Image Brightness Calculation

Theory

Brightness refers to the perceived lightness of an image. It is a critical parameter in image processing and computer vision, affecting tasks such as thresholding, contrast adjustment, and exposure evaluation.

1. Average Brightness

The average brightness is calculated as the mean of all pixel intensities in the image. For grayscale images, this is simply the mean of the single intensity channel. For color images, the mean is computed across all channels.

Formula:
Average Brightness = (Sum of all pixel values) / (Total number of pixels)

2. Applications

• Detecting underexposed or overexposed images.
• Normalizing images before further processing (e.g., histogram equalization).
• Adjusting brightness for better visualization or aesthetics.
• Preprocessing for machine learning models to maintain consistent lighting.

By understanding the average brightness of an image, we can make informed adjustments to improve its quality and prepare it for subsequent analysis.

Python Code

import cv2
import numpy as np

def calculate_brightness(image_path):
    # Load the image
    image = cv2.imread(image_path)

    if image is None:
        print("Error: Image not found!")
        return None
    
    # Calculate average brightness
    brightness = np.mean(image)
    return brightness

# Path to image
image_path = "assets/QR.png"

# Compute and display brightness
brightness = calculate_brightness(image_path)
if brightness is not None:
    print(f"Average Brightness: {brightness}")
        

Example Output

Average Brightness: 125.3