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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

1. Official Raspberry PI system

1. Raspberry Pi official system

32-bit system

The Raspberry Pi is a low-cost microcomputer targeted at education and the maker community. Its 32-bit operating system is designed to make full use of the Raspberry Pi's hardware resources. Typically based on ARM architecture, such operating systems offer lightweight performance and rich functionality. Raspbian is the officially recommended 32-bit operating system. It is based on Debian Linux and provides rich software packages and good support for Raspberry Pi hardware. In addition, you can also choose other 32-bit operating systems, such as Ubuntu MATE, Arch Linux, etc., to meet different needs.

These operating systems can be easily installed and booted from the Raspberry Pi's micro SD card. They support various development tools and programming languages such as Python, Scratch, etc., making them ideal for educational and learning purposes. In addition, the 32-bit operating system of Raspberry Pi can also be used as an experimental platform for the development of Internet of Things projects, home media center equipment, network servers, etc. Due to its low power consumption and small design, Raspberry Pi is also widely used for prototyping embedded systems and IoT devices.

64-bit system

The Raspberry Pi's 64-bit operating system is designed to better exploit the performance of its hardware. This type of operating system is usually based on the ARM architecture, which provides a larger memory addressing space and higher performance, allowing the Raspberry Pi to handle more complex tasks and applications. Currently, Raspberry Pi 3 and later models support 64-bit operating systems.

Officially recommended 64-bit operating systems include the Debian-based Raspberry Pi OS 64-bit version, as well as other Linux distributions such as Ubuntu Server, Fedora, etc. These operating systems typically support rich software packages and provide good support for Raspberry Pi hardware. They are also suitable for various development tools and programming languages, such as Python, C++, etc., to meet the needs of education, learning and practical applications.

The 64-bit operating system of Raspberry Pi can be used as a platform for developing high-performance Internet of Things devices, home media centers, network servers and other projects. Due to its larger memory addressing space and higher performance, 64-bit operating systems can also better support data-intensive applications, such as large-scale data processing, machine learning, etc.

Lightweight operating system

A lightweight operating system is an operating system specifically designed to run on resource-constrained devices and is typically small and efficient. The lightweight operating system on the Raspberry Pi is primarily used for embedded development, IoT projects, and other applications with low resource requirements.

Raspberry Pi OS Lite is the officially recommended lightweight operating system for Raspberry Pi. It is a streamlined version based on Debian and does not include a desktop environment and a large number of pre-installed software. This allows it to have smaller storage space requirements and faster boot times, and is suitable for different models of Raspberry Pi.

Lightweight operating systems usually provide basic system functions and services. Users can install and configure required software packages according to actual needs, such as web servers, databases, etc. They usually also provide a wealth of development tools and programming language environments, such as Python, C++, etc., to meet the needs of embedded development.

The Raspberry Pi lightweight operating system is suitable for projects that do not require complex graphical interfaces and a large amount of pre-installed software, such as sensor data collection, remote control, Internet of Things devices, etc. They excel in resource utilization and performance, making the Raspberry Pi better suited for a variety of embedded and IoT applications.

Desktop system

The Raspberry Pi desktop operating system is an operating system designed by the Raspberry Pi Foundation for the Raspberry Pi single-board computer. It is based on the Linux operating system and has been optimized and customized to adapt to the Raspberry Pi hardware platform, providing a graphical desktop environment and rich applications.

The Raspberry Pi desktop operating system has a simple and easy-to-use user interface, and users can operate it through the mouse and keyboard. It has built-in some commonly used office software, Internet browsers and media players to facilitate users for office work, Internet access and entertainment.

In addition, the Raspberry Pi desktop operating system also supports the installation and running of various Linux applications. Users can easily obtain the required software through the software package manager and perform customized configurations.

The Raspberry Pi desktop operating system also has rich hardware support and community resources. Users can obtain technical support and exchange experience through official forums and community Q&A platforms.

Bookworm Operating System

The Raspberry Pi Bookworm operating system is a lightweight Linux distribution based on Debian, specially designed for Raspberry Pi computers. It aims to provide a simple and efficient operating system environment to meet the reading, writing and learning needs of Raspberry Pi users.

The Bookworm operating system uses a lightweight desktop environment, allowing the Raspberry Pi to run smoothly even with limited resources. The operating system is pre-installed with a series of reading-related software, such as LibreOffice Writer for document writing, Evince for PDF file reading, Caliber for e-book management, etc. These software aid users in reading and writing tasks, and promote learning and knowledge management.

In addition to reading and writing tools, Bookworm operating system also provides some basic applications, such as web browsers, email clients, etc., to meet daily office and study needs. In addition, due to the versatility and programmability of Raspberry Pi, the Bookworm operating system also supports programming languages such as Python, providing users with the possibility of learning programming and development.

BullseyeOS

The Raspberry Pi Bullseye operating system is the latest version of the Raspberry Pi based on Debian 11. This operating system has been comprehensively upgraded and improved, providing users with a more stable and efficient experience.

The Bullseye operating system uses the latest Linux kernel and software packages, bringing more powerful performance and functionality to the Raspberry Pi. It provides a modern desktop environment, pre-installed with new applications, such as Chromium browser, LibreOffice office suite, VLC media player, etc., to meet the needs of users for daily use.

In addition, the Bullseye operating system has also been optimized for the hardware characteristics of the Raspberry Pi, providing better hardware support and performance tuning. At the same time, it also supports Raspberry Pi's official hardware acceleration functions, such as OpenGL, video decoding, etc., allowing users to enjoy a smoother experience.

In addition, Bullseye operating system has also made improvements in security and stability, enhanced system protection and stability, and provided a more reliable system environment.

The above are the more common systems for Raspberry Pi. For Raspberry Pi beginners, we recommend the desktop system.

Power over Ethernet (PoE) connector
Ubuntu

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