点云数据的K均值聚类

与pcl通常的工作方式(以自定义点类型为中心)相比,此函数非常奇怪。基本上,奇怪的是,您必须通过指定的维度向量而不是自定义点类型输入点。下面是经过测试的功能示例代码:(显然,您需要提供自己的文件名,并且您可能需要调整集群大小)

int main(int argc, char** argv) {

    std::string filePath = "../PointCloudFiles/beaconJR.pcd";
    pcl::PointCloud<pcl::PointXYZ>::Ptr tempCloud(new pcl::PointCloud<pcl::PointXYZ>);
    if (pcl::io::loadPCDFile(filePath, *tempCloud) == -1) //* load the file
    {printf("failed file load!\n");}
    else
    {
        pcl::Kmeans real(static_cast<int> (tempCloud->points.size()), 3);
        real.setClusterSize(3); //it is important that you set this term appropriately for your application
        for (size_t i = 0; i < tempCloud->points.size(); i++)
        {
            std::vector<float> data(3);
            data[0] = tempCloud->points[i].x;
            data[1] = tempCloud->points[i].y;
            data[2] = tempCloud->points[i].z;
            real.addDataPoint(data);
        }

        real.kMeans();
        // get the cluster centroids 
        pcl::Kmeans::Centroids centroids = real.get_centroids();
        std::cout << "points in total Cloud : " << tempCloud->points.size() << std::endl;
        std::cout << "centroid count: " << centroids.size() << std::endl;
        for (int i = 0; i<centroids.size(); i++)
        {
            std::cout << i << "_cent output: x: " << centroids[i][0] << " ,";
            std::cout << "y: " << centroids[i][1] << " ,";
            std::cout << "z: " << centroids[i][2] << std::endl;
        }
    }

    std::cin.get();
    std::cin.get();
}

--编辑