C++ ReferenceCloud::getPoint方法代码示例

void ccAlignDlg::estimateDelta()
{
    unsigned i, nb;
    float meanDensity, meanSqrDensity, dev, value;
    ccProgressDialog pDlg(false,this);

    CCLib::ReferenceCloud *sampledData = getSampledData();
    //we have to work on a copy of the cloud in order to prevent the algorithms from modifying the original cloud.
    CCLib::ChunkedPointCloud* cloud = new CCLib::ChunkedPointCloud();
    cloud->reserve(sampledData->size());
    for(i=0; i<sampledData->size(); i++)
        cloud->addPoint(*sampledData->getPoint(i));
    cloud->enableScalarField();

    CCLib::GeometricalAnalysisTools::computeLocalDensity(cloud, &pDlg);
    nb = 0;
    meanDensity = 0.;
    meanSqrDensity = 0.;
    for(i=0; i<cloud->size(); i++)
    {
        value = cloud->getPointScalarValue(i);
        if(value > ZERO_TOLERANCE)
        {
            value = 1/value;
            meanDensity += value;
            meanSqrDensity += value*value;
            nb++;
        }
    }
    meanDensity /= (float)nb;
    meanSqrDensity /= (float)nb;
    dev = meanSqrDensity-(meanDensity*meanDensity);

    delta->setValue(meanDensity+dev);
    delete sampledData;
    delete cloud;
}

//.........这里部分代码省略.........
						for (std::list<Candidate>::iterator it = candidates.begin(); it !=candidates.end(); ++it)
							it->dist = (it->centroid-currentCentroid).norm2();

						//sort candidates by their distance
						candidates.sort(CandidateDistAscendingComparison);
					}
					
					//we will keep track of the best fused 'couple' at each pass
					std::list<Candidate>::iterator bestIt = candidates.end();
					CCLib::ReferenceCloud* bestFused = 0;
					CCVector3 bestNormal(0,0,0);
					double bestError = -1.0;

					unsigned skipCount = 0;
					for (std::list<Candidate>::iterator it = candidates.begin(); it != candidates.end(); /*++it*/)
					{
						assert(it->leaf && it->leaf->points);
						assert(currentPointSet->getAssociatedCloud() == it->leaf->points->getAssociatedCloud());

						//if the leaf orientation is too different
						if (fabs(CCVector3(it->leaf->planeEq).dot(currentNormal)) < c_minCosNormAngle)
						{
							it = candidates.erase(it);
							//++it;
							//++skipCount;
							continue;
						}

						//compute the minimum distance between the candidate centroid and the 'currentPointSet'
						PointCoordinateType minDistToMainSet = 0.0;
						{
							for (unsigned j=0; j<currentPointSet->size(); ++j)
							{
								const CCVector3* P = currentPointSet->getPoint(j);
								PointCoordinateType d2 = (*P-it->centroid).norm2();
								if (d2 < minDistToMainSet || j == 0)
									minDistToMainSet = d2;
							}
							minDistToMainSet = sqrt(minDistToMainSet);
						}
						
						//if the leaf is too far
						if (it->radius < minDistToMainSet / overlapCoef)
						{
							++it;
							++skipCount;
							continue;
						}

						//fuse the main set with the current candidate
						CCLib::ReferenceCloud* fused = new CCLib::ReferenceCloud(*currentPointSet);
						if (!fused->add(*(it->leaf->points)))
						{
							//not enough memory!
							ccLog::Warning("[ccKdTreeForFacetExtraction] Not enough memory!");
							delete fused;
							if (currentPointSet != currentCell->points)
								delete currentPointSet;
							return false;
						}

						//fit a plane and estimate the resulting error
						double error = -1.0;
						const PointCoordinateType* planeEquation = CCLib::Neighbourhood(fused).getLSPlane();
						if (planeEquation)
							error = CCLib::DistanceComputationTools::ComputeCloud2PlaneDistance(fused, planeEquation, errorMeasure);