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 Handtracker

# Initialize the tracker
detector = Handtracker()

# Capture video
cap = cv2.VideoCapture(0)

while True:
    success, img = cap.read()
    if not success:
        break
    
    hands, img = detector.identifyHands(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.identifyHands(img, draw=True)
  • Detects hands in an image/video frame.
  • Draws hand landmarks if draw=True.

🏷️ Landmark Extraction

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

✋ Finger State Detection

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

📏 Distance Calculation

length, img, points = detector.trackDistance(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-0.1.14.tar.gz (5.0 kB view details)

Uploaded Source

Built Distribution

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

metaidigitcv-0.1.14-py3-none-any.whl (5.5 kB view details)

Uploaded Python 3

File details

Details for the file metaidigitcv-0.1.14.tar.gz.

File metadata

  • Download URL: metaidigitcv-0.1.14.tar.gz
  • Upload date:
  • Size: 5.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.0

File hashes

Hashes for metaidigitcv-0.1.14.tar.gz
Algorithm Hash digest
SHA256 e2cea66ac7437dce30d7af93d480d2c0f1f20eecb780e76e367060663d0b1003
MD5 63283d8fd78a22a9cf79a7513c832afa
BLAKE2b-256 e62468b37e5621ce1cddb9abb4da498fb341bf719f0491521c03d09b6b12194a

See more details on using hashes here.

File details

Details for the file metaidigitcv-0.1.14-py3-none-any.whl.

File metadata

  • Download URL: metaidigitcv-0.1.14-py3-none-any.whl
  • Upload date:
  • Size: 5.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.0

File hashes

Hashes for metaidigitcv-0.1.14-py3-none-any.whl
Algorithm Hash digest
SHA256 8412aef4a9fa7f14bc40738d63208013a89a99649db90049a05fb586bc790c7e
MD5 7fd0e32b6fb69ce609025783ca4a1fc2
BLAKE2b-256 1943bec106518c9b869809c97b56d215c8aaa8e2386d7acbdcf5c7655c4f6467

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