Storyboard Automation

This project aims to develop a machine learning framework to automate the conversion of textual advertisement descriptions into visually compelling storyboards. This process enhances creativity and efficiency in digital advertising campaigns.

Project Structure

storyboard/
│
├── data/ # Data files
│ ├── raw/ # Raw data
│ └── processed/ # Processed data
│
├── notebooks/ # Jupyter notebooks for exploration and analysis
│ └── EDA.ipynb
│
├── src/ # Source code
│ ├── init.py # Makes src a module
│ ├── data_loader.py # Code for loading and processing data
│ ├── models.py # Code for defining ML models
│ ├── train.py # Code for training models
│ ├── evaluate.py # Code for evaluating models
│ └── utils.py # Utility functions
│
├── tests/ # Unit tests
│ ├── init.py
│ └── test_data_loader.py
│ └── test_models.py
│
├── venv/ # Virtual environment directory
│
├── .gitignore # Git ignore file
├── README.md # Project description
└── requirements.txt # List of required packages

Getting Started

Clone the repository:

git clone https://github.com/dev-abuke/Automated_Ad_Storyboard_Synthesis.git

Navigate to the project directory:
```
cd Automated_Ad_Storyboard_Synthesis
```

Set up the virtual environment:

python3 -m venv venv
source venv/bin/activate

Install the dependencies:
```
pip install -r requirements.txt
```

Work Plan

Implement more advanced image segmentation models.
Integrate additional text analysis techniques.
Develop a more sophisticated evaluation framework.
Optimize the performance of the ML models.
Expand the dataset with more diverse advertisement examples.

License

This project is licensed under the MIT License. See the LICENSE file for details.