📷 QR & Data Matrix Decoder Application

This project provides two intuitive interfaces to detect and decode QR codes and Data Matrix codes:

1. 🌐 A modern Flask-based web application
2. 🖥️ A desktop GUI application built with Tkinter

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🚀 Demo for the tkinter gui app

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🚀 Demo for the web app

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🚀 Features

• 🔍 Detects QR codes and Data Matrix codes in images
• 🧠 Utilizes YOLOv8 for robust and accurate detection
• 🖼️ Handles multiple codes per image
• 🧩 Displays decoded data with visual overlays
• 🌐 Web app supports drag-and-drop uploads
• 🖱️ Desktop app supports file selection dialog
• 📋 One-click copy of decoded content
• 🧪 Pre-packaged with sample images for testing

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📦 Installation

✅ Prerequisites

• Python 3.8+
• pip package manager

📥 Setup for Both Applications

1. Clone the repository:

   git clone https://github.com/Lamouchi-Bayrem/Data-Matrix-scanner.git
   cd Data-Matrix-scanner
   
   

2. Install dependencies:

   pip install -r requirements.txt

3. Download the YOLO model weights:

   • Place best.pt in the root directory.

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🌐 Web Application (Flask)

▶️ Running the Web App

python app.py

Access it at: http://localhost:5000

🧭 Usage

• Open the web interface in your browser
• Drag and drop an image or click to select
• View decoded codes with bounding boxes
• Copy results to clipboard

📁 Web App Structure

web-app/
├── app.py                # Flask backend
├── requirements.txt      # Web dependencies
├── best.pt               # YOLOv8 model weights
├── uploads/              # Uploaded images
└── templates/
    └── index.html        # Web interface

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🖥️ Desktop Application (Tkinter)

▶️ Running the Desktop App

python tkinter_app.py

🧭 Usage

• Launch the GUI
• Click "Open Image" to choose a file
• View decoded codes and visual highlights
• Save or copy results

📁 Desktop App Structure

desktop-app/
├── tkinter_app.py        # Desktop GUI
├── requirements.txt      # GUI dependencies
├── best.pt               # YOLOv8 model weights
└── samples/              # Example test images

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⚙️ Performance Notes

• ⏱️ Average processing time: 500–1500 ms per image (depending on hardware)
• 📐 Recommended image size: 800–2000px per side
• 🖼️ Supported formats: PNG, JPG, JPEG, BMP

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🧰 Troubleshooting

❌ No codes detected?

• Ensure the image has good lighting
• Try a higher resolution image
• Lower the confidence threshold if needed

❌ Installation errors?

• Verify Python version (3.8+)
• If using GPU, ensure CUDA is configured correctly

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📄 License

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