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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

8.ROS topic subscribers

8.ROS topic subscribers

The previous section introduced how to write a publisher node program in ros. This section introduces how to write a subscriber program.

8.1 Create a subscriber

The general creation steps are as follows:

  • Initialize ROS nodes
  • Create handle
  • Subscribe to the topics you need
  • Wait in a loop for topic messages, and enter the callback function after receiving the message
  • Complete message processing in the callback function

The callback function is an important part of the subscriber. It processes the subscribed message, then parses the message data, and determines how to proceed with the subsequent program based on the parsed message data.

8.2 C++ version

8.2.1 Writing source code

In the src folder of the function package learn_topic, create a C++ file (the file suffix is .cpp), name it turtle_pose_subscriber.cpp, and paste the following content into turtle_pose_subscriber.cpp,

/*Create a small turtle to receive the current pose information*/
#include <ros/ros.h>
#include "turtlesim/Pose.h"
// After receiving the message, you will enter the message callback function, which will process the received data.
void turtle_poseCallback(const turtlesim::Pose::ConstPtr& msg)
{
    // Print received messages
    ROS_INFO("Turtle pose: x:%0.3f, y:%0.3f", msg->x, msg->y);
}
int main(int argc, char **argv)
{
    ros::init(argc, argv, "turtle_pose_subscriber");// Initialize ROS node
    ros::NodeHandle n;//Here is create handle
    // Create a subscriber. The topic of subscription is the topic of /turtle1/pose. poseCallback is the callback function.
    ros::Subscriber pose_sub = n.subscribe("/turtle1/pose", 10,turtle_poseCallback);
    ros::spin(); // Loop waiting for callback function
    return 0;
}

8.2.2 Modify CMakelist.txt file

Configure in CMakelist.txt, under the build area, add the following content,

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add_executable(turtle_pose_subscriber src/turtle_pose_subscriber.cpp)
target_link_libraries(turtle_pose_subscriber ${catkin_LIBRARIES})

add_executable shows that the generated executable program file is turtle_pose_subscriber, and the compiled source code is turtle_pose_subscriber.cpp in the src directory.

target_link_libraries specifies the libraries that need to be linked when compiling and generating an executable file.

8.2.3 Compile

Terminal input,

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cd ~/ros_ws
catkin_make

image-20231023163058208

After the compilation is passed, you need to re-source the current environment variables to find or update the program. Enter in the terminal.

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cd ~/ros_ws
source devel/setup.bash

8.2.4 Running the program

Run roscore

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roscore

Run the little turtle node

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rosrun turtlesim turtlesim_node

Run the turtle speed control node

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rosrun turtlesim turtle_teleop_key

Run the subscriber node to continuously receive the pose data sent by the little turtle.

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rosrun learn_topic turtle_pose_subscriber

After all programs are running, click on the terminal running the turtle speed control node and press [up, down, left, and right] on the keyboard to control the movement of the turtle. The terminal running the subscriber will print the xy coordinate value of the turtle to the terminal in real time.As shown below,

image-20231023163258938

We can use the rqt_graph tool to view the communication between nodes and terminal input.

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rqt_graph

image-20231023163655762

The inside of the ellipse represents the node name, the inside of the rectangle represents the topic name, and the arrow represents the direction of topic message transmission. The subscriber node program turtle_pose_subscriber we wrote is subscribed to the topic message of /turtle1/pose.This is consistent with the code ros::Subscriber pose_sub = n.subscribe("/turtle1/pose", 10, turtle_poseCallback);, and then in the callback function, print the data, the code is as follows,

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void turtle_poseCallback(const turtlesim::Pose::ConstPtr& msg)
{
    // Print received messages
    ROS_INFO("Turtle pose: x:%0.3f, y:%0.3f", msg->x, msg->y);
}

8.2.5 Program flow chart

image-20231023164512450

8.3 Python version

8.3.1 Writing source code

Create a new python file (file suffix .py) in the scripts folder under the function package learn_topic, name it turtle_pose_subscriber.py, copy and paste the following program code into the turtle_pose_subscriber.py file,

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import rospy
from turtlesim.msg import Pose
def poseCallback(msg):
    rospy.loginfo("Turtle pose: x:%0.3f, y:%0.3f", msg.x, msg.y)
def turtle_pose_subscriber():
    rospy.init_node('turtle_pose_subscriber', anonymous=True)# ROS node initialization
    # Create a Subscriber, subscribe to the topic named /turtle1/pose, and register the callback function poseCallback
    rospy.Subscriber("/turtle1/pose", Pose, poseCallback)
    rospy.spin()# Loop waiting for callback function
if __name__ == '__main__':
    turtle_pose_subscriber()

The python program does not need to be compiled, but it needs to add executable permissions and enter it in the terminal.

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

8.3.2 Run

Open roscore,

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roscore

Run the little turtle node,

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rosrun turtlesim turtlesim_node

Run the turtle speed control node,

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rosrun turtlesim turtle_teleop_key

Run the subscriber node and continue to receive the pose data sent by the little turtle.

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rosrun learn_topic turtle_pose_subscriber.py

After all programs are running, click on the terminal running the turtle speed control node and press [up, down, left, and right] on the keyboard to control the movement of the turtle. The terminal running the subscriber will print the xy coordinate value of the turtle to the terminal in real time.As shown below,

image-20231023165328965

8.3.3 Program flow chart

image-20231023165516756

ROS topic publisher
ROS service client

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