rosbag 包转TUM数据集

参考链接：
ROS学习：制作自己的TUM数据集

配置环境

1.安装ROS
参考我的博客
https://blog.csdn.net/qin_liang/article/details/127035615

2.查看rosbag中的topic

rosbag info xxx.bag

3.创建catkin_ws/src文件夹
在src下运行

catkin_create_pkg rosbag2tum roscpp std_msgs sensor_msgs  cv_bridge

4.进入生成的rosbag2tum里面的src
在src文件夹下创建bagToTUM.cpp文件：
（修改自己对应的rosbag包里面的节点）

#include <cv_bridge/cv_bridge.h>
#include <opencv2/opencv.hpp>
#include <ros/ros.h>
#include <iostream>
#include <chrono>
#include <string> 
using namespace std;

//设置一些保存路径
string Path = "/home/ql/pan1/Dataset/test/seq3/";
string pathRGB = Path + "rgb.txt";
string pathDepth = Path + "depth.txt";
string dataRGB = Path + "rgb/";
string dataDepth = Path + "depth/";
//设置图像topic名称
string depth_topic = "/zed2i/zed_node/depth/depth_registered";
string rgb_topic = "/zed2i/zed_node/left_raw/image_raw_color";

//保存图像路径至txt文件下
void savePath(string &path, string &type,double time){
    
    
   ofstream of;
   of.open(path, std::ios_base::app);
   if(of.fail()){
    
    
       ROS_INFO("Fail to opencv file!!");
   }else{
    
    
       of<<endl;
       of<<std::fixed<< time <<" "<< type <<time<<".png"; 
       of.close();
   }
}
//RGB图像回调函数
void GrabRGB(const sensor_msgs::ImageConstPtr& msg)
{
    
    
    cv_bridge::CvImageConstPtr cv_ptr;
    try
    {
    
    
       //保存图像及路径
        cv_ptr = cv_bridge::toCvShare(msg);
        //颜色通道转换
        cv::Mat bgrImage;
        // cvtColor(cv_ptr->image, bgrImage, CV_BGR2RGB);
        //cv::imshow("RGB", bgrImage);

        double time = cv_ptr->header.stamp.toSec();
        string type = "rgb/";
        savePath(pathRGB, type, time);
        std::ostringstream osf;
        osf<< dataRGB <<std::fixed <<time << ".png";//图像以时间戳命名
        // cv::imwrite(osf.str(), bgrImage);
        cv::imwrite(osf.str(), cv_ptr->image);
    }
    catch (cv_bridge::Exception& e)
    {
    
    
        ROS_ERROR("cv_bridge exception: %s", e.what());
        return;
    }
     //cv::waitKey(2);    
}

//深度图像回调函数
void GrabDepth(const sensor_msgs::ImageConstPtr& msg)
{
    
    
    cv_bridge::CvImageConstPtr cv_ptr;
    try
    {
    
    
        cv_ptr = cv_bridge::toCvShare(msg);
        //imshow("Depth", cv_ptr->image);
        double time = cv_ptr->header.stamp.toSec();
        string type = "depth/";
        savePath(pathDepth, type, time);
        std::ostringstream osf;
        osf<< dataDepth <<std::fixed  <<time << ".png";
        cv::imwrite(osf.str(), cv_ptr->image);
    }
    catch (cv_bridge::Exception& e)
    {
    
    
        ROS_ERROR("cv_bridge exception: %s", e.what());
        return;
    }
     //cv::waitKey(2);
}

//节点主函数
int main(int argc,char **argv)
{
    
    
    ros::init(argc,argv,"bagToTUM");
    ros::start();
    ofstream of;
    of.open(pathRGB, std::ios_base::app);
    if(of.fail()){
    
    
       ROS_INFO("Fail to opencv file!!");
    }else{
    
    
       of<<"#------------start a new dataset-----------------"; 
       of.close();
    }
    of.open(pathDepth, std::ios_base::app);
    if(of.fail()){
    
    
       ROS_INFO("Fail to opencv file!!");
    }else{
    
    
       of<<"#------------start a new dataset-----------------"; 
       of.close();
    }

    //订阅图像话题
    ROS_INFO("bagToTUM is ready.");
    ros::NodeHandle nodeHandler;
    ros::Subscriber subRGB = nodeHandler.subscribe(rgb_topic, 5, &GrabRGB);
    ros::Subscriber subDepth = nodeHandler.subscribe(depth_topic, 5, &GrabDepth);

    ros::spin();
    return 0;
}

5.配置rosbag2tum文件夹下的CMakeLists.txt

cmake_minimum_required(VERSION 3.0.2)
project(rosbag2tum)

find_package(catkin REQUIRED COMPONENTS
  cv_bridge
  roscpp
  sensor_msgs
  std_msgs
)

find_package(OpenCV 3.0 QUIET)
if(NOT OpenCV_FOUND)
   find_package(OpenCV 2.4.3 QUIET)
   if(NOT OpenCV_FOUND)
      message(FATAL_ERROR "OpenCV > 2.4.3 not found.")
   endif()
endif()

catkin_package(
#  INCLUDE_DIRS include
#  LIBRARIES rosbag2tum
  CATKIN_DEPENDS cv_bridge roscpp sensor_msgs std_msgs
#  DEPENDS system_lib
)

include_directories(
# include
  ${OpenCV_INCLUDE_DIRS}
  ${catkin_INCLUDE_DIRS}
)

# add_dependencies(${PROJECT_NAME} ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})

 add_executable(bag2tum src/bagToTUM.cpp)
 target_link_libraries(bag2tum ${catkin_LIBRARIES} ${OpenCV_LIBS})

5.在catkin_ws文件夹下运行

catkin_make

如果是在anaconda环境下，需要在第一次编译的时候使用

catkin_make -DPYTHON_EXECUTABLE=/usr/bin/python3

转化

运行之前先在输出的文件夹的位置创建好depth rgb文件夹

1.打开终端运行

roscore

2.在类似这样的文件夹/home/ql/pan1/catkin_ws/devel/lib/rosbag2tum下运行

./rosbag2tum

这个文件夹下必须有这个可执行文件

3.播放对应的bag包

rosbag play  xxx.bag  -r 0.5

这样就可以转化出对应的图片了