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Jetson AGX Thor📖
1.Get started
  • 01.Learning Route
  • 02.Getting Started Quickly
2.Jetson basic course
  • 01.Jetson board introduction
  • 02.Flashing a Pure Jetson System via SDK
  • 03.Flashing a Pure Jetson System via USB Drive
  • 04.Flashing the Jetson Bootloader using L4T
  • 05.Replacing the SSD
  • 06.Install Jetpack environment
  • 07.Backup SSD system
  • 08.Restore factory image system
  • 09.SSD expansion
  • 10.SSD Speed Test
3.Basic setting course
  • 01.Basic knowledge
  • 02.Network configuration
  • 03.SSH remote login
  • 04.VNC remote control
  • 05.Remote file transfer
  • 06.Jtop tool
  • 07.Exchange space expansion
  • 08.Use VSCode
  • 09.Use VMware
  • 10.Change screen resolution
  • 11.Virtual Desktop
  • 12.Set root user password
  • 13.Configure sudo passwordless operation
  • 14.Managing custom services
  • 15.View system version information
4.OpenCV basic course
  • 01.Image reading
  • 02.Image saving
  • 03.Image modification
  • 04.Image scaling
  • 05.Image cropping
  • 06.Image translation
  • 07.Image mirroring
  • 08.Image grayscale conversion
  • 09.Image binarization
  • 10.Image edge detection
  • 11.Add line segments
  • 12.Add rectangles
  • 13.Add circular shape
  • 14.Add ellipses
  • 15.Add polygons
  • 16.Add Text
5.Vision basic course
  • 01.USB camera preview
  • 02.Use Jupyter Lab
  • 03.Use JetCam
6.Vision advanced course
01.Environment construction
  • 01.TensorFlow
  • 02.PyTorch
  • 03.Torchvision
  • 04.Torchaudio
02.DeepStream development
  • 01.DeepStream environment
  • 02.Four types of detection(traffic)
03.YOLO26 development
  • 01.YOLO Environment Setup
  • 02.CLI Usage
  • 03.Object Detection
  • 04.Instance Segmentation
  • 05.Pose Estimation
  • 06.Image Classification
  • 07.Oriented Bounding Boxes Object Detection
  • 08.Model Conversion
  • 09.Dataset Annotation
  • 10.Model Training_Conversion
  • 11.Model Prediction
04.Mediapipe development
  • 01.Mediapipe environment
  • 02.Hand detection
  • 03.Face detection
  • 04.Face effects
  • 05.3D object recognition
  • 06.Virtual brush
  • 07.Gesture recognition
  • 08.Finger control
  • 09.Posture estimation
  • 10.Face mask
  • 11.Overall detection
  • 12.Green scene removal(Human)
05.QR code recognition
  • 01.Barcode Recognition
  • 02.QR Code Recognition
  • 03.Machine Code Recognition
  • 04.Handwritten Digit Recognition
7.Docker basic course
  • 01.Docker introduction
  • 02.Docker installation
  • 03.Docker command
  • 04.Docker usage
  • 05.Docker interaction
8.ROS2-jazzy
1.ROS2 basic course
  • 1.Introduction to ROS2
  • 2.ROS2 install Jazzy
  • 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 course
  • 01.Camera preview (USB)
  • 02.Camera calibration (USB)
  • 03.AR vision (USB)
9.Offline AI model course
Offline large model (Text Interaction)
  • 1.Al large model environment deployment
  • 2.Install large model dialogue platform
  • 3.Meta Al Llama3.2 model
  • 4.Alibaba Cloud Qwen3 model
  • 5.Microsoft Phi-4-mini model
  • 6.DeepSeek DeepSeek-R1 model
  • 7.Alibaba Cloud Qwen2.5v visual multimodal large model
  • 8.Google Gemma3 visual multimodal large model
  • 9.Lava visual multimodal large model
  • 10.MiniCPM-V visual multimodal large model
  • 11.Multimodal visual understand application
  • 12.Multimodal text image application
  • 13.Multimodal video analysis application
  • 14.Multi module visual position application
  • 15.Multimodal table scanning application
  • 16.Multimodal autonomous agent application
Offline large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Offline speech to text (ASR)
  • 2.Offline text to speech (TTS)
  • 3.AI large model voice interaction
  • 4.Multimodal visual understand speech interaction
  • 5.Multimodal text and graphics application
  • 6.Multimodal video analysis application
  • 7.Multi module visual position application
  • 8.Multimodal table scanning application
  • 9.Multimodal autonomous proxy application
  • 10.AI large model offline voice assistant
10.Online AI model course
Online large model (Text Interaction)
  • 1.OpenRouter large model API aggregation platform
  • 2.Multimodal Video Analysis Applications
  • 3.Multimodal visual localization application
  • 4.Multimodal table scanning application
  • 5.Multimodal autonomous proxy application
Online large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Online speech to text (ASR)
  • 2.Online text to speech (TTS)
  • 3.AI large model voice interaction
  • 4.Multimodal visual understand speech interaction
  • 5.Multi module visual position application
  • 6.Multimodal table scanning application
  • 7.Multimodal autonomous proxy application
  • 8.AI large model offline voice assistant
11.Generative AI Applications
  • 0.Development Environment Setup
  • 01.Children's Storytelling Session
  • 02.Cooking Assistant
  • 03.Homework tutor
  • 04.Image to animation
12.OpenClaw Advanced Development
  • 00.OpenClaw Quick Start on Jetson Platform
  • 01.OpenClaw Installation
  • 02.OpenClaw Online Platform Deployment
  • 03.OpenClaw Local Model Deployment
  • 04.OpenClaw TUI Interaction
  • 05.OpenClaw WebChat Interaction
  • 06.OpenClaw Tools Introduction
  • 07.OpenClawGate Gateway User Manual
  • 08.OpenClaw Features Overview
  • 09.OpenClawClawHub Introduction (Skill Installation)
  • 10. OpenClaw Switching Models
  • 11.OpenClaw Application - File Management
  • 12.OpenClaw Application - Camera
  • 13.OpenClaw Application - Browser Control
  • 14.OpenClaw Application - Script Execution
  • 15.OpenClaw Application - AI Programming
  • 16.OpenClaw Application - Dedicated AI Assistant
  • 17.OpenClaw WhatsApp Plugin Application
  • 18.OpenClaw Voice Assistant
Download
  • Code
  • SystemFile
  • VM_File
  • Tools
  • Jetson boot files
Download ZIP

Welcome to Jetson AGX Thor repository

02.Cooking Assistant

Cooking Assistant

Cooking Assistant1. Feature Overview2. Hardware Connections and Setup3. Code Analysis4. Running the Code5. Experimental Results

1. Feature Overview

This example routine generates recipes for dishes, covering ingredients preparation, seasoning quantities, cooking instructions, and the generation of final dish images.

 

2. Hardware Connections and Setup

This example requires not only a connection to a power adapter but also a connection to a Large Language Model (LLM) module for voice input and output. Additionally, you must connect a DP monitor, mouse, and keyboard to interact with the user interface, or alternatively, access the system via a remote desktop connection.

image-20260408202523869

Open System Settings, click on "Sound," and set the "Output Device" to "Speakers - USB Audio Device" and the "Input Device" to "Microphone - USB Audio Device."

image-20260211115144308

3. Code Analysis

Source Code Path:

/home/jetson/showing/cooking_assistant/cooking_assistant.py

The program defaults to using Chinese voice prompts. If you require English voice prompts, please change the value of the language variable to "en"; the program will automatically switch to the English voice model and prompts.

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language = "en"

If you need to adjust the size of the image display window, modify the values ​​of self.win_width and self.win_height.

image-20260401163102448

A background task is launched to monitor for the voice wake-up command. When you speak the phrase "Nihao XiaYa" (Hello, XiaYa) into the voice module, the module will wake up and begin recording audio.

image-20260401163151001

If you need to adjust the recording duration, modify the value of the -d parameter; this value specifies the number of seconds after which the recording will automatically stop (the default duration is 10 seconds).

image-20260211143917038

If you need to force-feed specific text input, uncomment the text variable and insert your custom text.

image-20260401170216964

If you need to modify the opening prompt, search for OPENING_TEXT and adjust the relevant parameters. If you need to modify the content generated for the story and images, search for PROMPT_TEXT and adjust the relevant parameters.

Note: For Chinese, modify the variables located under the "zh" section; for English, modify the variables located under the "else" section.

image-20260401170332658

If you need to modify the serial port for the Large Language Model (LLM) module, the ComfyUI port, the Ollama server port, or the Piper model file, adjust the parameters shown below.

image-20260211150514779

4. Running the Code

Open a new system terminal and launch the ComfyUI service.

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cd ~/ComfyUI
source venv/bin/activate
python main.py

image-20260210162629624

image-20260210162433208

Open another new system terminal, activate the virtual environment, and launch the main storytelling program.

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cd ~/showing/
source venvs/bin/activate
cd cooking_assistant
python cooking_assistant.py

image-20260211155600711

5. Experimental Results

Once the program has launched:

At this point, say "Hi,yahboom" to the Large Language Model (LLM) module to wake it up. Then, state the dish you would like to eat. After the recording concludes, the speaker will play a prompt indicating that the recipe is being prepared; subsequently, the recipe will be generated and read aloud via speech synthesis. Finally, an image of the finished dish will be displayed.

image-20260211155828156

If you wish to stop the playback, you can interrupt the process by saying "Hi,yahboom" to the LLM module, which will allow you to restart the recording and recipe generation.

Children's Storytelling Session
Homework tutor

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