使用c++写一个pcl1.8.1可以运行的三维最小生成树算法
时间: 2024-02-05 13:11:16 浏览: 222
最小生成树有很多种生成方法,主要有普利姆算法和克鲁斯卡尔算法,通过C语言easyx图形库实现
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以下是一个使用PCL 1.8.1实现三维最小生成树算法的示例代码:
```c++
#include <iostream>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/PCLPointCloud2.h>
#include <pcl/common/distances.h>
#include <pcl/io/pcd_io.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/features/normal_3d.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/search/kdtree.h>
#include <pcl/common/common.h>
#include <pcl/surface/mls.h>
#include <pcl/segmentation/extract_clusters.h>
#include <pcl/registration/icp.h>
#include <pcl/filters/statistical_outlier_removal.h>
#include <pcl/surface/gp3.h>
#include <pcl/io/vtk_io.h>
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/search/flann_search.h>
#include <pcl/common/centroid.h>
#include <pcl/segmentation/region_growing.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/features/moment_of_inertia_estimation.h>
#include <pcl/common/transforms.h>
#include <pcl/filters/passthrough.h>
#include <pcl/segmentation/conditional_euclidean_clustering.h>
#include <pcl/kdtree/kdtree_flann.h>
using namespace std;
using namespace pcl;
typedef PointXYZ PointType;
typedef PointCloud<PointType> PointCloudType;
struct Edge
{
int start;
int end;
double cost;
};
struct Node
{
int parent;
int rank;
};
bool compareEdge(Edge e1, Edge e2)
{
return e1.cost < e2.cost;
}
void MakeSet(Node* nodes, int size)
{
for (int i = 0; i < size; i++)
{
nodes[i].parent = i;
nodes[i].rank = 0;
}
}
int FindSet(Node* nodes, int x)
{
if (nodes[x].parent != x)
{
nodes[x].parent = FindSet(nodes, nodes[x].parent);
}
return nodes[x].parent;
}
void Union(Node* nodes, int x, int y)
{
int xRoot = FindSet(nodes, x);
int yRoot = FindSet(nodes, y);
if (xRoot == yRoot)
return;
if (nodes[xRoot].rank < nodes[yRoot].rank)
{
nodes[xRoot].parent = yRoot;
}
else if (nodes[xRoot].rank > nodes[yRoot].rank)
{
nodes[yRoot].parent = xRoot;
}
else
{
nodes[yRoot].parent = xRoot;
nodes[xRoot].rank++;
}
}
vector<Edge> MinimumSpanningTree(PointCloudType::Ptr cloud)
{
int size = cloud->size();
vector<Node> nodes(size);
vector<Edge> edges(size*size);
for (int i = 0; i < size; i++)
{
for (int j = i + 1; j < size; j++)
{
double distance = euclideanDistance(cloud->points[i], cloud->points[j]);
Edge edge;
edge.start = i;
edge.end = j;
edge.cost = distance;
edges.push_back(edge);
}
}
sort(edges.begin(), edges.end(), compareEdge);
MakeSet(&nodes[0], size);
vector<Edge> result;
int count = 0;
for (int i = 0; i < edges.size(); i++)
{
if (count == size - 1)
break;
Edge edge = edges[i];
int start = edge.start;
int end = edge.end;
if (FindSet(&nodes[0], start) != FindSet(&nodes[0], end))
{
Union(&nodes[0], start, end);
result.push_back(edge);
count++;
}
}
return result;
}
int main(int argc, char** argv)
{
if (argc < 2)
{
cout << "Usage: ./pcl_mst [input_file]" << endl;
return -1;
}
PointCloudType::Ptr input_cloud(new PointCloudType);
if (io::loadPCDFile(argv[1], *input_cloud) == -1)
{
cout << "Error reading file " << argv[1] << endl;
return -1;
}
vector<Edge> mst = MinimumSpanningTree(input_cloud);
PointCloudType::Ptr output_cloud(new PointCloudType);
for (int i = 0; i < mst.size(); i++)
{
int start = mst[i].start;
int end = mst[i].end;
output_cloud->push_back(input_cloud->points[start]);
output_cloud->push_back(input_cloud->points[end]);
}
visualization::PCLVisualizer viewer("Minimum Spanning Tree");
viewer.addPointCloud(input_cloud, "input_cloud");
viewer.setPointCloudRenderingProperties(visualization::PCL_VISUALIZER_POINT_SIZE, 3, "input_cloud");
viewer.addPointCloud(output_cloud, "output_cloud");
viewer.setPointCloudRenderingProperties(visualization::PCL_VISUALIZER_COLOR, 1, 0, 0, "output_cloud");
viewer.setPointCloudRenderingProperties(visualization::PCL_VISUALIZER_LINE_WIDTH, 3, "output_cloud");
while (!viewer.wasStopped())
{
viewer.spinOnce();
}
return 0;
}
```
该代码实现了一个简单的Prim算法,用于计算三维点云的最小生成树。它首先计算所有点之间的距离,并将它们存储在一个边列表中。然后,按照边的成本对列表进行排序,以便按顺序处理它们。在处理每个边时,它检查该边的两个端点是否在同一集合中。如果不在同一集合中,则将它们合并,并将边添加到输出中。最后,它显示了原始点云和最小生成树的可视化效果。
注意,这只是一个简单的实现,它没有考虑优化技巧,如使用Fibonacci堆来管理边列表。
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