本文整理汇总了C++中Octree::getNodes方法的典型用法代码示例。如果您正苦于以下问题：C++ Octree::getNodes方法的具体用法？C++ Octree::getNodes怎么用？C++ Octree::getNodes使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Octree的用法示例。
在下文中一共展示了Octree::getNodes方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1:
sure::Scalar sure::keypoints::calculateEntropyWithCrossproductsPairwise(const Octree& octree, Node* node, Scalar normalSamplingrate, Scalar radius, Scalar influenceRadius)
{
sure::normal::CrossProductHistogram histogram;
histogram.setInfluenceRadius(influenceRadius);
NodeVector nodes = octree.getNodes(node->fixed().getMeanPosition(), radius, normalSamplingrate);
for(unsigned int i=0; i<nodes.size(); ++i)
{
Node* firstNode = nodes[i];
NormalPayload* firstPayload = static_cast<NormalPayload*>(firstNode->opt());
const Normal& firstNormal = firstPayload->normal_;
if( !firstNormal.isStable() )
{
continue;
}
for(unsigned int j=i+1; j<nodes.size(); ++j)
{
Node* secondNode = nodes[j];
CrossProductPayload* secondPayload = static_cast<CrossProductPayload*>(secondNode->opt());
const Normal& secondNormal = secondPayload->normal_;
if( secondNormal.isStable() )
{
histogram.insertCrossProduct(firstNormal.vector(), secondNormal.vector());
}
}
return histogram.calculateEntropy();
}
return 0.0;
}示例2: mean
sure::Scalar sure::keypoints::calculateCornerness(const Octree& octree, Node* node, Scalar radius)
{
Octree::NodeVector vec;
vec = octree.getNodes(node, octree.getUnitSize(radius));
Vector3 mean(Vector3::Zero());
Scalar weight(0.0);
for(unsigned int i=0; i<vec.size(); ++i)
{
Node* currNode = vec[i];
sure::payload::EntropyPayload* payload = static_cast<EntropyPayload*>(currNode->opt());
if( payload->entropy_ > 0.0 )
{
mean += (payload->entropy_ * currNode->fixed().getMeanPosition());
// mean[0] += (payload->entropy_ * currNode->fixed().getMeanPosition()[0]);
// mean[1] += (payload->entropy_ * currNode->fixed().getMeanPosition()[1]);
// mean[2] += (payload->entropy_ * currNode->fixed().getMeanPosition()[2]);
weight += payload->entropy_;
}
}
if( weight > 0.0 )
{
mean /= weight;
}
else
{
return 0.f;
}
Matrix3 covariance(Matrix3::Zero());
for(unsigned int i=0; i<vec.size(); ++i)
{
Node* currNode = vec[i];
EntropyPayload* payload = static_cast<EntropyPayload*>(currNode->opt());
if( payload->entropy_ > 0.0 )
{
// Vector3 d;
// d[0] = mean[0] - currNode->fixed().getMeanPosition()[0];
// d[1] = mean[1] - currNode->fixed().getMeanPosition()[1];
// d[2] = mean[2] - currNode->fixed().getMeanPosition()[2];
covariance += payload->entropy_ * ( (mean - currNode->fixed().getMeanPosition()) * ((mean - currNode->fixed().getMeanPosition()).transpose()) );
// covariance += payload->entropy_ * ( d * d.transpose() );
}
}
covariance /= weight;
Vector3 eigenValues;
pcl::eigen33(covariance, eigenValues);
return (Scalar) (eigenValues[0] / eigenValues[2]);
}示例3: keypoints
unsigned sure::keypoints::extractKeypoints(Octree& octree, Scalar samplingrate, Scalar searchRadius, Scalar featureRadius, std::vector<Feature>& features, std::vector<Node*>& keypointNodes)
{
unsigned samplingDepth = octree.getDepth(samplingrate);
unsigned keypoints(0);
for(unsigned int i=0; i<octree[samplingDepth].size(); ++i)
{
Node* currNode = octree[samplingDepth][i];
EntropyPayload* payload = static_cast<EntropyPayload*>(currNode->opt());
if( payload->flag_ != POSSIBLE )
{
continue;
}
NodeVector neighbors = octree.getNodes(currNode, octree.getUnitSize(searchRadius));
for(unsigned int j=0; j<neighbors.size(); ++j)
{
Node* currNeighbor = neighbors[j];
EntropyPayload* neighborPayload = static_cast<EntropyPayload*>(currNeighbor->opt());
if( neighborPayload->flag_ == IS_MAXIMUM )
{
payload->flag_ = SUPPRESSED;
break;
}
else if( neighborPayload->flag_ == POSSIBLE )
{
if( payload->entropy_ < neighborPayload->entropy_ )
{
payload->flag_ = SUPPRESSED;
break;
}
}
else
{
continue;
}
}
if( payload->flag_ == POSSIBLE )
{
payload->flag_ = IS_MAXIMUM;
sure::feature::Feature f;
f.radius() = featureRadius;
f.position() = currNode->fixed().getMeanPosition();
features.push_back(f);
keypointNodes.push_back(currNode);
keypoints++;
}
}
return keypoints;
}示例4: summedMean
void sure::keypoints::improveLocalization(const Octree& octree, Scalar samplingrate, Scalar radius, Vector3& position)
{
for(unsigned iteration=0; iteration<NUMBER_OF_MEAN_SHIFT_ITERATIONS; ++iteration)
{
NodeVector neighbors = octree.getNodes(position, radius, samplingrate);
Scalar summedMean(0.0);
int count(0);
for(unsigned int n=0; n<neighbors.size(); ++n)
{
Node* neighbor = neighbors[n];
EntropyPayload* neighborPayload = static_cast<EntropyPayload*>(neighbor->opt());
if( neighbor->fixed().getPointFlag() != ARTIFICIAL )
{
summedMean += neighborPayload->entropy_;
count++;
}
}
if( count == 0 )
{
break;
}
Scalar mean = summedMean / (Scalar) count;
Scalar summedVariance(0.0);
for(unsigned int n=0; n<neighbors.size(); ++n)
{
Node* neighbor = neighbors[n];
EntropyPayload* neighborPayload = static_cast<EntropyPayload*>(neighbor->opt());
if( neighbor->fixed().getPointFlag() != ARTIFICIAL )
{
summedVariance += (neighborPayload->entropy_ - mean) * (neighborPayload->entropy_ - mean);
}
}
Scalar variance = summedVariance / (Scalar) count;
Vector3 shiftedPosition(0.0, 0.0, 0.0);
Scalar summedShift(0.0);
for(unsigned int n=0; n<neighbors.size(); ++n)
{
Node* neighbor = neighbors[n];
EntropyPayload* neighborPayload = static_cast<EntropyPayload*>(neighbor->opt());
if( neighborPayload->entropy_ > mean )
{
Scalar entropyDifference = mean - neighborPayload->entropy_;
Scalar kernelElements = (entropyDifference*entropyDifference) / (variance);
shiftedPosition += neighbor->fixed().getMeanPosition() * exp(-0.5*kernelElements);
summedShift += exp(-0.5*kernelElements);
}
}
if( summedShift != 0 )
{
shiftedPosition = shiftedPosition * (1.f / summedShift);
}
if( sure::eigen_is_finite(shiftedPosition) )
{
// Scalar dist = (position - shiftedPosition).norm();
position = shiftedPosition;
}
}
}