Python image processing: compare the difference between two pictures

Python image processing: compare the difference between two pictures

introduction

In the field of image processing, comparing the differences between two images is a common task. Python provides many powerful tools and libraries that allow us to achieve this easily. This article will show you how to use Python to compare two images, detect and visualize the differences between them.

Definition of Image Difference

Before we start, we first need to define image differences. Image difference can be understood as the degree of difference between two pictures at the pixel level. Typically, we compute the difference for each pixel in the two images and generate an image representing the difference.

Image Difference Algorithm

Python provides a variety of image difference algorithms, and you can choose the appropriate algorithm according to your needs. The following are some commonly used image difference algorithms:

Pixel-level difference: The simplest method is to compare the RGB values ​​of two images pixel by pixel and calculate the difference. This can be done efficiently using the NumPy library.

Structural Similarity Index (SSIM): SSIM is an algorithm that measures the similarity of two images. It takes into account the differences between brightness, contrast and structure and generates a score between -1 and 1. In Python, the SSIM can be calculated using the ssim function from the scikit-image library.

Mean Squared Error (Mean Squared Error, MSE): MSE is a method of measuring the difference between two pictures, by calculating the sum of the squares of the difference between each pixel to get a value. A lower MSE value indicates that the two images are more similar. In Python, the MSE can be calculated using the OpenCV library.

Example Python implementation

Next, we'll use Python to compare the differences between two images. We will demonstrate the use of pixel-level differences and structural similarity indices to compute image differences and visualize the results using the Matplotlib library.

1. Import the required libraries:

import cv2
import numpy as np
from skimage.metrics import structural_similarity as ssim
import matplotlib.pyplot as plt

2. Load two images to be compared:

image1 = cv2.imread('image1.jpg')
image2 = cv2.imread('image2.jpg')

3. Compute pixel-level differences:

pixel_diff = cv2.absdiff(image1, image2)

4. Calculate the structural similarity index:

gray1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
ssim_score = ssim(gray1, gray2)

5. Visualize the diff results:

plt.subplot(1, 3, 1)
plt.imshow(cv2.cvtColor(image1, cv2.COLOR_BGR2RGB))
plt.title('Image 1')

plt.subplot(1, 3, 2)
plt.imshow(cv2.cvtColor(image2, cv2.COLOR_BGR2RGB))
plt.title('Image 2')

plt.subplot(1, 3, 3)
plt.imshow(pixel_diff, cmap='gray')
plt.title(f'Pixel Difference\nSSIM Score: {
      
      ssim_score:.2f}')

plt.show()

in conclusion

This article introduces how to compare the differences between two pictures using Python. We explore algorithms such as pixel-level differences and structural similarity indices, with examples of their Python implementations. By comparing the differences between pictures, we can better understand the applications in the field of image processing and obtain valuable information from them.