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Raspberry Pi 5📖
00.Quick Start (Must See)
  • 1.Quick Start (Must See)
  • 2.TF card expand capacity
  • 3.Raspberry Pi 5 - 1GB Version Must - See Before Use
01.Introduction to Raspberry PI 5
  • 1. Introduction to Raspberry PI 5
  • 2. Raspberry PI system installation and backup
  • 3. Powering the Raspberry PI 5
  • 4. Startup of Raspberry PI 5
  • 5. Installation and introduction of shell and active radiator
  • 6. GPIO and 40-pin pins
  • 7. Tips for using Raspberry PI 5
  • 8. Raspberry PI PCIe connector
  • 9. MIPI CSI DSI connector
  • 10. UART connector
  • 11. Real-time clock
  • 12. Power over Ethernet (PoE) connector
02.Introduction to common mirror systems
  • 1. Official Raspberry PI system
  • 2. Ubuntu
03.Basic settings of Raspberry Pi
  • 1. Update and upgrade operating system
  • 2.Introduction to raspi-config tool
  • 3.config.txt file description
  • 4.Network Configuration
  • 5.remote access
  • 6.Transfer files remotely
  • 7.Set display resolution and rotation
  • 8.Set screen to sleep
  • 9.Get real-time temperature of Raspberry Pi
  • 10.Play audio and video
  • 11.Using USB camera
  • 12.Using MIPI camera
04.Basic programming course
1. C language programming
  • 1. C language environment construction
  • 2. Print Hello World
2. Python programming
  • 1.Python usage
  • 2.Use APT to install Python packages
  • 3.Python virtual environment
  • 4.GPIO Zero
  • 5.Pin output high and low levels
  • 6.Pin reads high and low levels
  • 7.Pin output PWM
3.Jupyter lab programming
  • 1.Jupyter Lab environment construction
  • 2.Basic use of Jupyter Lab
  • 3.Jupyter Lab plug-in installation
  • 4.Basic use of Jupyter Widgets
05.Advanced AI course
5.Advanced AI gameplay
  • 1. Human pose estimation
  • 2. target detection
  • 3.yolov5
  • 4. Camera display
  • 5. QR code
  • 6. face detection
  • 7. Color recognition
2.Getting started with Open Source CV
  • 1. Introduction to Open Source CV
  • 2. Read and display Open Source CV images
  • 3. Open Source CV image writing
  • 4. Open Source CV image quality
  • 5.Open Source CV pixel operation
3.Open Source CV Geometric Transformations
  • 1. Image resizing
  • 2. Cut the image
  • 3. Image translation
  • 4. Image mirroring
  • 5. Affine transformation
  • 6. Rotate the image
  • 7. Perspective transformation
4.Text Picture Drawing Open Source CV image processing and drawing text line segments
  • 1、Grey Processing
  • 2、image binaryzation
  • 3、edge detection
  • 4、Line segment drawing
  • 5、Rectangle and circle drawing
  • 6、Text image drawing
1.Open Source CV environment setup
  • Open Source CV environment setup
6.YOLOv11 development
  • 1. YOLOv11 environment construction
  • 2. CLI Usage
  • 3. Object Detection
  • 4. Instance Segmentation
  • 5. Pose Estimation
  • 6. Image Classification
  • 7. Oriented Bounding Boxes Object Detection
  • 8. Model conversion
  • 9. Dataset Annotation
  • 10. Model training_conversion
  • 11. Model prediction
7.YOLO26 development
  • 01.YOLO26 environment
  • 02.CLI use
  • 03.Object detection
  • 04.Instance segmentation
  • 05.Pose estimation
  • 06.Image classification
  • 07.Oriented bounding box object detection
  • 08.Model conversion
  • 09.Dataset annotation
  • 10.Model training - conversion
  • 11.Model prediction
06.Mediapipe course
  • 1. Face special effects
  • 2. Face detection
  • 3. Three-dimensional object recognition
  • 4. Paintbrush
  • 5. Finger control
  • 6. Gesture recognition
07.Docker Tutorial
  • 1.Overview and installation
  • 2.Common commands
  • 3.Understand and publish images
  • 4.Hardware interaction data processing
  • 5.Enter docker container
  • 6.Update docker images
  • 7. docker container to install ROS using accessories
08.ROS1 advanced tutorial
1.ROS1 basic course
  • 1.ROS introduction
  • 2.ROS installation
  • 3.ROS common command tools
  • 4.ROS workspace
  • 5.ROS function package
  • 6.ROS node
  • 7.ROS topic publisher
  • 8.ROS topic subscribers
  • 9.ROS service client
  • 10.ROS service server
  • 11.ROS action client
  • 12.ROS action server
  • 13.ROS custom message reception
  • 14.ROS-launch file
  • 15.ROS-TF transformation
  • 16.ROS parameter service
  • 17.ROS-rviz use
  • 18.ROS-rqt tool usage
  • 19.Topic message recording and playback
  • 20.urdf model introduction
  • 21.gazebo introduction
  • 22.ROS distributed communication
2.ROS1 vision application
  • 0.Precautions
  • 1.AR vision
  • 2.AR QR code
  • 3.ROS+Opencv basics
  • 4.Face recognition
  • 5.harris corner detection
  • 6.Target tracking algorithm
  • 7.Contour moment
  • 8.Polygon outline
  • 9.Discrete fourier transform algorithm
  • 10.Edge detection algorithm
  • 11.Face detection algorithm
  • 12.Optical flow detection algorithm
  • 13.Contour detection
  • 14.General contour detection
  • 15.Feature point tracking
  • 16.HLS color filtering
  • 17.Hough circle detection
  • 18.Hough linear detection
  • 19.HSV color filtering
  • 20.LK optical flow algorithm
  • 21.detection algorithm
  • 22.Phase dependent displacement detection
  • 23.Image pyramid sampling algorithm
  • 24.RGB color filtering
  • 25.Clear background detection algorithm
  • 26.Simplified optical flow algorithm
  • 27.Simple filter
  • 28.Threshold image processing
  • 29.Watershed segmentation algorithm
09.ROS2 advanced tutorial
1.ROS2 basic course
  • 1. Introduction to ROS2
  • 2.ROS2 install Humble
  • 3.ROS2 development environment
  • 4.ROS2 workspace
  • 5.ROS2 function package
  • 6.ROS2 node
  • 7.ROS2 topic communication
  • 8.ROS2 service communication
  • 9.ROS2 action communication
  • 10.ROS2 custom interface message
  • 11.ROS2 parameter service case
  • 12.ROS2 meta-function package
  • 13.ROS2 distributed communication
  • 14.ROS2 DDS
  • 15.ROS2 time related API
  • 16.ROS2 common command tools
  • 17.ROS2 rviz2 use
  • 18.ROS2 rqt toolbox
  • 19.ROS2 Launch startup file configuration
  • 20.ROS2 recording and playback tool
  • 21.ROS2 URDF model
  • 22.ROS2 Gazebo simulation platform
  • 23.ROS2 TF2 coordinate transformation
2.ROS2 vision application
  • 0.Precautions
  • 1.Opencv basic course
  • 2.ROS+opencv application
  • 3.QR code creation and recognition
  • 4.AR vision
  • 5.Interesting gameplay of mediapipe
10.Offline AI model coursenew
Offline large model (Text Interaction)
  • 0. AI large model image description
  • 1. Deployment of large AI model environments
  • 2. Switch Chinese input method
  • 3. Installation of large model dialogue platform
  • 4. Meta AI Llama 3.2 model
  • 5. Alibaba Cloud Qwen3 Model
  • 6. Microsoft Phi-4-mini model
  • 7. DeepSeek DeepSeek-R1 model
  • 8. Alibaba Cloud Qwen2.5vl Visual Multimodal Large Model
  • 9. Google Gemma3 Visual Multimodal Large Model
  • 10. Llava Visual Multimodal Large Model
  • 11.MiniCPM-V Visual Multimodal Large Model
  • 12. Multimodal Visual Understanding Applications
  • 13. Multimodal Text-Based Image Application
  • 14. Multimodal Video Analysis Application
  • 15. Multimodal visual positioning applications
  • 16. Multimodal table scanning application
  • 17. Multimodal Autonomous Agent Application
Offline large model (Voice Interaction)
  • 0. Voice Interaction Hardware Connection (Must Read)
  • 1. Offline speech-to-text (ASR)
  • 2. Offline Text-to-Speech (TTS)
  • 3. AI large model voice interaction
  • 4. Multimodal visual understanding and voice interaction
  • 5. Multimodal Text-Based Image Application
  • 6. Multimodal Video Analysis Application
  • 7. Multimodal visual positioning applications
  • 8. Multimodal table scanning application
  • 9. Multimodal Autonomous Agent Applications
  • 10. AI large model offline voice assistant
11.Online AI model coursenew
Online large model (text interaction)
  • 1.OpenRouter Large Model API Aggregation Platform
  • 2. Multimodal Visual Understanding Applications
  • 3. Multimodal visual positioning applications
  • 4. Multimodal table scanning application
  • 5. Multimodal Autonomous Agent Applications
Online large model (voice interaction)
  • 0. Voice Interaction Hardware Connection (Must Read)
  • 1. Online speech-to-text (ASR)
  • 2. Online Text-to-Speech (TTS)
  • 3. AI large model voice interaction
  • 4. Multimodal visual and voice interaction
  • 5. Multimodal visual positioning applications
  • 6. Multimodal table scanning application
  • 7. Multimodal Autonomous Agent Applications
  • 8. AI large model online voice assistant
12.OpenClaw Deployment and Basic Usagenew
  • 01. OpenClaw Deployment
  • 02. OpenClaw WhatsApp Plugin Application
  • 03. OpenClaw WebChat Interaction
  • 04. OpenClaw TUI Interaction
  • 05. OpenClaw Tools Introduction
  • 06. OpenClawGate Gateway User Manual
  • 07. OpenClaw Features Overview
  • 08. OpenClawHub Introduction (Skill Installation)
  • 09. OpenClaw Application - File Management
  • 10. OpenClaw Application - Camera
  • 11. OpenClaw Application - Browser Control
  • 12. OpenClaw Application - Script Execution
  • 13. OpenClaw Application - AI Programming (Peripheral & GPIO Control)
  • 14. OpenClaw Application - Dedicated AI Assistant
13.OpenClaw Advanced Developmentnew
1.Preparation Before Use
  • 01. Peripheral Hardware Configuration
  • 02. OpenClaw API-KEY Configuration
  • 03. OpenClaw Model Switching
  • 04. OpenClaw Prompts
  • 05. AI Voice Interaction Configuration
  • 06. 3D Configuration Tests
2.OpenClaw AI Programming Peripheral Control
  • 1. Servo Control
  • 2. RGB Strip
  • 3. OLED Display
  • 4. Temperature and Humidity Sensor
  • 5. Camera Application
  • 6. AI Script Peripheral Control
3.OpenClaw Peripheral Integration Applications
  • 1. Plant Care Assistant
  • 2. AI Calorie Estimation
  • 3. AI Rock Paper Scissors Game
  • 4. AI Pet
  • 5. AI Weather Station
  • 6. Temperature and Humidity Dashboard
  • 7. Scheduled Tasks
Download
  • SystemFile
  • Code
  • SoftWare
  • 3D_Model_File
  • GuideManual
Download ZIP

Welcome to Raspberry Pi 5 repository

13.ROS custom message reception

13.ROS custom message reception

Although ros provides us with a variety of message types, sometimes we need to define some data types ourselves to use. Commonly used ros include topic data and service data.This course explains how to customize the interface message type and how to call it in the program.

13.1 Customized topic messages and usage

Generally, we will create a new folder under the function package folder, named msg, to store customized topic messages. Therefore, we will create a new folder msg under the directory of the learn_topic function package and enter it in the terminal.

cd ~/ros_ws/src/learn_topic
mkdir msg

13.1.1 Define msg file

Switch to the msg directory and create a new blank msg file. The suffix msg indicates that it is an msg file. Here we take Information.msg as an example. Copy the following code into the msg file you just created.

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string company
string city

13.1.2 Add function package dependencies in package.xml

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<build_depend>message_generation</build_depend>
<exec_depend>message_runtime</exec_depend>

13.1.3 Add compilation options to CMakeLists.txt

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#Add message_generation to find_package
add_message_files(FILES Information.msg)
generate_messages(DEPENDENCIES std_msgs)

13.1.4 Compile files

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cd ~/ros_ws
catkin_make

After compilation is completed, enter rosmsg show learn_topic/Information to query the customized message type.

image-20231025155312061

When the above screen appears, it means that a custom message type has been generated. Next, let’s see how to use it.

13.1.5 C++ program uses custom topic message type

We write a publisher and a subscriber to use custom topic messages. Create two new files in the src directory of the function package learn_topic. The commands are Information_publisher.cpp and Information_subscriber.cpp respectively. Copy the following codes into them.

Information_publisher.cpp

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/**
* This routine will publish the /company_info topic, the message type is the custom learn_topic::Information
*/
#include <ros/ros.h>
#include "learn_topic/Information.h"
int main(int argc, char **argv)
{
    // ROS node initialization
    ros::init(argc, argv, "company_Information_publisher");
    // Create node handle
    ros::NodeHandle n;
    // Create a Publisher, publish a topic named /company_info, the message type is learn_topic::Person, and the queue length is 10
    ros::Publisher Information_pub = n.advertise<learn_topic::Information>("/company_info", 10);
    // Set the loop frequency
    ros::Rate loop_rate(1);
    int count = 0;
    while (ros::ok())
    {
        // Initialize messages of type learn_topic::Information
        learn_topic::Information info_msg;
        info_msg.company = "Yahboom";
        info_msg.city = "Shenzhen";
        // Release the news
        Information_pub.publish(info_msg);
        ROS_INFO("Information: company:%s city:%s ",info_msg.company.c_str(), info_msg.city.c_str());
        loop_rate.sleep();// Delay according to cycle frequency
    }
    return 0;
}

Information_subscriber.cpp

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/**
* This routine will subscribe to the /company_info topic and customize the message type learn_topic::Information
*/
#include <ros/ros.h>
#include "learn_topic/Information.h"
// After receiving the subscribed message, the message callback function will be entered to process the data.
void CompanyInfoCallback(const learn_topic::Information::ConstPtr& msg)
{
    // Print received messages
    ROS_INFO("This is: %s in %s", msg->company.c_str(), msg->city.c_str());
}
int main(int argc, char **argv)
{
    ros::init(argc, argv, "company_Information_subscriber");// Initialize ROS node
    ros::NodeHandle n;// Here is the created node handle
    // Create a Subscriber, subscribe to the topic named topic/company_info, and register the callback function CompanyInfoCallback
    ros::Subscriber person_info_sub = n.subscribe("/company_info", 10,CompanyInfoCallback);
    ros::spin();// Loop waiting for callback function
    return 0;
}

Modify the CMakeLists.txt file and add the following content,

​x
add_executable(Information_publisher src/Information_publisher.cpp)
target_link_libraries(Information_publisher ${catkin_LIBRARIES})
add_dependencies(Information_publisher ${PROJECT_NAME}_generate_messages_cpp)

add_executable(Information_subscriber src/Information_subscriber.cpp)
target_link_libraries(Information_subscriber ${catkin_LIBRARIES})
add_dependencies(Information_subscriber ${PROJECT_NAME}_generate_messages_cpp)

Compile the function package and enter it in the terminal,

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cd ~/ros_ws
catkin_make

After the compilation is passed, reopen the two terminals and run Information_publisher and Information_subscriber respectively. Start roscore first and enter in the terminal.

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roscore

Open another terminal,

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rosrun learn_topic Information_publisher

Open another terminal,

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rosrun learn_topic Information_subscriber 

image-20231025161328201

  • Information_publisher, as the publisher, continuously publishes message content to the topic "/company_info" and prints the published message; and Information_subscriber, as the subscriber, also continuously receives the content of the topic "/company_info" and then prints it out in the callback function. .

    So how does the program call custom data? The core parts are as follows:

  • Introduce header files

    xxxxxxxxxx
    #include "learn_topic/Information.h"

    The former learn_topic is the name of the function package, and the latter Information.h is the name of the header file generated by the msg file just created.

  • Use custom message files

    xxxxxxxxxx
    //Define custom message type variables
    learning_topic::Information info_msg; 
    //To copy a defined message, company and city are members of the message
    info_msg.company = "Yahboom";
    info_msg.city = "Shenzhen";
    //Callback function definition data type
    void CompanyInfoCallback(const learning_topic::Information::ConstPtr& msg) 

13.1.6 Python program uses custom message types

In the scripts folder of the function package, create two new files, named Information_publisher.py and Information_subscriber.py, and copy the following codes into them respectively.

xxxxxxxxxx
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import rospy
from learn_topic.msg import Information #Import custom msg
def information_publisher():
    rospy.init_node('information_publisher', anonymous=True)# ROS node initialization
    # Create a Publisher and publish a topic named /company_info. The message type is learn_topic::Information and the queue length is 6.
    info_pub = rospy.Publisher('/company_info', Information, queue_size=6)
    rate = rospy.Rate(10) #Set the loop frequency
    while not rospy.is_shutdown():
    # Initialize messages of type learn_topic::Information
        info_msg = Information()
        info_msg.company = "Yahboom";
        info_msg.city = "Shenzhen";
        info_pub.publish(info_msg)# Release the news
        rospy.loginfo("This is %s in %s.", info_msg.company, info_msg.city)# Print release message
        rate.sleep()# Delay according to cycle frequency
if __name__ == '__main__':
    try:
        information_publisher()
    except rospy.ROSInterruptException:
        pass

Information_subscriber.py

xxxxxxxxxx
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import rospy
from learning_topic.msg import Information #Import custom msg
def CompanyInfoCallback(msg):
    rospy.loginfo("company: name:%s city:%s ", msg.company, msg.city)#Print subscription received information
def Infomation_subscriber():
    rospy.init_node('Infomation_subscriber', anonymous=True)# ROS node initialization
    #Create a Subscriber, subscribe to the topic named /company_info, and register the callback function personInfoCallback
    rospy.Subscriber("/company_info", Information, CompanyInfoCallback)
    rospy.spin()# Loop waiting for callback function
if __name__ == '__main__':
    Infomation_subscriber()

After saving the two files, execution permissions need to be given.

xxxxxxxxxx
cd ~/ros_ws/src/learn_topic/scripts
sudo chmod a+x Information_publisher.py
sudo chmod a+x Information_subscriber.py

Similarly, let's run these two programs to see, first start roscore,

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roscore

Reopen the terminal and enter,

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rosrun learn_topic Information_publisher.py

Enter from another terminal,

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rosrun learn_topic Information_subscriber.py

image-20231025163754902

Python programs do not need to add header files, but they need to import libraries. Please refer to the following content for import and use.

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#Import, learn_topic represents the function package, msg represents the msg folder under the function package, and Infomation represents the name of the custom msg file.
from learn_topic.msg import Information

use

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info_msg = Information()
info_msg.company = "Yahboom";
info_msg.city = "Shenzhen";

13.2 Customized service messages and usage

Generally, we will create a new folder under the function package folder, named srv, to store customized topic messages. Therefore, we will create a new folder srv under the directory of the learn_service function package and enter it in the terminal.

xxxxxxxxxx
cd ~/ros_ws/src/learn_service
mkdir srv

13.2.1 Define srv file

Switch to the srv directory and create a new blank srv file. The suffix srv indicates that it is an srv file. Here we take IntPlus.srv as an example. Copy the following code into the srv file you just created.

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uint8 a
uint8 b
---
uint8 result

Here is an explanation of the structure of the srv file, which is divided into upper and lower parts by the symbol ---. The upper part represents the request and the lower part represents the response.

13.2.2 Add function package dependencies in package.xml

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<build_depend>message_generation</build_depend>
<exec_depend>message_runtime</exec_depend>

13.2.3 Add compilation options to CMakeList.txt

xxxxxxxxxx
#Add message_generation to find_package
add_service_files(FILES IntPlus.srv)
generate_messages(DEPENDENCIES std_msgs)

13.2.4 Compile function package

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cd  ~/ros_ws
catkin_make

After the compilation is successful, enter the following command in the terminal to check whether a customized service message is generated.

xxxxxxxxxx
rossrv show learn_service/IntPlus

image-20231025170506495

The above screen appears, indicating that a customized service message has been generated. Next, let’s talk about how to use it in the program.

13.2.5 C++ program uses custom topic message type

We write a client and a server to use customized topic messages. Create two new files in the src directory of the function package learn_service. The commands are IntPlus_client.cpp and IntPlus_server.cpp respectively. Copy the following codes into them.

IntPlus_client.cpp

xxxxxxxxxx
/*
This routine will request the /Two_Int_Plus service, service data type learn_service::IntPlus
Add two integers and find the sum
*/
#include <ros/ros.h>
#include "learn_service/IntPlus.h"
#include <iostream>
using namespace std;
int main(int argc, char** argv)
{
    int i,k;
    cin>>i;
    cin>>k;
    ros::init(argc, argv, "IntPlus_client");// Initialize ROS node
    ros::NodeHandle node;// Create node handle
    // After discovering the /Two_Int_Plus service, create a service client
    ros::service::waitForService("/Two_Int_Plus");
    ros::ServiceClient IntPlus_client = node.serviceClient<learn_service::IntPlus>("/Two_Int_Plus");
    // Initialize the request data of learn_service::IntPlus
    learn_service::IntPlus srv;
    srv.request.a = i;
    srv.request.b = k;
    ROS_INFO("Call service to plus %d and %d", srv.request.a, srv.request.b);//Request service call
    IntPlus_client.call(srv);
    // Display service call results
    ROS_INFO("Show the result : %d", srv.response.result);// Display service call results
    return 0;
}

IntPlus_server.cpp

xxxxxxxxxx
/**
*This routine will execute the /Two_Int_Plus service, the service data type learn_service::IntPlus
*/
#include <ros/ros.h>
#include "learn_service/IntPlus.h"
// service callback function, input parameter req, output parameter res
bool IntPlusCallback(learn_service::IntPlus::Request &req,learn_service::IntPlus::Response &res)
{
    ROS_INFO("number 1 is:%d ,number 2 is:%d ", req.a, req.b);// Show request data
    res.result = req.a + req.b ;// The feedback result is the sum of two numbers
    return res.result;
}
int main(int argc, char **argv)
{
    ros::init(argc, argv, "IntPlus_server"); //ROS node initialization
    ros::NodeHandle n;// Create node handle
    // Create a server and register the callback function IntPlusCallback
    ros::ServiceServer Int_Plus_service = n.advertiseService("/Two_Int_Plus",IntPlusCallback);
    // Loop waiting for callback function
    ROS_INFO("Ready to caculate.");
    ros::spin();
    return 0;
}

Modify the CMakeLists.txt file and add the following content,

xxxxxxxxxx
add_executable(IntPlus_server src/IntPlus_server.cpp)
target_link_libraries(IntPlus_server ${catkin_LIBRARIES})
add_dependencies(IntPlus_server ${PROJECT_NAME}_generate_messages_cpp)

add_executable(IntPlus_client src/IntPlus_client.cpp)
target_link_libraries(IntPlus_client ${catkin_LIBRARIES})
add_dependencies(IntPlus_client ${PROJECT_NAME}_generate_messages_cpp)

Compile the function package and enter it in the terminal,

xxxxxxxxxx
cd ~/ros_ws
catkin_make

After the compilation is passed, reopen the two terminals and run IntPlus_server and IntPlus_client respectively. Start roscore first and enter in the terminal.

xxxxxxxxxx
roscore

Then reopen a terminal,

xxxxxxxxxx
rosrun learn_service IntPlus_server

image-20231025173344023

Open another terminal and enter,

xxxxxxxxxx
rosrun learn_service IntPlus_client

Then enter two numbers in the terminal and press Enter. The server will calculate and return the value. The returned value is printed on the client terminal.

image-20231025173532123

  • IntPlus_serverr serves as the server. After running, it provides the operation of calculating the addition of two int data. When no client calls the service, it will display and print Ready to caculate. When IntPlus_client is run, it will look for /Two_Int_Plus For the service, after entering two int-type data, press Enter to confirm; the server starts computing after receiving the data, and then returns a result to the client, and the client terminal will print out this value. So how does the program call custom data? The core parts are as follows:

  • Introduce header files

    xxxxxxxxxx
    #include "learn_service/IntPlus.h"

    The former learn_service is the name of the function package, and the latter IntPlus.h is the name of the header file generated by the srv file just created.

  • Use custom service files

    xxxxxxxxxx
    client:
    learning_server::IntPlus srv;
    srv.request.a = i;
    srv.request.b = k;
    #i, k is the addend input by the terminal
    ros::ServiceClient IntPlus_client = node.serviceClient<learning_server::IntPlus>
    ("/Two_Int_Plus");
    IntPlus_client.call(srv);
    server:
    ros::ServiceServer Int_Plus_service = n.advertiseService("/Two_Int_Plus",IntPlusCallback);
    bool IntPlusCallback(learning_server::IntPlus::Request &req,learning_server::IntPlus::Response &res)
13.2.6 Python program uses custom message types

In the scripts folder of the function package, create two new files, named IntPlus_client.py and IntPlus_server.py, and copy the following codes into them respectively.

IntPlus_client.py

xxxxxxxxxx
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import rospy
from learn_service.srv import IntPlus, IntPlusRequest
def Plus_client():
    #ROS node initialization
    rospy.init_node('IntPlus_client')
    rospy.wait_for_service('/Two_Int_Plus')
    try:
        Plus_client = rospy.ServiceProxy('/Two_Int_Plus', IntPlus)
        response = Plus_client(22, 20)# Request service call, enter request data
        return response.result
    except rospy.ServiceException as e:
        print ("failed to call service : %s"%e)
if __name__ == "__main__":
    #Call the service and display the call results
    print ("Show two_int_plus result : %s" %(Plus_client()))

IntPlus_server.py

xxxxxxxxxx
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import rospy
from learn_service.srv import IntPlus, IntPlusResponse
def IntPlusCallback(req):
    rospy.loginfo("Ints: a:%d b:%d", req.a, req.b)# Show request data
    return IntPlusResponse(req.a+req.b)# feedback data
def IntPlus_server():
    rospy.init_node('IntPlus_server')# ROS node initialization
    # Create a server and register the callback function IntPlusCallback
    s = rospy.Service('/Two_Int_Plus', IntPlus, IntPlusCallback)
    print ("Ready to caculate two ints.")# Loop waiting for callback function
    rospy.spin()
if __name__ == "__main__":
    IntPlus_server()

After saving the two files, execution permissions need to be given.

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cd ~/ros_ws/src/learn_topic/scripts
sudo chmod a+x IntPlus_client.py
sudo chmod a+x IntPlus_server.py

Similarly, let's run these two programs to see, first start roscore,

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roscore

Open another terminal and enter,

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rosrun learn_service IntPlus_server.py

Open another terminal and enter,

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rosrun learn_service IntPlus_client.py

The effect is half that of the C++ version, but there is no need to enter numbers after running IntPlus_client.py here. 22 and 20 have been specified in the program. After finding the service, the server directly calculates the result, and then returns it to the client for display.

image-20231025182550348

Python programs do not need to add header files, but they need to import libraries. Please refer to the following content for import and use.

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#server
from learning_server.srv import IntPlus, IntPlusResponse
#client
from learning_server.srv import IntPlus, IntPlusRequest

use,

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#server
s = rospy.Service('/Two_Int_Plus', IntPlus, IntPlusCallback)
return IntPlusResponse(req.a+req.b)#feedback data
#client
response = Plus_client(12, 20)# Request service call, enter request data
return response.result

 

ROS action server
ROS-launch file

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