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Comparing similar images with Python PIL
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Download this code from https://codegive.com • Image comparison is a common task in computer vision and image processing. In this tutorial, we will explore how to compare similar images using Python and the Python Imaging Library (PIL), which is now known as the Pillow library. • Before we begin, make sure you have Python installed on your system. You can download it from python.org. Additionally, install the Pillow library using the following command: • Let's create a simple Python script to compare two images using the Structural Similarity Index (SSI) provided by the scikit-image library, which can be installed with: • Now, let's create a script named image_comparison.py. • Replace path/to/image1.jpg and path/to/image2.jpg with the actual paths of the images you want to compare. • Import Libraries: Import necessary libraries, including PIL, scikit-image, matplotlib, and numpy. • Function Definition: Define a function compare_images that takes the paths of two images as input. • Open Images: Open the images using Pillow and convert them to grayscale. • Convert to Numpy Arrays: Convert the grayscale images to numpy arrays. • Compute Structural Similarity Index (SSI): Use the ssim function from the scikit-image library to compute the Structural Similarity Index. • Display Images: Display the original images using matplotlib for visual inspection. • Print Results: Print the computed SSI and compare it against a predefined threshold to determine the similarity of the images. • Main Execution Block: Specify the paths of the images to be compared and call the compare_images function. • This tutorial demonstrates a simple method to compare similar images using Python and the Pillow library. The Structural Similarity Index provides a quantitative measure of similarity, and the threshold can be adjusted based on the specific requirements of your application. Further enhancements and optimizations can be made based on the complexity of your use case. • ChatGPT
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