ROS机械臂——ROS结合OpenCV案例（含资源）

纲要

摄像头驱动

图像属性

图像压缩

### Realsense摄像头

点云展示

### 点云图像属性
## 摄像头标定

摄像头标定流程

如何使用标定文件

OpenCV

ROS与OpenCV的集成框架

![在这里插入图片描述](https://i-blog.csdnimg.cn/direct/b0ff143b710543839325d19c7a3c04c5.png

ROS+OpenCV 图像绘制（cpp）

cpp 复制代码

#include <ros/ros.h>
#include <image_transport/image_transport.h>
#include <sensor_msgs/image_encodings.h>
#include <cv_bridge/cv_bridge.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){
             //订阅图像输入流 、发布图像输出流(话题名称，处理队列的大小为1，回调函数的名称，回调函数所处的类)
             image_sub = it.subscribe("/usb_cam/image_raw",1,&ImageConverter::imageCb,this);
             image_pub = it.advertise("cv_bridge_image",1);

             cv::namedWindow(OPENCV_WINDOW);
       }

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

      void imageCb(const sensor_msgs::ImageConstPtr& msg){
          //msg为图像信息在ROS中的地址
           cv_bridge::CvImagePtr cv_ptr;

           try{
            //创建一个图像数据的拷贝
            //toCvCopy复制数据并返回复制数据地址指针cv_bridge::CvImagePtr
            //将ROS图像消息转换为了CvImage以在OpenCV中使用
            //sensor_msgs::image_encodings::BGR8是"bgr8"字符串常量。
               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;
           }
       
       //在opencv窗口画红圈
       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));

       //生成opencv窗口
       cv::imshow(OPENCV_WINDOW,cv_ptr->image);
       cv::waitKey(3);

       //将opencv生成的话题信息，转换为Image图像信息，通过iamge_pub发布出去，可以通过rqt_image_view订阅话题，查看opencv生成的红圈
       image_pub.publish(cv_ptr->toImageMsg());    
    }
};

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

}

ROS+OpenCV 图像绘制（python）

python 复制代码

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import rospy
import cv2
from cv_bridge import CvBridge, CvBridgeError
from sensor_msgs.msg import Image

class image_converter:
    def __init__(self):    
        # 创建cv_bridge，声明图像的发布者和订阅者
        self.image_pub = rospy.Publisher("cv_bridge_image", Image, queue_size=1)
        self.bridge = CvBridge()
        self.image_sub = rospy.Subscriber("/usb_cam/image_raw", Image, self.callback)

    def callback(self,data):
        # 使用cv_bridge将ROS的图像数据转换成OpenCV的图像格式
        try:
            cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
        except CvBridgeError as e:
            print e

        # 在opencv的显示窗口中绘制一个圆，作为标记
        (rows,cols,channels) = cv_image.shape
        if cols > 60 and rows > 60 :
            cv2.circle(cv_image, (60, 60), 30, (0,0,255), -1)

        # 显示Opencv格式的图像
        cv2.imshow("Image window", cv_image)
        cv2.waitKey(3)

        # 再将opencv格式额数据转换成ros image格式的数据发布
        try:
            self.image_pub.publish(self.bridge.cv2_to_imgmsg(cv_image, "bgr8"))
        except CvBridgeError as e:
            print e

if __name__ == '__main__':
    try:
        # 初始化ros节点
        rospy.init_node("cv_bridge_test")
        rospy.loginfo("Starting cv_bridge_test node")
        image_converter()
        rospy.spin()
    except KeyboardInterrupt:
        print "Shutting down cv_bridge_test node."
        cv2.destroyAllWindows()

ROS+OpenCV物体识别

源码

get2DLocation()：获取桌面以及桌面物体的坐标

设置红色、绿色通道，通过像素点灰度值判断出黑色桌子的具体位置，将黑色像素点具体位置的二进制数值设置为255，非黑设置为0，计算出桌子中心点，最后将非0点画上方框
找红点、画蓝框

识别绿色圆柱（contours:轮廓）