How to Evaluate Image Similarity Using Python

Understanding the similarity between two images can be crucial in various applications, from image processing to quality assessment in photography. In this article, we will explore how to use the Image Similarity Measures Python package and command line tool to evaluate image similarity using different metrics. We will guide you through installation, usage, and some troubleshooting tips.

Installation

Before you can begin measuring image similarity, you’ll need to install the package. It’s straightforward! Just follow these steps:

• Open your command line interface (CLI).
• For Python version 3.9, run the following command:

pip install image-similarity-measures

• Optional: For faster evaluation of the FSIM metric, you can install the pyfftw package using:

pip install image-similarity-measures[speedups]

• Optional: For reading TIFF images with rasterio instead of OpenCV, install using:

pip install image-similarity-measures[rasterio]

Usage on the Command Line

To evaluate the similarity between two images, execute the following command in your CLI:

image-similarity-measures --org_img_path=a.tif --pred_img_path=b.tif

Note: Make sure the images you’re using for evaluation are in channel last format. The results are printed in a machine-readable JSON format, which you can redirect into a file.

Parameters

Here are the parameters you can use:

• –org_img_path FILE: Path to the original input image.
• –pred_img_path FILE: Path to the predicted image.
• –metric METRIC: Select an evaluation metric (options include fsim, issm, psnr, rmse, sam, sre, ssim, uiq, or all). This can be repeated for multiple metrics.

Usage in Python

You can also evaluate image similarity directly in your Python scripts. Here’s how:

from image_similarity_measures.evaluate import evaluation
evaluation(org_img_path='examplelafayette_org.tif',
           pred_img_path='examplelafayette_pred.tif',
           metrics=['rmse', 'psnr'])

For specific metrics like RMSE, use:

from image_similarity_measures.quality_metrics import rmse
rmse(org_img=np.random.rand(3,2,1), pred_img=np.random.rand(3,2,1))

Understanding the Evaluation Metrics

The package includes several evaluation metrics to assess similarity:

• 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)

Troubleshooting

If you encounter issues while using the package, consider the following troubleshooting tips:

• Ensure that your image files are correctly formatted and accessible.
• Check that you have installed all required dependencies for the desired metrics.
• Confirm that your images are in the channel last format as required by the evaluation.

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Conclusion

The Image Similarity Measures package provides a comprehensive framework for evaluating how similar two images are, leveraging various metrics to suit your needs. It’s a powerful tool for developers and researchers alike in the domain of image processing.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.