How to Calculate Image Similarity Using Python

Utilize built-in evaluation metrics with the image-similarity-measures Python library.

Photo by Carson Arias on Unsplash

Today, detecting image similarity is increasingly relevant. Contemporary platforms such as Pinterest employ visual search engines to deliver results based on visual resemblances.

This article demonstrates how to utilize the image-similarity-measures library for image comparison. I selected this library for its range of pre-built evaluation metrics, offering a straightforward and efficient approach to explore this complex field.

Let’s dive into it!

Introduction

According to the library’s documentation, we can use eight different evaluation metrics to calculate the similarity between images:

• Root mean square error (RMSE),

• Peak signal-to-noise ratio (PSNR),

• Structural Similarity Index (SSIM),

• Feature-based similarity index (FSIM),

• Information theoretic-based Statistic Similarity Measure (ISSM),

• Signal to reconstruction error ratio (SRE),

• Spectral angle mapper (SAM)

• Universal image quality index (UIQ)

We’re not going to discuss how each of these metrics works. If you’re eager to learn how they work on a deeper level, check out the provided links.

Fortunately, all of the scary math has been implemented for us and we can start measuring the image similarities right away. We just need to call the name of the chosen evaluation metric and pass two images as parameters. For example:

rmse(query_image, data_image)sam(query_image, data_image)

Note that the default metric is psnr.

There are two ways to use this package: You can execute commands in your terminal or write a separate Python script.

If you want to do a quick evaluation between two images, run this command in your terminal:

Optionally, add the — metric flag to indicate which evaluation metric to use.

For this article, I’ve chosen three evaluation metrics: rmse, psnr, and ssim.

You’ll see examples of how the results can differ.

Scenario

First, let’s understand what program we’re going to build:

• We’ll pass an original image as an argument to our program.

• The code will walk through all images in a dataset folder and calculate the similarity value between each image and the original one.

• In the end, we’ll print the individual results from each evaluation metric.