PoseMate is an advanced human pose estimation app that leverages cutting-edge machine learning techniques to analyze and predict human poses in real time. It can detect and track key body points, making it ideal for applications in fitness, healthcare, gaming, and augmented reality.
- Real-time pose estimation: Analyze poses from live camera feeds or uploaded videos/images.
- Keypoint detection: Detects joints and key body landmarks (e.g., shoulders, elbows, knees, etc.).
- High accuracy: Powered by state-of-the-art deep learning models.
- Cross-platform support: Available on web and mobile platforms.
- Use cases: Fitness tracking, physical therapy, gaming, and more.
- Frontend: Streamlit
- Machine Learning: TensorFlow / PyTorch / Computer Vision
- Other Tools: OpenCV
Try the app live: PoseMate Live Demo
Make sure you have the following installed:
- Python (3.7 or higher)
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Clone the repository:
git clone https://github.com/arihantjain6739/posemate.git cd posemate -
Install dependencies:
pip install -r requirements.txt
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Run the application:
streamlit run app.py
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Access the app in your browser:
http://localhost:8501
The following Python dependencies are required:
opencv_python_headless==4.5.1.48
streamlit==0.76.0
numpy==1.18.5
matplotlib==3.3.2
Pillow==8.1.2
- Upload an image/video or enable your webcam.
- View the detected keypoints and pose visualization in real time.
- Analyze results or export them for further use.
PoseMate uses [model name] for pose estimation, which has been fine-tuned for accuracy and performance.
We welcome contributions! Here’s how you can help:
- Fork the repository.
- Create a feature branch:
git checkout -b feature-name. - Commit your changes:
git commit -m "Add new feature". - Push to your branch:
git push origin feature-name. - Open a pull request.
This project is licensed under the MIT License.
Feel free to raise an issue or contribute to improve the app. Happy coding!