Product Inspector AI

Overview

Product Inspector AI is an open-source quality inspection system designed for production lines and manufacturing environments. Built with Streamlit and OpenCV, this application provides real-time product quality assessment, defect detection, and comprehensive reporting capabilities.

Features

• Real-time Quality Inspection: Detect and analyze products on production lines using computer vision
• Defect Recognition: Identify product defects with customizable detection thresholds
• Product Management: Add, edit, and manage product information and inspection criteria
• Batch Processing: Organize inspections by batches for better traceability
• Comprehensive Reporting: Generate PDF and Excel reports with quality statistics
• Interactive Dashboard: View inspection metrics and performance analytics
• Customizable Settings: Adjust detection parameters to your specific requirements

Installation

# Clone the repository
git clone https://github.com/liveupx/QualityInspector.git
cd product-inspector-ai

# Install required dependencies
pip install -r requirements.txt

# Run the application
streamlit run app.py

System Requirements

• Python 3.7+
• OpenCV
• Streamlit
• Pandas
• Matplotlib
• Plotly
• FPDF

Usage

Main Inspection Screen

The main screen provides a live view of the inspection process:

1. Configure product information in the sidebar
2. Adjust detection thresholds as needed
3. Click "Start Inspection" to begin the quality assessment
4. View real-time statistics of processed products

Product Setup

Use the Product Setup page to:

1. Add new products with detailed information
2. Configure inspection criteria (Standard, Strict, or Permissive)
3. Manage existing products and their parameters

Dashboard

The dashboard provides analytical insights:

1. View overall inspection statistics
2. Analyze quality distribution and trends
3. Compare performance across different batches
4. Filter data by date ranges

Reports

Generate and export detailed reports:

1. Create PDF reports with graphs and statistics
2. Export Excel spreadsheets with raw inspection data
3. Filter reports by time period or batch number

Camera Integration

The system supports multiple camera sources:

• Local webcams
• IP cameras
• Video files (for testing)
• Demo mode with simulated products

Extending the System

Custom Detection Models

You can integrate your own machine learning models by:

1. Implementing the model in the ProductDetector class
2. Adjusting the detection threshold based on your model's characteristics
3. Customizing the visualization of detection results

Additional Features

The modular architecture allows for easy extensions:

• Add new quality metrics
• Implement different types of defect detection
• Connect to databases for persistent storage
• Integrate with production line control systems

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• OpenCV for the computer vision capabilities
• Streamlit for the interactive web interface
• The open-source community for various libraries used in this project

Contact

LiveupX - @liveupx