Learn to decode body language using AI in this comprehensive 90-minute Python tutorial. Leverage MediaPipe to estimate facial and body landmarks, then build custom pose classification models for fine-grained body language analysis. Set up MediaPipe for Python, estimate face and body poses using a webcam and OpenCV, collect and process joint coordinates with Pandas, train a custom pose classification model using Scikit-Learn, and decode body language in real-time. Customize the project for various applications like drowsiness detection or extended pose classification with hand models. Follow along with step-by-step instructions, from installation to implementation, and gain practical skills in AI-powered body language interpretation.
AI Body Language Decoder with MediaPipe and Python
Overview
Syllabus
- Start
- Introduction
- How it Works
- Tutorial Start
- Installing Mediapipe and Dependencies
- Capture Landmarks using OpenCV and CSV
- Load Pose and Face Data using Pandas
- Train Sciki-Learn Pose Classification Model
- Evaluate Classification Model and Pickle
- Making Detections using the Model
- Decoded Body Language Demo
- Displaying Probabilities
- Adding in New Poses
- Wrap Up
Taught by
Nicholas Renotte
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