Here’s a clean, professional, GitHub-ready README tailored exactly to your project and code. You can copy-paste this into README.md.

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📦 YOLO-Based Barcode & QR Code Scanner

An end-to-end Barcode and QR Code detection & decoding system built using YOLO, OpenCV, pyzbar, and Streamlit. The system first detects barcodes/QR codes using a trained YOLO model, then decodes them using enhanced image preprocessing techniques for higher accuracy.

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🚀 Features

• 🔍 Object Detection using a custom YOLO model
• 🧠 Robust Decoding with pyzbar and image enhancement (CLAHE + thresholding)
• 🖼️ Supports multiple barcodes/QR codes in one image
• ⚡ Efficient image resizing for faster inference
• 🌐 Interactive Web App powered by Streamlit
• 📄 Detailed logging for debugging and monitoring

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🛠️ Tech Stack

• Python
• YOLO (Ultralytics)
• OpenCV
• pyzbar
• NumPy
• Streamlit
• PIL

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📂 Project Structure

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├── app.py              # Main Streamlit application
├── best.pt             # Trained YOLO model (required)
├── README.md
└── requirements.txt

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⚙️ Installation

1. Clone the repository

git clone https://github.com/your-username/barcode-scanner.git
cd barcode-scanner

2. Install dependencies

pip install -r requirements.txt

3. Add YOLO model

• Place your trained YOLO model as best.pt in the project root directory.

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🧪 How It Works

1. User uploads an image via the Streamlit UI

2. YOLO detects barcode / QR code regions

3. Each detected region is cropped

4. Image preprocessing is applied:

   • Grayscale conversion
   • CLAHE contrast enhancement
   • Adaptive thresholding

5. pyzbar attempts decoding using multiple strategies

6. Decoded text is displayed to the user

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📊 Output

• Supports multiple detections per image
• Displays decoded text clearly in the UI
• Gracefully handles unreadable or missing barcodes

Just tell me 👌