//"PER-CELL" METHOD: FLAG DUPLICATE POINTS
//ADDITIONAL PARAMETERS (1):
// [0] -> (double*) maxSquareDistBetweenPoints: max square distance between points
bool GeometricalAnalysisTools::flagDuplicatePointsInACellAtLevel( const DgmOctree::octreeCell& cell,
void** additionalParameters,
NormalizedProgress* nProgress/*=0*/)
{
//parameter(s)
double minDistBetweenPoints = *static_cast<double*>(additionalParameters[0]);
//structure for nearest neighbors search
DgmOctree::NearestNeighboursSphericalSearchStruct nNSS;
nNSS.level = cell.level;
nNSS.prepare(static_cast<PointCoordinateType>(minDistBetweenPoints),cell.parentOctree->getCellSize(nNSS.level));
cell.parentOctree->getCellPos(cell.truncatedCode,cell.level,nNSS.cellPos,true);
cell.parentOctree->computeCellCenter(nNSS.cellPos,cell.level,nNSS.cellCenter);
unsigned n = cell.points->size(); //number of points in the current cell
//for each point in the cell
for (unsigned i=0; i<n; ++i)
{
//don't process points already flagged as 'duplicate'
if (cell.points->getPointScalarValue(i) == 0)
{
cell.points->getPoint(i,nNSS.queryPoint);
//look for neighbors in a sphere
//warning: there may be more points at the end of nNSS.pointsInNeighbourhood than the actual nearest neighbors (neighborCount)!
unsigned neighborCount = cell.parentOctree->findNeighborsInASphereStartingFromCell(nNSS,minDistBetweenPoints,false);
if (neighborCount > 1) //the point itself lies in the neighborhood
{
unsigned iIndex = cell.points->getPointGlobalIndex(i);
for (unsigned j=0; j<neighborCount; ++j)
{
if (nNSS.pointsInNeighbourhood[j].pointIndex != iIndex)
{
//flag this point as 'duplicate'
cell.points->getAssociatedCloud()->setPointScalarValue(nNSS.pointsInNeighbourhood[j].pointIndex,static_cast<ScalarType>(1));
}
}
}
}
if (nProgress && !nProgress->oneStep())
return false;
}
return true;
}
//"PER-CELL" METHOD: LOCAL DENSITY
//ADDITIONNAL PARAMETERS (2):
// [0] -> (PointCoordinateType*) kernelRadius : spherical neighborhood radius
// [1] -> (ScalarType*) sphereVolume : spherical neighborhood volume
bool GeometricalAnalysisTools::computePointsDensityInACellAtLevel( const DgmOctree::octreeCell& cell,
void** additionalParameters,
NormalizedProgress* nProgress/*=0*/)
{
//parameter(s)
PointCoordinateType radius = *static_cast<PointCoordinateType*>(additionalParameters[0]);
double dimensionalCoef = *static_cast<double*>(additionalParameters[1]);
assert(dimensionalCoef > 0);
//structure for nearest neighbors search
DgmOctree::NearestNeighboursSphericalSearchStruct nNSS;
nNSS.level = cell.level;
nNSS.prepare(radius,cell.parentOctree->getCellSize(nNSS.level));
cell.parentOctree->getCellPos(cell.truncatedCode,cell.level,nNSS.cellPos,true);
cell.parentOctree->computeCellCenter(nNSS.cellPos,cell.level,nNSS.cellCenter);
unsigned n = cell.points->size(); //number of points in the current cell
//for each point in the cell
for (unsigned i=0; i<n; ++i)
{
cell.points->getPoint(i,nNSS.queryPoint);
//look for neighbors inside a sphere
//warning: there may be more points at the end of nNSS.pointsInNeighbourhood than the actual nearest neighbors (neighborCount)!
unsigned neighborCount = cell.parentOctree->findNeighborsInASphereStartingFromCell(nNSS,radius,false);
ScalarType density = static_cast<ScalarType>(neighborCount/dimensionalCoef);
cell.points->setPointScalarValue(i,density);
if (nProgress && !nProgress->oneStep())
return false;
}
return true;
}
ReferenceCloud* CloudSamplingTools::resampleCloudSpatially(GenericIndexedCloudPersist* theCloud,
PointCoordinateType minDistance,
DgmOctree* theOctree/*=0*/,
GenericProgressCallback* progressCb/*=0*/)
{
assert(theCloud);
unsigned cloudSize = theCloud->size();
DgmOctree *_theOctree=theOctree;
if (!_theOctree)
{
_theOctree = new DgmOctree(theCloud);
if (_theOctree->build()<(int)cloudSize)
{
delete _theOctree;
return 0;
}
}
ReferenceCloud* sampledCloud = new ReferenceCloud(theCloud);
if (!sampledCloud->reserve(cloudSize))
{
if (!theOctree)
delete _theOctree;
return 0;
}
GenericChunkedArray<1,bool>* markers = new GenericChunkedArray<1,bool>(); //DGM: upgraded from vector, as this can be quite huge!
if (!markers->resize(cloudSize,true,true))
{
markers->release();
if (!theOctree)
delete _theOctree;
delete sampledCloud;
return 0;
}
NormalizedProgress* normProgress=0;
if (progressCb)
{
progressCb->setInfo("Spatial resampling");
normProgress = new NormalizedProgress(progressCb,cloudSize);
progressCb->reset();
progressCb->start();
}
//for each point in the cloud that is still 'marked', we look
//for its neighbors and remove their own marks
DgmOctree::NearestNeighboursSphericalSearchStruct nss;
nss.level = _theOctree->findBestLevelForAGivenNeighbourhoodSizeExtraction(minDistance);
markers->placeIteratorAtBegining();
for (unsigned i=0; i<cloudSize; i++, markers->forwardIterator())
{
//progress indicator
if (normProgress && !normProgress->oneStep())
{
//cancel process
delete sampledCloud;
sampledCloud = 0;
break;
}
//no mark? we skip this point
if (!markers->getCurrentValue())
continue;
//init neighbor search structure
theCloud->getPoint(i,nss.queryPoint);
bool inbounds = false;
_theOctree->getTheCellPosWhichIncludesThePoint(&nss.queryPoint, nss.cellPos, nss.level, inbounds);
nss.truncatedCellCode = (inbounds ? _theOctree->generateTruncatedCellCode(nss.cellPos, nss.level) : DgmOctree::INVALID_CELL_CODE);
_theOctree->computeCellCenter(nss.cellPos, nss.level, nss.cellCenter);
//add the points that lie in the same cell (faster)
{
ReferenceCloud* Y = _theOctree->getPointsInCell(nss.truncatedCellCode, nss.level, true);
unsigned count = Y->size();
try
{
nss.pointsInNeighbourhood.resize(count);
}
catch (std::bad_alloc) //out of memory
{
//stop process
delete sampledCloud;
sampledCloud = 0;
break;
}
unsigned realCount = 0;
DgmOctree::NeighboursSet::iterator it = nss.pointsInNeighbourhood.begin();
for (unsigned j=0; j<count; ++j)
{
unsigned index = Y->getPointGlobalIndex(j);
if (index != i && markers->getValue(index)) //no need to add the point itself and those already flagged off
{
it->point = Y->getPointPersistentPtr(j);
it->pointIndex = index;
++it;
++realCount;
}
}
nss.pointsInNeighbourhood.resize(realCount); //should be ok as realCount<=count
nss.alreadyVisitedNeighbourhoodSize = 1;
}
#ifdef TEST_CELLS_FOR_SPHERICAL_NN
nss.pointsInSphericalNeighbourhood.clear();
#endif
nss.prepare(minDistance,_theOctree->getCellSize(nss.level));
//look for neighbors and 'de-mark' them
{
unsigned nbNeighbors = _theOctree->findNeighborsInASphereStartingFromCell(nss, minDistance, false);
DgmOctree::NeighboursSet::iterator it = nss.pointsInNeighbourhood.begin();
for (unsigned j=0; j<nbNeighbors; ++j, ++it)
if (it->pointIndex != i)
markers->setValue(it->pointIndex,false);
}
//At this stage, the ith point is the only one marked in a radius of <minDistance>.
//Therefore it will necessarily be in the final cloud!
if (!sampledCloud->addPointIndex(i)) //not enough memory
{
//stop process
delete sampledCloud;
sampledCloud = 0;
break;
}
}
if(normProgress)
{
delete normProgress;
normProgress = 0;
}
if (!theOctree)
delete _theOctree;
markers->release();
return sampledCloud;
}
bool CloudSamplingTools::applyNoiseFilterAtLevel( const DgmOctree::octreeCell& cell,
void** additionalParameters,
NormalizedProgress* nProgress/*=0*/)
{
ReferenceCloud* cloud = static_cast<ReferenceCloud*>(additionalParameters[0]);
PointCoordinateType kernelRadius = *static_cast<PointCoordinateType*>(additionalParameters[1]);
double nSigma = *static_cast<double*>(additionalParameters[2]);
bool removeIsolatedPoints = *static_cast<bool*>(additionalParameters[3]);
bool useKnn = *static_cast<bool*>(additionalParameters[4]);
int knn = *static_cast<int*>(additionalParameters[5]);
bool useAbsoluteError = *static_cast<bool*>(additionalParameters[6]);
double absoluteError = *static_cast<double*>(additionalParameters[7]);
//structure for nearest neighbors search
DgmOctree::NearestNeighboursSphericalSearchStruct nNSS;
nNSS.level = cell.level;
nNSS.prepare(kernelRadius,cell.parentOctree->getCellSize(nNSS.level));
if (useKnn)
{
nNSS.minNumberOfNeighbors = knn;
}
cell.parentOctree->getCellPos(cell.truncatedCode,cell.level,nNSS.cellPos,true);
cell.parentOctree->computeCellCenter(nNSS.cellPos,cell.level,nNSS.cellCenter);
unsigned n = cell.points->size(); //number of points in the current cell
//for each point in the cell
for (unsigned i=0; i<n; ++i)
{
cell.points->getPoint(i,nNSS.queryPoint);
//look for neighbors (either inside a sphere or the k nearest ones)
//warning: there may be more points at the end of nNSS.pointsInNeighbourhood than the actual nearest neighbors (neighborCount)!
unsigned neighborCount = 0;
if (useKnn)
neighborCount = cell.parentOctree->findNearestNeighborsStartingFromCell(nNSS);
else
neighborCount = cell.parentOctree->findNeighborsInASphereStartingFromCell(nNSS,kernelRadius,false);
if (neighborCount > 3) //we want 3 points or more (other than the point itself!)
{
//find the query point in the nearest neighbors set and place it at the end
const unsigned globalIndex = cell.points->getPointGlobalIndex(i);
unsigned localIndex = 0;
while (localIndex < neighborCount && nNSS.pointsInNeighbourhood[localIndex].pointIndex != globalIndex)
++localIndex;
//the query point should be in the nearest neighbors set!
assert(localIndex < neighborCount);
if (localIndex+1 < neighborCount) //no need to swap with another point if it's already at the end!
{
std::swap(nNSS.pointsInNeighbourhood[localIndex],nNSS.pointsInNeighbourhood[neighborCount-1]);
}
unsigned realNeighborCount = neighborCount-1;
DgmOctreeReferenceCloud neighboursCloud(&nNSS.pointsInNeighbourhood,realNeighborCount); //we don't take the query point into account!
Neighbourhood Z(&neighboursCloud);
const PointCoordinateType* lsPlane = Z.getLSPlane();
if (lsPlane)
{
double maxD = absoluteError;
if (!useAbsoluteError)
{
//compute the std. dev. to this plane
double sum_d = 0;
double sum_d2 = 0;
for (unsigned j=0; j<realNeighborCount; ++j)
{
const CCVector3* P = neighboursCloud.getPoint(j);
double d = CCLib::DistanceComputationTools::computePoint2PlaneDistance(P,lsPlane);
sum_d += d;
sum_d2 += d*d;
}
double stddev = sqrt(fabs(sum_d2*realNeighborCount - sum_d*sum_d))/realNeighborCount;
maxD = stddev * nSigma;
}
//distance from the query point to the plane
double d = fabs(CCLib::DistanceComputationTools::computePoint2PlaneDistance(&nNSS.queryPoint,lsPlane));
if (d <= maxD)
cloud->addPointIndex(globalIndex);
}
else
{
//TODO: ???
}
}
else
{
//not enough points to fit a plane AND compute distances to it
if (!removeIsolatedPoints)
{
//we keep the point
unsigned globalIndex = cell.points->getPointGlobalIndex(i);
cloud->addPointIndex(globalIndex);
}
}
if (nProgress && !nProgress->oneStep())
{
return false;
}
}
return true;
}
//"PER-CELL" METHOD: CURVATURE ESTIMATION (WITH QUADRATIC FIT)
//ADDITIONAL PARAMETERS (2):
// [0] -> (CC_CURVATURE_TYPE*) cType : curvature type
// [1] -> (PointCoordinateType*) radius : sphere radius
bool GeometricalAnalysisTools::computeCellCurvatureAtLevel( const DgmOctree::octreeCell& cell,
void** additionalParameters,
NormalizedProgress* nProgress/*=0*/)
{
//parameters
Neighbourhood::CC_CURVATURE_TYPE cType = *static_cast<Neighbourhood::CC_CURVATURE_TYPE*>(additionalParameters[0]);
PointCoordinateType radius = *static_cast<PointCoordinateType*>(additionalParameters[1]);
//structure for nearest neighbors search
DgmOctree::NearestNeighboursSphericalSearchStruct nNSS;
nNSS.level = cell.level;
nNSS.prepare(radius,cell.parentOctree->getCellSize(nNSS.level));
cell.parentOctree->getCellPos(cell.truncatedCode,cell.level,nNSS.cellPos,true);
cell.parentOctree->computeCellCenter(nNSS.cellPos,cell.level,nNSS.cellCenter);
unsigned n = cell.points->size(); //number of points in the current cell
//we already know some of the neighbours: the points in the current cell!
{
try
{
nNSS.pointsInNeighbourhood.resize(n);
}
catch (.../*const std::bad_alloc&*/) //out of memory
{
return false;
}
DgmOctree::NeighboursSet::iterator it = nNSS.pointsInNeighbourhood.begin();
for (unsigned i=0; i<n; ++i,++it)
{
it->point = cell.points->getPointPersistentPtr(i);
it->pointIndex = cell.points->getPointGlobalIndex(i);
}
}
nNSS.alreadyVisitedNeighbourhoodSize = 1;
//for each point in the cell
for (unsigned i=0; i<n; ++i)
{
ScalarType curv = NAN_VALUE;
cell.points->getPoint(i,nNSS.queryPoint);
//look for neighbors in a sphere
//warning: there may be more points at the end of nNSS.pointsInNeighbourhood than the actual nearest neighbors (neighborCount)!
unsigned neighborCount = cell.parentOctree->findNeighborsInASphereStartingFromCell(nNSS,radius,false);
//neighborCount = std::min(neighborCount,16);
if (neighborCount > 5)
{
//current point index
unsigned index = cell.points->getPointGlobalIndex(i);
//current point index in neighbourhood (to compute curvature at the right position!)
unsigned indexInNeighbourhood = 0;
DgmOctreeReferenceCloud neighboursCloud(&nNSS.pointsInNeighbourhood,neighborCount);
Neighbourhood Z(&neighboursCloud);
//look for local index
for (unsigned j=0;j<neighborCount;++j)
{
if (nNSS.pointsInNeighbourhood[j].pointIndex == index)
{
indexInNeighbourhood = j;
break;
}
}
curv = Z.computeCurvature(indexInNeighbourhood,cType);
}
cell.points->setPointScalarValue(i,curv);
if (nProgress && !nProgress->oneStep())
return false;
}
return true;
}
//"PER-CELL" METHOD: ROUGHNESS ESTIMATION (LEAST SQUARES PLANE FIT)
//ADDITIONNAL PARAMETERS (1):
// [0] -> (PointCoordinateType*) kernelRadius : neighbourhood radius
bool GeometricalAnalysisTools::computePointsRoughnessInACellAtLevel(const DgmOctree::octreeCell& cell,
void** additionalParameters,
NormalizedProgress* nProgress/*=0*/)
{
//parameter(s)
PointCoordinateType radius = *static_cast<PointCoordinateType*>(additionalParameters[0]);
//structure for nearest neighbors search
DgmOctree::NearestNeighboursSphericalSearchStruct nNSS;
nNSS.level = cell.level;
nNSS.prepare(radius,cell.parentOctree->getCellSize(nNSS.level));
cell.parentOctree->getCellPos(cell.truncatedCode,cell.level,nNSS.cellPos,true);
cell.parentOctree->computeCellCenter(nNSS.cellPos,cell.level,nNSS.cellCenter);
unsigned n = cell.points->size(); //number of points in the current cell
//for each point in the cell
for (unsigned i=0; i<n; ++i)
{
ScalarType d = NAN_VALUE;
cell.points->getPoint(i,nNSS.queryPoint);
//look for neighbors inside a sphere
//warning: there may be more points at the end of nNSS.pointsInNeighbourhood than the actual nearest neighbors (= neighborCount)!
unsigned neighborCount = cell.parentOctree->findNeighborsInASphereStartingFromCell(nNSS,radius,false);
if (neighborCount > 3)
{
//find the query point in the nearest neighbors set and place it at the end
const unsigned globalIndex = cell.points->getPointGlobalIndex(i);
unsigned localIndex = 0;
while (localIndex < neighborCount && nNSS.pointsInNeighbourhood[localIndex].pointIndex != globalIndex)
++localIndex;
//the query point should be in the nearest neighbors set!
assert(localIndex < neighborCount);
if (localIndex+1 < neighborCount) //no need to swap with another point if it's already at the end!
{
std::swap(nNSS.pointsInNeighbourhood[localIndex],nNSS.pointsInNeighbourhood[neighborCount-1]);
}
DgmOctreeReferenceCloud neighboursCloud(&nNSS.pointsInNeighbourhood,neighborCount-1); //we don't take the query point into account!
Neighbourhood Z(&neighboursCloud);
const PointCoordinateType* lsPlane = Z.getLSPlane();
if (lsPlane)
d = fabs(DistanceComputationTools::computePoint2PlaneDistance(&nNSS.queryPoint,lsPlane));
//swap the points back to their original position (DGM: not necessary)
//if (localIndex+1 < neighborCount)
//{
// std::swap(nNSS.pointsInNeighbourhood[localIndex],nNSS.pointsInNeighbourhood[neighborCount-1]);
//}
}
cell.points->setPointScalarValue(i,d);
if (nProgress && !nProgress->oneStep())
return false;
}
return true;
}
//FONCTION "CELLULAIRE" DE CALCUL DU FILTRE GAUSSIEN (PAR PROJECTION SUR LE PLAN AUX MOINDRES CARRES)
//DETAIL DES PARAMETRES ADDITIONNELS (2) :
// [0] -> (PointCoordinateType*) sigma : gauss function sigma
// [1] -> (PointCoordinateType*) sigmaSF : used when in "bilateral modality" - if -1 pure gaussian filtering is performed
bool ScalarFieldTools::computeCellGaussianFilter( const DgmOctree::octreeCell& cell,
void** additionalParameters,
NormalizedProgress* nProgress/*=0*/)
{
//variables additionnelles
PointCoordinateType sigma = *((PointCoordinateType*)additionalParameters[0]);
PointCoordinateType sigmaSF = *((PointCoordinateType*)additionalParameters[1]);
//we use only the squared value of sigma
PointCoordinateType sigma2 = 2*sigma*sigma;
PointCoordinateType radius = 3*sigma; //2.5 sigma > 99%
//we use only the squared value of sigmaSF
PointCoordinateType sigmaSF2 = 2*sigmaSF*sigmaSF;
//number of points inside the current cell
unsigned n = cell.points->size();
//structures pour la recherche de voisinages SPECIFIQUES
DgmOctree::NearestNeighboursSphericalSearchStruct nNSS;
nNSS.level = cell.level;
nNSS.prepare(radius,cell.parentOctree->getCellSize(nNSS.level));
cell.parentOctree->getCellPos(cell.truncatedCode,cell.level,nNSS.cellPos,true);
cell.parentOctree->computeCellCenter(nNSS.cellPos,cell.level,nNSS.cellCenter);
//we already know the points lying in the first cell (this is the one we are treating :)
try
{
nNSS.pointsInNeighbourhood.resize(n);
}
catch (.../*const std::bad_alloc&*/) //out of memory
{
return false;
}
DgmOctree::NeighboursSet::iterator it = nNSS.pointsInNeighbourhood.begin();
{
for (unsigned i=0; i<n; ++i,++it)
{
it->point = cell.points->getPointPersistentPtr(i);
it->pointIndex = cell.points->getPointGlobalIndex(i);
}
}
nNSS.alreadyVisitedNeighbourhoodSize = 1;
const GenericIndexedCloudPersist* cloud = cell.points->getAssociatedCloud();
//Pure Gaussian Filtering
if (sigmaSF == -1)
{
for (unsigned i=0; i<n; ++i) //for each point in cell
{
//we get the points inside a spherical neighbourhood (radius: '3*sigma')
cell.points->getPoint(i,nNSS.queryPoint);
//warning: there may be more points at the end of nNSS.pointsInNeighbourhood than the actual nearest neighbors (k)!
unsigned k = cell.parentOctree->findNeighborsInASphereStartingFromCell(nNSS,radius,false);
//each point adds a contribution weighted by its distance to the sphere center
it = nNSS.pointsInNeighbourhood.begin();
double meanValue = 0.0;
double wSum = 0.0;
for (unsigned j=0;j<k;++j,++it)
{
double weight = exp(-(it->squareDistd)/sigma2); //PDF: -exp(-(x-mu)^2/(2*sigma^2))
ScalarType val = cloud->getPointScalarValue(it->pointIndex);
//scalar value must be valid
if (ScalarField::ValidValue(val))
{
meanValue += static_cast<double>(val) * weight;
wSum += weight;
}
}
ScalarType newValue = (wSum > 0.0 ? static_cast<ScalarType>(meanValue / wSum) : NAN_VALUE);
cell.points->setPointScalarValue(i,newValue);
if (nProgress && !nProgress->oneStep())
return false;
}
}
//Bilateral Filtering using the second sigma parameters on values (when given)
else
{
for (unsigned i=0;i<n;++i) //for each point in cell
{
ScalarType queryValue = cell.points->getPointScalarValue(i); //scalar of the query point
//we get the points inside a spherical neighbourhood (radius: '3*sigma')
cell.points->getPoint(i,nNSS.queryPoint);
//warning: there may be more points at the end of nNSS.pointsInNeighbourhood than the actual nearest neighbors (k)!
unsigned k = cell.parentOctree->findNeighborsInASphereStartingFromCell(nNSS,radius,false);
//each point adds a contribution weighted by its distance to the sphere center
it = nNSS.pointsInNeighbourhood.begin();
double meanValue = 0.0;
double wSum = 0.0;
for (unsigned j=0;j<k;++j,++it)
{
ScalarType val = cloud->getPointScalarValue(it->pointIndex);
ScalarType dSF = queryValue - val;
double weight = exp(-(it->squareDistd)/sigma2) * exp(-(dSF*dSF)/sigmaSF2);
//scalar value must be valid
if (ScalarField::ValidValue(val))
{
meanValue += (double)val * weight;
wSum += weight;
}
}
cell.points->setPointScalarValue(i,wSum > 0.0 ? (ScalarType)(meanValue / wSum) : NAN_VALUE);
if (nProgress && !nProgress->oneStep())
return false;
}
}
return true;
}
bool ScalarFieldTools::computeMeanGradientOnPatch( const DgmOctree::octreeCell& cell,
void** additionalParameters,
NormalizedProgress* nProgress/*=0*/)
{
//additional parameters
bool euclideanDistances = *reinterpret_cast<bool*>(additionalParameters[0]);
PointCoordinateType radius = *reinterpret_cast<PointCoordinateType*>(additionalParameters[1]);
ScalarField* theGradientNorms = reinterpret_cast<ScalarField*>(additionalParameters[2]);
//number of points inside the current cell
unsigned n = cell.points->size();
//spherical neighborhood extraction structure
DgmOctree::NearestNeighboursSphericalSearchStruct nNSS;
nNSS.level = cell.level;
nNSS.prepare(radius,cell.parentOctree->getCellSize(nNSS.level));
cell.parentOctree->getCellPos(cell.truncatedCode,cell.level,nNSS.cellPos,true);
cell.parentOctree->computeCellCenter(nNSS.cellPos,cell.level,nNSS.cellCenter);
//we already know the points inside the current cell
{
try
{
nNSS.pointsInNeighbourhood.resize(n);
}
catch (.../*const std::bad_alloc&*/) //out of memory
{
return false;
}
DgmOctree::NeighboursSet::iterator it = nNSS.pointsInNeighbourhood.begin();
for (unsigned j=0; j<n; ++j,++it)
{
it->point = cell.points->getPointPersistentPtr(j);
it->pointIndex = cell.points->getPointGlobalIndex(j);
}
nNSS.alreadyVisitedNeighbourhoodSize = 1;
}
const GenericIndexedCloudPersist* cloud = cell.points->getAssociatedCloud();
for (unsigned i=0; i<n; ++i)
{
ScalarType gN = NAN_VALUE;
ScalarType d1 = cell.points->getPointScalarValue(i);
if (ScalarField::ValidValue(d1))
{
cell.points->getPoint(i,nNSS.queryPoint);
//we extract the point's neighbors
//warning: there may be more points at the end of nNSS.pointsInNeighbourhood than the actual nearest neighbors (k)!
unsigned k = cell.parentOctree->findNeighborsInASphereStartingFromCell(nNSS,radius,true);
//if more than one neighbour (the query point itself)
if (k > 1)
{
CCVector3d sum(0,0,0);
unsigned counter = 0;
//j = 1 because the first point is the query point itself --> contribution = 0
for (unsigned j=1; j<k; ++j)
{
ScalarType d2 = cloud->getPointScalarValue(nNSS.pointsInNeighbourhood[j].pointIndex);
if (ScalarField::ValidValue(d2))
{
CCVector3 u = *nNSS.pointsInNeighbourhood[j].point - nNSS.queryPoint;
double norm2 = u.norm2d();
if (norm2 > ZERO_TOLERANCE)
{
double dd = d2 - d1;
if (!euclideanDistances || dd*dd < 1.01 * norm2)
{
dd /= norm2;
sum.x += u.x * dd; //warning: and here 'dd'=dd/norm2 ;)
sum.y += u.y * dd;
sum.z += u.z * dd;
++counter;
}
}
}
}
if (counter != 0)
gN = static_cast<ScalarType>(sum.norm()/counter);
}
}
if (theGradientNorms)
//if "IN" and "OUT" SFs are the same
theGradientNorms->setValue(cell.points->getPointGlobalIndex(i),gN);
else
//if "IN" and "OUT" SFs are different
cell.points->setPointScalarValue(i,gN);
if (nProgress && !nProgress->oneStep())
return false;
}
return true;
}