ROS学习笔记（一）：在ROS中使用USB网络摄像头传输图像

在现实中，我们一般是使用摄像头来获取图像，这次要实现在ROS中使用摄像头传输图像，我使用的是usb网络摄像头（不需要驱动，我使用的是不需要驱动的usb摄像头），首先我们可以获取当前电脑连接的usb设备，代码如下所示:

~$ lsusb  

如果需要驱动，可以参考我引用的文章：http://blog.csdn.net/yake827/article/details/44983093
下面我详细介绍如何一步一步操作（给初学者一个参考，大牛绕过）
首先创建一个工作空间：

~$ mkdir -p ~/ros_ws/src
~$ cd ~/ros_ws/
~$ catkin_make
~$ source devel/setup.bash

再建立一个程序包

~$ cd ~/ros_ws/src
~$ catkin_create_pkg learning_image_transport roscpp std_msgs cv_bridge image_transport sensor_msgs 

然后在程序包learning_image_transport下的src目录中建立两个cpp文件

~$ cd ~/ros_ws/src/learning_image_transport/src/
~$ gedit my_publisher.cpp

将下列代码复制进去

    #include   
    #include   
    #include   
    #include   
    #include  // for converting the command line parameter to integer  

    int main(int argc, char** argv)  
    {  
      // Check if video source has been passed as a parameter  
      if(argv[1] == NULL)   
        {  
                ROS_INFO("argv[1]=NULL\n");  
            return 1;  
        }  

      ros::init(argc, argv, "image_publisher");  
      ros::NodeHandle nh;  
      image_transport::ImageTransport it(nh);  
      image_transport::Publisher pub = it.advertise("camera/image", 1);  

      // Convert the passed as command line parameter index for the video device to an integer  
      std::istringstream video_sourceCmd(argv[1]);  
      int video_source;  
      // Check if it is indeed a number  
      if(!(video_sourceCmd >> video_source))   
      {  
          ROS_INFO("video_sourceCmd is %d\n",video_source);  
          return 1;  
      }  

      cv::VideoCapture cap(video_source);  
      // Check if video device can be opened with the given index  
      if(!cap.isOpened())   
      {  
          ROS_INFO("can not opencv video device\n");  
          return 1;  
      }  
      cv::Mat frame;  
      sensor_msgs::ImagePtr msg;  

      ros::Rate loop_rate(5);  
      while (nh.ok()) {  
        cap >> frame;  
        // Check if grabbed frame is actually full with some content  
        if(!frame.empty()) {  
          msg = cv_bridge::CvImage(std_msgs::Header(), "bgr8", frame).toImageMsg();  
          pub.publish(msg);  
          //cv::Wait(1);  
        }  

        ros::spinOnce();  
        loop_rate.sleep();  
      }  
    }  

保存以后，继续创建my_subscriber.cpp

~$ gedit my_subscriber.cpp

复制下列代码

    #include   
    #include   
    #include   
    #include   

    void imageCallback(const sensor_msgs::ImageConstPtr& msg)  
    {  
      try  
      {  
        cv::imshow("view", cv_bridge::toCvShare(msg, "bgr8")->image);  
       // cv::waitKey(30);  
      }  
      catch (cv_bridge::Exception& e)  
      {  
        ROS_ERROR("Could not convert from '%s' to 'bgr8'.", msg->encoding.c_str());  
      }  
    }  

    int main(int argc, char **argv)  
    {  
      ros::init(argc, argv, "image_listener");  
      ros::NodeHandle nh;  
      cv::namedWindow("view");  
      cv::startWindowThread();  
      image_transport::ImageTransport it(nh);  
      image_transport::Subscriber sub = it.subscribe("camera/image", 1, imageCallback);  
      ros::spin();  
      cv::destroyWindow("view");  
    }  

接下来要把涉及到的各种包和opencv在CMakeList中声明一下。
回到程序包目录下

-$ cd ~/ros_ws/src/learning_image_transport/
-$ gedit CMakeLists.txt

添加以下语句

find_package(OpenCV REQUIRED)  
# add the publisher example  
add_executable(my_publisher src/my_publisher.cpp)  

target_link_libraries(my_publisher ${catkin_LIBRARIES} ${OpenCV_LIBRARIES})  

# add the subscriber example  
add_executable(my_subscriber src/my_subscriber.cpp)  
target_link_libraries(my_subscriber ${catkin_LIBRARIES} ${OpenCV_LIBRARIES}) 

将这个包进行编译

-$ cd ~/ros_ws/
-$ catkin_make  

接下来开始运行程序,首先启动ROS。

~$ roscore 

运行my_publisher节点.(如果运行不起来,需要先source devel/setup.bash)

$ rosrun learning_image_transport my_publisher 0 

这时候会看到我们的摄像头灯已经亮起来了,0代表默认摄像头,如果有多个摄像头,则第二个是1,依次类推.

接下来运行my_subscriber节点来接收图像

-$ rosrun learning_image_transport my_subscriber

这时候如果没有错误的话就会弹出图像窗口