This repository contains a Python-based project that utilizes computer vision techniques and popular libraries such as OpenCV, Scikit-learn, and Mediapipe to analyze hand gestures in real-time using a webcam. The goal of this project is to create a system capable of recognizing and classifying different hand gestures, allowing users to interact with technology in a more intuitive and natural way, ultimately making it more accessible.
- Real-time hand gesture recognition using a webcam.
- Execution of various commands on a PC based on recognized gestures.
- Utilizes computer vision algorithms for background subtraction, hand segmentation, feature extraction, and classification.
- Supports a wide range of hand gestures for controlling devices or software.
Before you begin, make sure you have the following dependencies installed:
- Python (3.6 or higher)
- OpenCV
- Scikit-learn
- Mediapipe
- Webcam or camera device
You can install the required Python packages using pip:
pip install opencv-python scikit-learn mediapipe- Clone this repository:
git clone https://github.com/yourusername/hand-gesture-recognition.git
cd hand-gesture-recognition- Run the main script:
python main.py- Use your webcam to interact with the system by performing various hand gestures. The system will recognize the gestures and trigger corresponding actions.
Throughout the project, we employ various methodologies and algorithms to process video frames and extract meaningful hand gesture information. Some of the key technical aspects include:
- Background subtraction: Removing the background to isolate the hand.
- Hand segmentation: Identifying and isolating the hand within the frame.
- Feature extraction: Extracting relevant features from the hand gesture.
- Classification techniques: Using machine learning to classify the recognized gesture.
For more detailed technical information, refer to the project's documentation and code.
While developing this project, we encountered several challenges in achieving accurate gesture recognition. Some of the common challenges include:
- Accurate background subtraction to isolate the hand.
- Robust hand segmentation techniques, especially in varying lighting conditions.
- Effective feature extraction to capture the essence of each gesture.
- Training and fine-tuning classification models for high accuracy.
We welcome contributions from the community. If you have ideas, bug fixes, or improvements, feel free to open an issue or submit a pull request.
Biziura Olha,
Lin Can,
Srymova Aruta.
This project is licensed under the MIT License.
We would like to express our gratitude to the open-source community and the developers of the libraries and tools that made this project possible.
By contributing to this project, you are helping to advance the field of computer vision and gesture-based control, making technology more accessible and intuitive for everyone. Enjoy experimenting with hand gestures and exploring the possibilities of this project!