This project aims to provide a comprehensive solution for image and video colorization using deep learning techniques. Using convolutional neural networks (CNNs) and modern web technologies, the project enables users to easily add color to grayscale images and videos.
- Image colorization: Convert grayscale images to colorized versions.
- Video colorization: Extend image colorization to video content.
- User-friendly interface: Web-based interface for easy interaction and colorization.
- Real-time processing: Instant colorization of uploaded images and videos.
- Streamlit integration: Utilizes Streamlit for web application development and deployment.
- Python 3.110
- OpenCV
- Streamlit
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Clone the repository: git clone https://github.com/saadii007/Image-Video-Colorization-using-LAB-Space.git
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Install dependencies: pip install -r requirements.txt
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Run the application: streamlit run app.py
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Access the application: Open your web browser and navigate to http://localhost:8501.
- The colorization model is based on research by Richard Zhang, Phillip Isola, and Alexei A. Efros.
