Learn to implement AI hand pose estimation using MediaPipe and Python in this comprehensive tutorial video. Set up MediaPipe for Python, estimate hand poses using MediaPipe Hands, and output images with detections using OpenCV. Follow step-by-step instructions to install dependencies, make real-time detections, access webcam feed, apply and interpret hand pose results, render detections, apply styling, flip images horizontally, and save the output. Gain practical skills in AI and machine learning while working with efficient Python libraries for hand detection and tracking.
AI Hand Pose Estimation with MediaPipe and Python
Overview
Syllabus
- Start
- Introduction
- How it Works
- Tutorial Start
- Install and Import Dependencies
- Make Detections in Real Time
- Accessing a Real Time Webcam Feed Using OpenCV
- Applying MediaPipe Hand Pose
- Interpreting Hand Pose Results
- Rendering Using OpenCV
- Applying Styling to MediaPipe Detections
- Horizontally Flipping Images using OpenCV
- Save Images using OpenCV
- Ending
Taught by
Nicholas Renotte
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