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Hand-Gesture-To-Control-PC

This is project based on opencv and python by which we can control our pc by using our hand and webcam

Environment

  • OS: Windows
  • Platform: Python 3
  • Librarys:
    • OpenCV 3
    • appscript

Requirements

  • import cv2
  • import numpy as np
  • import copy
  • import math
  • import pyautogui
  • import time

How to run it?

  • run it in python
  • command : python hand-gesture.py (For hand recognition)
  • 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
  • command : python hand_voice.py (For hand + speech recognition)

Process

Capture original image

Capture video from camera and pick up a frame.

Alt text

Capture background model & Background subtraction

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

For more information about the method, check Zivkovic2004

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

Alt text

Gaussian blur & Threshold

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.

Alt text

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

We use thresholding to create binary images from grayscale images.

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Contour & Hull & Convexity

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)

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Using on Chrome

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Gesture to Operate

  • 1.Switch right
  • 2.Switch left
  • 3.Scroll down
  • 4.Scroll up
  • 5.Maximize
  • 0.Minimize

Speech Recognition

Added speech recognition using python in this project for voice controlling of pc . Run speech.py file for the same . And working on it for making it run simultaneously with gesture recognition module.

Voice Commands

  • 1.Zoom In : To zoom in the tab
  • 2.Zoom Out : To zoom out the tab
  • 3.Close Tab : To close the current tab on web browser
  • 4.Screenshot : To take screenshot of the screen
  • 5.Open Tab/New Tab :To open new the tab on web browser
  • 6.Close Window : To close application
  • 7.Open Terminal : To open command prompt on windows

References & Tutorials

  1. OpenCV documentation: http://docs.opencv.org/2.4.13/
  2. Opencv python hand gesture recognition: http://creat-tabu.blogspot.com/2013/08/opencv-python-hand-gesture-recognition.html
  3. Speech Recognition: https://pypi.org/project/SpeechRecognition