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

![visitors](https://visitor-badge.glitch.me/badge?page_id=SouravJohar.visitor-badge)

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

![Alt Text](sample.gif)

```sh
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
```

## Get started
1. Clone or download the repo, and then,
```sh
$ 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.

## Note
Please use OpenCV version 4 to use Handy.

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

## Purpose
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.