- OS: MacOS El Capitan

- Platform: Python 3

- Librarys:

- OpenCV 3

- appscript

- Youtube: [Click here](https://youtu.be/CmBxUnp7XwM)

- Youku: [Click here](http://v.youku.com/v_show/id_XMTc3MjI4MjQwOA==.html)

- run it in python

- press `'b'` to capture the background model (Remember to move your hand out of the blue rectangle)

- press `'r'` to reset the backgroud model

- press `'ESC'` to exit

Capture video from camera and pick up a frame.


Use background subtraction method called **Gaussian Mixture-based Background/Foreground Segmentation Algorithm** to subtract background.

For more information about the method, check [Zivkovic2004](http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf)

Here I use the OpenCV's built-in function `BackgroundSubtractorMOG2` to subtract background.

bgModel = cv2.BackgroundSubtractorMOG2(0, bgSubThreshold)

Build a background subtractor model

fgmask = bgModel.apply(frame)

Apply the model to a frame

res = cv2.bitwise_and(frame, frame, mask=fgmask)

Get the foreground(hand) image


gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

First convert the image to gray scale.

blur = cv2.GaussianBlur(gray, (blurValue, blurValue), 0)

By Gaussian blurring, we create smooth transition from one color to another and reduce the edge content.


ret, thresh = cv2.threshold(blur, threshold, 255, cv2.THRESH_BINARY)

We use thresholding to create binary images from grayscale images.


We now need to find out the hand contour from the binary image we created before and detect fingers (or in other words, recognize gestures)

contours, hierarchy = cv2.findContours(thresh1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

This function will find all the contours from the binary image. We need to get the biggest contours (our hand) based on their area since we can assume that our hand will be the biggest contour in this situation. (it's obvious)

After picking up our hand, we can create its hull and detect the defects by calling :

hull = cv2.convexHull(res)

defects = cv2.convexityDefects(res, hull)


Now we have the number of fingers. How to use this information? It's based on your imagination...

I add in a keyboard simulation package named **appscript** as interface to control Chrome's dinosaur game.


1. OpenCV documentation:

2. Opencv python hand gesture recognition:

3. Mahaveerverma's hand gesture recognition project:

[hand-gesture-recognition-opencv](https://github.com/mahaveerverma/hand-gesture-recognition-opencv)