metaidigitcv 0.1.16

computer vision and machine learning library

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.

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🔥 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.
✅ Angle Calculation – Calculates the angle (in degrees) between three 3D points. ✅ Text Overlay with Background – Draws text on the image with a customizable background rectangle.. ✅ Optimized Performance – Uses MediaPipe for fast processing.

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📦 Installation

pip install metaidigitcv

Ensure you have OpenCV and MediaPipe installed:

pip install opencv-python mediapipe numpy

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🎯 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()

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✨ 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).

📏 Angle Calculation

angle = detector.calculate_angle(point_a_coords, point_b_coords, point_c_coords)

• Calculates the angle (in degrees) between three 3D points, with point_b_coords as the vertex.
• point_a_coords, point_b_coords, point_c_coords should be lists or tuples of [x, y] coordinates from lmList.

📏 Text Overlay with Background

img_with_text = detector.putTextRect(img, "Hand Detected!", (50, 100), scale=1, thickness=1, 
                                     colorT=(255, 255, 255), colorR=(255, 0, 255), offset=10)

• Draws text on the image with a customizable background rectangle.
• Enhances text readability, especially on busy backgrounds.

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🎯 Applications

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

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📜 License

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

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🤝 Contributing

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

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📧 Contact

👤 Author: Suhal Samad ✉️ Email: [e-mail] 🌐 Personal Website: metaidigit.link ☕ Buy me a cup of coffee: https://gumroad.com/products

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

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