cluster3d::VoronoiPathFinder::~VoronoiPathFinder ( )

Destructor.

Definition at line 122 of file VoronoiPathFinder_tool.cc.

123 {

124 }

Member Function Documentation

reco::ClusterParametersList::iterator lar_cluster3d::VoronoiPathFinder::breakIntoTinyBits	(	reco::ClusterParameters &	cluster,
		reco::ClusterParametersList::iterator	positionItr,
		reco::ClusterParametersList &	outputClusterList,
		int	level = `0`
	)		const

private

Use PCA to try to find path in cluster.

Parameters

clusterParameters	The given cluster parameters object to try to split
clusterParametersList	The list of clusters

Definition at line 225 of file VoronoiPathFinder_tool.cc.

References buildConvexHull(), reco::PrincipalComponents::getAveHitDoca(), reco::PrincipalComponents::getAvePosition(), reco::ClusterParameters::getBestEdgeList(), reco::PrincipalComponents::getEigenValues(), reco::PrincipalComponents::getEigenVectors(), reco::ClusterParameters::getFullPCA(), reco::ClusterParameters::getHitPairListPtr(), reco::PrincipalComponents::getNumHitsUsed(), reco::ClusterHit3D::getPosition(), reco::ClusterParameters::getSkeletonPCA(), reco::PrincipalComponents::getSvdOK(), art::detail::indent(), art::left(), m_minTinyClusterSize, m_pcaAlg, lar_cluster3d::PrincipalComponentsAlg::PCAAnalysis_3D(), lar_cluster3d::PrincipalComponentsAlg::PCAAnalysis_calc3DDocas(), art::right(), reco::ClusterParameters::UpdateParameters(), and reco::ClusterHit2D::USED.

Referenced by getTimeToExecute(), and ModifyClusters().

 {
     // This needs to be a recursive routine...
     // Idea is to take the input cluster and order 3D hits by arclength along PCA primary axis
     // If the cluster is above the minimum number of hits then we divide into two and call ourself
     // with the two halves. This means we form a new cluster with hits and PCA and then call ourself
     // If the cluster is below the minimum then we can't break any more, simply add this cluster to
     // the new output list.
     
     // set an indention string
     std::string pluses(level/2, '+');
     std::string indent(level/2, ' ');
     
     indent += pluses;
     
     reco::ClusterParametersList::iterator inputPositionItr = positionItr;
     
     // To make best use of this we'll also want the PCA for this cluster... so...
     // Recover the prime ingredients
     reco::PrincipalComponents& fullPCA     = clusterToBreak.getFullPCA();
     std::vector<double>        eigenValVec = {3. * std::sqrt(fullPCA.getEigenValues()[0]),
                                               3. * std::sqrt(fullPCA.getEigenValues()[1]),
                                               3. * std::sqrt(fullPCA.getEigenValues()[2])};
     
     std::cout << indent << ">>> breakIntoTinyBits with " << clusterToBreak.getHitPairListPtr().size() << " input hits " << std::endl;
     
     // It turns out that computing the convex hull surrounding the points in the 2D projection onto the
     // plane of largest spread in the PCA is a good way to break up the cluster... and we do it here since
     // we (currently) want this to be part of the standard output
     buildConvexHull(clusterToBreak, level+2);
 
     bool storeCurrentCluster(true);
     int  minimumClusterSize(m_minTinyClusterSize);
     
     // Create a rough cut intended to tell us when we've reached the land of diminishing returns
     if (clusterToBreak.getBestEdgeList().size()   > 6 &&
         clusterToBreak.getHitPairListPtr().size() > size_t(2 * minimumClusterSize))
     {
         // The plan we think we need here:
         // 1) recover the list of edges
         // 2) sort by length
         // 3) then find the defect point with the furthest distance perpendicular to this edge
         // 4) Use this as the breakpoint, sort points according to their position along the PCA and
         //    side of the perpendicular edge...
         reco::EdgeList& bestEdgeList = clusterToBreak.getBestEdgeList();
         
         const reco::EdgeTuple& longEdge = *std::max_element(bestEdgeList.begin(),bestEdgeList.end(),[](const auto& first, const auto& second){return std::get<2>(first) < std::get<2>(second);});
         
         // Get a vector representing this edge
         const reco::ClusterHit3D* firstEdgeHit  = std::get<0>(longEdge);
         const reco::ClusterHit3D* secondEdgeHit = std::get<1>(longEdge);
         double                    edgeLen       = std::get<2>(longEdge);
         Eigen::Vector3f           edgeVec(secondEdgeHit->getPosition()[0] - firstEdgeHit->getPosition()[0],
                                           secondEdgeHit->getPosition()[1] - firstEdgeHit->getPosition()[1],
                                           secondEdgeHit->getPosition()[2] - firstEdgeHit->getPosition()[2]);
         
         // normalize it
         edgeVec.normalize();
         
         // Keep track of the winner
         const reco::ClusterHit3D* furthestHit(NULL);
         float                     furthestDistance(0.);
         
         // Now loop through all the edges and search for the furthers point
         for(const auto& edge : bestEdgeList)
         {
             const reco::ClusterHit3D* nextEdgeHit = std::get<0>(edge);  // recover the first point
             
             // Create vector to this point from the longest edge
             Eigen::Vector3f hitToEdgeVec(nextEdgeHit->getPosition()[0] - firstEdgeHit->getPosition()[0],
                                          nextEdgeHit->getPosition()[1] - firstEdgeHit->getPosition()[1],
                                          nextEdgeHit->getPosition()[2] - firstEdgeHit->getPosition()[2]);
             
             // Get projection
             float hitProjection = hitToEdgeVec.dot(edgeVec);
 
             // Require that the point is really "opposite" the longest edge
             if (hitProjection > 0. && hitProjection < edgeLen)
             {
                 Eigen::Vector3f distToHitVec = hitToEdgeVec - hitProjection * edgeVec;
                 float           distToHit    = distToHitVec.norm();
                 
                 if (distToHit > furthestDistance)
                 {
                     furthestDistance = distToHit;
                     furthestHit      = nextEdgeHit;
                 }
             }
         }
         
         // Make sure we have a hit
         if (furthestHit)
         {
             // Recover the list of 3D hits associated to this cluster
             reco::HitPairListPtr& clusHitPairVector = clusterToBreak.getHitPairListPtr();
         
             // Calculate the doca to the PCA primary axis for each 3D hit
             // Importantly, this also gives us the arclength along that axis to the hit
             m_pcaAlg.PCAAnalysis_calc3DDocas(clusHitPairVector, fullPCA);
         
             // Sort the hits along the PCA
             clusHitPairVector.sort([](const auto& left, const auto& right){return left->getArclenToPoca() < right->getArclenToPoca();});
             
             // Now find the hit identified above as furthest away
             reco::HitPairListPtr::iterator vertexItr = std::find(clusHitPairVector.begin(),clusHitPairVector.end(),furthestHit);
             
             // Now we create a list of pairs of iterators to the start and end of each subcluster
             using Hit3DItrPair   = std::pair<reco::HitPairListPtr::iterator,reco::HitPairListPtr::iterator>;
             using VertexPairList = std::list<Hit3DItrPair>;
             
             VertexPairList vertexPairList;
 
             // Make sure enough hits either side
             if (std::distance(clusHitPairVector.begin(),vertexItr) > minimumClusterSize && std::distance(vertexItr,clusHitPairVector.end()) > minimumClusterSize)
             {
                 vertexPairList.emplace_back(Hit3DItrPair(clusHitPairVector.begin(),vertexItr));
                 vertexPairList.emplace_back(Hit3DItrPair(vertexItr,clusHitPairVector.end()));
             }
 
             storeCurrentCluster = false;
             
             // Ok, now loop through our pairs
             for(auto& hit3DItrPair : vertexPairList)
             {
                 reco::ClusterParameters clusterParams;
                 reco::HitPairListPtr&   hitPairListPtr = clusterParams.getHitPairListPtr();
             
                 std::cout << indent << "+>    -- building new cluster, size: " << std::distance(hit3DItrPair.first,hit3DItrPair.second) << std::endl;
 
                 // size the container...
                 hitPairListPtr.resize(std::distance(hit3DItrPair.first,hit3DItrPair.second));
 
                 // and copy the hits into the container
                 std::copy(hit3DItrPair.first,hit3DItrPair.second,hitPairListPtr.begin());
             
                 // First stage of feature extraction runs here
                 m_pcaAlg.PCAAnalysis_3D(hitPairListPtr, clusterParams.getFullPCA());
                 
                 // Recover the new fullPCA
                 reco::PrincipalComponents& newFullPCA = clusterParams.getFullPCA();
 
                 // Must have a valid pca
                 if (newFullPCA.getSvdOK())
                 {
                     std::cout << indent << "+>    -- >> cluster has a valid Full PCA" << std::endl;
 
                     // Need to check if the PCA direction has been reversed
                     Eigen::Vector3f fullPrimaryVec(fullPCA.getEigenVectors()[0][0],fullPCA.getEigenVectors()[0][1],fullPCA.getEigenVectors()[0][2]);
                     Eigen::Vector3f newPrimaryVec(newFullPCA.getEigenVectors()[0][0],newFullPCA.getEigenVectors()[0][1],newFullPCA.getEigenVectors()[0][2]);
                     
                     // If the PCA's are opposite the flip the axes
                     if (fullPrimaryVec.dot(newPrimaryVec) < 0.)
                     {
                         reco::PrincipalComponents::EigenVectors eigenVectors;
                         
                         eigenVectors.resize(3);
                         
                         for(size_t vecIdx = 0; vecIdx < 3; vecIdx++)
                         {
                             eigenVectors[vecIdx].resize(3,0.);
                             
                             eigenVectors[vecIdx][0] = -newFullPCA.getEigenVectors()[vecIdx][0];
                             eigenVectors[vecIdx][1] = -newFullPCA.getEigenVectors()[vecIdx][1];
                             eigenVectors[vecIdx][2] = -newFullPCA.getEigenVectors()[vecIdx][2];
                         }
                         
                         newFullPCA = reco::PrincipalComponents(true,
                                                                newFullPCA.getNumHitsUsed(),
                                                                newFullPCA.getEigenValues(),
                                                                eigenVectors,
                                                                newFullPCA.getAvePosition(),
                                                                newFullPCA.getAveHitDoca());
                     }
 
                     // Set the skeleton PCA to make sure it has some value
                     clusterParams.getSkeletonPCA() = clusterParams.getFullPCA();
                 
                     positionItr = breakIntoTinyBits(clusterParams, positionItr, outputClusterList, level+4);
                 }
             }
         }
     }
 
     // First question, are we done breaking?
     if (storeCurrentCluster)
     {
         // I think this is where we fill out the rest of the parameters?
         // Start by adding the 2D hits...
         // See if we can avoid duplicates by temporarily transferring to a set
         std::set<const reco::ClusterHit2D*> hitSet;
         
         // Loop through 3D hits to get a set of unique 2D hits
         for(const auto& hit3D : clusterToBreak.getHitPairListPtr())
         {
             for(const auto& hit2D : hit3D->getHits())
             {
                 if (hit2D) hitSet.insert(hit2D);
             }
         }
         
         // Now add these to the new cluster
         for(const auto& hit2D : hitSet)
         {
             hit2D->setStatusBit(reco::ClusterHit2D::USED);
             clusterToBreak.UpdateParameters(hit2D);
         }
         
         std::cout << indent << "*********>>> storing new subcluster of size " << clusterToBreak.getHitPairListPtr().size() << std::endl;
         
         positionItr = outputClusterList.insert(positionItr,clusterToBreak);
         
         // The above points to the element, want the next element
         positionItr++;
     }
     else if (inputPositionItr == positionItr) std::cout << indent << "***** DID NOT STORE A CLUSTER *****" << std::endl;
     
     return positionItr;
 }

void lar_cluster3d::VoronoiPathFinder::buildConvexHull	(	reco::ClusterParameters &	clusterParameters,
		int	level = `0`
	)		const

private

Definition at line 447 of file VoronoiPathFinder_tool.cc.

References reco::PrincipalComponents::getAvePosition(), reco::ClusterParameters::getBestEdgeList(), lar_cluster3d::ConvexHull::getConvexHull(), lar_cluster3d::ConvexHull::getConvexHullArea(), reco::PrincipalComponents::getEigenVectors(), reco::ClusterParameters::getFullPCA(), reco::ClusterParameters::getHit3DToEdgeMap(), reco::ClusterParameters::getHitPairListPtr(), reco::ClusterHit3D::getPosition(), art::detail::indent(), art::left(), max, and art::right().

Referenced by breakIntoTinyBits().

 {
     // set an indention string
     std::string minuses(level/2, '-');
     std::string indent(level/2, ' ');
     
     indent += minuses;
 
     // The plan is to build the enclosing 2D polygon around the points in the PCA plane of most spread for this cluster
     // To do so we need to start by building a list of 2D projections onto the plane of most spread...
     reco::PrincipalComponents& pca = clusterParameters.getFullPCA();
 
     // Recover the parameters from the Principal Components Analysis that we need to project and accumulate
     Eigen::Vector3f pcaCenter(pca.getAvePosition()[0],pca.getAvePosition()[1],pca.getAvePosition()[2]);
     Eigen::Vector3f planeVec0(pca.getEigenVectors()[0][0],pca.getEigenVectors()[0][1],pca.getEigenVectors()[0][2]);
     Eigen::Vector3f planeVec1(pca.getEigenVectors()[1][0],pca.getEigenVectors()[1][1],pca.getEigenVectors()[1][2]);
     Eigen::Vector3f pcaPlaneNrml(pca.getEigenVectors()[2][0],pca.getEigenVectors()[2][1],pca.getEigenVectors()[2][2]);
 
     // Let's get the rotation matrix from the standard coordinate system to the PCA system.
     Eigen::Matrix3f rotationMatrix;
     
     rotationMatrix << planeVec0(0),    planeVec0(1),    planeVec0(2),
                       planeVec1(0),    planeVec1(1),    planeVec1(2),
                       pcaPlaneNrml(0), pcaPlaneNrml(1), pcaPlaneNrml(2);
 
     //dcel2d::PointList pointList;
     using Point     = std::tuple<float,float,const reco::ClusterHit3D*>;
     using PointList = std::list<Point>;
     PointList pointList;
 
     // Loop through hits and do projection to plane
     for(const auto& hit3D : clusterParameters.getHitPairListPtr())
     {
         Eigen::Vector3f pcaToHitVec(hit3D->getPosition()[0] - pcaCenter(0),
                                     hit3D->getPosition()[1] - pcaCenter(1),
                                     hit3D->getPosition()[2] - pcaCenter(2));
         Eigen::Vector3f pcaToHit = rotationMatrix * pcaToHitVec;
 
         pointList.emplace_back(dcel2d::Point(pcaToHit(0),pcaToHit(1),hit3D));
     }
     
     // Sort the point vec by increasing x, then increase y
     pointList.sort([](const auto& left, const auto& right){return (std::abs(std::get<0>(left) - std::get<0>(right)) > std::numeric_limits<float>::epsilon()) ? std::get<0>(left) < std::get<0>(right) : std::get<1>(left) < std::get<1>(right);});
     
     // containers for finding the "best" hull...
     std::vector<ConvexHull> convexHullVec;
     std::vector<PointList>  rejectedListVec;
     bool                    increaseDepth(pointList.size() > 5);
     float                   lastArea(std::numeric_limits<float>::max());
     
     while(increaseDepth)
     {
         // Get another convexHull container
         convexHullVec.push_back(ConvexHull(pointList));
         rejectedListVec.push_back(PointList());
 
         const ConvexHull& convexHull       = convexHullVec.back();
         PointList&        rejectedList     = rejectedListVec.back();
         const PointList&  convexHullPoints = convexHull.getConvexHull();
         
         increaseDepth = false;
         
         if (convexHull.getConvexHullArea() > 0.)
         {
             std::cout << indent << "-> built convex hull, 3D hits: " << pointList.size() << " with " << convexHullPoints.size() << " vertices" << ", area: " << convexHull.getConvexHullArea() << std::endl;
             std::cout << indent << "-> -Points:";
             for(const auto& point : convexHullPoints)
                 std::cout << " (" << std::get<0>(point) << "," << std::get<1>(point) << ")";
             std::cout << std::endl;
             
             if (convexHullVec.size() < 2 || convexHull.getConvexHullArea() < 0.8 * lastArea)
             {
                 for(auto& point : convexHullPoints)
                 {
                     pointList.remove(point);
                     rejectedList.emplace_back(point);
                 }
                 lastArea      = convexHull.getConvexHullArea();
 //                increaseDepth = true;
             }
         }
     }
     
     // do we have a valid convexHull?
     while(!convexHullVec.empty() && convexHullVec.back().getConvexHullArea() < 0.5)
     {
         convexHullVec.pop_back();
         rejectedListVec.pop_back();
     }
 
     // If we found the convex hull then build edges around the region
     if (!convexHullVec.empty())
     {
         size_t               nRejectedTotal(0);
         reco::HitPairListPtr hitPairListPtr = clusterParameters.getHitPairListPtr();
         
         for(const auto& rejectedList : rejectedListVec)
         {
             nRejectedTotal += rejectedList.size();
             
             for(const auto& rejectedPoint : rejectedList)
             {
                 std::cout << indent << "-> -- Point is " << convexHullVec.back().findNearestDistance(rejectedPoint) << " from nearest edge" << std::endl;
                 
                 if (convexHullVec.back().findNearestDistance(rejectedPoint) > 0.5)
                     hitPairListPtr.remove(std::get<2>(rejectedPoint));
             }
         }
         
         std::cout << indent << "-> Removed " << nRejectedTotal << " leaving " << pointList.size() << "/" << hitPairListPtr.size() << " points" << std::endl;
         
         // Now add "edges" to the cluster to describe the convex hull (for the display)
         reco::Hit3DToEdgeMap& edgeMap      = clusterParameters.getHit3DToEdgeMap();
         reco::EdgeList&       bestEdgeList = clusterParameters.getBestEdgeList();
 
         Point lastPoint = convexHullVec.back().getConvexHull().front();
     
         for(auto& curPoint : convexHullVec.back().getConvexHull())
         {
             if (curPoint == lastPoint) continue;
         
             const reco::ClusterHit3D* lastPoint3D = std::get<2>(lastPoint);
             const reco::ClusterHit3D* curPoint3D  = std::get<2>(curPoint);
         
             float distBetweenPoints = (curPoint3D->getPosition()[0] - lastPoint3D->getPosition()[0]) * (curPoint3D->getPosition()[0] - lastPoint3D->getPosition()[0])
                                     + (curPoint3D->getPosition()[1] - lastPoint3D->getPosition()[1]) * (curPoint3D->getPosition()[1] - lastPoint3D->getPosition()[1])
                                     + (curPoint3D->getPosition()[2] - lastPoint3D->getPosition()[2]) * (curPoint3D->getPosition()[2] - lastPoint3D->getPosition()[2]);
         
             distBetweenPoints = std::sqrt(distBetweenPoints);
         
             reco::EdgeTuple edge(lastPoint3D,curPoint3D,distBetweenPoints);
         
             edgeMap[lastPoint3D].push_back(edge);
             edgeMap[curPoint3D].push_back(edge);
             bestEdgeList.emplace_back(edge);
         
             lastPoint = curPoint;
         }
     }
 
     return;
 }

void lar_cluster3d::VoronoiPathFinder::buildVoronoiDiagram	(	reco::ClusterParameters &	clusterParameters,
		int	level = `0`
	)		const

private

Definition at line 591 of file VoronoiPathFinder_tool.cc.

References voronoi2d::VoronoiDiagram::buildVoronoiDiagram(), reco::PrincipalComponents::getAvePosition(), reco::ClusterParameters::getBestEdgeList(), reco::PrincipalComponents::getEigenVectors(), reco::ClusterParameters::getFaceList(), reco::ClusterParameters::getFullPCA(), reco::ClusterParameters::getHalfEdgeList(), reco::ClusterParameters::getHit3DToEdgeMap(), reco::ClusterParameters::getHitPairListPtr(), reco::ClusterHit3D::getPosition(), reco::ClusterParameters::getVertexList(), art::left(), and art::right().

Referenced by ModifyClusters().

 {
     // The plan is to build the enclosing 2D polygon around the points in the PCA plane of most spread for this cluster
     // To do so we need to start by building a list of 2D projections onto the plane of most spread...
     reco::PrincipalComponents& pca = clusterParameters.getFullPCA();
     
     // Recover the parameters from the Principal Components Analysis that we need to project and accumulate
     Eigen::Vector3f pcaCenter(pca.getAvePosition()[0],pca.getAvePosition()[1],pca.getAvePosition()[2]);
     Eigen::Vector3f planeVec0(pca.getEigenVectors()[0][0],pca.getEigenVectors()[0][1],pca.getEigenVectors()[0][2]);
     Eigen::Vector3f planeVec1(pca.getEigenVectors()[1][0],pca.getEigenVectors()[1][1],pca.getEigenVectors()[1][2]);
     Eigen::Vector3f pcaPlaneNrml(pca.getEigenVectors()[2][0],pca.getEigenVectors()[2][1],pca.getEigenVectors()[2][2]);
     
     // Leave the following code bits as an example of a more complicated way to do what we are trying to do here
     // (but in case I want to use quaternions again!)
     //
     //Eigen::Vector3f unitVecX(1.,0.,0.);
     //
     //Eigen::Quaternionf rotationMatrix = Eigen::Quaternionf::FromTwoVectors(planeVec0,unitVecX);
     //
     //Eigen::Matrix3f Rmatrix    = rotationMatrix.toRotationMatrix();
     //Eigen::Matrix3f RInvMatrix = rotationMatrix.inverse().toRotationMatrix();
     
     // Let's get the rotation matrix from the standard coordinate system to the PCA system.
     Eigen::Matrix3f rotationMatrix;
     
     rotationMatrix << planeVec0(0),    planeVec0(1),    planeVec0(2),
                       planeVec1(0),    planeVec1(1),    planeVec1(2),
                       pcaPlaneNrml(0), pcaPlaneNrml(1), pcaPlaneNrml(2);
     
     dcel2d::PointList pointList;
     
     // Loop through hits and do projection to plane
     for(const auto& hit3D : clusterParameters.getHitPairListPtr())
     {
         Eigen::Vector3f pcaToHitVec(hit3D->getPosition()[0] - pcaCenter(0),
                                     hit3D->getPosition()[1] - pcaCenter(1),
                                     hit3D->getPosition()[2] - pcaCenter(2));
         Eigen::Vector3f pcaToHit = rotationMatrix * pcaToHitVec;
         
         pointList.emplace_back(dcel2d::Point(pcaToHit(0),pcaToHit(1),hit3D));
     }
     
     // Sort the point vec by increasing x, then increase y
     pointList.sort([](const auto& left, const auto& right){return (std::abs(std::get<0>(left) - std::get<0>(right)) > std::numeric_limits<float>::epsilon()) ? std::get<0>(left) < std::get<0>(right) : std::get<1>(left) < std::get<1>(right);});
     
     // Set up the voronoi diagram builder
     voronoi2d::VoronoiDiagram voronoiDiagram(clusterParameters.getHalfEdgeList(),clusterParameters.getVertexList(),clusterParameters.getFaceList());
     
     // And make the diagram
     voronoiDiagram.buildVoronoiDiagram(pointList);
 
     // Recover the voronoi diagram vertex list and the container to store them in
     dcel2d::VertexList& vertexList = clusterParameters.getVertexList();
     
     // Now get the inverse of the rotation matrix so we can get the vertex positions,
     // which lie in the plane of the two largest PCA axes, in the standard coordinate system
     Eigen::Matrix3f rotationMatrixInv = rotationMatrix.inverse();
     
     // Translate and fill
     for(auto& vertex : vertexList)
     {
         Eigen::Vector3f coords = rotationMatrixInv * vertex.getCoords();
         
         coords += pcaCenter;
         
         vertex.setCoords(coords);
     }
     
     // Now do the Convex Hull
     // Now add "edges" to the cluster to describe the convex hull (for the display)
     reco::Hit3DToEdgeMap& edgeMap      = clusterParameters.getHit3DToEdgeMap();
     reco::EdgeList&       bestEdgeList = clusterParameters.getBestEdgeList();
     
 //    const dcel2d::PointList& edgePoints = voronoiDiagram.getConvexHull();
 //    PointList                localList;
 //
 //    for(const auto& edgePoint : edgePoints) localList.emplace_back(std::get<0>(edgePoint),std::get<1>(edgePoint),std::get<2>(edgePoint));
 //
 //    // Sort the point vec by increasing x, then increase y
 //    localList.sort([](const auto& left, const auto& right){return (std::abs(std::get<0>(left) - std::get<0>(right)) > std::numeric_limits<float>::epsilon()) ? std::get<0>(left) < std::get<0>(right) : std::get<1>(left) < std::get<1>(right);});
 //
 //    // Why do I need to do this?
 //    ConvexHull convexHull(localList);
 //
 //    Point lastPoint = convexHull.getConvexHull().front();
     dcel2d::Point lastPoint = voronoiDiagram.getConvexHull().front();
 
 //    std::cout << "@@@@>> Build convex hull, voronoi handed " << edgePoints.size() << " points, convexHull cut to " << convexHull.getConvexHull().size() << std::endl;
     
 //    for(auto& curPoint : convexHull.getConvexHull())
     for(auto& curPoint : voronoiDiagram.getConvexHull())
     {
         if (curPoint == lastPoint) continue;
         
         const reco::ClusterHit3D* lastPoint3D = std::get<2>(lastPoint);
         const reco::ClusterHit3D* curPoint3D  = std::get<2>(curPoint);
         
         float distBetweenPoints = (curPoint3D->getPosition()[0] - lastPoint3D->getPosition()[0]) * (curPoint3D->getPosition()[0] - lastPoint3D->getPosition()[0])
                                 + (curPoint3D->getPosition()[1] - lastPoint3D->getPosition()[1]) * (curPoint3D->getPosition()[1] - lastPoint3D->getPosition()[1])
                                 + (curPoint3D->getPosition()[2] - lastPoint3D->getPosition()[2]) * (curPoint3D->getPosition()[2] - lastPoint3D->getPosition()[2]);
         
         distBetweenPoints = std::sqrt(distBetweenPoints);
         
         reco::EdgeTuple edge(lastPoint3D,curPoint3D,distBetweenPoints);
         
         edgeMap[lastPoint3D].push_back(edge);
         edgeMap[curPoint3D].push_back(edge);
         bestEdgeList.emplace_back(edge);
         
         lastPoint = curPoint;
     }
 
     std::cout << "****> vertexList containted " << vertexList.size() << " vertices" << std::endl;
     
     return;
 }

float lar_cluster3d::VoronoiPathFinder::closestApproach	(	const Eigen::Vector3f &	P0,
		const Eigen::Vector3f &	u0,
		const Eigen::Vector3f &	P1,
		const Eigen::Vector3f &	u1,
		Eigen::Vector3f &	poca0,
		Eigen::Vector3f &	poca1
	)		const

private

Definition at line 709 of file VoronoiPathFinder_tool.cc.

References d, DEFINE_ART_CLASS_TOOL, den, and e.

Referenced by getTimeToExecute().

 {
     // Technique is to compute the arclength to each point of closest approach
     Eigen::Vector3f w0 = P0 - P1;
     float a(1.);
     float b(u0.dot(u1));
     float c(1.);
     float d(u0.dot(w0));
     float e(u1.dot(w0));
     float den(a * c - b * b);
     
     float arcLen0 = (b * e - c * d) / den;
     float arcLen1 = (a * e - b * d) / den;
     
     poca0 = P0 + arcLen0 * u0;
     poca1 = P1 + arcLen1 * u1;
     
     return (poca0 - poca1).norm();
 }

virtual void lar_cluster3d::IClusterModAlg::configure ( const fhicl::ParameterSet & )

pure virtualinherited

Interface for configuring the particular algorithm tool.

Parameters

ParameterSet The input set of parameters for configuration

void lar_cluster3d::VoronoiPathFinder::configure ( fhicl::ParameterSet const & pset )

override

Definition at line 128 of file VoronoiPathFinder_tool.cc.

References fhicl::ParameterSet::get(), m_clusterAlg, m_enableMonitoring, m_geometry, m_minTinyClusterSize, and m_timeToProcess.

Referenced by VoronoiPathFinder().

 {
     m_enableMonitoring   = pset.get<bool>  ("EnableMonitoring",  true  );
     m_minTinyClusterSize = pset.get<size_t>("MinTinyClusterSize",40);
     m_clusterAlg         = art::make_tool<lar_cluster3d::IClusterAlg>(pset.get<fhicl::ParameterSet>("ClusterAlg"));
 
     art::ServiceHandle<geo::Geometry> geometry;
     
     m_geometry = &*geometry;
     
     m_timeToProcess = 0.;
     
     return;
 }

float lar_cluster3d::VoronoiPathFinder::getTimeToExecute ( ) const

inlineoverridevirtual

If monitoring, recover the time to execute a particular function.

Implements lar_cluster3d::IClusterModAlg.

Definition at line 72 of file VoronoiPathFinder_tool.cc.

References breakIntoTinyBits(), closestApproach(), and m_timeToProcess.

72 {return m_timeToProcess;}

lar_cluster3d::VoronoiPathFinder::m_timeToProcess

float m_timeToProcess

Definition: VoronoiPathFinder_tool.cc:106

void lar_cluster3d::VoronoiPathFinder::ModifyClusters ( reco::ClusterParametersList & clusterParametersList ) const

overridevirtual

Scan an input collection of clusters and modify those according to the specific implementing algorithm.

Parameters

clusterParametersList A list of cluster objects (parameters from associated hits)

Top level interface for algorithm to consider pairs of clusters from the input list and determine if they are consistent with each other and, therefore, should be merged. This is done by looking at the PCA for each cluster and looking at the projection of the primary axis along the vector connecting their centers.

Implements lar_cluster3d::IClusterModAlg.

Definition at line 143 of file VoronoiPathFinder_tool.cc.

References breakIntoTinyBits(), buildVoronoiDiagram(), reco::ClusterParameters::daughterList(), reco::ClusterParameters::getHitPairListPtr(), m_clusterAlg, m_enableMonitoring, m_minTinyClusterSize, and m_timeToProcess.

 {
     // Initial clustering is done, now trim the list and get output parameters
     cet::cpu_timer theClockBuildClusters;
     
     // Start clocks if requested
     if (m_enableMonitoring) theClockBuildClusters.start();
     
     int countClusters(0);
 
     // This is the loop over candidate 3D clusters
     // Note that it might be that the list of candidate clusters is modified by splitting
     // So we use the following construct to make sure we get all of them
     reco::ClusterParametersList::iterator clusterParametersListItr = clusterParametersList.begin();
     
     while(clusterParametersListItr != clusterParametersList.end())
     {
         // Dereference to get the cluster paramters
         reco::ClusterParameters& clusterParameters = *clusterParametersListItr;
         
         std::cout << "**> Looking at Cluster " << countClusters++ << std::endl;
         
         // It turns out that computing the convex hull surrounding the points in the 2D projection onto the
         // plane of largest spread in the PCA is a good way to break up the cluster... and we do it here since
         // we (currently) want this to be part of the standard output
         buildVoronoiDiagram(clusterParameters);
 
         // Make sure our cluster has enough hits...
         if (clusterParameters.getHitPairListPtr().size() > m_minTinyClusterSize)
         {
             // Get an interim cluster list
             reco::ClusterParametersList reclusteredParameters;
             
             // Call the main workhorse algorithm for building the local version of candidate 3D clusters
             m_clusterAlg->Cluster3DHits(clusterParameters.getHitPairListPtr(), reclusteredParameters);
             
             std::cout << ">>>>>>>>>>> Reclustered to " << reclusteredParameters.size() << " Clusters <<<<<<<<<<<<<<<" << std::endl;
             
             // Only process non-empty results
             if (!reclusteredParameters.empty())
             {
                 // Loop over the reclustered set
                 for (auto& cluster : reclusteredParameters)
                 {
                     std::cout << "****> Calling breakIntoTinyBits" << std::endl;
                     
                     // Break our cluster into smaller elements...
                     breakIntoTinyBits(cluster, cluster.daughterList().end(), cluster.daughterList(), 4);
         
                     std::cout << "****> Broke Cluster with " << cluster.getHitPairListPtr().size() << " into " << cluster.daughterList().size() << " sub clusters";
                     for(auto& clus : cluster.daughterList()) std::cout << ", " << clus.getHitPairListPtr().size();
                     std::cout << std::endl;
             
                     // Add the daughters to the cluster
                     clusterParameters.daughterList().insert(clusterParameters.daughterList().end(),cluster); 
                 }
             }
         }
     
         // Go to next cluster parameters object
         clusterParametersListItr++;
     }
 
     if (m_enableMonitoring)
     {
         theClockBuildClusters.stop();
         
         m_timeToProcess = theClockBuildClusters.accumulated_real_time();
     }
     
     mf::LogDebug("Cluster3D") << ">>>>> Cluster Path finding done" << std::endl;
     
     return;
 }

Member Data Documentation

std::unique_ptr<lar_cluster3d::IClusterAlg> lar_cluster3d::VoronoiPathFinder::m_clusterAlg

private

Algorithm to do 3D space point clustering.

Definition at line 110 of file VoronoiPathFinder_tool.cc.

Referenced by configure(), and ModifyClusters().

bool lar_cluster3d::VoronoiPathFinder::m_enableMonitoring

private

Data members to follow.

Definition at line 104 of file VoronoiPathFinder_tool.cc.

Referenced by configure(), and ModifyClusters().

geo::Geometry* lar_cluster3d::VoronoiPathFinder::m_geometry

private

Definition at line 108 of file VoronoiPathFinder_tool.cc.

Referenced by configure().

size_t lar_cluster3d::VoronoiPathFinder::m_minTinyClusterSize

private

Minimum size for a "tiny" cluster.

Definition at line 105 of file VoronoiPathFinder_tool.cc.

Referenced by breakIntoTinyBits(), configure(), and ModifyClusters().

PrincipalComponentsAlg lar_cluster3d::VoronoiPathFinder::m_pcaAlg

private

Definition at line 111 of file VoronoiPathFinder_tool.cc.

Referenced by breakIntoTinyBits().

float lar_cluster3d::VoronoiPathFinder::m_timeToProcess

mutableprivate

Definition at line 106 of file VoronoiPathFinder_tool.cc.

Referenced by configure(), getTimeToExecute(), and ModifyClusters().

The documentation for this class was generated from the following file:

larreco/v06_64_02/source/larreco/RecoAlg/Cluster3DAlgs/VoronoiPathFinder_tool.cc

Public Member Functions

Private Types

Private Member Functions

Private Attributes

Detailed Description

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation