hand-gesture-engine 0.1.1

A lightweight hand gesture recognition engine

Hand Gesture Engine

A modular, extensible hand gesture recognition engine built in Python using MediaPipe landmarks. Designed for real-time applications, clean architecture, and easy integration into games, apps, and AI projects. Perfect for:

• Computer Vision projects
• Gesture-controlled apps
• Games & UI interaction
• esearch & prototyping

Features

• Modular gesture detection system
• Real-time hand landmark processing
• Centralized gesture recognizer (brain logic)
• Configurable thresholds (pinch, thumb, etc.)
• Built-in logging (debug & production ready)
• CPU / GPU backend auto-selection
• Clean project structure
• YAML configuration support
• Confidence scoring
• Stable public API
• Ready for extension with custom gestures

Supported Gestrues

Gesture	Name Returned
✊ Fist	FIST
✋ Open Hand	OPEN HAND
👍 Thumbs Up	THUMBS UP
👎 Thumbs Down	THUMBS DOWN
✌ Peace	PEACE
☝ Pointing	POINTING
👌 OK	OK
🤏 Pinch	PINCH
🖖 Three Fingers	THREE

Installation

pip install mediapipe opencv-python

Then:

pip install hand-gesture-engine==0.1.1

Quick Start

Sample Code:

import cv2
from hand_gesture import GestureEngine, GestureConfig

config = GestureConfig.from_yaml("gesture_config.yaml")
engine = GestureEngine(config=config)

cap = cv2.VideoCapture(0)

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break

    frame, gesture = engine.process(frame)

    if gesture:
        print("Detected:", gesture)

    cv2.imshow("Gesture Engine", frame)
    if cv2.waitKey(1) == 27:
        break

Configuration

IMPORTANT:

Add/Create gesture_config.yaml in the same working directory:

backend: AUTO

stability:
  hold_time: 0.35
  min_confidence: 0.6

thresholds:
  pinch: 0.04
  thumb_extended: 0.25
  finger_tolerance: 0.05

GerstureEngine

engine = GestureEngine(
    backend="AUTO",  # AUTO | CPU | GPU
    config=config
)

Backends
AUTO - selects best available backend
CPU - forced CPU execution
GPU - uses GPU if supported

Logging

Enable logging via config:

GestureConfig(enable_logging=True)

Logs include:

• Backend selection
• Detection failures
• Gesture recognition results

Future Ideas

Roadmap

• Gesture smoothing
• Multi-hand support
• Custom gesture training
• Mobile optimization
• ONNX / TensorRT backend

Credit

https://choosealicense.com/
This site helps you choose an open source license for your repository/project.
You can get to know about the licenses and which license to choose.
Very useful!

Author

Karan Vishwakarma
Built with Python and Mediapipe(Google)

License

MIT License
Free to use, modify, and distribute.

Final Note

If this project helps you:

• Star it on GitHub
• Share it on PyPI
• Build cool gesture-powered apps