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🛠️ Tech Stack

  • Programming Language: Python
  • Libraries:
    • OpenCV – Real-time computer vision
    • MediaPipe – Human pose estimation
    • Streamlit – Interactive frontend for user interface

🧠 How it Works

  1. MediaPipe Pose Estimation is used to extract 33 key landmarks from the human body.
  2. OpenCV captures the live webcam feed and displays annotated frames.
  3. Custom logic in plank.py and pushups.py measures:
    • Elbow, knee, shoulder, and hip angles
    • Repetition counts based on movement patterns
  4. stream.py offers a visual interface to run pose detection with simple UI.

🏃‍♀️ Use Cases

  • Fitness tracking and automated workout rep counting
  • Gym form correction using pose analytics
  • Biomechanical movement analysis
  • Interactive fitness applications

▶️ Getting Started

🔧 Requirements

pip install opencv-python mediapipe streamlit

![image](https://github.com/user-attachments/assets/f1284bb5-3032-4143-9952-b8934b67024c)