Rosepetal Node-RED Documentation

Welcome to the Rosepetal Node-RED documentation. This collection provides powerful custom nodes for industrial automation, computer vision, AI inference, and advanced data processing workflows.

Overview

Rosepetal extends Node-RED with specialized nodes for:

• High-Performance Computing: Non-blocking JavaScript and remote Python execution
• Computer Vision: Barcode reading, AI inference, OCR, and image processing
• Dataset Management: Firebase-integrated data pipeline for ML workflows
• Industrial Hardware: Camera control and sensor integration
• AI/ML Integration: YOLO, PaddleOCR, and custom model deployment

Quick Navigation

🚀 Getting Started

Browse the available custom nodes for Node-RED below.

📚 Node Categories

Function & Processing Nodes

High-performance computing nodes for complex operations:

• Worker Function - Non-blocking JavaScript execution in worker threads
• Code Executor - Remote Python execution with AI-powered assistant

Computer Vision & AI

Advanced image processing and machine learning:

• Barcode Reader - Multi-decoder barcode and QR code scanner

Hardware Integration

Industrial camera and sensor control:

• Sentech Cams - Control Sentech industrial cameras via Docker

Common Workflows

🎯 Quality Control with AI

[Camera] → [Inferencer] → [Filter Defects] → [Alert/Log]

Capture images, run AI detection, filter results, and trigger alerts for defects.

📦 Barcode Scanning Pipeline

[Camera] → [Barcode Reader] → [Database Lookup] → [Action]

Scan barcodes/QR codes and trigger actions based on product data.

🤖 ML Dataset Creation

[Camera] → [Image Processing] → [Dataset Upload] → Firebase

Capture and upload training data for machine learning models.

🔄 Batch Image Processing

[From Dataset] → [Inferencer: Promise Mode] → [Promise Reader] → [Results]

Process large datasets efficiently with async batch inference.

🐍 Custom Python Processing

[Image Input] → [Code Executor] → [Custom Algorithm] → [Output]

Execute custom Python code for specialized image processing or data analysis.

Key Features

Vision Platform

The Vision Platform is a comprehensive computer vision solution with Firebase integration and Docker-based AI inference. For detailed information, refer to the separate Vision Platform documentation.

Worker Function

Execute CPU-intensive tasks without blocking Node-RED:

• Separate Threads: Node.js Worker Threads API
• Queue Management: Automatic message queuing
• Buffer Support: SharedArrayBuffer for binary data
• Lifecycle Hooks: Init/finalize code sections
• Module Support: Require external npm packages

Learn more about Worker Function →

Code Executor

Remote Python execution with AI assistance:

• gRPC Connection: High-performance communication
• AI Assistant: GPT-powered code generation
• Package Management: Dynamic pip install
• Persistent Context: Maintain variables across calls
• Code Editor: Built-in syntax highlighting

Learn more about Code Executor →

Barcode Reader

Multi-decoder barcode scanning:

• Three Decoders: ZBar, ZXing, Quagga2
• Flexible Preprocessing: Original, histogram equalization, Otsu threshold
• Execution Modes: Parallel (accuracy) or sequential (performance)
• Batch Processing: Process arrays of images
• Smart Deduplication: Merge duplicate detections

Learn more about Barcode Reader →

System Requirements

General Requirements

• Node-RED: Version 3.0.0 or higher
• Node.js: Version 14.x or higher
• Operating System: Linux, macOS, or Windows

Vision Platform Requirements

• Firebase Account: Free or paid tier
• Docker: Version 20.10.0 or higher
• Python: 3.9+ (for inference containers)
• Network: Internet access for Firebase

Optional Dependencies

• ZBar: For barcode scanning (install via system package manager)
• OpenCV: For advanced image processing
• CUDA: For GPU-accelerated inference (optional)

Installation

Install from npm

cd ~/.node-red
npm install rosepetal-node-red-contrib

Install from source

git clone https://github.com/rosepetal/node-red-contrib.git
cd node-red-contrib
npm install
npm link

Restart Node-RED

node-red-restart
# or
systemctl restart nodered

Configuration

Firebase Setup (for Vision Platform)

1. Create Firebase project at Firebase Console
2. Enable Firestore, Storage, and Authentication
3. Get Firebase configuration JSON
4. Configure in firebase-config node

Docker Setup (for Inferencer)

1. Install Docker and Docker Compose
2. Pull inference images or build from source
3. Configure inference servers in inferencer node
4. Verify connectivity

Camera Setup (for Sentech Cams)

1. Install camera drivers
2. Configure Docker Compose files
3. Set device IDs in sentech-cams node
4. Test camera connectivity

Getting Help

Documentation

• Browse node-specific documentation in the sidebar
• Check examples and troubleshooting sections
• Review integration patterns and best practices

Community & Support

• Node-RED Forum
• GitHub Issues
• Stack Overflow

Additional Resources

• Node-RED Documentation
• Firebase Documentation
• Docker Documentation
• YOLO Documentation
• PaddleOCR Documentation

License

This project is licensed under the terms specified in the package.

Contributing

Contributions are welcome! Please follow these guidelines:

1. Fork the repository
2. Create a feature branch
3. Make your changes with tests
4. Submit a pull request

Version Compatibility

Node-RED Version	Rosepetal Version	Status
3.0.x	1.0.x	✅ Supported
2.x	-	❌ Not supported

What's New

Version 1.0.0

• Initial release with 13 custom nodes
• Vision Platform with Firebase integration
• Worker Function for non-blocking operations
• Code Executor with AI assistant
• Multi-decoder Barcode Reader
• Industrial camera support

Next Steps

Ready to get started? Here are some suggested paths:

1. For Barcode Scanning: Check out Barcode Reader
2. For Performance: Learn about Worker Function
3. For Python Integration: Explore Code Executor
4. For Industrial Vision: Review Sentech Cams

Browse the documentation for each node above to learn more about configuration, usage examples, and best practices.