https://github.com/rathod-shubham/mediapipe_gestures
Project uses MediaPipe to recognize and track hand gestures from both live video and images. By identifying 3D hand landmarks in real time, it enables intuitive gesture-based control. Have successfully applied this in robotics where a robotic arm mirrors human hand movements demonstrating its potential for interactive and touchless control systems.
https://github.com/rathod-shubham/mediapipe_gestures
cv2 gesture-recognition livestream mediapipe python3 robotics tracking
Last synced: 4 months ago
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Project uses MediaPipe to recognize and track hand gestures from both live video and images. By identifying 3D hand landmarks in real time, it enables intuitive gesture-based control. Have successfully applied this in robotics where a robotic arm mirrors human hand movements demonstrating its potential for interactive and touchless control systems.
- Host: GitHub
- URL: https://github.com/rathod-shubham/mediapipe_gestures
- Owner: RATHOD-SHUBHAM
- Created: 2025-06-15T16:55:31.000Z (4 months ago)
- Default Branch: master
- Last Pushed: 2025-06-15T17:00:59.000Z (4 months ago)
- Last Synced: 2025-06-15T18:28:45.025Z (4 months ago)
- Topics: cv2, gesture-recognition, livestream, mediapipe, python3, robotics, tracking
- Language: Python
- Homepage:
- Size: 6.13 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# MediaPipe_Gestures
# 🖐️ Hand Gesture Recognition and Tracking using MediaPipe
This repository contains Python code that leverages **Google’s MediaPipe** to recognize and track hand gestures from both live camera streams and static images.
Using real-time hand landmark detection, the system can track individual fingers, detect various hand poses, and interpret gestures that can be used in a wide range of applications.
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## 🔍 Overview
At the core of this project is MediaPipe’s **Hand Tracking solution**, which provides 21 3D landmarks per detected hand with high accuracy and real-time performance. This enables gesture classification, pose estimation, and motion tracking with minimal setup.
The code in this repository supports:
- 🎥 Real-time gesture recognition using live video streams
- 🖼️ Gesture detection from saved images
- ✨ Preprocessing and annotation utilities for visualization
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## 🤖 Applications
Hand gesture recognition has wide applicability across domains such as:
- **Human-computer interaction**
- **Sign language interpretation**
- **AR/VR input systems**
- **Touchless interfaces**
- **Gaming and immersive environments**
### 🚀 Use Case in Robotics
I integrated this system into a **robotics project** where a **robotic arm mimicked human hand movements** in real-time. Using the detected hand landmarks, joint angles were inferred and relayed to the robot's servos, enabling intuitive and natural gesture-based control.
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## 🛠️ Tech Stack
- 🐍 **Python 3**
- 🪞 **MediaPipe Hands** solution
- 🎥 **OpenCV** for real-time video handling and visualization
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## 📁 Contents
- `LiveSteam` – Detect and visualize hand gestures in real-time from webcam
- `Image` – Perform gesture recognition on static images
- Utility scripts and drawing helpers
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