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Brightness Control With Hand Detection using OpenCV in Python

Brightness Control With Hand Detection using OpenCV in Python

Last Updated : 23 Jul, 2025

In this article, we are going to make a Python project that uses OpenCV and Mediapipe to see hand gesture and accordingly set the brightness of the system from a range of 0-100. 

We have used a HandTracking module that tracks all points on the hand and detects hand landmarks, calculate the distance between thumb tip and index fingertip and maps the distance between thumb tip and index fingertip with brightness range.

Required Libraries 
pip install mediapipe  
pip install opencv-python
pip install screen-brightness-control
pip install numpy
Stepwise Implementation Step 1: Import all required libraries Python3 
# Importing Libraries
import cv2
import mediapipe as mp
from math import hypot
import screen_brightness_control as sbc
import numpy as np
Step 2: Initializing Hands model Python3 
# Initializing the Model
mpHands = mp.solutions.hands
hands = mpHands.Hands(
    static_image_mode=False,
    model_complexity=1,
    min_detection_confidence=0.75,
    min_tracking_confidence=0.75,
    max_num_hands=2)

Draw = mp.solutions.drawing_utils

Let us look into the parameters for the Hands Model:

Hands( static_image_mode=False, model_complexity=1 min_detection_confidence=0.75, min_tracking_confidence=0.75, max_num_hands=2 )

Where:

Step 3: Process the image and apply brightness based on the distance between thumb and index fingertip

Capture the frames continuously from the camera using OpenCV and Convert BGR image to an RGB image and make predictions using initialized hands model. Prediction made by the model is saved in the results variable from which we can access landmarks using results.multi_hand_landmarks and if hands are present in the frame, detect hand landmarks after that calculate the distance between thumb tip and index finger tip. Map the distance of thumb tip and index fingertip with brightness range i.e according to distance between them brightness of system will change.

Python3 
# Start capturing video from webcam
cap = cv2.VideoCapture(0)

while True:
    # Read video frame by frame
    _,frame = cap.read()
    
    #Flip image 
    frame=cv2.flip(frame,1)
    
    # Convert BGR image to RGB image
    frameRGB = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
    
    # Process the RGB image
    Process = hands.process(frameRGB)
    
    landmarkList = []
    # if hands are present in image(frame)
    if Process.multi_hand_landmarks:
        # detect handmarks
        for handlm in Process.multi_hand_landmarks:
            for _id,landmarks in enumerate(handlm.landmark):
                # store height and width of image
                height,width,color_channels = frame.shape
                
                # calculate and append x, y coordinates
                # of handmarks from image(frame) to lmList
                x,y = int(landmarks.x*width),int(landmarks.y*height)              
                landmarkList.append([_id,x,y]) 
            
            # draw Landmarks
            Draw.draw_landmarks(frame,handlm,mpHands.HAND_CONNECTIONS)
    
    # If landmarks list is not empty
    if landmarkList != []:
        # store x,y coordinates of (tip of) thumb  
        x_1,y_1 = landmarkList[4][1],landmarkList[4][2]
        
        # store x,y coordinates of (tip of) index finger
        x_2,y_2 = landmarkList[8][1],landmarkList[8][2]
        
        # draw circle on thumb and index finger tip
        cv2.circle(frame,(x_1,y_1),7,(0,255,0),cv2.FILLED)
        cv2.circle(frame,(x_2,y_2),7,(0,255,0),cv2.FILLED)
        
        # draw line from tip of thumb to tip of index finger
        cv2.line(frame,(x_1,y_1),(x_2,y_2),(0,255,0),3)
        
        # calculate square root of the sum
        # of squares of the specified arguments.
        L = hypot(x_2-x_1,y_2-y_1)
        
        # 1-D linear interpolant to a function
        # with given discrete data points 
        # (Hand range 15 - 220, Brightness range 0 - 100),
        # evaluated at length.
        b_level = np.interp(L,[15,220],[0,100])
    
        # set brightness
        sbc.set_brightness(int(b_level))

    # Display Video and when 'q' is entered,
    # destroy the window
    cv2.imshow('Image', frame)
    if cv2.waitKey(1) & 0xff == ord('q'):
        break
Below is the complete implementation: Python3 
# Importing Libraries
import cv2
import mediapipe as mp
from math import hypot
import screen_brightness_control as sbc
import numpy as np

# Initializing the Model
mpHands = mp.solutions.hands
hands = mpHands.Hands(
    static_image_mode=False,
    model_complexity=1,
    min_detection_confidence=0.75,
    min_tracking_confidence=0.75,
    max_num_hands=2)

Draw = mp.solutions.drawing_utils

# Start capturing video from webcam
cap = cv2.VideoCapture(0)

while True:
    # Read video frame by frame
    _, frame = cap.read()

    # Flip image
    frame = cv2.flip(frame, 1)

    # Convert BGR image to RGB image
    frameRGB = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

    # Process the RGB image
    Process = hands.process(frameRGB)

    landmarkList = []
    # if hands are present in image(frame)
    if Process.multi_hand_landmarks:
        # detect handmarks
        for handlm in Process.multi_hand_landmarks:
            for _id, landmarks in enumerate(handlm.landmark):
                # store height and width of image
                height, width, color_channels = frame.shape

                # calculate and append x, y coordinates
                # of handmarks from image(frame) to lmList
                x, y = int(landmarks.x*width), int(landmarks.y*height)
                landmarkList.append([_id, x, y])

            # draw Landmarks
            Draw.draw_landmarks(frame, handlm,
                                mpHands.HAND_CONNECTIONS)

    # If landmarks list is not empty
    if landmarkList != []:
        # store x,y coordinates of (tip of) thumb
        x_1, y_1 = landmarkList[4][1], landmarkList[4][2]

        # store x,y coordinates of (tip of) index finger
        x_2, y_2 = landmarkList[8][1], landmarkList[8][2]

        # draw circle on thumb and index finger tip
        cv2.circle(frame, (x_1, y_1), 7, (0, 255, 0), cv2.FILLED)
        cv2.circle(frame, (x_2, y_2), 7, (0, 255, 0), cv2.FILLED)

        # draw line from tip of thumb to tip of index finger
        cv2.line(frame, (x_1, y_1), (x_2, y_2), (0, 255, 0), 3)

        # calculate square root of the sum of
        # squares of the specified arguments.
        L = hypot(x_2-x_1, y_2-y_1)

        # 1-D linear interpolant to a function
        # with given discrete data points
        # (Hand range 15 - 220, Brightness
        # range 0 - 100), evaluated at length.
        b_level = np.interp(L, [15, 220], [0, 100])

        # set brightness
        sbc.set_brightness(int(b_level))

    # Display Video and when 'q' is entered, destroy 
    # the window
    cv2.imshow('Image', frame)
    if cv2.waitKey(1) & 0xff == ord('q'):
        break

Output:

 
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