Description: Developed a Python-based project using the Gradio library for yoga pose detection. The system allows users to upload 2D images of yoga poses and accurately identifies the correct pose, providing instant feedback. Future plans involve upgrading the project to real-time pose detection and generating feedback for improved yoga practice.
Key Features:
- Utilized Gradio library for creating a user-friendly interface to upload and process 2D images of yoga poses.
- Integrated computer vision technique CNN with Keras basic model to accurately detect and recognize yoga poses from the uploaded images.
- Provided instant feedback by identifying the correct pose and displaying it to the user.
- Future plans to upgrade the project include implementing real-time pose detection for live video streams and generating personalized feedback to assist users in improving their yoga practice.
- Ensured scalability and extensibility of the system to accommodate additional yoga poses and support advanced pose detection techniques in future upgrades.
By combining the power of Gradio, computer vision, and machine learning, this Yoga Pose Detection and Feedback System aims to enhance users' yoga practice by accurately identifying poses from uploaded images and providing valuable feedback. Future enhancements will enable real-time pose detection and personalized feedback, taking the project to new heights of interactivity and usefulness.
After uploading an image:
