Snap2Sketch

A web application for converting images into pencil sketches with adjustable quality levels and step-by-step transformation features. Built with Python, OpenCV, and Streamlit for an intuitive user experience.

Features

• Convert uploaded images to pencil sketches
• Adjustable sketch quality settings
• Step-by-step transformation visualization
• Real-time image processing using OpenCV
• Clean, responsive web interface
• Support for common image formats (JPEG, PNG, etc.)

Live Demo

Snap2Sketch Web Application

Technical Stack

• Frontend: Streamlit web framework
• Image Processing: OpenCV (cv2)
• Computing: NumPy for numerical operations
• Image Handling: Pillow (PIL) for image manipulation
• Deployment: Streamlit Cloud

Local Installation

1. Clone the repository:

git clone https://github.com/RoyAalekh/Snap2Sketch.git
cd Snap2Sketch

2. Install dependencies:

pip install -r requirements.txt

3. Run the application:

streamlit run app.py

4. Open your browser to http://localhost:8501

How It Works

The application uses computer vision techniques to convert photographs into pencil sketch-like drawings:

1. Image Upload: Users upload an image through the web interface
2. Preprocessing: Image is converted to grayscale
3. Edge Detection: Canny edge detection identifies prominent features
4. Sketch Generation: Combination of blur and edge enhancement creates sketch effect
5. Quality Control: Adjustable parameters for fine-tuning the output

Project Structure

Snap2Sketch/
├── app.py                 # Main Streamlit application
├── utils.py               # Image processing utilities
├── requirements.txt       # Python dependencies
├── components/            # Modular UI components
│   ├── controls.py        # Control panel interface
│   ├── display.py         # Image display panel
│   ├── header.py          # Application header
│   ├── footer.py          # Application footer
│   └── image_processor.py # Core image processing logic
└── .devcontainer/         # Development container configuration

Usage

1. Access the web application through the live demo link
2. Upload an image using the file upload interface
3. Adjust quality settings as desired
4. View the generated pencil sketch
5. Download the processed image

Development

The application is built with modular components for maintainability:

• ControlPanel: Handles user inputs and parameter adjustments
• DisplayPanel: Manages image display and processing results
• ImageProcessor: Contains core OpenCV-based processing algorithms

Requirements

• Python 3.7+
• Streamlit
• OpenCV (opencv-python-headless)
• NumPy
• Pillow

License

MIT License