Posture Detection Project

A real-time ergonomic posture analyzer built with Python, MediaPipe Pose & OpenCV. Computes joint angles, classifies “good” vs. “slouching” posture, overlays visual feedback on webcam feed, and logs session statistics for offline analysis.

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🔍 Features

• Pose Estimation
  Uses MediaPipe Pose to extract 33 3D landmarks per frame (shoulders, spine, neck, hips, etc.).

• Kinematic Computations
  Calculates key joint angles (e.g. back tilt, neck flexion) via NumPy and compares against calibrated thresholds.

• Real‑Time Visualization
  Overlays landmarks, angle values, and posture status (✅ Good / ⚠️ Slouching) on live webcam feed with OpenCV.

• Session Logging & Analytics
  Streams timestamped posture labels into Pandas DataFrames, exports CSV for time‑series analysis or dashboarding.

• Demo Notebook
  Jupyter Notebook walkthrough showing setup, landmark plotting, threshold tuning, and interactive session reporting.

🚀 Quick Start

1. Clone the repo

   git clone https://github.com/zainibaloch/Posture-Detection-Project.git
   cd Posture-Detection-Project
   
   

2. Create & activate virtual environment

   python3 -m venv venv        #conda create -n newenv python==3.10 --y
   source venv/bin/activate    # Linux/Mac
   venv\Scripts\activate       # Windows

3. Install dependencies

   pip install streamlit opencv-python mediapipe tensorflow numpy pillow

4. Run real‑time detection

   streamlit run app.py

5. Explore the demo Notebook Open demo/Posture_Detection_Demo.ipynb in Jupyter to see angle calculations, threshold tuning, and data logging examples.

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📁 Project Structure Overview

• Driver Safety: CSVs, preprocessing.pkl, analysis notebook
• Exercise:
  • Yoga pose folders (train/test)
  • Model training script, evaluation report & confusion matrix
• Handley: YOLOv5 model config, training output
• Sitting: Image dataset (train/test/val), result folders, model script
• Main Files: main.py, README, and a screenshot

⚠️ Images and model weights are excluded. DM for full access.

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⚙️ Dependencies

• Python 3.10+
• opencv-python
• mediapipe
• numpy
• pandas

Install via:

pip install opencv-python mediapipe numpy pandas

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🤝 Contributing

1. Fork the repo
2. Create a feature branch (git checkout -b feature/awesome)
3. Commit your changes (git commit -m "Add awesome feature")
4. Push to branch (git push origin feature/awesome)
5. Open a Pull Request