CV2 Threshold Visualizer

A powerful desktop GUI application for experimenting with OpenCV threshold operations. Upload any image and visualize the effects of different thresholding techniques in real-time.

✨ Features

• 🖼️ Image Upload: Load images in various formats (PNG, JPG, JPEG, BMP, TIFF, WebP)
• 📊 Classic Threshold: Apply simple thresholding with adjustable value (0-255)
• 🔄 Adaptive Threshold: Apply adaptive thresholding with MEAN or GAUSSIAN methods
• 👁️ Side-by-Side Preview: Compare original and processed images instantly
• 💾 Save Results: Export processed images to your desired location
• 🎛️ Real-time Controls: Adjust parameters with intuitive sliders

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• pip package manager

Installation

1. Clone the repository

   git clone https://github.com/bugraskl/cv2-threshold-visualizer.git
   cd cv2-threshold-visualizer

2. Install dependencies

   pip install -r requirements.txt

3. Run the application

   python app.py

📖 Usage

Loading an Image

1. Click the "📁 Load Image" button
2. Select an image file from your computer
3. The original image will appear in the left panel

Classic Threshold

Classic thresholding converts pixels to black or white based on a single threshold value.

1. Adjust the Threshold Value slider (0-255)
2. Select a Threshold Type from the dropdown:
   • BINARY: Pixels > threshold → white, else → black
   • BINARY_INV: Inverse of BINARY
   • TRUNC: Pixels > threshold → threshold value, else → unchanged
   • TOZERO: Pixels > threshold → unchanged, else → black
   • TOZERO_INV: Inverse of TOZERO
3. Click "✓ Apply Classic Threshold"

Adaptive Threshold

Adaptive thresholding calculates threshold values for smaller regions, making it ideal for images with varying lighting conditions.

1. Select a Method:
   • MEAN: Threshold = mean of neighborhood area
   • GAUSSIAN: Threshold = Gaussian-weighted sum of neighborhood
2. Adjust Block Size (must be odd): Size of the neighborhood area
3. Adjust C Constant: Value subtracted from the calculated threshold
4. Click "✓ Apply Adaptive Threshold"

Saving Results

1. Apply a threshold to your image
2. Click "💾 Save Result"
3. Choose the destination and format (PNG/JPG)

🎯 When to Use Each Method

Method	Best For
Classic BINARY	Simple images with uniform lighting
Classic TRUNC	Reducing bright areas while preserving darks
Adaptive MEAN	Images with gradual lighting changes
Adaptive GAUSSIAN	Images with sharp text or fine details

📁 Project Structure

cv2-threshold-visualizer/
├── app.py              # Main application
├── requirements.txt    # Python dependencies
├── README.md           # This file
├── LICENSE             # MIT License
├── .gitignore          # Git ignore rules
└── screenshots/        # Application screenshots
    └── demo.png

🛠️ Technical Details

Dependencies

Package	Version	Purpose
opencv-python	≥4.5.0	Image processing
Pillow	≥9.0.0	Image display in Tkinter
numpy	≥1.20.0	Array operations

Threshold Functions Used

# Classic Threshold
cv2.threshold(gray_image, threshold_value, 255, cv2.THRESH_BINARY)

# Adaptive Threshold
cv2.adaptiveThreshold(
    gray_image,
    255,
    cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
    cv2.THRESH_BINARY,
    block_size,
    c_value
)

🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add 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 powerful image processing library
• Tkinter for the GUI framework
• The Python community for continuous support

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Made with ❤️ for the image processing community