后續使用了fpfh特征作為訓練數據，遇到了一些困難
首先是flann沖突，這個將opcv中的flann都改成了flann2就可以運行
后面在將得到的33特征值進行訓練的時候一直內存超限，傳輸的不太好，到現在還是不行，改了三天還是沒有改好，先放這里吧，等后續有時間進行修改，我感覺是傳輸的問題。

#pragma warning(disable:4996)
#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/visualization/pcl_visualizer.h>
//點云顯示
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/visualization/point_cloud_color_handlers.h>
//數據組織
#include <boost/thread/thread.hpp>
#include <boost/thread/thread_time.hpp>
#include<flann/flann.h>
#include <thread>
#include <pcl/search/kdtree.h>
//
#include <omp.h>
//補充點云特征
#include <pcl/features/normal_3d.h>
#include <pcl/features/normal_3d_omp.h>
#include <pcl/features/shot_omp.h>
#include <pcl/features/shot.h>
#include <pcl/features/fpfh_omp.h>
#include <pcl/features/pfh.h>
#include <pcl/features/normal_3d.h>#include <opencv2/opencv.hpp>int main() {// 讀取初始點云pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);pcl::PCDReader reader;reader.read("svmtest.pcd", *cloud);cout << "初始點云讀取完成" << endl;// 讀取帶標簽的點云pcl::PointCloud<pcl::PointXYZL>::Ptr labeledCloud(new pcl::PointCloud<pcl::PointXYZL>);reader.read("svmlearn_xyzl.pcd", *labeledCloud);cout << "標簽點云讀取完成" << endl;// 計算法線pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);pcl::NormalEstimation<pcl::PointXYZL, pcl::Normal> normalEstimation;normalEstimation.setInputCloud(labeledCloud);pcl::search::KdTree<pcl::PointXYZL>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZL>);normalEstimation.setSearchMethod(kdtree);normalEstimation.setKSearch(20);  // 設置法線估計時近鄰點的數量normalEstimation.compute(*normals); cout << "發線計算完成" << endl;將法線和原始點云拼接起來//pcl::PointCloud<pcl::PointNormal>::Ptr cloudWithNormals(new pcl::PointCloud<pcl::PointNormal>);//pcl::concatenateFields(*cloud, *normals, *cloudWithNormals);// 將法線和原始點云拼接起來//pcl::PointCloud<pcl::PointNormal>::Ptr cloudWithNormals(new pcl::PointCloud<pcl::PointNormal>);pcl::PointCloud<pcl::PointXYZLNormal>::Ptr cloudWithNormals(new pcl::PointCloud<pcl::PointXYZLNormal>);cloudWithNormals->resize(cloud->size());for (size_t i = 0; i < cloud->size(); ++i) {cloudWithNormals->points[i].x = cloud->points[i].x;cloudWithNormals->points[i].y = cloud->points[i].y;cloudWithNormals->points[i].z = cloud->points[i].z;cloudWithNormals->points[i].normal_x = normals->points[i].normal_x;cloudWithNormals->points[i].normal_y = normals->points[i].normal_y;cloudWithNormals->points[i].normal_z = normals->points[i].normal_z;}cout << "cloudWithNormals的點云數量為" << cloudWithNormals->size() << endl;cout << "法線和原始點云合并完成" << endl;// 讀取法線和曲率特征// pcl::PointCloud<pcl::FPFHSignature33>::Ptr features(new pcl::PointCloud<pcl::FPFHSignature33>);// 計算帶標簽點云的FPFH特征pcl::FPFHEstimationOMP<pcl::PointXYZL, pcl::Normal, pcl::FPFHSignature33> fpfh_src;fpfh_src.setInputCloud(labeledCloud);fpfh_src.setInputNormals(normals);fpfh_src.setNumberOfThreads(10);pcl::search::KdTree<pcl::PointXYZL>::Ptr kdtree2(new pcl::search::KdTree<pcl::PointXYZL>);fpfh_src.setSearchMethod(kdtree2);cout << "開始計算點云特征" << endl;pcl::PointCloud<pcl::FPFHSignature33>::Ptr features(new pcl::PointCloud<pcl::FPFHSignature33>());fpfh_src.setKSearch(20);fpfh_src.compute(*features);// 開始計算前上鎖omp_lock_t lock;omp_init_lock(&lock);// 使用 OpenMP 設置鎖
#pragma omp parallel{
#pragma omp single{
#pragma omp task{fpfh_src.compute(*features);}}}// 計算完成后解鎖omp_destroy_lock(&lock);cout << "讀取法線和曲率特征完成" << endl;// 準備訓練數據和標簽cv::Mat trainingData(labeledCloud->size(), 33, CV_32FC1);  // 注意特征的維度cv::Mat labels(labeledCloud->size(), 1, CV_32SC1);std::cout << "labeledCloud size: " << labeledCloud->size() << std::endl;std::cout << "features size: " << features->size() << std::endl;for (size_t i = 0; i < labeledCloud->size(); ++i){// 使用法線和曲率特征for (int j = 0; j < 33; ++j){if (i < features->size()){  // 添加索引范圍檢查trainingData.at<float>(i, j) = features->points[i].histogram[j];}else {std::cerr << "Index out of range for features at i=" << i << " and j=" << j << std::endl;}}// 根據點的標簽設置標簽數據if (i < labeledCloud->size()) {  // 添加索引范圍檢查labels.at<int>(i, 0) = labeledCloud->points[i].label;}else {std::cerr << "Index out of range for labeledCloud at i=" << i << std::endl;}}cout << "根據點的標簽設置標簽數據完成" << endl;// 創建并訓練SVM分類器cv::Ptr<cv::ml::SVM> svm = cv::ml::SVM::create();svm->setType(cv::ml::SVM::C_SVC);svm->setKernel(cv::ml::SVM::RBF);svm->setC(10);svm->setGamma(0.001);svm->train(trainingData, cv::ml::ROW_SAMPLE, labels);cout << "創建并訓練SVM分類器完成，正在開始對點云進行分類" << endl;// 對初始點云進行分類//cv::Mat testData(cloud->size(), 3, CV_32FC1);//for (size_t i = 0; i < cloud->size(); ++i) //{//    testData.at<float>(i, 0) = cloud->points[i].x;//    testData.at<float>(i, 1) = cloud->points[i].y;//    testData.at<float>(i, 2) = cloud->points[i].z;//}cv::Mat testData(cloud->size(), 33, CV_32FC1);for (size_t i = 0; i < cloud->size(); ++i){for (int j = 0; j < 33; ++j) {testData.at<float>(i, j) = features->points[i].histogram[j];}}cv::Mat predictedLabels;/* svm->predict(testData, predictedLabels);*/try {svm->predict(testData, predictedLabels);}catch (cv::Exception& e) {std::cerr << "OpenCV Exception: " << e.what() << std::endl;}cout << "正在將分類結果添加到點云中" << endl;// 將分類結果添加到點云中pcl::PointCloud<pcl::PointXYZL>::Ptr classifiedCloud(new pcl::PointCloud<pcl::PointXYZL>);classifiedCloud->resize(cloud->size());for (size_t i = 0; i < cloud->size(); ++i) {classifiedCloud->points[i].x = cloud->points[i].x;classifiedCloud->points[i].y = cloud->points[i].y;classifiedCloud->points[i].z = cloud->points[i].z;// 修正標簽值（假設標簽是 0 或 1）classifiedCloud->points[i].label = static_cast<int>(predictedLabels.at<float>(i, 0)) + 1;}pcl::PCDWriter writer;writer.write("lable.pcd", *classifiedCloud);cout << "lable.pcd已完成儲存，請查看" << endl;//----------------------------根據分類標簽可視化-----------------------------boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("3D Viewer"));pcl::visualization::PointCloudColorHandlerGenericField<pcl::PointXYZL>fildColor(classifiedCloud, "label");viewer->setBackgroundColor(0, 0, 0);viewer->setWindowName("點云按分類標簽顯示");viewer->addText("Point clouds are shown by label", 50, 50, 0, 1, 0, "v1_text");viewer->addPointCloud<pcl::PointXYZL>(classifiedCloud, fildColor, "sample cloud");viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "sample cloud");while (!viewer->wasStopped()){viewer->spinOnce(100);boost::this_thread::sleep(boost::posix_time::microseconds(100000));}return 0;
}