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The application uses machine learning and computer vision to enable players to play the classic game through hand gesture detection via camera input.\n\n## Key Features\n- Real-time gesture recognition using Convolutional Neural Networks (CNNs)\n- Single-player and multiplayer game modes\n- Web-based application with responsive interface\n- Database integration for game statistics\n\n## Technologies Used\n\n### Backend\n- **Language**: Python\n- **Web Framework**: Flask (chosen for lightweight nature and flexibility)\n- **Machine Learning**:\n  - TensorFlow\n  - Keras (with SqueezeNet architecture)\n- **Computer Vision**: OpenCV\n\n### Database\n- **Database Management System**: MySQL\n- **ORM**: SQLAlchemy\n- **Database Features**:\n  - Game state tracking\n  - Player statistics storage\n  - Potential for future leaderboard implementation\n\n### Frontend\n- HTML\n- Bootstrap\n- JavaScript\n\n## Web Application\nThe project includes a fully-developed web application framework using Flask, designed to provide a seamless and interactive user experience. The web interface offers:\n- Dynamic route handling\n- Real-time game state rendering\n- Responsive design using HTML and Bootstrap\n- Integrated OpenCV video processing\n\n![image](https://github.com/user-attachments/assets/8cf8d74c-3749-4bd5-80ce-4298b9464ade)\n\n\n## Machine Learning Model Details\n- **Model Architecture**: Convolutional Neural Network (CNN)\n- **Framework**: Keras with SqueezeNet\n- **Training Dataset**: \n  - Four labels: rock, paper, scissors, none\n  - Diverse images covering variations in:\n    - Lighting conditions\n    - Sizes\n    - Skin tones\n- **Data Augmentation**: \n  - Used ImageDataGenerator\n  - Augmentation techniques: rotation, zoom\n\n## Game Modes\n1. **Single-player**: \n   - Player competes against computer\n\n![image](https://github.com/user-attachments/assets/1c261d66-36e5-4cdf-bfb5-c4d2d3ba778f)\n\n\n2. **Multiplayer**: \n   - Two players compete using camera inputs\n   - Real-time gesture recognition\n   - Winner calculated dynamically\n\n![image](https://github.com/user-attachments/assets/d204577c-ad3b-49da-b802-14649d179bb4)\n\n\n## Future Enhancements\n- Leaderboard integration\n- Settings menu\n- In-game chat feature\n- Improved UI/UX\n\n___________________________________________\n\n## Requirements\n- Python 3\n- Keras\n- TensorFlow\n- OpenCV\n- MySQL\n\n## Installation Guide\n1. Clone the repository:\n```sh\n$ git clone https://github.com/Aaminah2611/FYP_Motion_Detection.git\n```\n\n2. Navigate to the project directory:\n```sh\n$ cd rock-paper-scissors\n```\n\n3. Install dependencies:\n```sh\n$ pip install -r requirements.txt\n```\n\n4. Prepare Machine Learning Model:\n   - Ensure the machine learning model is present\n   - Model should be titled: `Rock-Paper-Scissors.keras`\n   - Place the model in the `Keras` directory\n\n5. Running the Application:\n   - Single-player mode:\n     ```sh\n     $ py singleplayer.py\n     ```\n   - Multiplayer mode:\n     ```sh\n     $ py multiplayer.py\n     ```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faaminah2611%2Frps_gesture_recognition_application","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faaminah2611%2Frps_gesture_recognition_application","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faaminah2611%2Frps_gesture_recognition_application/lists"}