Body Measurement project

Software supplier


United States


N/A name


The task was to create a solution to precisely calculate anthropometric measurements from a photo person and input basic parameters. The acceptable standard error of the measurements is 5%.

The solution included using Computer Vision and Machine Learning. A person inputs their weight, height, and gender as the basic parameters and takes two full-size photos from the front and the side.

Technology Stack

  • Python 3.7
  • PyTorch
  • Mediapipe
  • Docker
  • Flask
  • PostgreSQL
  • Angular

The algorithm works as follows:

  • A body frame is detected by using CV;
  • Inside the frame, a body contour is calculated using a gradient filter;
  • Using Google’s Mediapipe, the body contour is mapped to a pose model;
  • Cross-width of the contour is calculated at the predefined landmarks, which are mapped to the contour using the pose;
  • ML regression is used for converting linear measurements into circumferences;
  • ML outlier detection and regression are used to validate the calculated measurements and their improvement.

The algorithm calculates ten measurements of different parts of the person’s body: Neck, Elbow, Wrist, Chest, Waist, Hip, Thigh, Knee, Calf, and Ankle. The measurements are given as circumferences. No helper markers are used in the photos.


worked on this project



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