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README.md

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-# SafeLicensing
-
-This project demonstrates a pipeline for detecting license plates in images using YOLOv8 and encrypting the detected regions with a Chaotic Logistic Map encryption algorithm. It provides a user-friendly interface built with Streamlit.
-
->[!TIP]
-> You can directly test the application on the web using the following link: [![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://share.streamlit.io/fahimfba/safelicensing/main/app.py)
-
-## Features
-
-- **License Plate Detection**: Uses the YOLOv8 model to detect license plates in uploaded images.
-- **Chaotic Encryption**: Encrypts the detected license plate regions using a two-layer XOR-based chaotic logistic map algorithm.
-- **Streamlit Web App**: A simple interface to upload images, detect license plates, encrypt them, and download the results.
-
-## Installation
-
-1. Clone the repository:
-   ```bash
-   git clone https://github.com/FahimFBA/SafeLicensing.git
-   cd SafeLicensing
-   ```
-
-2. Install `ffmpeg` for video processing (Linux):
-   ```bash
-   sudo apt-get install ffmpeg
-   ```
-
-   or, for macOS:
-   ```bash
-   brew install ffmpeg
-   ```
-
-3. Install the required dependencies:
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-4. Download the YOLOv8 weights file (`best.pt`) and place it in the root directory of the project. You can train your own model or use a pre-trained one. This repository already have our model from [SEncrypt](https://github.com/IsratIJK/SEncrypt) located in [best.pt](./best.pt) file.
-
-## Usage
-
-1. Run the Streamlit app:
-   ```bash
-   streamlit run app.py
-   ```
-
-2. Open the app in your browser (typically at `http://localhost:8501`).
-
-3. Follow the steps:
-   - Upload an image or provide a URL.
-   - Adjust the encryption key seed using the slider.
-   - Click the "Detect & Encrypt" button to process the image.
-
-4. Download the encrypted image directly from the app.
-
-## Workflow
-
-1. **License Plate Detection**:
-   - The YOLOv8 model is used to detect license plates in the input image. The model has been taken from [SEncrypt](https://github.com/IsratIJK/SEncrypt).
-   - Detected regions are highlighted with bounding boxes.
-
-2. **Chaotic Logistic Map Encryption**:
-   - A chaotic logistic map generates two XOR-based encryption keys.
-   - Pixels in the license plate regions are shuffled and encrypted in two stages.
-   - The encrypted region replaces the original plate in the image.
-
-3. **Visualization and Download**:
-   - The original, detected, and encrypted images are displayed in the app.
-   - Encrypted images can be downloaded as PNG files.
-
-## Files
-
-- `app.py`: The main Streamlit app file.
-- `requirements.txt`: Python dependencies for the project.
-- `best.pt`: YOLOv8 weights file (not included, add your own).
-
-## Key Parameters
-
-- **Encryption Key Seed**: A slider in the app adjusts the seed value for the chaotic logistic map, affecting the encryption's randomness.
-
-## Example Screenshots
-
-### Original Image
-
-![Original Image](./img/lpr-tesla-license-plate-recognition-1910x1000.jpg)
-
-### Encrypted Image
-
-![Encrypted Image](./img/encrypted_plate.png)
-
-## License
-
-This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
-
-## Contact
-
-For any queries, feel free to reach out:
-
-- **Author**: Md. Fahim Bin Amin
-- **GitHub**: [FahimFBA](https://github.com/FahimFBA)
-
-- **Other Authors**: [Rafid Mehda](https://github.com/rafid29mehda), [Israt Jahan Khan](https://github.com/IsratIJK)
-