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Posture correction using computer vision and Mediapipe library enables the detection and correction of poor posture in images and live videos, promoting a healthier lifestyle in the digital era.

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SuriyaaVijay/Posture-Recognition-using-Opencv-and-Mediapipe

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Posture-Recognition

Mail to : [email protected] for Full Project Report and Full Project Code. This does not have the code files !

OpenCV and Mediapipe based good and bad posture Recognition

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Posture correction is an important aspect of maintaining a healthy lifestyle, especially in today's digital age where people spend a significant amount of time in front of screens. Computer vision techniques, combined with the powerful Mediapipe library from Google, offer a promising solution for detecting and correcting poor posture in both images and live videos.

Screenshots

MediaPipe Architecture
MP - screenshot
Good Posture
GP - screenshot
Bad Posture
BP - screenshot

Mediapipe Library

Mediapipe is a powerful computer vision and deep learning framework developed by Google. It provides a collection of pre-built, customizable models and tools for building applications that process images and videos. The library offers a wide range of functionalities, including face detection, hand tracking, pose estimation, and more.

Usage

  • Download the .ipynb file
  • Open it using Google Colab or Jupyter Notebook
  • Add a "/content/images" folder with ur selfie images facing the computer
  • Run the Code Blocks

Pre-Requisites

  • Python
  • Mediapipe
  • OpenCV
  • ipynb notebooks

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Posture correction using computer vision and Mediapipe library enables the detection and correction of poor posture in images and live videos, promoting a healthier lifestyle in the digital era.

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