An easy to use wrapper for hand recognition, made using OpenCV 4.

A gesture controlled media player I made using handy: https://www.youtube.com/watch?v=-_9WFzgI7ak

import handy
import cv2

cap = cv2.VideoCapture(0)
hist = handy.capture_histogram(source=0)

while True:
    ret, frame = cap.read()
    
    # detect the hand
    hand = handy.detect_hand(frame, hist)
    
    # plot the fingertips
    for fingertip in hand.fingertips:
        cv2.circle(hand.outline, fingertip, 5, (0, 0, 255), -1)

    cv2.imshow("Handy", hand.outline)
    
    k = cv2.waitKey(5)
    if k == ord('q'):
        break

1. Clone or download the repo, and then,

$ cd handy-master
$ pip install -r requirements.txt
$ python test.py

2. When the program starts, it'll pop open a web cam feed and you have to place a part of your hand in the rectangle shown and press the key 'a' to calibrate the system with your skin color and the detection process will start.

Please use OpenCV version 4 to use Handy.

I didn't want to make a full, proper documentation. 😅 However, test.py contains all the functions and their usage.

The purpose of this project was to detect hands in images/videos without using Machine/Deep Learning. So, this has been done using only Image Processing, and it is much faster than ML/DL solutions on a normal system. However, it is not as as accurate (backgrounds with similar color as that of skin can fool the detector). Also note that, this isn't really a "Hand detector". It is just an Object Detector, using color. You can play around and modify the code to detect other objects as well, pretty easily.