【ROS+OpenCV实践】基于ROS系统开启USB相机，完成视频图像的边缘检测

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前言

功能包资源下载：http://www.hzcourse.com/oep/image/ueditor/jsp/upload/file/20190812/62199-精通ROS机器人编程（原书第2版）_配书资源.rar

一、导入和编译功能包

方法1

cd ~/catkin_ws/src  
git clone https://github.com/bosch-ros-pkg/usb_cam.git  
cd ~/catkin_ws  
catkin_make  
echo "source /home/zjc/catkin_ws/devel/setup.bash" >> ~/.bashrc
source ~/.bashrc

方法2

1.创建一个名为cv_bridge_tutorial_pkg的工作空间

catkin_create_pkg cv_bridge_tutorial_pkg sensor_msgs cv_bridge roscpp std_msgs image_transport

2.编译

catkin_make

二、启动相机进行边缘检测

终端1：

roslaunch usb_cam usb_cam-test.launch 

终端2：

rosrun cv_bridge_tutorial_pkg sample_cv_bridge_node

三、代码展示

#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 = "Raw Image window";
static const std::string OPENCV_WINDOW_1 = "Edge Detection";

class Edge_Detector
{
  ros::NodeHandle nh_;
  image_transport::ImageTransport it_;
  image_transport::Subscriber image_sub_;
  image_transport::Publisher image_pub_;
  
public:
  Edge_Detector()
    : it_(nh_)
  {
    // Subscribe to input video feed and publish output video feed
    image_sub_ = it_.subscribe("/usb_cam/image_raw", 1, 
      &Edge_Detector::imageCb, this);
    image_pub_ = it_.advertise("/edge_detector/raw_image", 1);
    cv::namedWindow(OPENCV_WINDOW);
  }

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

  void imageCb(const sensor_msgs::ImageConstPtr& msg)
  {
    cv_bridge::CvImagePtr cv_ptr;
    namespace enc = sensor_msgs::image_encodings;

    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 > 400 && cv_ptr->image.cols > 600){

	detect_edges(cv_ptr->image);
    	image_pub_.publish(cv_ptr->toImageMsg());

	}
  }
  void detect_edges(cv::Mat img)
  {
   	cv::Mat src, src_gray;
	cv::Mat dst, detected_edges;

	int edgeThresh = 1;
	int lowThreshold = 200;
	int highThreshold =300;
	int kernel_size = 5;

	img.copyTo(src);

	cv::cvtColor( img, src_gray, CV_BGR2GRAY );
        cv::blur( src_gray, detected_edges, cv::Size(5,5) );
	cv::Canny( detected_edges, detected_edges, lowThreshold, highThreshold, kernel_size );

  	dst = cv::Scalar::all(0);
  	img.copyTo( dst, detected_edges);
	dst.copyTo(img);

    	cv::imshow(OPENCV_WINDOW, src);
    	cv::imshow(OPENCV_WINDOW_1, dst);
    	cv::waitKey(3);
  }	
};

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

四、代码优化

修改代码的变化，之前边缘检测保留了颜色，修改后去除了颜色，只有灰色；此外，去除了显示原图视频的代码，只显示处理后的视频流；简化了部分代码。
源代码解读：

#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>

class Edge_Detector
{
  ros::NodeHandle nh_;
  image_transport::ImageTransport it_;
  image_transport::Subscriber image_sub_;
  image_transport::Publisher image_pub_;
  
public:
  Edge_Detector()
    : it_(nh_)
  {
    image_sub_ = it_.subscribe("/usb_cam/image_raw", 1,  
      &Edge_Detector::imageCb, this);//订阅输入视频
    image_pub_ = it_.advertise("/edge_detector/raw_image", 1); //发布输出视频
  }
  
    if (cv_ptr->image.rows > 400 && cv_ptr->image.cols > 600)//检测输入图像的是否满足尺寸要求，满足将会执行边缘检测的函数
   {
    detect_edges(cv_ptr->image);
    image_pub_.publish(cv_ptr->toImageMsg());//opencv图像转化为ROS图像，并进行发布。
	}
  }
  
//使用cv_bridge将opencv转化为ROS图像
 void imageCb(const sensor_msgs::ImageConstPtr& msg)//图像的回调函数
  {
    cv_bridge::CvImagePtr cv_ptr;
    namespace enc = sensor_msgs::image_encodings;
    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;
    }
    
void detect_edges(cv::Mat img)
  {
   	cv::Mat src, src_gray, detected_edges;
	img.copyTo(src);
	cv::cvtColor( img, src_gray, CV_BGR2GRAY );
    cv::blur( src_gray, detected_edges, cv::Size(5,5) );
    int edgeThresh = 1;
	int lowThreshold = 200;
	int highThreshold =300;
	int kernel_size = 5;
	cv::Canny( detected_edges, detected_edges, lowThreshold, highThreshold, kernel_size );

    cv::imshow("Edge Detection", dst);
    cv::waitKey(3);
  }	
};

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

五、修改后再编译

在修改sample_cv_bridge_node.cpp中的代码后需要进行一次编译，以更新sample_cv_bridge_node文件，且该节点文件存放在/home/zjc/catkin_make/devel/lib/cv_bridge_tutorial_pkg目录下