Image Colorization App

The Image Colorization App is a Streamlit web application that leverages deep learning models to colorize grayscale images. Two pre-trained models—ECCV16 and SIGGRAPH17—are used to generate vibrant colorizations from user-uploaded images. The app offers GPU acceleration (if available) to speed up processing.

Features

• Easy-to-Use Interface:
  Upload an image in JPG, JPEG, or PNG format and view the colorization results side by side.

• Dual Model Comparison:
  Compare the colorization outputs from two different models:

  • ECCV16
  • SIGGRAPH17

• GPU Support:
  Option to use GPU for faster inference when available.

• Interactive Results:
  View the original image, the grayscale input, and the two colorized outputs in a clear and organized layout.

Installation

1. Clone the Repository:

   git clone https://github.com/Jnan-py/image-colorization-app.git
   cd image-colorization-app

2. Create a Virtual Environment (Recommended):

   python -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install Dependencies:

   pip install -r requirements.txt

Usage

1. Run the Application:

   streamlit run main.py

2. Upload an Image:

   • Click the file uploader to select an image (jpg, jpeg, or png).
   • Optionally, check the "Use GPU" checkbox if you have GPU support enabled.

3. View Results:

   • The app displays:
     • The grayscale version of your image.
     • The colorized output using the ECCV16 model.
     • The original image.
     • The colorized output using the SIGGRAPH17 model.

Project Structure

image-colorization-app/
│
├── main.py                  # Main Streamlit application
├── models/                 # Folder containing model definitions and preprocessing utilities:
│   ├── eccv16.py           # ECCV16 model definition
│   ├── siggraph17.py       # SIGGRAPH17 model definition│
├── README.md               # Project documentation
└── requirements.txt        # Python dependencies

Technologies Used

• Streamlit: For building the interactive web interface.
• PyTorch: For loading and running the deep learning models.
• NumPy: For numerical operations.
• Matplotlib: For plotting (if needed).
• Pillow: For image processing.
• Custom Models: Pre-trained ECCV16 and SIGGRAPH17 models along with associated preprocessing utilities.

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Save these files in your project directory. To run the app, use the following command:

streamlit run main.py