Brightness Control With Hand Detection OpenCV Python With Source Code
This 2022 Brightness Control With Hand Detection OpenCV Python With Source Code was developed using Python OpenCV, This Python OpenCV Project With Source Code we are going Building 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. For 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.
Time needed: 5 minutes.
These are the steps on how to run Brightness Control With Hand Detection OpenCV Python With Source Code
- Step 1: Download the given source code below.
First, download the given source code below and unzip the source code.
- Step 2: Import the project to your PyCharm IDE.
Next, import the source code you’ve download to your PyCharm IDE.
- Step 3: Run the project.
last, run the project with the command “py main.py”
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,lmList x2,y2 = lmList,lmList 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
Brightness Control With Hand Detection Python: Project Information
|Project Name:||Brightness Control With Hand Detection OpenCV Python|
|Language/s Used:||Python OpenCV|
|Python version (Recommended):||3.8|
Download 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 2021.
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.
In this Python OpenCV Project also includes a downloadable source code for free.
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