Ros利用usb_cam进行opencv测试

调了几天遇到很多坑啊！第一次玩Ros的痛！！
1、首先看usb_cam：
直接下载代码：
进入创建好的工作空间：

cd ~/catkin_ws/src
git clone https://github.com/bosch-ros-pkg/usb_cam.git

然后退回到工作空间，编译代码：

cd ~/catkin_ws
catkin_make

编译好之后，添加编译好的文件到环境变量：

source devel/setup.bash

然后接下来测试usb_cam：
先运行usb_cam节点：

rosrun usb_cam usb_cam_node

运行上面命令发现没有显示图像，只看到摄像头打开了。这是因为ros发布的topic是/usb_cam/image_raw。新打开一个终端，可以通过如下命令查看：

rostopic list

结果如下：

/rosout
/rosout_agg
/usb_cam/camera_info
/usb_cam/image_raw
/usb_cam/image_raw/compressed
/usb_cam/image_raw/compressed/parameter_descriptions
/usb_cam/image_raw/compressed/parameter_updates
/usb_cam/image_raw/compressedDepth
/usb_cam/image_raw/compressedDepth/parameter_descriptions
/usb_cam/image_raw/compressedDepth/parameter_updates
/usb_cam/image_raw/theora
/usb_cam/image_raw/theora/parameter_descriptions
/usb_cam/image_raw/theora/parameter_updates

所以我们需要运行如下命令才可以看到图像：

rosrun image_view image_view image:=/usb_cam/image_raw

或者直接写launch文件，这样就不用一个终端运行node，一个终端看图像。新建usb_cam_test.launch：

  
  "usb_cam" pkg="usb_cam" type="usb_cam_node" output="screen" >  
    <param name="video_device" value="/dev/video0" />  
    <param name="image_width" value="640" />  
    <param name="image_height" value="480" />  
    <param name="pixel_format" value="yuyv" />  
    <param name="camera_frame_id" value="usb_cam" />  
    <param name="io_method" value="mmap"/>  
    
  "image_view" pkg="image_view" type="image_view" respawn="false" output="screen">  
    from="image" to="/usb_cam/image_raw"/>  
    <param name="autosize" value="true" />  
    
  

其中字段的意义可按照字面理解，这里不再解释。
然后终端直接运行：

roslaunch usb_cam usb_cam_test.launch

2、接下来，我们看usb_cam采集的图像怎么让opencv处理。
ros提供了一个cv_bridge用以转换Ros采集到图像到opencv能处理的图像。
这里主要参考官网对于cv_bridge的解释及其使用：
http://wiki.ros.org/cv_bridge/Tutorials/UsingCvBridgeToConvertBetweenROSImagesAndOpenCVImages
包创建：http://wiki.ros.org/ROS/Tutorials/CreatingPackage
这里需要新建opecv的测试工作空间，opencv测试依赖于：

sensor_msgs
cv_bridge
roscpp
std_msgs
image_transport

进入～/catkin_ws/src,创建测试包：

catkin_create_pkg opencvtest sensor_msgs cv_bridge roscpp std_msgs image_transport  rospy roscpp

创建好包之后，进入~/catkin_ws/src/opencvtest/src，将官网的代码保存到此处，命名为opencv_testcam.cpp：

#include <ros/ros.h>
#include<image_transport/image_transport.h>
#include<cv_bridge/cv_bridge.h>
#include<sensor_msgs/image_encodings.h>
#include<opencv2/imgproc/imgproc.hpp>
#include<opencv2/highgui/highgui.hpp>

static const std::string OPENCV_WINDOW = "Image window";

class ImageConverter
{
  ros::NodeHandle nh_;
  image_transport::ImageTransport it_;
  image_transport::Subscriber image_sub_;
  image_transport::Publisher image_pub_;

public:
  ImageConverter()
    : it_(nh_)
  {
    // Subscrive to input video feed and publish output video feed
    image_sub_ = it_.subscribe("/usb_cam/image_raw", 1, 
      &ImageConverter::imageCb, this);
    image_pub_ = it_.advertise("/image_converter/output_video", 1);

    cv::namedWindow(OPENCV_WINDOW);
  }

  ~ImageConverter()
  {
    cv::destroyWindow(OPENCV_WINDOW);
  }

  void imageCb(const sensor_msgs::ImageConstPtr& msg)
  {
    cv_bridge::CvImagePtr cv_ptr;
    try
    {
      cv_ptr = cv_bridge::toCvCopy(msg, sensor_msgs::image_encodings::BGR8);
    }
    catch (cv_bridge::Exception& e)
    {
      ROS_ERROR("cv_bridge exception: %s", e.what());
      return;
    }

    // Draw an example circle on the video stream
    if (cv_ptr->image.rows > 60 && cv_ptr->image.cols > 60)
      cv::circle(cv_ptr->image, cv::Point(50, 50), 10, CV_RGB(255,0,0));

    // Update GUI Window
    cv::imshow(OPENCV_WINDOW, cv_ptr->image);
    cv::waitKey(3);

    // Output modified video stream
    image_pub_.publish(cv_ptr->toImageMsg());
  }
};

int main(int argc, char** argv)
{
  ros::init(argc, argv, "image_converter");
  ImageConverter ic;
  ros::spin();
  return 0;
}

以上代码不解释了，可以查看官网的解释。这里需要注意的是代码：

image_sub_ = it_.subscribe("/usb_cam/image_raw", 1, 
      &ImageConverter::imageCb, this);

这里是usb摄像头的topic，官网默认是/camera/image_raw，这里修改为usb摄像头。
保存退出后，进入上个目录，修改CmakeList.txt，在文件最后添加：

find_package(OpenCV)
include_directories(${OpenCV_INCLUDE_DIRS})
add_executable(opencv_testcam src/opencv_testcam.cpp)
target_link_libraries(opencv_testcam ${catkin_LIBRARIES} ${OpenCV_LIBRARIES})

然后返回工作空间~/catkin_ws执行catkin_make编译工程。
编译完成后，执行

source devel/setup.bash

先打开一个终端运行roscore，用以节点之间的通信交互。
再打开一个终端运行rosrun usbcam usbcam_node
再打开一个终端运行rosrun opencvtest opencv_testcam
之后即可看到opencv处理后摄像头的图像。