加速这一段代码#include <thread> #include <mutex> // 用于保护m_vpdEdgePoints和m_vdEdgeGradient的锁 std::mutex g_mutex; void process_edges(const cv::Mat& RoiMat, const std::vectorcv::Point2d& m_vpdEquinoxPoints, const double m_dMeasureLength, const double m_dMeasureHeight, const double m_dSigma, const int m_nThresholdCircle, const int m_nTranslationCircle, const std::vector<double>& m_vdMeasureAngle, std::vectorcv::Point2d& m_vpdEdgePoints, std::vector<double>& m_vdEdgeGradient, int start_idx, int end_idx, Extract1DEdgeCircle Extract1DEdgeCircle) { std::vector<Edge1D_Result> edges; for (int i = start_idx; i < end_idx; i++) { edges = Extract1DEdgeCircle.Get1DEdge(RoiMat, m_vpdEquinoxPoints[i], m_dMeasureLength, m_dMeasureHeight,m_vdMeasureAngle[i], m_dSigma, m_nThresholdCircle, m_nTranslationCircle == 1 ? Translation::Poisitive : Translation::Negative, Selection::Strongest); // 使用锁保护m_vpdEdgePoints和m_vdEdgeGradient //std::lock_guardstd::mutex lock(g_mutex); for (int j = 0; j < edges.size(); j++) { m_vpdEdgePoints.push_back(edges[j].m_pdEdgePoint); m_vdEdgeGradient.push_back(edges[j].m_dGradient); } } } const int num_threads = 10; std::vectorstd::thread threads(num_threads); std::vector<std::vectorcv::Point2d> edge_points(num_threads); std::vector<std::vector<double>> edge_gradients(num_threads); for (int i = 0; i < num_threads; i++) { int start_idx = i * m / num_threads; int end_idx = (i + 1) * m / num_threads; threads[i] = std::thread(process_edges, std::ref(RoiMat), std::ref(m_vpdEquinoxPoints), m_dMeasureLength, m_dMeasureHeight, m_dSigma, m_nThresholdCircle, m_nTranslationCircle, std::ref(m_vdMeasureAngle), std::ref(edge_points[i]), std::ref(edge_gradients[i]), start_idx, end_idx, Extract1DEdgeCircle); } for (int i = 0; i < num_threads; i++) { threads[i].join(); // 合并结果 m_vpdEdgePoints.insert(m_vpdEdgePoints.end(), edge_points[i].begin(), edge_points[i].end()); m_vdEdgeGradient.insert(m_vdEdgeGradient.end(), edge_gradients[i].begin(), edge_gradients[i].end()); }

C++11 并发指南之std::mutex详解

在C++11中，std::mutex是并发编程中的基础组件，它提供了一种机制来保护共享资源免受并发访问的影响。互斥量（Mutex）确保在任何时刻只有一个线程能持有对资源的访问权，从而防止数据竞争和其他并发问题。在...

SimpleC++Examples.zip_Linux/Unix编程_C/C++_

标题中的"SimpleC++Examples.zip"表明这是一个包含C++简单示例程序的压缩文件，而标签则强调了这些示例主要是针对Linux/Unix环境的编程，同时也涉及到C/C++语言。压缩包内的四个子文件"problem2.2.cpp"、"problem2.1...

加速这一段代码#include <thread> #include <mutex> // 用于保护m_vpdEdgePoints和m_vdEdgeGradient的锁 std::mutex g_mutex; void process_edges(const cv::Mat& RoiMat, const std::vector<cv::Point2d>& m_vpdEquinoxPoints, const double m_dMeasureLength, const double m_dMeasureHeight, const double m_dSigma, const int m_nThresholdCircle, const int m_nTranslationCircle, const std::vector<double>& m_vdMeasureAngle, std::vector<cv::Point2d>& m_vpdEdgePoints, std::vector<double>& m_vdEdgeGradient, int start_idx, int end_idx, Extract1DEdgeCircle Extract1DEdgeCircle) { std::vector<Edge1D_Result> edges; for (int i = start_idx; i < end_idx; i++) { edges = Extract1DEdgeCircle.Get1DEdge(RoiMat, m_vpdEquinoxPoints[i], m_dMeasureLength, m_dMeasureHeight,m_vdMeasureAngle[i], m_dSigma, m_nThresholdCircle, m_nTranslationCircle == 1 ? Translation::Poisitive : Translation::Negative, Selection::Strongest); // 使用锁保护m_vpdEdgePoints和m_vdEdgeGradient //std::lock_guard<std::mutex> lock(g_mutex); for (int j = 0; j < edges.size(); j++) { m_vpdEdgePoints.push_back(edges[j].m_pdEdgePoint); m_vdEdgeGradient.push_back(edges[j].m_dGradient); } } } const int num_threads = 10; std::vector<std::thread> threads(num_threads); std::vector<std::vector<cv::Point2d>> edge_points(num_threads); std::vector<std::vector<double>> edge_gradients(num_threads); for (int i = 0; i < num_threads; i++) { int start_idx = i * m / num_threads; int end_idx = (i + 1) * m / num_threads; threads[i] = std::thread(process_edges, std::ref(RoiMat), std::ref(m_vpdEquinoxPoints), m_dMeasureLength, m_dMeasureHeight, m_dSigma, m_nThresholdCircle, m_nTranslationCircle, std::ref(m_vdMeasureAngle), std::ref(edge_points[i]), std::ref(edge_gradients[i]), start_idx, end_idx, Extract1DEdgeCircle); } for (int i = 0; i < num_threads; i++) { threads[i].join(); // 合并结果 m_vpdEdgePoints.insert(m_vpdEdgePoints.end(), edge_points[i].begin(), edge_points[i].end()); m_vdEdgeGradient.insert(m_vdEdgeGradient.end(), edge_gradients[i].begin(), edge_gradients[i].end()); }

可以尝试使用并行 STL 算法来加速代码，例如 std::for_each() 或 std::transform()。这些算法可以利用多线程来并行化循环。具体来说，你可以将 process_edges() 函数中的循环替换为一个并行算法，例如： ...

加速这一段代码例程#include <thread> #include <mutex> // 用于保护m_vpdEdgePoints和m_vdEdgeGradient的锁 std::mutex g_mutex; void process_edges(const cv::Mat& RoiMat, const std::vectorcv::Point2d& m_vpdEquinoxPoints, const double m_dMeasureLength, const double m_dMeasureHeight, const double m_dSigma, const int m_nThresholdCircle, const int m_nTranslationCircle, const std::vector<double>& m_vdMeasureAngle, std::vectorcv::Point2d& m_vpdEdgePoints, std::vector<double>& m_vdEdgeGradient, int start_idx, int end_idx, Extract1DEdgeCircle Extract1DEdgeCircle) { std::vector<Edge1D_Result> edges; for (int i = start_idx; i < end_idx; i++) { edges = Extract1DEdgeCircle.Get1DEdge(RoiMat, m_vpdEquinoxPoints[i], m_dMeasureLength, m_dMeasureHeight,m_vdMeasureAngle[i], m_dSigma, m_nThresholdCircle, m_nTranslationCircle == 1 ? Translation::Poisitive : Translation::Negative, Selection::Strongest); // 使用锁保护m_vpdEdgePoints和m_vdEdgeGradient //std::lock_guardstd::mutex lock(g_mutex); for (int j = 0; j < edges.size(); j++) { m_vpdEdgePoints.push_back(edges[j].m_pdEdgePoint); m_vdEdgeGradient.push_back(edges[j].m_dGradient); } } } int main() { int m = m_vpdEquinoxPoints.size(); const int num_threads = 10; std::vectorstd::thread threads(num_threads); std::vector<std::vectorcv::Point2d> edge_points(num_threads); std::vector<std::vector<double>> edge_gradients(num_threads); for (int i = 0; i < num_threads; i++) { int start_idx = i * m / num_threads; int end_idx = (i + 1) * m / num_threads; threads[i] = std::thread(process_edges, std::ref(RoiMat), std::ref(m_vpdEquinoxPoints), m_dMeasureLength, m_dMeasureHeight, m_dSigma, m_nThresholdCircle, m_nTranslationCircle, std::ref(m_vdMeasureAngle), std::ref(edge_points[i]), std::ref(edge_gradients[i]), start_idx, end_idx, Extract1DEdgeCircle); } for (int i = 0; i < num_threads; i++) { threads[i].join(); // 合并结果 m_vpdEdgePoints.insert(m_vpdEdgePoints.end(), edge_points[i].begin(), edge_points[i].end()); m_vdEdgeGradient.insert(m_vdEdgeGradient.end(), edge_gradients[i].begin(), edge_gradients[i].end()); } return 0; }

可以考虑使用OpenMP来并行化这段代码，OpenMP是一个支持多平台的共享内存并行编程API。可以在循环中使用#pragma omp parallel for指令来并行化循环，具体如下： #include <omp.h> void process_edges(const cv...

#include <vector> #include <queue> #include <thread> #include <mutex> #include <condition_variable> #include <functional> #include <future> class ThreadPool { public: ThreadPool(size_t threads) : stop(false) { for (size_t i = 0; i < threads; ++i) { workers.emplace_back([this] { for (;;) { std::function<void()> task; { std::unique_lock<std::mutex> lock(this->queue_mutex); this->condition.wait(lock, [this] { return this->stop || !this->tasks.empty(); }); if (this->stop && this->tasks.empty()) return; task = std::move(this->tasks.front()); this->tasks.pop(); } task(); } }); } } ~ThreadPool() { { std::unique_lock<std::mutex> lock(queue_mutex); stop = true; } condition.notify_all(); for (std::thread &worker : workers) worker.join(); } template<class F, class... Args> auto enqueue(F&& f, Args&&... args) -> std::future<typename std::result_of<F(Args...)>::type> { using return_type = typename std::result_of<F(Args...)>::type; auto task = std::make_shared<std::packaged_task<return_type()>>(std::bind(std::forward<F>(f), std::forward<Args>(args)...)); std::future<return_type> res = task->get_future(); { std_lock<std::mutex> lock(queue_mutex); if (stop) throw std::runtime_error("enqueue on stopped ThreadPool"); tasks.emplace([task] { (*task)(); }); } condition.notify_one(); return res; } private: std::vector<std::thread> workers; std::queue<std::function<void()>> tasks; std::mutex queue_mutex; std::condition_variable condition; bool stop; };

这是一个 C++ 实现的线程池类。...这个实现使用了 C++11 中的标准库，包括 std::thread、std::mutex、std::condition_variable 等。它还提供了一个 std::future 对象，可以用来获取任务执行的结果。

#include <mutex> #include <thread> #include <iostream> struct Box { explicit Box(int num) :

#include <mutex>、<thread> 和 <iostream> 这些头文件在 C++ 中用于处理线程相关的功能。std::mutex 是互斥锁，它用于同步对共享资源的访问，防止多个线程同时修改数据导致的竞态条件。 struct Box 定义...

使用#include <semaphore> #include <thread>

使用#include <semaphore>和#include <thread>可以实现多线程编程中的信号量机制。信号量是一种用于多线程编程的同步原语，它可以用来保护共享资源，避免多个线程同时访问同一个资源而导致的数据竞争问题。在C++11...

优化这段代码 #include <iostream> #include <thread> #include <chrono> #include <mutex> #include <semaphore.h> using namespace std; // shared data resource int shared_data = 0; // semaphores for synchronization sem_t mutex, rw_mutex; // number of readers int num_readers = 0; // reader function void reader(int id) { while (true) { // acquire mutex to update the number of readers sem_wait(&mutex); num_readers++; if (num_readers == 1) { // if this is the first reader, acquire the rw_mutex sem_wait(&rw_mutex); } sem_post(&mutex); // read the shared data cout << "Reader " << id << " read shared data: " << shared_data << endl; // release mutex sem_wait(&mutex); num_readers--; if (num_readers == 0) { // if this is the last reader, release the rw_mutex sem_post(&rw_mutex); } sem_post(&mutex); // sleep for a random amount of time this_thread::sleep_for(chrono::milliseconds(rand() % 1000)); } } // writer function void writer(int id) { while (true) { // acquire the rw_mutex sem_wait(&rw_mutex); // write to the shared data shared_data++; cout << "Writer " << id << " wrote to shared data: " << shared_data << endl; // release the rw_mutex sem_post(&rw_mutex); // sleep for a random amount of time this_thread::sleep_for(chrono::milliseconds(rand() % 1000)); } } int main() { // initialize semaphores sem_init(&mutex, 0, 1); sem_init(&rw_mutex, 0, 1); // create reader threads thread readers[8]; for (int i = 0; i < 8; i++) { readers[i] = thread(reader, i); } // create writer threads thread writers[2]; for (int i = 0; i < 2; i++) { writers[i] = thread(writer, i); } // join threads for (int i = 0; i < 8; i++) { readers[i].join(); } for (int i = 0; i < 2; i++) { writers[i].join(); } // destroy semaphores sem_destroy(&mutex); sem_destroy(&rw_mutex); return 0; }

这段代码是使用信号量实现读写锁的示例，但是存在一些需要优化的地方： 1. 不支持优先级反转：在这个实现中，读者和写者都使用了同一个互斥信号量(mutex)来保证对num_readers的互斥访问。但是，如果一个写者在等待...

#include <iostream>#include <thread>#include <mutex>#include <condition_variable>using namespace std;mutex father_mutex;mutex son_mutex;mutex daughter_mutex;condition_variable father_cv;condition_variable son_cv;condition_variable daughter_cv;int fruit_count = 0;void father() { while (true) { unique_lock<mutex> lock(father_mutex); while (fruit_count > 0) { father_cv.wait(lock); } fruit_count = 1; cout << "Father puts an apple into the plate." << endl; son_cv.notify_one(); daughter_cv.notify_one(); }}void son() { while (true) { unique_lock<mutex> lock(son_mutex); while (fruit_count == 0) { son_cv.wait(lock); } fruit_count = 0; cout << "Son eats an orange from the plate." << endl; father_cv.notify_one(); }}void daughter() { while (true) { unique_lock<mutex> lock(daughter_mutex); while (fruit_count == 1) { daughter_cv.wait(lock); } fruit_count = 1; cout << "Daughter eats an apple from the plate." << endl; father_cv.notify_one(); }}int main() { thread father_thread(father); thread son_thread(son); thread daughter_thread(daughter); father_thread.join(); son_thread.join(); daughter_thread.join(); return 0;}

这是一个简单的多线程程序，模拟了一个父亲、儿子、女儿共同分享果盘中水果的场景。程序中使用了三个互斥量（father_mutex、son_mutex、daughter_mutex）和三个条件变量（father_cv、son_cv、daughter_cv）来同步...

#include <thread>

#include <thread> 是C++11中的一个标准库头文件，用于支持多线程编程。它包含了一些用于创建、操作和管理线程的类和函数。其中最常用的是std::thread类，它表示一个线程的对象。通过std::thread可以创建新的线程，...

帮我修改下面的代码。要求建立一次weboscket链接，链接到wss://autopilot-test.t3go.cn:443/api/v1/vehicle/push/message/LFB1FV696M2L43840，当订阅到感知话题调用perceptionCallback时，通过wss发送serialized_data：#include "ros/ros.h" #include "std_msgs/String.h" #include <boost/thread/locks.hpp> #include <boost/thread/shared_mutex.hpp> #include "third_party/apollo/proto/perception/perception_obstacle.pb.h" #include "t3_perception.pb.h" #include <iostream> #include <websocketpp/config/asio_client.hpp> #include <websocketpp/client.hpp> #include <websocketpp/common/thread.hpp> apollo::perception::PerceptionObstacles perception_obstacles_; typedef websocketpp::clientwebsocketpp::config::asio_tls_client client; void perceptionCallback(const std_msgs::String& msg) { ROS_WARN("t3 perceptionCallback parse"); if (perception_obstacles_.ParseFromString(msg.data)) { double timestamp = perception_obstacles_.header().timestamp_sec(); ROS_INFO("t3 perceptionCallback timestamp %f count:%d", timestamp, perception_obstacles_.perception_obstacle().size()); std::string data; perception_obstacles_.SerializeToString(&data); VehData veh_data; veh_data.set_messagetype(5); veh_data.set_messagedes("PerceptionObstacles"); veh_data.set_contents(data); std::string serialized_data; veh_data.SerializeToString(&serialized_data); } else { ROS_ERROR("t3 perceptionCallback parse fail!"); } } int main(int argc, char **argv) { ros::init(argc, argv, "listener"); ros::NodeHandle n; ros::Subscriber sub = n.subscribe("/perception_node/perception_objects", 1000, perceptionCallback); ros::spin(); return 0; }

#include <boost/thread/shared_mutex.hpp> #include "third_party/apollo/proto/perception/perception_obstacle.pb.h" #include "t3_perception.pb.h" #include <iostream> #include <websocketpp/config/asio_...

#include <iostream> #include <thread> #include <mutex> #include <condition_variable> using namespace std; const int N = 5; // 哲学家数量 mutex forks[N]; // 叉子互斥锁 condition_variable cv[N]; // 条件变量，用于线程同步 void philosopher(int i) { while (true) { // 模拟思考 cout << "Philosopher " << i << " is hungry." << endl; // 拿起左手的叉子 forks[i].lock(); cout << "Philosopher " << i << " picks up left fork." << endl; // 尝试拿起右手的叉子 unique_lock<mutex> lock(forks[(i + 1) % N], defer_lock); while (!lock.try_lock()) { cv[i].wait(lock); } cout << "Philosopher " << i << " picks up right fork." << endl; // 进餐 cout << "Philosopher " << i << " starts eating." << endl; this_thread::sleep_for(chrono::milliseconds(rand() % 1000)); cout << "Philosopher " << i << " finishes eating." << endl; // 放下右手的叉子 lock.unlock(); cv[(i + 1) % N].notify_one(); cout << "Philosopher " << i << " puts down right fork." << endl; // 放下左手的叉子 forks[i].unlock(); cout << "Philosopher " << i << " puts down left fork." << endl; } } int main() { thread philosophers[N]; for (int i = 0; i < N; i++) { philosophers[i] = thread(philosopher, i); } for (int i = 0; i < N; i++) { philosophers[i].join(); } return 0; }分析案例

这是一个经典的哲学家就餐问题，使用了互斥锁和条件变量来实现线程同步。在哲学家就餐问题中，有5个哲学家围坐在一张圆桌前，每个哲学家面前都有一只叉子。哲学家只有在同时拿到左右两只叉子时才能进餐，进餐完毕...

#include "stdafx.h" #include <windows.h> #include #include <iostream> #include <fstream> using namespace std; HANDLE evRead, evFinish; void ReadThread(LPVOID param) { WaitForSingleObject (evRead ,INFINITE); cout<<"Reading"<<endl; SetEvent (evFinish); } void WriteThread(LPVOID param) { cout<<"Writing"<<endl; SetEvent (evRead); } int main(int argc , char * argv[]) { evRead = CreateEvent (NULL ,FALSE ,FALSE ,NULL) ; evFinish = CreateEvent (NULL ,FALSE ,FALSE ,NULL) ; _beginthread(ReadThread , 0 , NULL) ; _beginthread(WriteThread , 0 , NULL) ; WaitForSingleObject (evFinish,INFINITE) ; cout<<"The Program is End"<<endl; return 0 ; }用互斥量代替事件实现线程间同步

好的，下面是使用互斥量代替事件实现线程间同步的代码： cpp #include "stdafx.h" #include <windows.h> #include <process.h> #include <iostream> #include <fstream> using namespace std; HANDLE ...

#include "stdafx.h" #include <windows.h> #include <iostream> //#include <fstream> using namespace std; #define THREAD_INSTANCE_NUMBER 3 LONG g_fResourceInUse = FALSE; LONG g_lCounter = 0; DWORD ThreadProc(void * pData) { int ThreadNumberTemp = ((int) pData); HANDLE hMutex; if ((hMutex = OpenMutex(MUTEX_ALL_ACCESS, FALSE, "Mutex.Test")) == NULL) { cout << "Open Mutex error!" << endl; } cout << "ThreadProc: " << ThreadNumberTemp << " is running!" << endl; cout << "ThreadProc " << ThreadNumberTemp << " gets the mutex"<< endl; ReleaseMutex(hMutex); CloseHandle(hMutex); return 0; } int main(int argc, char* argv[]) { int i; DWORD ID[THREAD_INSTANCE_NUMBER]; HANDLE h[THREAD_INSTANCE_NUMBER]; HANDLE hMutex; if ( (hMutex = OpenMutex(MUTEX_ALL_ACCESS, FALSE, "Mutex.Test")) == NULL) { if ((hMutex = CreateMutex(NULL, FALSE, "Mutex.Test")) == NULL ) { cout << "Create Mutex error!" << endl; return 0; } } for (i=0;i<THREAD_INSTANCE_NUMBER;i++) { h[i] = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE) ThreadProc, (void *)&ID[i], 0, &(ID[i])); if (h[i] == NULL) cout << "CreateThread error" << ID[i] << endl; else cout << "CreateThread: " << ID[i] << endl; } WaitForMultipleObjects(THREAD_INSTANCE_NUMBER,h,TRUE,INFINITE); cout << "Close the Mutex Handle! " << endl; CloseHandle(hMutex); return 0; }运行这段代码并解释运行结果

这段代码是一个使用了Windows API的多线程程序，它创建了三个线程并尝试使用互斥量来控制对共享资源的访问。在主函数中，程序首先尝试打开一个名为"Mutex.Test"的互斥量，如果互斥量不存在则创建一个新的。接着...

帮我修改下面的代码。要求建立一次weboscket链接，链接到wss://autopilot-test.t3go.cn:443/api/v1/vehicle/push/message/LFB1FV696M2L43840，当订阅到感知话题调用perceptionCallback时，通过wss发送serialized_data：#include "ros/ros.h" #include "std_msgs/String.h" #include <boost/thread/locks.hpp> #include

具体来说，它使用了一个发送队列和一个互斥锁来保证消息的线程安全，同时使用了io_service来异步发送消息。这样做可以避免阻塞ROS事件循环，提高程序的响应性能。另外，它还使用了ROS的日志功能，用于输出调试信息...

将serialized_data用websocket wss发送，#include "ros/ros.h" #include "std_msgs/String.h" #include <boost/thread/locks.hpp> #include <boost/thread/shared_mutex.hpp> #include "third_party/apollo/proto/perception/perception_obstacle.pb.h" #include "t3_perception.pb.h" apollo::perception::PerceptionObstacles perception_obstacles_; void perceptionCallback(const std_msgs::String& msg) { ROS_WARN("t3 perceptionCallback parse"); if (perception_obstacles_.ParseFromString(msg.data)) { double timestamp = perception_obstacles_.header().timestamp_sec(); ROS_INFO("t3 perceptionCallback timestamp %f count:%d", timestamp, perception_obstacles_.perception_obstacle().size()); std::string data; perception_obstacles_.SerializeToString(&data); VehData veh_data; veh_data.set_messagetype(5); veh_data.set_messagedes("PerceptionObstacles"); veh_data.set_contents(data); std::string serialized_data; veh_data.SerializeToString(&serialized_data); } else { ROS_ERROR("t3 perceptionCallback parse fail!"); } } int main(int argc, char **argv) { ros::init(argc, argv, "listener"); ros::NodeHandle n; ros::Subscriber sub = n.subscribe("/perception_node/perception_objects", 1000, perceptionCallback); ros::spin(); return 0; }

这段代码的功能是订阅ROS节点中的/perception_node/perception_objects主题，当有数据到来时，会调用perceptionCallback()函数进行处理。在这个函数中，首先尝试将接收到的数据解析为PerceptionObstacles类型的数据...

std::lock_guard<std::mutex> guard(mutex_);

std::lock_guard<std::mutex> 是 C++ 中用于简化线程安全区管理的一种工具。当你想要在一段代码中保证对某个共享资源（如 std::mutex）的互斥访问时，可以使用它。它的工作原理是在构造时自动尝试获取锁，并在...

在ros项目中添加发送websocket wss消息的功能，修改如下代码并在CmakeLists.txt中添加依赖，实现将serialized_data发送到wss://autopilot-test.t3go.cn:443/api/v1/vehicle/push/message/LFB1FV696M2L43840。main.cpp：#include "ros/ros.h" #include "std_msgs/String.h" #include <boost/thread/locks.hpp> #include <boost/thread/shared_mutex.hpp> #include "third_party/apollo/proto/perception/perception_obstacle.pb.h" #include "t3_perception.pb.h" apollo::perception::PerceptionObstacles perception_obstacles_; void perceptionCallback(const std_msgs::String& msg) { ROS_WARN("t3 perceptionCallback parse"); if (perception_obstacles_.ParseFromString(msg.data)) { double timestamp = perception_obstacles_.header().timestamp_sec(); ROS_INFO("t3 perceptionCallback timestamp %f count:%d", timestamp, perception_obstacles_.perception_obstacle().size()); std::string data; perception_obstacles_.SerializeToString(&data); VehData veh_data; veh_data.set_messagetype(5); veh_data.set_messagedes("PerceptionObstacles"); veh_data.set_contents(data); std::string serialized_data; veh_data.SerializeToString(&serialized_data); } else { ROS_ERROR("t3 perceptionCallback parse fail!"); } } int main(int argc, char **argv) { ros::init(argc, argv, "listener"); ros::NodeHandle n; ros::Subscriber sub = n.subscribe("/perception_node/perception_objects", 1000, perceptionCallback); ros::spin(); return 0; }CMakeLists.txt：cmake_minimum_required(VERSION 3.0.2) project(t3) find_package(catkin REQUIRED COMPONENTS roscpp rospy pcl_ros std_msgs third_party ) find_package(Protobuf REQUIRED) include_directories(${Protobuf_INCLUDE_DIRS} ${CMAKE_CURRENT_BINARY_DIR}/..) find_package(Boost REQUIRED) include_directories(${Boost_INCLUDE_DIRS}) set(ixwebsocket_INCLUDE_DIR "/usr/local/include/ixwebsocket") set(ixwebsocket_LIBRARIES "/usr/local/lib/libixwebsocket.a") include_directories(${ixwebsocket_INCLUDE_DIR}) include_directories(${CATKIN_DEVEL_PREFIX}/${CATKIN_GLOBAL_INCLUDE_DESTINATION}/${PROJECT_NAME}) include_directories(${CATKIN_DEVEL_PREFIX}/${CATKIN_GLOBAL_INCLUDE_DESTINATION}/smartview) catkin_package(INCLUDE_DIRS ${PROJECT_INCLUDE_DIRS} DEPENDS ${GFLAGS_LIBRARIES} ) include_directories( ${catkin_INCLUDE_DIRS} ${PROTOBUF_INCLUDE_DIR} ${PROJECT_SOURCE_DIR}/.. ) add_executable(${PROJECT_NAME}_node src/main.cpp ) add_dependencies(${PROJECT_NAME}_node ${catkin_EXPORTED_TARGETS}) target_link_libraries(${PROJECT_NAME}_node ${catkin_LIBRARIES} ${PROTOBUF_LIBRARIES} smartview_proto ) install(TARGETS ${PROJECT_NAME}_node ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} RUNTIME DESTINATION ${CATKIN_GLOBAL_BIN_DESTINATION} )

#include <boost/thread/shared_mutex.hpp> #include "third_party/apollo/proto/perception/perception_obstacle.pb.h" #include "t3_perception.pb.h" #include <ixwebsocket/IXWebSocket.h> using namespace ix...

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