Brightness Control with Hand Detection OpenCV Python Source Code

Brightness Control With Hand Detection OpenCV Python With Source Code

This Brightness Control With Hand Detection OpenCV Python With Source Code was developed using Python OpenCV.

In this Python OpenCV Project With Source Code, we are going to build a Brightness Controller with OpenCV, to change the brightness of a computer.

Building a Brightness 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. In my case, distance between thumb tip and index finger tip was within the range of 15 – 220 and the volume range was from 0 – 100.

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

Installed Libraries

import cv2 
import mediapipe as mp
from math import hypot
import screen_brightness_control as sbc
import numpy as np 

Complete Source Code

import cv2 
import mediapipe as mp
from math import hypot
import screen_brightness_control as sbc
import numpy as np 

cap = cv2.VideoCapture(0)

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

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)

        bright = np.interp(length,[15,220],[0,100])
        print(bright,length)
        sbc.set_brightness(int(bright))
        
        # Hand range 15 - 220
        # Brightness range 0 - 100

    cv2.imshow('Image',img)
    if cv2.waitKey(1) & 0xff==ord('q'):
        break

Output

Brightness Control With Hand Detection Python: Project Information

Download the Source Code below

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 .

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 a computer camera. The computer then makes use of this data as input to handle applications.

In this Python OpenCV Project also includes a downloadable source code for free.

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Inquiries

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