C++ ReferenceCloud::size方法代码示例

bool ccKdTree::convertCellIndexToRandomColor()
{
	if (!m_associatedGenericCloud || !m_associatedGenericCloud->isA(CC_TYPES::POINT_CLOUD))
		return false;

	//get leaves
	std::vector<Leaf*> leaves;
	if (!getLeaves(leaves) || leaves.empty())
		return false;

	ccPointCloud* pc = static_cast<ccPointCloud*>(m_associatedGenericCloud);
	if (!pc->resizeTheRGBTable())
		return false;

	//for each cell
	for (size_t i=0; i<leaves.size(); ++i)
	{
		colorType col[3];
		ccColor::Generator::Random(col);
		CCLib::ReferenceCloud* subset = leaves[i]->points;
		if (subset)
		{
			for (unsigned j=0; j<subset->size(); ++j)
				pc->setPointColor(subset->getPointGlobalIndex(j),col);
		}
	}

	pc->showColors(true);

	return true;
}

void ccAlignDlg::dataSamplingRateChanged(double value)
{
    QString message("An error occured");

    CC_SAMPLING_METHOD method = getSamplingMethod();
    float rate = (float)dataSamplingRate->value()/(float)dataSamplingRate->maximum();
    if(method == SPACE)
        rate = 1.0f-rate;
    dataSample->setSliderPosition((unsigned)((float)dataSample->maximum()*rate));

    switch(method)
    {
        case SPACE:
			{
				CCLib::ReferenceCloud* tmpCloud = getSampledData(); //DGM FIXME: wow! you generate a spatially sampled cloud just to display its size?!
				if (tmpCloud)
				{
					message = QString("distance units (%1 remaining points)").arg(tmpCloud->size());
					delete tmpCloud;
				}
			}
            break;
        case RANDOM:
			{
				message = QString("remaining points (%1%)").arg(rate*100.0f,0,'f',1);
			}
            break;
        case OCTREE:
			{
				CCLib::ReferenceCloud* tmpCloud = getSampledData();  //DGM FIXME: wow! you generate a spatially sampled cloud just to display its size?!
				if (tmpCloud)
				{
					message = QString("%1 remaining points").arg(tmpCloud->size());
					delete tmpCloud;
				}
			}
            break;
        default:
			{
				unsigned remaining = (unsigned)(rate * (float)dataObject->size());
				message = QString("%1 remaining points").arg(remaining);
			}
            break;
    }
    dataRemaining->setText(message);
}

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;
}

int ccFastMarchingForNormsDirection::updateResolvedTable(ccGenericPointCloud* theCloud,
                                                            GenericChunkedArray<1,uchar> &resolved,
                                                            NormsIndexesTableType* theNorms)
{
	if (!initialized)
		return -1;

	int count=0;
	for (unsigned i=0;i<activeCells.size();++i)
	{
		DirectionCell* aCell = (DirectionCell*)theGrid[activeCells[i]];
		CCLib::ReferenceCloud* Yk = theOctree->getPointsInCell(aCell->cellCode,gridLevel,true);
		if (!Yk)
			continue;

		Yk->placeIteratorAtBegining();
		
		for (unsigned k=0;k<Yk->size();++k)
		{
			unsigned index = Yk->getCurrentPointGlobalIndex();
			resolved.setValue(index,1); //resolvedValue=1

			const normsType& norm = theNorms->getValue(index);
			if (CCVector3::vdot(ccNormalVectors::GetNormal(norm),aCell->N)<0.0)
			{
				PointCoordinateType newN[3];
				const PointCoordinateType* N = ccNormalVectors::GetNormal(norm);
				newN[0]=-N[0];
				newN[1]=-N[1];
				newN[2]=-N[2];
				theNorms->setValue(index,ccNormalVectors::GetNormIndex(newN));
			}

			//norm = NormalVectors::getNormIndex(aCell->N);
			//theNorms->setValue(index,&norm);

			theCloud->setPointScalarValue(index,aCell->T);
			//theCloud->setPointScalarValue(index,aCell->v);
			Yk->forwardIterator();
			++count;
		}
	}

	return count;
}

bool ccKdTree::convertCellIndexToSF()
{
	if (!m_associatedGenericCloud || !m_associatedGenericCloud->isA(CC_TYPES::POINT_CLOUD))
		return false;

	//get leaves
	std::vector<Leaf*> leaves;
	if (!getLeaves(leaves) || leaves.empty())
		return false;

	ccPointCloud* pc = static_cast<ccPointCloud*>(m_associatedGenericCloud);

	const char c_defaultSFName[] = "Kd-tree indexes";
	int sfIdx = pc->getScalarFieldIndexByName(c_defaultSFName);
	if (sfIdx < 0)
		sfIdx = pc->addScalarField(c_defaultSFName);
	if (sfIdx < 0)
	{
		ccLog::Error("Not enough memory!");
		return false;
	}
	pc->setCurrentScalarField(sfIdx);

	//for each cell
	for (size_t i=0; i<leaves.size(); ++i)
	{
		CCLib::ReferenceCloud* subset = leaves[i]->points;
		if (subset)
		{
			for (unsigned j=0; j<subset->size(); ++j)
				subset->setPointScalarValue(j,(ScalarType)i);
		}
	}

	pc->getScalarField(sfIdx)->computeMinAndMax();
	pc->setCurrentDisplayedScalarField(sfIdx);
	pc->showSF(true);

	return true;
}

bool ccPointPairRegistrationDlg::convertToSphereCenter(CCVector3d& P, ccHObject* entity, PointCoordinateType& sphereRadius)
{
	sphereRadius = -PC_ONE;
	if (	!entity
		||	!useSphereToolButton->isChecked()
		||	!entity->isKindOf(CC_TYPES::POINT_CLOUD) ) //only works with cloud right now
	{
		//nothing to do
		return true;
	}

	//we'll now try to detect the sphere
	double searchRadius = radiusDoubleSpinBox->value();
	double maxRMSPercentage = maxRmsSpinBox->value() / 100.0;
	ccGenericPointCloud* cloud = static_cast<ccGenericPointCloud*>(entity);
	assert(cloud);

	//crop points inside a box centered on the current point
	ccBBox box;
	box.add(CCVector3::fromArray((P - CCVector3d(1,1,1)*searchRadius).u));
	box.add(CCVector3::fromArray((P + CCVector3d(1,1,1)*searchRadius).u));
	CCLib::ReferenceCloud* part = cloud->crop(box,true);

	bool success = false;
	if (part && part->size() > 16)
	{
		PointCoordinateType radius;
		CCVector3 C;
		double rms;
		ccProgressDialog pDlg(true, this);
		//first roughly search for the sphere
		if (CCLib::GeometricalAnalysisTools::detectSphereRobust(part,0.5,C,radius,rms,&pDlg,0.9))
		{
			if (radius / searchRadius < 0.5 || radius / searchRadius > 2.0)
			{
				ccLog::Warning(QString("[ccPointPairRegistrationDlg] Detected sphere radius (%1) is too far from search radius!").arg(radius));
			}
			else
			{
				//now look again (more precisely)
				{
					delete part;
					box.clear();
					box.add(C - CCVector3(1,1,1)*radius*static_cast<PointCoordinateType>(1.05)); //add 5%
					box.add(C + CCVector3(1,1,1)*radius*static_cast<PointCoordinateType>(1.05)); //add 5%
					part = cloud->crop(box,true);
					if (part && part->size() > 16)
						CCLib::GeometricalAnalysisTools::detectSphereRobust(part,0.5,C,radius,rms,&pDlg,0.99);
				}
				ccLog::Print(QString("[ccPointPairRegistrationDlg] Detected sphere radius = %1 (rms = %2)").arg(radius).arg(rms));
				if (radius / searchRadius < 0.5 || radius / searchRadius > 2.0)
				{
					ccLog::Warning("[ccPointPairRegistrationDlg] Sphere radius is too far from search radius!");
				}
				else if (rms / searchRadius >= maxRMSPercentage)
				{
					ccLog::Warning("[ccPointPairRegistrationDlg] RMS is too high!");
				}
				else
				{
					sphereRadius = radius;
					P = CCVector3d::fromArray(C.u);
					success = true;
				}
			}
		}
		else
		{
			ccLog::Warning("[ccPointPairRegistrationDlg] Failed to fit a sphere around the picked point!");
		}
	}
	else
	{
		//not enough memory? No points inside the 
		ccLog::Warning("[ccPointPairRegistrationDlg] Failed to crop points around the picked point?!");
	}

	if (part)
		delete part;

	return success;
}

//.........这里部分代码省略.........
		oldDataSfIdx = pc->getCurrentInScalarFieldIndex();
		dataSfIdx = pc->getScalarFieldIndexByName(REGISTRATION_DISTS_SF);
		if (dataSfIdx < 0)
			dataSfIdx = pc->addScalarField(REGISTRATION_DISTS_SF);
		if (dataSfIdx >= 0)
			pc->setCurrentScalarField(dataSfIdx);
		else
		{
			ccLog::Error("[ICP] Couldn't create temporary scalar field! Not enough memory?");
			return false;
		}
	}
	else
	{
		if (!dataCloud->enableScalarField())
		{
			ccLog::Error("[ICP] Couldn't create temporary scalar field! Not enough memory?");
			return false;
		}
	}

	//add a 'safety' margin to input ratio
	static double s_overlapMarginRatio = 0.2;
	finalOverlapRatio = std::max(finalOverlapRatio, 0.01); //1% minimum
	//do we need to reduce the input point cloud (so as to be close
	//to the theoretical number of overlapping points - but not too
	//low so as we are not registered yet ;)
	if (finalOverlapRatio < 1.0 - s_overlapMarginRatio)
	{
		//DGM we can now use 'approximate' distances as SAITO algorithm is exact (but with a coarse resolution)
		//level = 7 if < 1.000.000
		//level = 8 if < 10.000.000
		//level = 9 if > 10.000.000
		int gridLevel = static_cast<int>(floor(log10(static_cast<double>(std::max(dataCloud->size(), modelCloud->size()))))) + 2;
		    gridLevel = std::min(std::max(gridLevel, 7), 9);
		int result = -1;
		if (modelMesh)
		{
			CCLib::DistanceComputationTools::Cloud2MeshDistanceComputationParams c2mParams;
			c2mParams.octreeLevel = gridLevel;
			c2mParams.maxSearchDist = 0;
			c2mParams.useDistanceMap = true;
			c2mParams.signedDistances = false;
			c2mParams.flipNormals = false;
			c2mParams.multiThread = false;
			result = CCLib::DistanceComputationTools::computeCloud2MeshDistance(dataCloud, modelMesh, c2mParams, progressDlg.data());
		}
		else
		{
			result = CCLib::DistanceComputationTools::computeApproxCloud2CloudDistance(	dataCloud,
																						modelCloud,
																						gridLevel,
																						-1,
																						progressDlg.data());
		}

		if (result < 0)
		{
			ccLog::Error("Failed to determine the max (overlap) distance (not enough memory?)");
			return false;
		}

		//determine the max distance that (roughly) corresponds to the input overlap ratio
		ScalarType maxSearchDist = 0;
		{
			unsigned count = dataCloud->size();

void qHPR::doAction()
{
	assert(m_app);
	if (!m_app)
		return;

	const ccHObject::Container& selectedEntities = m_app->getSelectedEntities();
	size_t selNum = selectedEntities.size();
	if (	selNum != 1
		||	!selectedEntities.front()->isA(CC_TYPES::POINT_CLOUD))
	{
		m_app->dispToConsole("Select only one cloud!",ccMainAppInterface::ERR_CONSOLE_MESSAGE);
		return;
	}

	ccPointCloud* cloud = static_cast<ccPointCloud*>(selectedEntities[0]);

	ccGLWindow* win = m_app->getActiveGLWindow();
	if (!win)
	{
		m_app->dispToConsole("No active window!",ccMainAppInterface::ERR_CONSOLE_MESSAGE);
		return;
	}

	//display parameters
	const ccViewportParameters& params =  win->getViewportParameters();
	if (!params.perspectiveView)
	{
		m_app->dispToConsole("Perspective mode only!",ccMainAppInterface::ERR_CONSOLE_MESSAGE);
		return;
	}

	ccHprDlg dlg(m_app->getMainWindow());
	if (!dlg.exec())
		return;

	//progress dialog
	ccProgressDialog progressCb(false,m_app->getMainWindow());

	//unique parameter: the octree subdivision level
	int octreeLevel = dlg.octreeLevelSpinBox->value();
	assert(octreeLevel >= 0 && octreeLevel <= CCLib::DgmOctree::MAX_OCTREE_LEVEL);

	//compute octree if cloud hasn't any
	ccOctree::Shared theOctree = cloud->getOctree();
	if (!theOctree)
	{
		theOctree = cloud->computeOctree(&progressCb);
		if (theOctree && cloud->getParent())
		{
			m_app->addToDB(cloud->getOctreeProxy());
		}
	}

	if (!theOctree)
	{
		m_app->dispToConsole("Couldn't compute octree!",ccMainAppInterface::ERR_CONSOLE_MESSAGE);
		return;
	}

	CCVector3d viewPoint = params.cameraCenter;
	if (params.objectCenteredView)
	{
		CCVector3d PC = params.cameraCenter - params.pivotPoint;
		params.viewMat.inverse().apply(PC);
		viewPoint = params.pivotPoint + PC;
	}

	//HPR
	CCLib::ReferenceCloud* visibleCells = 0;
	{
		QElapsedTimer eTimer;
		eTimer.start();

		CCLib::ReferenceCloud* theCellCenters = CCLib::CloudSamplingTools::subsampleCloudWithOctreeAtLevel(	cloud,
																											static_cast<unsigned char>(octreeLevel),
																											CCLib::CloudSamplingTools::NEAREST_POINT_TO_CELL_CENTER,
																											&progressCb,
																											theOctree.data());
		if (!theCellCenters)
		{
			m_app->dispToConsole("Error while simplifying point cloud with octree!",ccMainAppInterface::ERR_CONSOLE_MESSAGE);
			return;
		}

		visibleCells = removeHiddenPoints(theCellCenters,viewPoint,3.5);
	
		m_app->dispToConsole(QString("[HPR] Cells: %1 - Time: %2 s").arg(theCellCenters->size()).arg(eTimer.elapsed()/1.0e3));

		//warning: after this, visibleCells can't be used anymore as a
		//normal cloud (as it's 'associated cloud' has been deleted).
		//Only its indexes are valid! (they are corresponding to octree cells)
		delete theCellCenters;
		theCellCenters = 0;
	}

	if (visibleCells)
	{
		//DGM: we generate a new cloud now, instead of playing with the points visiblity! (too confusing for the user)
		/*if (!cloud->isVisibilityTableInstantiated() && !cloud->resetVisibilityArray())
//.........这里部分代码省略.........

bool ccKdTreeForFacetExtraction::FuseCells(	ccKdTree* kdTree,
											double maxError,
											CCLib::DistanceComputationTools::ERROR_MEASURES errorMeasure,
											double maxAngle_deg,
											PointCoordinateType overlapCoef/*=1*/,
											bool closestFirst/*=true*/,
											CCLib::GenericProgressCallback* progressCb/*=0*/)
{
	if (!kdTree)
		return false;

	ccGenericPointCloud* associatedGenericCloud = kdTree->associatedGenericCloud();
	if (!associatedGenericCloud || !associatedGenericCloud->isA(CC_TYPES::POINT_CLOUD) || maxError < 0.0)
		return false;

	//get leaves
	std::vector<ccKdTree::Leaf*> leaves;
	if (!kdTree->getLeaves(leaves) || leaves.empty())
		return false;

	//progress notification
	CCLib::NormalizedProgress nProgress(progressCb, static_cast<unsigned>(leaves.size()));
	if (progressCb)
	{
		progressCb->update(0);
		if (progressCb->textCanBeEdited())
		{
			progressCb->setMethodTitle("Fuse Kd-tree cells");
			progressCb->setInfo(qPrintable(QString("Cells: %1\nMax error: %2").arg(leaves.size()).arg(maxError)));
		}
		progressCb->start();
	}

	ccPointCloud* pc = static_cast<ccPointCloud*>(associatedGenericCloud);

	//sort cells based on their population size (we start by the biggest ones)
	SortAlgo(leaves.begin(), leaves.end(), DescendingLeafSizeComparison);

	//set all 'userData' to -1 (i.e. unfused cells)
	{
		for (size_t i=0; i<leaves.size(); ++i)
		{
			leaves[i]->userData = -1;
			//check by the way that the plane normal is unit!
			assert(static_cast<double>(fabs(CCVector3(leaves[i]->planeEq).norm2()) - 1.0) < 1.0e-6);
		}
	}

	// cosine of the max angle between fused 'planes'
	const double c_minCosNormAngle = cos(maxAngle_deg * CC_DEG_TO_RAD);

	//fuse all cells, starting from the ones with the best error
	const int unvisitedNeighborValue = -1;
	bool cancelled = false;
	int macroIndex = 1; //starts at 1 (0 is reserved for cells already above the max error)
	{
		for (size_t i=0; i<leaves.size(); ++i)
		{
			ccKdTree::Leaf* currentCell = leaves[i];
			if (currentCell->error >= maxError)
				currentCell->userData = 0; //0 = special group for cells already above the user defined threshold!

			//already fused?
			if (currentCell->userData != -1)
			{
				if (progressCb && !nProgress.oneStep()) //process canceled by user
				{
					cancelled = true;
					break;
				}
				continue;
			}

			//we create a new "macro cell" index
			currentCell->userData = macroIndex++;

			//we init the current set of 'fused' points with the cell's points
			CCLib::ReferenceCloud* currentPointSet = currentCell->points;
			//get current fused set centroid and normal
			CCVector3 currentCentroid = *CCLib::Neighbourhood(currentPointSet).getGravityCenter();
			CCVector3 currentNormal(currentCell->planeEq);

			//visited neighbors
			ccKdTree::LeafSet visitedNeighbors;
			//set of candidates
			std::list<Candidate> candidates;

			//we are going to iteratively look for neighbor cells that could be fused to this one
			ccKdTree::LeafVector cellsToTest;
			cellsToTest.push_back(currentCell);

			if (progressCb && !nProgress.oneStep()) //process canceled by user
			{
				cancelled = true;
				break;
			}

			while (!cellsToTest.empty() || !candidates.empty())
			{
				//get all neighbors around the 'waiting' cell(s)
//.........这里部分代码省略.........

ccHObject* qFacets::createFacets(	ccPointCloud* cloud,
								CCLib::ReferenceCloudContainer& components,
								unsigned minPointsPerComponent,
								double maxEdgeLength,
								bool randomColors,
								bool& error)
{
	if (!cloud)
		return 0;

	//we create a new group to store all input CCs as 'facets'
	ccHObject* ccGroup = new ccHObject(cloud->getName()+QString(" [facets]"));
	ccGroup->setDisplay(cloud->getDisplay());
	ccGroup->setVisible(true);

	bool cloudHasNormal = cloud->hasNormals();

	//number of input components
	size_t componentCount = components.size();

	//progress notification
	ccProgressDialog pDlg(true,m_app->getMainWindow());
	pDlg.setMethodTitle("Facets creation");
	pDlg.setInfo(qPrintable(QString("Components: %1").arg(componentCount)));
	pDlg.setMaximum(static_cast<int>(componentCount));
	pDlg.show();
	QApplication::processEvents();

	//for each component
	error = false;
	while (!components.empty())
	{
		CCLib::ReferenceCloud* compIndexes = components.back();
		components.pop_back();

		//if it has enough points
		if (compIndexes && compIndexes->size() >= minPointsPerComponent)
		{
			ccPointCloud* facetCloud = cloud->partialClone(compIndexes);
			if (!facetCloud)
			{
				//not enough  memory!
				error = true;
				delete facetCloud;
				facetCloud = 0;
			}
			else
			{
				ccFacet* facet = ccFacet::Create(facetCloud,static_cast<PointCoordinateType>(maxEdgeLength),true);
				if (facet)
				{
					QString facetName = QString("facet %1 (rms=%2)").arg(ccGroup->getChildrenNumber()).arg(facet->getRMS());
					facet->setName(facetName);
					if (facet->getPolygon())
					{
						facet->getPolygon()->enableStippling(false);
						facet->getPolygon()->showNormals(false);
					}
					if (facet->getContour())
					{
						facet->getContour()->setGlobalScale(facetCloud->getGlobalScale());
						facet->getContour()->setGlobalShift(facetCloud->getGlobalShift());
					}

					//check the facet normal sign
					if (cloudHasNormal)
					{
						CCVector3 N = ccOctree::ComputeAverageNorm(compIndexes,cloud);

						if (N.dot(facet->getNormal()) < 0)
							facet->invertNormal();
					}

#ifdef _DEBUG
					facet->showNormalVector(true);
#endif

					//shall we colorize it with a random color?
					ccColor::Rgb col, darkCol;
					if (randomColors)
					{
						col = ccColor::Generator::Random();
						assert(c_darkColorRatio <= 1.0);
						darkCol.r = static_cast<ColorCompType>(static_cast<double>(col.r) * c_darkColorRatio);
						darkCol.g = static_cast<ColorCompType>(static_cast<double>(col.g) * c_darkColorRatio);
						darkCol.b = static_cast<ColorCompType>(static_cast<double>(col.b) * c_darkColorRatio);
					}
					else
					{
						//use normal-based HSV coloring
						CCVector3 N = facet->getNormal();
						PointCoordinateType dip, dipDir;
						ccNormalVectors::ConvertNormalToDipAndDipDir(N, dip, dipDir);
						FacetsClassifier::GenerateSubfamilyColor(col,dip,dipDir,0,1,&darkCol);
					}
					facet->setColor(col);
					if (facet->getContour())
					{
						facet->getContour()->setColor(darkCol);
						facet->getContour()->setWidth(2);
//.........这里部分代码省略.........