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📜 Brightness control

🎯 AIM

To develop a real-time brightness control system using hand gestures, leveraging OpenCV and MediaPipe for hand detection and brightness adjustment.

No dataset used

https://drive.google.com/file/d/1q7kraajGykfc2Kb6-84dCOjkrDGhIQcy/view?usp=sharing

⚙️ TECH STACK

Category Technologies
Languages Python
Libraries/Frameworks OpenCV, NumPy, MediaPipe, cvzone
Tools Jupyter Notebook, Local Python IDE

📝 DESCRIPTION

What is the requirement of the project?

  • The project requires a webcam to capture real-time video and detect hand gestures for brightness control.
How is it beneficial and used?
  • Allows users to control screen brightness without physical touch, making it useful for touchless interfaces.
  • Ideal for applications in smart home systems and assistive technologies.
How did you start approaching this project? (Initial thoughts and planning)
  • Identified the need for a touchless brightness control system.
  • Selected OpenCV for video processing and MediaPipe for efficient hand tracking.
  • Developed a prototype to calculate brightness based on hand distance.
Mention any additional resources used (blogs, books, chapters, articles, research papers, etc.).
  • OpenCV documentation for video processing.
  • MediaPipe's official guide for hand tracking.

🔍 EXPLANATION

🧩 DETAILS OF THE DIFFERENT FEATURES

🛠 Developed Features

Feature Name Description Reason
Hand Detection Detects hand gestures in real-time To control brightness with gestures
Distance Calculation Calculates distance between fingers To adjust brightness dynamically
Brightness Mapping Maps hand distance to brightness levels Ensures smooth adjustment of brightness

🛤 PROJECT WORKFLOW

Project workflow

  graph LR
A[Start] --> B[Initialize Webcam];
B --> C[Detect Hand Gestures];
C --> D[Calculate Distance];
D --> E[Adjust Brightness];
E --> F[Display Output];
  • Initialize the webcam using OpenCV.
  • Use MediaPipe to detect hands in the video feed.
  • Calculate the distance between two fingers (e.g., thumb and index).
  • Map the distance to a brightness range.
  • Display the adjusted brightness on the video feed.

🖥 CODE EXPLANATION

  • The program begins by setting up the webcam to capture frames with a resolution of 640x480 pixels. This ensures consistent processing and visualization of the video stream.

    cap = cv2.VideoCapture(0)  
cap.set(3, 640)  # Set width  
cap.set(4, 480)  # Set height

  • Using the HandDetector from cvzone, the program tracks one hand (maxHands=1). The brightness of the video frame is dynamically adjusted based on the distance between the thumb and index finger.

    detector = HandDetector(detectionCon=0.8, maxHands=1)  
brightness = 1.0  # Default brightness level

  • The HandDetector detects hand landmarks in each frame with a confidence threshold of 0.8. The initial brightness is set to 1.0 (normal).

    hands, img = detector.findHands(frame, flipType=False)  

if hands:  
    hand = hands[0]  
    lm_list = hand['lmList']  
    if len(lm_list) > 8:  
        thumb_tip = lm_list[4]  
        index_tip = lm_list[8]  
        distance = int(((thumb_tip[0] - index_tip[0]) ** 2 + (thumb_tip[1] - index_tip[1]) ** 2) ** 0.5)  
        brightness = np.interp(distance, [20, 200], [0, 1])

  • The program calculates the distance between the thumb tip (lmList[4]) and index finger tip (lmList[8]). This distance is mapped to a brightness range of 0 to 1 using np.interp.

  • The captured frame's brightness is modified by scaling the value (V) channel in the HSV color space according to the calculated brightness level.

    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)  
hsv[..., 2] = np.clip(hsv[..., 2] * brightness, 0, 255).astype(np.uint8)  
frame_bright = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)  
cv2.imshow("Brightness Controller", frame_bright)

  • This technique ensures smooth, real-time brightness adjustments based on the user's hand gestures. The output frame is displayed with the adjusted brightness level.

⚖️ PROJECT TRADE-OFFS AND SOLUTIONS

  • Real-time processing vs. computational efficiency: Optimized hand detection by limiting the maximum number of detectable hands to 1.
  • Precision in brightness control vs. usability: Adjusted mapping function to ensure smooth transitions.

🖼 SCREENSHOTS

Working of the model

image

✅ CONCLUSION

🔑 KEY LEARNINGS

Insights gained from the data

  • Improved understanding of real-time video processing.
  • Learned to integrate gesture detection with hardware functionalities.
Improvements in understanding machine learning concepts
  • Gained insights into MediaPipe's efficient hand detection algorithms.

🌍 USE CASES

  • Touchless brightness control for smart home displays.
  • Brightness adjustment for users with limited mobility.