an advanced computer vision project using OpenCV that combines image recognition with hand gesture detection. The system recognizes various hand gestures and objects in real-time, allowing for intuitive human-computer interaction.
GestureSense is an AI-driven project designed to revolutionize human-computer interaction. By leveraging OpenCV and machine learning, this system recognizes objects and hand gestures in real-time. It allows for a seamless, touchless interface with potential applications in automation, gaming, accessibility, and more.
- Real-time hand gesture detection: Detects gestures like hand waves, fist bumps, and other actions, enabling touchless control.
- Object recognition: Identifies common objects like pens, apples, and mobile phones with bounding boxes.
- Multi-person recognition: Adjusts the bounding box color based on different users for personalized interaction.
- Intuitive interface: Offers a smooth, interactive UI with labels for gestures and objects in the video feed.
- Expandable design: Easily integrates with other systems for further use cases, such as gesture-based automation or gaming controls.
- Python: Core programming language used for the project.
- OpenCV: For real-time computer vision and image processing.
- Machine Learning: Used for object and gesture classification.
- TensorFlow: (If applicable) For training custom models.
- YOLO (You Only Look Once): Used for fast object detection.
- Gesture Recognition: Custom algorithms built using OpenCV for real-time hand detection and classification.
Here’s a glimpse of how GestureSense works:
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Real-Time Object Detection: Using OpenCV and YOLO, the system can recognize objects like apples, pens, and mobile phones in real-time. These objects are tracked with bounding boxes, and labels are displayed dynamically on the screen.
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Hand Gesture Recognition: The system detects common hand gestures such as waving or shaking hands, capturing the action and displaying the corresponding label.
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User Detection: The project incorporates facial recognition, changing the color of bounding boxes when different users are detected.
To get this project running locally, follow these steps:
git clone https://github.com/yourusername/GestureSense.git
cd GestureSenseEnsure you have Python 3.6+ and pip installed.
pip install -r requirements.txtpython main.pyNote: Ensure you have a webcam connected for real-time detection.
- OpenCV: Install using
pip install opencv-python. - TensorFlow: (Optional) Install using
pip install tensorflowif using custom models. - PyTorch: If using YOLO for object detection.
Using YOLO, objects in the video feed are detected and classified based on the pre-trained model. Bounding boxes are drawn dynamically around detected objects, and labels are applied with confidence levels.
Using OpenCV, the system analyzes hand movements and classifies gestures such as waving or making a fist. The detected gestures are displayed on the screen in real-time.
GestureSense has wide-ranging potential, with future enhancements such as:
- Adding more complex hand gestures.
- Integrating voice recognition for hybrid touchless interfaces.
- Expanding the object database for broader use cases.
- Improving multi-person interaction to adjust gestures dynamically.
We welcome contributions from the community! If you have ideas, bug reports, or feature requests, feel free to:
- Fork the repository.
- Create a new branch (
git checkout -b feature-branch). - Commit your changes (
git commit -m "Add a feature"). - Push to the branch (
git push origin feature-branch). - Open a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
- Special thanks to the OpenCV and YOLO community for their awesome libraries and documentation.
- Thanks to [your name] for developing and maintaining GestureSense.
- TensorFlow User Group for inspiration.
Feel free to reach out if you have any questions!
- LinkedIn: Your LinkedIn Profile
- GitHub: Your GitHub Profile
- Email: bishwapalar8@gmail.com
Make sure to replace the placeholder URLs for the images and any specific technology choices you made with your actual project details! If you need further help with images, just let me know!