Ros Kinetic 配置 OpenCV2和CV_bridge (Python, C++)

本篇介绍如何在Ros-kinetic环境下运用opencv2进行开发的配置，系统平台为64位Ubuntu16.04。

需要系统环境：

1.Ros kinetic版本，一般自带cv_bridge, 若没有可以通过apt下载

sudo apt-get install ros-kinetic-cv-bridge

2.OpenCV 2.4.9版本，一般来说cv_bridge依赖的OpenCV版本为2.4.8，亲测2.4.9可以用，安装可以参考https://blog.csdn.net/u013250416/article/details/78913126

　　2.1  先下载OpenCV的源码http://opencv.org/downloads.html并解压到任意目录

　　2.2 事先安装下列软件

  sudo apt-get install build-essential cmake libgtk2.0-dev pkg-config python-dev python-numpy libavcodec-dev libavformat-dev libswscale-dev 

　　2.3 进入cmake并编译

cd cmake
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..  
sudo make install  

　　2.4 测试

　　新建C++代码test.cpp：

#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
using namespace cv;
using namespace std;
int main (int argc, char **argv)
{
    Mat image, image_gray;
    image = imread(argv[1], CV_LOAD_IMAGE_COLOR );
    if (argc != 2 || !image.data) {
        cout << "No image data\n";
        return -1;
    }

    cvtColor(image, image_gray, CV_RGB2GRAY);
    namedWindow("image", CV_WINDOW_AUTOSIZE);
    namedWindow("image gray", CV_WINDOW_AUTOSIZE);

    imshow("image", image);
    imshow("image gray", image_gray);

    waitKey(0);
    return 0;
}

　　新建makefile文件：

CC = g++
# 可执行文件
TARGET = test
# C文件
SRCS = main.cpp
# 目标文件
OBJS = $(SRCS:.cpp=.o)
# 库文件
DLIBS = -lopencv_core -lopencv_imgproc -lopencv_highgui
# 链接为可执行文件
$(TARGET):$(OBJS)
    $(CC) -o $@ $^ $(DLIBS)  
clean:
    rm -rf $(TARGET) $(OBJS)
# 编译规则 $@代表目标文件 $< 代表第一个依赖文件
%.o:%.cpp
    $(CC) -o $@ -c $<

　　make，产生了两个文件：test文件和test.o文件 ，并测试：

 

./test lena.bmp

 

注：程序运行的过程中，常见错误error while loading shared libraries: libopencv_highgui.so.2.4: cannot open，解决方法：

　　1.用gedit打开/etc/ld.so.conf，注意要用sudo打开获得权限，不然无法修改，
如：sudo gedit /etc/ld.so.conf，在文件中加上一行 /usr/loacal/lib，/user/loacal是opencv安装路径 就是makefile中指定的安装路径
加了之后的变为

 

 2.再运行sudo ldconfig,
修改bash.bashrc文件，sudo gedit /etc/bash.bashrc
在文件末尾加入：
PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib/pkgconfig
export PKG_CONFIG_PATH
加后的图：

3.

source /etc/bash.bashrc

然后就可以正常运行了！

Python环境配置：

python下由于ros一般自带cv_bridge,系统同样自带cv2，处理起来比较简单，当摄像机采出的图像以rostopic发布时，可以用以下代码进行测试：

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

class Image_Receiver:
    def __init__(self):
        rospy.Subscriber('/camera/rgb/image_color', Image, callback=self.image_rgb_callback, queue_size=1000)
        self.cv_bridge = CvBridge()
        rospy.spin()

    def image_rgb_callback(self, data):
        cv_image = self.cv_bridge.imgmsg_to_cv2(data, "bgr8")
        (rows, cols, channnels) = cv_image.shape
        if cols > 60 and rows > 60:
            cv2.circle(cv_image, (50, 50), 10, 255)
        cv2.imshow("Image Window", cv_image)
        cv2.waitKey(3)

if __name__ == '__main__':
    rospy.init_node('rgb_image_get_node')
    Image_Receiver()

C++ QT环境配置：

由于本人开发环境为qt，则需要在qmake执行.pro文件时同时加载ros和opencv2的环境，在.pro文件中添加以下依赖：

 

# ROS Depends
INCLUDEPATH += /opt/ros/kinetic/include \

DEPENDPATH += /opt/ros/kinetic/include \

LIBS += -L/opt/ros/kinetic/lib \
        -L/usr/local/lib \
        -lroscpp \
        -lrospack \
        -lpthread \
        -lrosconsole \
        -lrosconsole_log4cxx \
        -lrosconsole_backend_interface \
        -lxmlrpcpp \
        -lroscpp_serialization \
        -lrostime \
        -lcpp_common \
        -lroslib \
        -ltf \
        -lyaml-cpp \
        -lkdl_conversions \
        -lcv_bridge \
        -lboost_system

# OpenCV Depends
LIBS += -lopencv_core \
        -lopencv_imgproc \
        -lopencv_highgui

 

编写测试代码如下：

#include <ros/ros.h>
#include <sensor_msgs/Image.h>
#include <cv_bridge/cv_bridge.h>
class Image_Receiver
{
public:
    Image_Receiver()
    {
        subsRgbImage = nh.subscribe<sensor_msgs::Image>("/camera/rgb/image_color", 1000, &Image_Receiver::image_rgb_callback, this);
        ros::spin();
    }
    ~Image_Receiver()
    {

    }
    void image_rgb_callback(const sensor_msgs::Image::ConstPtr& data)
    {
        cv_bridge::CvImagePtr cv_ptr;
        try{
            cv_ptr = cv_bridge::toCvCopy(data, sensor_msgs::image_encodings::BGR8);
        } catch (cv_bridge::Exception& e) {
            std::cout << e.what() << std::endl;
        }
        receiveFlag = true;
    }

private:
    sensor_msgs::Image::ConstPtr m_data;
    ros::NodeHandle nh;
    ros::Subscriber subsRgbImage;
    bool receiveFlag;
};

int main(int argc, char** argv)
{
    ros::init(argc, argv, "image_receive_node");
    Image_Receiver image_receiver_instance;
    return 0;
}