Skip to main content

computer vision and machine learning library

Project description

metaidigitcv

🚀 Hand Tracking & Gesture Recognition with metaidigitcv

metaidigitcv is an advanced computer vision and machine learning library designed to detect, track, and analyze hand keypoints using MediaPipe and OpenCV. Whether you're building interactive applications, gesture-based controls, or AI-powered hand recognition systems, metaidigitcv simplifies the process with efficient hand tracking and keypoint extraction.


🔥 Features

Hand Detection – Accurately detects multiple hands in real-time.
Landmark Tracking – Tracks 21 keypoints per hand.
Finger Recognition – Identifies raised fingers for gesture recognition.
Distance Measurement – Computes distance between two keypoints.
Optimized Performance – Uses MediaPipe for fast processing.


📦 Installation

pip install metaidigitcv

Ensure you have OpenCV and MediaPipe installed:

pip install opencv-python mediapipe numpy

🎯 Quick Start

import cv2
from metaidigitcv.main import HandKeypointTracker

# Initialize the tracker
detector = HandKeypointTracker()

# Capture video
cap = cv2.VideoCapture(0)

while True:
    success, img = cap.read()
    if not success:
        break
    
    hands, img = detector.detectHands(img)
    if hands:
        lmList = hands[0]["lmList"]  # List of hand landmarks
        print("Thumb Tip Position:", lmList[4])
    
    cv2.imshow("Hand Tracking", img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

✨ Key Functions

🖐 Hand Detection

hands, img = detector.detectHands(img, draw=True)
  • Detects hands in an image/video frame.
  • Draws hand landmarks if draw=True.

🏷️ Landmark Extraction

lmList, bbox = detector.findPosition(img)
  • Returns a list of hand landmark positions.
  • Provides the bounding box of the detected hand.

✋ Finger State Detection

fingers = detector.getRaisedFingers(hands[0])
  • Returns a list [1, 0, 1, 1, 0] where 1 means the finger is up.

📏 Distance Calculation

length, img, points = detector.findDistance(4, 8, img)
  • Measures the Euclidean distance between two keypoints (e.g., thumb and index finger).

🎯 Applications

🔹 Gesture-based UI controls 🎮
🔹 Sign language recognition 🤟
🔹 Virtual painting & drawing 🎨
🔹 Touchless interaction systems 🤖
🔹 AI-powered hand gesture games 🎭


📜 License

This project is licensed under the MIT License – see the LICENSE file for details.


🤝 Contributing

We welcome contributions! Feel free to fork the repository and submit a pull request.


📧 Contact

👤 Author: Suhal Samad
✉️ Email: samadsuhal@gmail.com

If you like metaidigitcvcv, don't forget to ⭐ the repository!


🚀 Let's build amazing hand-tracking applications together! 🚀

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

metaidigitcv-0.1.5.tar.gz (4.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

metaidigitcv-0.1.5-py3-none-any.whl (5.2 kB view details)

Uploaded Python 3

File details

Details for the file metaidigitcv-0.1.5.tar.gz.

File metadata

  • Download URL: metaidigitcv-0.1.5.tar.gz
  • Upload date:
  • Size: 4.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.0

File hashes

Hashes for metaidigitcv-0.1.5.tar.gz
Algorithm Hash digest
SHA256 1bbf1b3253621a0d2b1a0fc57e3483fbc2cd24c56afea680c70f6b36ee252e8b
MD5 72b6fe837d592be63650a0a5948e0784
BLAKE2b-256 019debb6792afaa4f57872d68a150961e2844d770b78b0aa48eae0d8ca737a69

See more details on using hashes here.

File details

Details for the file metaidigitcv-0.1.5-py3-none-any.whl.

File metadata

  • Download URL: metaidigitcv-0.1.5-py3-none-any.whl
  • Upload date:
  • Size: 5.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.0

File hashes

Hashes for metaidigitcv-0.1.5-py3-none-any.whl
Algorithm Hash digest
SHA256 931a7d1d73112de70619b6efcfadcc90f71869cbaa02c51ebbbb481b336d34de
MD5 6d96c0d9bf3099f2a0817a250d0dbb16
BLAKE2b-256 b52a84b202fc9daf2c0383fd7f4b73aee9655618752da70b2af4dcec468f1acf

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page