Volume Control With Hand Detection OpenCV Python With Source Code

Volume Control With Hand Detection OpenCV Python With Source Code

The Volume Control With Hand Detection OpenCV Python was developed using Python OpenCV, In this Python OpenCV Project With Source Code we are going Building a Volume Controller with OpenCV , To change the volume of a computer.

We first look into hand tracking and then we will use the hand landmarks to find gesture of our hand to change the volume. This project is module based which means we will be using a previously created hand module which makes the hand tracking very easy.

Building a Volume Controller with OpenCV can be accomplished in just 3 simple steps:

• Step 1. Detect Hand landmarks
• Step 2. Calculate the distance between thumb tip and index finger tip.
• Step 3. Map the distance of thumb tip and index finger tip with volume range. For my case, distance between thumb tip and index finger tip was within the range of 15 – 220 and the volume range was from -63.5 – 0.0.

What is OpenCV?

OpenCV is short for Open Source Computer Vision. Intuitively by the name, it is an open-source Computer Vision and Machine Learning library. This library is capable of processing real-time image and video while also boasting analytical capabilities. It supports the Deep Learning frameworks.

In this Python OpenCV Project also includes a downloadable Python Project With Source Code for free, just find the downloadable source code below and click to start downloading.

By the way if you are new to python programming and you don’t know what would be the the Python IDE to use, I have here a list of Best Python IDE for Windows, Linux, Mac OS that will suit for you. I also have here How to Download and Install Latest Version of Python on Windows.

To start executing Volume Control With Hand Detection OpenCV Python With Source Code, make sure that you have installed Python 3.9 and PyCharm in your computer.

Volume Control With Hand Detection OpenCV Python With Source Code : Steps on how to run the project

Installed Libraries

import cv2 
import mediapipe as mp
from math import hypot
from ctypes import cast, POINTER
from comtypes import CLSCTX_ALL
from pycaw.pycaw import AudioUtilities, IAudioEndpointVolume
import numpy as np

Complete Source Code

import cv2 
import mediapipe as mp
from math import hypot
from ctypes import cast, POINTER
from comtypes import CLSCTX_ALL
from pycaw.pycaw import AudioUtilities, IAudioEndpointVolume
import numpy as np 

cap = cv2.VideoCapture(0)

mpHands = mp.solutions.hands 
hands = mpHands.Hands()
mpDraw = mp.solutions.drawing_utils


devices = AudioUtilities.GetSpeakers()
interface = devices.Activate(IAudioEndpointVolume._iid_, CLSCTX_ALL, None)
volume = cast(interface, POINTER(IAudioEndpointVolume))

volMin,volMax = volume.GetVolumeRange()[:2]

while True:
    success,img = cap.read()
    imgRGB = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
    results = hands.process(imgRGB)

    lmList = []
    if results.multi_hand_landmarks:
        for handlandmark in results.multi_hand_landmarks:
            for id,lm in enumerate(handlandmark.landmark):
                h,w,_ = img.shape
                cx,cy = int(lm.x*w),int(lm.y*h)
                lmList.append([id,cx,cy]) 
            mpDraw.draw_landmarks(img,handlandmark,mpHands.HAND_CONNECTIONS)
    
    if lmList != []:
        x1,y1 = lmList[4][1],lmList[4][2]
        x2,y2 = lmList[8][1],lmList[8][2]

        cv2.circle(img,(x1,y1),4,(255,0,0),cv2.FILLED)
        cv2.circle(img,(x2,y2),4,(255,0,0),cv2.FILLED)
        cv2.line(img,(x1,y1),(x2,y2),(255,0,0),3)

        length = hypot(x2-x1,y2-y1)

        vol = np.interp(length,[15,220],[volMin,volMax])
        print(vol,length)
        volume.SetMasterVolumeLevel(vol, None)

        # Hand range 15 - 220
        # Volume range -63.5 - 0.0
        
    cv2.imshow('Image',img)
    if cv2.waitKey(1) & 0xff==ord('q'):
        break

Output

Volume Control With Hand Detection OpenCV Python: Project Information

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Anyway, if you want to level up your programming knowledge, especially Python OpenCV, try this new article I’ve made for you Best OpenCV Projects With Source Code For Beginners 2021.

Summary

Gesture recognition helps computers to understand human body language. This helps to build a more potent link between humans and machines, rather than just the basic text user interfaces or graphical user interfaces (GUIs).

In this project for gesture recognition, the human body’s motions are read by computer camera. The computer then makes use of this data as input to handle applications.

The objective of this project is to develop an interface which will capture human hand gesture dynamically and will control the volume level. For this, Deep Learning techniques such as Yolo model, Inception Net model+LSTM, 3-D CNN+LSTM and Time Distributed CNN+LSTM have been studied to compare the results of hand detection.

The results of Yolo model outperform the other three models. The models were trained using Kaggle and 20% of the videos available in 20 billion jester dataset. After the hand detection in captured frames, the next step is to control the system volume depending on direction of hand movement.

The hand movement direction is determined by generating and locating the bounding box on the detected hand.

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