OpenCV 3d点云

data.push\_back(m);
}

//这样之后data[i].cloud就代表一个点云，共六个
 //批量存储点云
 for (int i = 0; i < numberOfViews; ++i)
 {
 std::string fname = prefix + num2str(i) + “_rotate” + extension;
 pcl::io::savePLYFile(fname, *data[i].cloudWithNormal, true);
 }return 0;}
还可以批量进行其他操作，比如离群点滤波//-----------------------去除离群点------------------------
std::vector<PCD, Eigen::aligned_allocator<PCD> > data1; //这里新定义了一个算是数组？好像滤波之后的点云不能重复赋值到这个数组里
pcl::RadiusOutlierRemoval<pcl::PointXYZ> outrem;
outrem.setRadiusSearch(0.01);
outrem.setMinNeighborsInRadius(5);
for (int i = 0; i < numberOfViews; ++i)
{
	PLY a;//这里新定义了一个结构体
	outrem.setInputCloud(data[i].cloud);
	outrem.filter(\*a.cloud);
	data1.push\_back(a);
}### 注意！


批量处理点云时会出现定义新点云以及赋值的问题，这里一定要注意在赋值时先给他一个空间，否则如果只是一个指针的话很容易报错，什么指针溢出啥的，最后处理完成一定要设置好点云的长宽来保存点云  
 ![在这里插入图片描述]()


## 2. 定义一个新的点云


\*cloud是已知的点云  
 \*newcloud是新点云pcl::PointCloudpcl::PointXYZRGB::Ptr newcloud(new pcl::PointCloudpcl::PointXYZRGB); // 创建点云（指针）
newcloud->height = cloud->height;//或者直接这个newcloud->height = 1;一般好像点云这个都是1
 newcloud->width = cloud->width;//*cloud是已知的点云
 newcloud->is_dense = false;也可以直接复制点云pcl::PointCloudpcl::PointXYZRGB::Ptr newcloud(new pcl::PointCloudpcl::PointXYZRGB); // 创建点云（指针）
pcl::copyPointCloud(*cloud, *newcloud);//把cloud复制给newcloud（然后就可以直接用newcloud了，里面就有相应点的坐标了，相当于把指针赋值了吧）注意一定要加这个，否则没法给新点云的点赋值啥的
 //一顿操作之后newcloud里面一些点被修改，需要保存起来，如下：
 newcloud->height = 1;
 newcloud->width = newcloud->points.size();
 newcloud->is_dense = false;创建新点云并且给它赋值：pcl::PointCloudpcl::PointXYZ::Ptr centorid_points(new pcl::PointCloudpcl::PointXYZ);
for (int i = 0; i < numberOfViews; i++)
{
	pcl::PointXYZ point;　
	Eigen::Vector4f centroid;					// 质心
	pcl::compute3DCentroid(\*data1[i].cloud, centroid);	// 齐次坐标，（c0,c1,c2,1）
	point.x = centroid(0);　／／这里直接给点云赋值（centorid_points－＞ｐｏｉｎｔｓ［ｉ］．ｘ＝centroid(0);）是不行的，要先给点赋值，然后再把点ｐｕsｈ＿ｂａｃｋ到点云中
	point.y = centroid(1);
	point.z = centroid(2);
	//cout << centroid(0)<<endl;
	centorid_points->points.push\_back(point);
}
centorid_points->height = 1;
centorid_points->width = numberOfViews;
centorid_points->is_dense = false;有序点云的保存pcl::PointCloud<pcl::PointXYZ> newcloud;
newcloud.width = 424;
newcloud.height = 512;
newcloud.points.resize(newcloud.width\*newcloud.height);//要加这个，否则会报错
newcloud.is_dense = false;//点云中有NAN值，若没有则true，或者说数值是有限则是true
for (int i = 0; i < cloud->size(); i++)
{
	newcloud.points[i].x = cloud->points[i].x;
	newcloud.points[i].y = cloud->points[i].y;
	newcloud.points[i].z = cloud->points[i].z;
	/\*std::cout << i << endl;\*/
}
pcl::io::savePLYFile("newcloud.ply", newcloud);## 3. 去除NAN点#include <pcl/filters/extract_indices.h>
 #include <pcl/filters/filter.h> //这两个任意一个头文件好像就可以
 //去除NAN点
 std::vector mapping;
 pcl::removeNaNFromPointCloud(*pcloud, *pcloud, mapping);
 std::cout << "原始点云去除NaN点后数量: " << pcloud->points.size() << std::endl;注意这里如果是复制的指针，去除之前一定要先保存点云，否则没啥效果pcl::copyPointCloud(\*source_key_Neigh, \*source_key_Neighbors);
for (int n = 0; n < source_key_Neigh->size(); n++)
{
	for (int m = 0; m < source_key_Neigh->size(); m++)
	{
		if ((source_key_Neigh->points[n].x == source_key_Neighbors->points[m].x) && (source_key_Neigh->points[n].y == source_key_Neighbors->points[m].y) && (source_key_Neigh->points[n].z == source_key_Neighbors->points[m].z) && (m != n))
		{
			source_key_Neighbors->points[m].x = NAN;
			source_key_Neighbors->points[m].y = NAN;
			source_key_Neighbors->points[m].z = NAN;
		}
	}
}
// 设置并保存点云\*\*\*\*\*\*\*\*\*\*\*保存这里很重要
source_key_Neighbors->height = 1;
source_key_Neighbors->width = source_key_Neighbors->points.size();
source_key_Neighbors->is_dense = false;
//去除NAN点
std::vector<int> mapping;
pcl::removeNaNFromPointCloud(\*source_key_Neighbors, \*source_key_Neighbors, mapping);## 4. 遍历点云


C++里，是编号是从0开始for (int n = 0; n < source_key_Neigh->size(); n++)
 {
 for (int m = 0; m < source_key_Neigh->size(); m++)
 {
 if ((source_key_Neigh->points[n].x == source_key_Neighbors->points[m].x) && (source_key_Neigh->points[n].y == source_key_Neighbors->points[m].y) && (source_key_Neigh->points[n].z == source_key_Neighbors->points[m].z) && (m != n))
 {
 source_key_Neighbors->points[m].x = NAN;
 source_key_Neighbors->points[m].y = NAN;
 source_key_Neighbors->points[m].z = NAN;
 }
 }
 }matlab里编号是从1 开始ptCloud = pcread(‘source_key_Neigh.ply’);
 location=ptCloud.Location;
 x = location(:,1);
 y = location(:,2);
 z = location(:,3);
 location1=location;
 x1=x;
 y1=y;
 z1=z;
 for i=1:size(x)
 for j=1:size(x)
 if((x1(j)==x(i))&&(y1(j)==y(i))&&(z1(j)==z(i))&&(i~=j))
 location1(j,1)=nan;
 location1(j,2)=nan;
 location1(j,3)=nan;
 end
 end
 end
 location1(all(isnan(location1), 2)😅 = [];## 5. 查看点云点数#include <pcl/ModelCoefficients.h>
 #include <pcl/io/pcd_io.h>
 #include <pcl/io/ply_io.h>
 #include <pcl/point_types.h>using namespace std;
 typedef pcl::PointXYZRGBA PointT;int main()
 {
 pcl::PointCloudpcl::PointXYZRGB::Ptr cloud(new pcl::PointCloudpcl::PointXYZRGB);
 pcl::PointCloudpcl::PointXYZRGB::Ptr cloud_filtered(new pcl::PointCloudpcl::PointXYZRGB);
 std::string prefix(“2020-10-14-15-19-55_ConditionAnd_RadiusOutlierRemoval”);
 std::string extension(“.ply”);
 std::string fname = prefix + extension;
 if (pcl::io::loadPLYFilepcl::PointXYZRGB(fname, *cloud) == -1) //* load the file
 {
 PCL_ERROR(“Couldn’t read file. \n”);
 system(“PAUSE”);
 return (-1);
 }
 std::cout << cloud->size() << endl;