[CC]手动点云分割
CloudCompare中手动点云分割功能ccGraphicalSegmentationTool,
点击应用按钮后将现有的点云分成segmented和remaining两个点云,
//停用点云分割功能
void MainWindow::deactivateSegmentationMode(bool state)
是通过ccPointCloud的可视选择集来实现的。其中用到了点云的swap需要参考!
//创建新的点云,可视的选择集
ccGenericPointCloud* ccPointCloud::createNewCloudFromVisibilitySelection(bool removeSelectedPoints)
{
if (!isVisibilityTableInstantiated())
{
ccLog::Error(QString("[Cloud %1] Visibility table not instantiated!").arg(getName()));
return 0;
} //we create a new cloud with the "visible" points
ccPointCloud* result = 0;
{
//we create a temporary entity with the visible points only
CCLib::ReferenceCloud* rc = getTheVisiblePoints();
if (!rc)
{
//a warning message has already been issued by getTheVisiblePoints!
//ccLog::Warning("[ccPointCloud::createNewCloudFromVisibilitySelection] An error occurred during points selection!");
return 0;
}
assert(rc->size() != 0); //convert selection to cloud
result = partialClone(rc); //don't need this one anymore
delete rc;
rc = 0;
} if (!result)
{
ccLog::Warning("[ccPointCloud::createNewCloudFromVisibilitySelection] An error occurred during segmentation!");
return 0;
} result->setName(getName()+QString(".segmented"));//切割出来的点云 //shall the visible points be erased from this cloud?
if (removeSelectedPoints && !isLocked())
{
//we drop the octree before modifying this cloud's contents
deleteOctree();
clearLOD(); unsigned count = size(); //we have to take care of scan grids first
{
//we need a map between old and new indexes
std::vector<int> newIndexMap(size(), -1);
{
unsigned newIndex = 0;
for (unsigned i=0; i<count; ++i)
{
if (m_pointsVisibility->getValue(i) != POINT_VISIBLE)
newIndexMap[i] = newIndex++;
}
} //then update the indexes
UpdateGridIndexes(newIndexMap, m_grids); //and reset the invalid (empty) ones
//(DGM: we don't erase them as they may still be useful?)
for (size_t i=0; i<m_grids.size(); ++i)
{
Grid::Shared& scanGrid = m_grids[i];
if (scanGrid->validCount == 0)
{
scanGrid->indexes.clear();
}
}
} //we remove all visible points
unsigned lastPoint = 0;
for (unsigned i=0; i<count; ++i)
{
//i持续增长,而lastPoint遇到==POINT_VISIBLE则跳过,起到迁移的效果
if (m_pointsVisibility->getValue(i) != POINT_VISIBLE)
{
if (i != lastPoint)
swapPoints(lastPoint,i);
++lastPoint;
}
} //TODO: handle associated meshes resize(lastPoint); refreshBB(); //calls notifyGeometryUpdate + releaseVBOs
} return result;
}
调用的方法getTheVisiblePoints()
CCLib::ReferenceCloud* ccGenericPointCloud::getTheVisiblePoints() const
{
unsigned count = size();
assert(count == m_pointsVisibility->currentSize()); if (!m_pointsVisibility || m_pointsVisibility->currentSize() != count)
{
ccLog::Warning("[ccGenericPointCloud::getTheVisiblePoints] No visibility table instantiated!");
return 0;
} //count the number of points to copy
unsigned pointCount = 0;
{
for (unsigned i=0; i<count; ++i)
if (m_pointsVisibility->getValue(i) == POINT_VISIBLE)
++pointCount;
} if (pointCount == 0)
{
ccLog::Warning("[ccGenericPointCloud::getTheVisiblePoints] No point in selection");
return 0;
} //we create an entity with the 'visible' vertices only
CCLib::ReferenceCloud* rc = new CCLib::ReferenceCloud(const_cast<ccGenericPointCloud*>(this));
if (rc->reserve(pointCount))
{
for (unsigned i=0; i<count; ++i)
if (m_pointsVisibility->getValue(i) == POINT_VISIBLE)
rc->addPointIndex(i); //can't fail (see above)
}
else
{
delete rc;
rc = 0;
ccLog::Error("[ccGenericPointCloud::getTheVisiblePoints] Not enough memory!");
} return rc;
}
[CC]手动点云分割的更多相关文章
- 基于传统方法点云分割以及PCL中分割模块
之前在微信公众号中更新了以下几个章节 1,如何学习PCL以及一些基础的知识 2,PCL中IO口以及common模块的介绍 3,PCL中常用的两种数据结构KDtree以及Octree树的介绍 ...
- PCL—低层次视觉—点云分割(基于凹凸性)
1.图像分割的两条思路 场景分割时机器视觉中的重要任务,尤其对家庭机器人而言,优秀的场景分割算法是实现复杂功能的基础.但是大家搞了几十年也还没搞定——不是我说的,是接下来要介绍的这篇论文说的.图像分割 ...
- PCL点云分割(1)
点云分割是根据空间,几何和纹理等特征对点云进行划分,使得同一划分内的点云拥有相似的特征,点云的有效分割往往是许多应用的前提,例如逆向工作,CAD领域对零件的不同扫描表面进行分割,然后才能更好的进行空洞 ...
- PCL—点云分割(基于凹凸性) 低层次点云处理
博客转载自:http://www.cnblogs.com/ironstark/p/5027269.html 1.图像分割的两条思路 场景分割时机器视觉中的重要任务,尤其对家庭机器人而言,优秀的场景分割 ...
- PCL—低层次视觉—点云分割(基于形态学)
1.航空测量与点云的形态学 航空测量是对地形地貌进行测量的一种高效手段.生成地形三维形貌一直是地球学,测量学的研究重点.但对于城市,森林,等独特地形来说,航空测量会受到影响.因为土地表面的树,地面上的 ...
- PCL—低层次视觉—点云分割(超体聚类)
1.超体聚类——一种来自图像的分割方法 超体(supervoxel)是一种集合,集合的元素是“体”.与体素滤波器中的体类似,其本质是一个个的小方块.与之前提到的所有分割手段不同,超体聚类的目的并不是分 ...
- PCL—低层次视觉—点云分割(最小割算法)
1.点云分割的精度 在之前的两个章节里介绍了基于采样一致的点云分割和基于临近搜索的点云分割算法.基于采样一致的点云分割算法显然是意识流的,它只能割出大概的点云(可能是杯子的一部分,但杯把儿肯定没分割出 ...
- PCL—低层次视觉—点云分割(RanSaC)
点云分割 点云分割可谓点云处理的精髓,也是三维图像相对二维图像最大优势的体现.不过多插一句,自Niloy J Mitra教授的Global contrast based salient region ...
- segMatch:基于3D点云分割的回环检测
该论文的地址是:https://arxiv.org/pdf/1609.07720.pdf segmatch是一个提供车辆的回环检测的技术,使用提取和匹配分割的三维激光点云技术.分割的例子可以在下面的图 ...
随机推荐
- 2.goldengate日常维护命令(转载)
goldengate日常维护命令 发表于 2013 年 7 月 4 日 由 Asysdba 1.查看进程状态 GGSCI (PONY) 2> info all 2.查看进程详细状态,有助于排错 ...
- (一)洞悉linux下的Netfilter&iptables:什么是Netfilter?
转自:http://blog.chinaunix.net/uid-23069658-id-3160506.html 本人研究linux的防火墙系统也有一段时间了,由于近来涉及到的工作比较纷杂,久而久之 ...
- LeetCode——Single Number II(找出数组中只出现一次的数2)
问题: Given an array of integers, every element appears three times except for one. Find that single o ...
- AlertDialog对话框简单案例
什么是Dialog? Dialog类,是一切对话框的基类,需要注意的是,Dialog类虽然可以在界面上显示,但是并非继承于View类,而是直接从java.lang.Object开始构造出的.类似于Ac ...
- CentOS 6.5 下安装 Redis 2.8.7
wget http://download.redis.io/redis-stable.tar.gz tar xvzf redis-stable.tar.gz cd redis-stable make ...
- 自己写了一个无缝滚动的插件(jQuery)
效果图: html代码: 1 <h1>无缝滚动,向右滚动</h1> 2 <ul id="guoul1"> 3 <li><img ...
- 洛谷 P2735 电网 Electric Fences Label:计算几何--皮克定理
题目描述 在本题中,格点是指横纵坐标皆为整数的点. 为了圈养他的牛,农夫约翰(Farmer John)建造了一个三角形的电网.他从原点(0,0)牵出一根通电的电线,连接格点(n,m)(0<=n& ...
- Using MySQL Connector .NET 6.6.4 with Entity Framework 5
I had been waiting for the latest MySQL connector for .NET to come out so I can move on to the new a ...
- 【BZOJ】3542: DZY Loves March
题意 \(m * m\)的网格,有\(n\)个点.\(t\)个询问:操作一:第\(x\)个点向四个方向移动了\(d\)个单位.操作二:询问同行同列其他点到这个点的曼哈顿距离和.强制在线.(\(n \l ...
- *HDU3339 最短路+01背包
In Action Time Limit: 2000/1000 MS (Java/Others) Memory Limit: 32768/32768 K (Java/Others)Total S ...