This repository hosts the codebase for a simple Rock-Scissors-Paper game developed in Python, leveraging the OpenCV library. The project employs OpenCV's image processing capabilities to recognize player's hand gestures (Rock, Scissors, or Paper) via a webcam feed.

• Features
• Repository Structure
• Installation
• Usage
• License
• Contributing
• Acknowledgements
• Future Enhancements

• Hand Gesture Recognition: Implements OpenCV, potentially in conjunction with the MediaPipe library, to perform real-time detection and classification of hand gestures corresponding to Rock, Scissors, and Paper from a webcam stream.
• Automated Opponent: Enables gameplay against a virtual opponent (computer) employing a pseudo-random selection mechanism.
• Result Visualization: Displays both the player's and the computer's selections, along with the outcome of each round.
• Basic Graphical Interface: A rudimentary graphical interface may be constructed using OpenCV functionalities or integrated with external GUI libraries, contingent on further development.

├── GoogleColabProject_RockScissorsPaper_Game.ipynb # Jupyter Notebook containing the primary game logic.
├── hand_detect_lib.py                           # Potentially houses custom functions for hand detection tasks.
├── SetUp.txt                                    # Lists the requisite libraries for project execution.
├── pic/                                         # Directory potentially containing supplementary image assets.   
│ ├── 0_Scissor.png                              # Image asset representing the Scissors gesture.
│ ├── 1_rock.png                                 # Image asset representing the Rock gesture.
│ └── 2_paper.png                                 # Image asset representing the Paper gesture.                                                       
└── README.md                                  # Project documentation file.
└── LICENSE                                      

1. Clone the repository:

   git clone [https://github.com/paht2005/PythonGame_Rock-Scissors-Paper.git](https://github.com/paht2005/PythonGame_Rock-Scissors-Paper.git)
   cd PythonGame_Rock-Scissors-Paper

2. Install Dependencies: Utilize pip to install the necessary libraries specified in the SetUp.txt file:

   pip install -r SetUp.txt

   Alternatively, install them individually:

   pip install mediapipe
   pip install opencv-python

3. (Optional) Jupyter Notebook: To execute the .ipynb file, ensure you have Jupyter Notebook or JupyterLab installed:

   pip install notebook
   # or
   pip install jupyterlab

1. Execute the Game:

   • If using Jupyter Notebook, open the GoogleColabProject_RockScissorsPaper_Game.ipynb file and execute the code cells sequentially.
   • If the project is developed as a standalone Python script (.py), run it from the terminal:

     python your_game_script.py

2. Follow On-screen Prompts:

   • The application may request access to your webcam to capture video frames for gesture recognition.
   • Present hand gestures corresponding to Rock, Scissors, or Paper when prompted.
   • The outcome of each round will be displayed on the interface.

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

Contributions to enhance this project are welcome! Feel free to:

• Submit pull requests for implemented features or bug fixes.
• Report any identified issues or bugs.
• Propose new features or improvements. Contact for work: Nguyễn Công Phát – [email protected]

• OpenCV: For providing a robust library for computer vision and image processing tasks in Python.
• MediaPipe: (If utilized) For offering a framework for constructing multimodal applied machine learning pipelines.
• Python: The versatile and accessible programming language employed for this project.
• GitHub: The platform hosting this open-source project for collaborative development.

• Improved Gesture Recognition Accuracy: Fine-tuning parameters and algorithms to enhance the robustness and accuracy of hand gesture recognition across varying lighting conditions and backgrounds.
• Multiplayer Mode: Development of a two-player mode (if feasible) or exploration of alternative game variations.
• Enhanced User Interface: Integration of more sophisticated GUI libraries (e.g., Tkinter, PyQt) for a more interactive and visually appealing user experience.
• Persistent Score Tracking: Implementation of a mechanism to maintain and display a running score across multiple rounds of gameplay.