The Finger Movement Tracker is a Python-based project that utilizes TensorFlow and OpenCV to track and recognize finger gestures in real time. By identifying and analyzing key pivot points on the fingers, this system can be used to create interactive applications for device control, virtual/augmented reality (VR/AR) environments, gaming, accessibility tools, and more. The project aims to provide seamless human-computer interaction through precise and efficient gesture recognition.
Features Real-Time Gesture Tracking: Tracks the movement of fingers with high precision using the TensorFlow library, enabling near-instant feedback and control. Pivot Point Detection: Identifies the key joints of the fingers (such as knuckles and fingertips) to track movements and detect gestures accurately. Gesture Recognition: Classifies various hand gestures in real time, allowing the system to interpret specific actions and commands. Multi-Platform Support: Can be integrated with different devices, including PCs, laptops, and smartphones, to provide flexibility in usage. Customizable Gesture Models: Users can train and add new gestures, making the system adaptable for a variety of use cases. Technologies Used Programming Language: Python Libraries & Frameworks: TensorFlow: Used for creating and training machine learning models that interpret the finger movements and gestures. OpenCV: Used for processing video feeds, capturing real-time input from the camera, and detecting the positions of the fingers. NumPy: Supports efficient numerical operations and data manipulation, especially for matrix operations related to the coordinates of pivot points. Installation Prerequisites Before installing, ensure that you have Python 3.7+ and pip installed on your system.
Clone the repository to your local machine:
bash Copy code git clone https://github.com/sagexd08/finger-movement-tracker.git Navigate to the project directory:
bash Copy code cd finger-movement-tracker Create a virtual environment (optional but recommended):
bash Copy code python -m venv venv source venv/bin/activate # For macOS/Linux venv\Scripts\activate # For Windows Install required dependencies:
bash Copy code pip install -r requirements.txt Ensure that you have OpenCV and TensorFlow set up properly by testing the installation:
bash Copy code python -c "import cv2; import tensorflow" Run the application:
bash Copy code python app.py Usage Setup: Connect a camera or use the built-in camera on your device to capture the hand gestures. Ensure the camera is positioned for clear visibility of your hands. Start the Application: Launch the program by running: bash Copy code python app.py Perform Gestures: In front of the camera, perform specific hand gestures to see them tracked in real time. The system will recognize the gesture and display the result on the screen. Project Workflow
- Input Capture The camera feed is captured using OpenCV, which continuously processes the video stream and sends frames for analysis.
- Feature Extraction The key joints and pivot points of the fingers (e.g., knuckles, fingertips) are detected using OpenCV’s feature detection functions.
- Model Processing A TensorFlow model, pre-trained on gesture data, classifies the movements by analyzing the relationships between the detected pivot points. The model uses neural networks for gesture recognition.
- Output Display Based on the recognized gesture, the system outputs the corresponding action on the screen (e.g., controlling a device, providing feedback, etc.). Applications Device Control:
Control smart devices or applications using predefined gestures (e.g., turning lights on/off, playing music, etc.). Virtual Reality (VR) and Augmented Reality (AR):
Enhance user interaction by using hand gestures to navigate in VR/AR environments, such as selecting items, controlling avatars, or manipulating objects. Accessibility:
Implement gesture-based input systems for individuals with disabilities, offering an alternative to traditional input methods like keyboards and mice. Gaming:
Integrate the system into video games to provide gesture-based controls, creating an immersive experience. Sign Language Translation:
Recognize sign language gestures and translate them into text or speech for better communication. Future Improvements Enhanced Accuracy: Implement advanced neural network architectures such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to improve gesture classification accuracy and handle more complex gestures.
Integration with IoT: Extend the functionality to interact with Internet of Things (IoT) devices, allowing users to control various smart devices using hand gestures.
Multi-Hand Tracking: Enable tracking of both hands simultaneously for more complex interactions.
Custom Gesture Training: Allow users to define their own gestures and train the model on custom data to cater to specific needs.
Contributing Contributions are welcome! To contribute to the project, follow these steps:
Fork the repository to your own GitHub account. Clone your forked repository: bash Copy code git clone https://github.com/your-username/finger-movement-tracker.git Create a new branch for your feature or bug fix: bash Copy code git checkout -b feature-name Make changes and commit them: bash Copy code git commit -m "Add feature or fix issue" Push your changes to your forked repository: bash Copy code git push origin feature-name Create a pull request on GitHub to propose your changes to the main project. License This project is licensed under the MIT License. See the LICENSE file for more information.
Contact For questions or feedback, feel free to reach out:
Email: [email protected] GitHub: sagexd08