How to use Python to perform lighting compensation on pictures

How to use Python to perform lighting compensation on pictures

Abstract:
For digital image processing, uneven lighting is one of the common problems. This article will introduce how to use the OpenCV library in the Python programming language to perform lighting compensation on pictures. We will extract the lighting information of the image, and then adjust the brightness and contrast of the image based on this information to achieve the effect of uniform lighting.

1. Import the required libraries
   First, we need to import the OpenCV library in Python and other basic libraries:

import cv2
import numpy as np
import matplotlib.pyplot as plt

2. Load and display the original Image
   Load the image that requires lighting compensation and display it using OpenCV's imshow function:

image = cv2.imread('image.jpg',1)
cv2.imshow('Original Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

3. Extract lighting information
   In order to extract the lighting information of the image, we can use Statistically based methods. Among them, a common method is to obtain the light intensity of the image by calculating the average value of the image:

gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
mean = np.mean(gray_image)

4. Adjust the brightness and contrast of the image
   According to the lighting information, we can pass Adjust the brightness and contrast of the image to achieve a uniform lighting effect. Specifically, we will use the cv2.convertScaleAbs function from the OpenCV library to adjust the brightness and contrast of the image:

alpha = 1.0 / mean
beta = 0
light_compensated_image = cv2.convertScaleAbs(image, alpha=alpha, beta=beta)

5. Display and save the processed image
   Finally, we can use OpenCV's imshow function displays the processed image and uses the cv2.imwrite function to save it to the disk:

cv2.imshow('Light Compensated Image', light_compensated_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

cv2.imwrite('light_compensated_image.jpg', light_compensated_image)

6. Results and Discussion
   Through the above steps, we successfully Lighting compensation was performed on the original image to eliminate the problem of uneven lighting. By adjusting the brightness and contrast of the image, we give the entire image a more even lighting effect. Finally, we can verify our results by displaying and saving the processed image.

Conclusion:
This article demonstrates the process of lighting compensation for images by using Python and the OpenCV library. Uneven lighting is a common problem in digital image processing, but by extracting lighting information and adjusting the brightness and contrast of the image, we can effectively achieve uniform lighting effects. As a powerful programming language, Python provides various image processing tools and libraries, making illumination compensation simpler and more efficient.

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