image-similarity-measures
Evaluation metrics to assess the similarity between two images.
Decision gist · record as of 2026-08-14
Yes, if you need multiple standard image similarity metrics in one package and can accept no future maintenance. The package is stable, permissively licensed, and has low install friction. However, do not rely on it for critical production systems or expect bug fixes—consider it a snapshot tool. For active development or long-term support, evaluate maintained alternatives.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.8; images must be in channel-last format; depends on numpy, scikit-image, opencv-python, and phasepack.
- Low friction install with a pure-Python wheel.
- Package is abandoned (last release 2023-05-04, no activity for 1198 days), so expect no maintenance or bug fixes going forward.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use this package freely in commercial and private projects with minimal restrictions.
last release 2023-05-04 (1198 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 173,615 downloads/mo, #10,303 on PyPI
Alternatives
Verify before relying
from image_similarity_measures.evaluate import evaluation
evaluation(org_img_path="original.tif", pred_img_path="predicted.tif", metrics=["rmse", "psnr"])- Whether the package works reliably with Python 3.10 and 3.11 given its abandoned status and lack of recent testing.
- Whether all eight metrics produce numerically correct results or if any have known issues that were never fixed.
- Performance characteristics of each metric on typical image sizes and whether optional speedups (pyfftw, rasterio) are still maintained.
What it is and what it does
Image Similarity Measures is a Python package that computes eight standard metrics to quantify how similar two images are. It wraps implementations of RMSE, PSNR, SSIM, FSIM, ISSM, SRE, SAM, and UIQ into a unified API, with both a Python function interface and a command-line tool. The package depends on numpy, scikit-image, opencv-python, and phasepack to handle image I/O and metric computation.
The package is mature and stable (marked Production/Stable), but is no longer actively maintained—the last release was in May 2023 with no commits since. It works with Python 3.8 through 3.11 and installs with low friction as a pure wheel. Optional dependencies (pyfftw for faster FSIM, rasterio for TIFF reading) can be added at install time.
Use it for
- Evaluate image reconstruction quality in super-resolution or denoising projects by comparing output to reference images.
- Benchmark image compression algorithms by measuring similarity loss across different compression levels.
- Validate image processing pipelines in remote sensing or satellite imagery workflows.
- Assess quality of generated or predicted images in machine learning model evaluation.
- Compare image enhancement or restoration results quantitatively in research or production workflows.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need multiple standard image similarity metrics in one package and can accept no future maintenance.
The package is stable, permissively licensed, and has low install friction. However, do not rely on it for critical production systems or expect bug fixes—consider it a snapshot tool. For active development or long-term support, evaluate maintained alternatives.
Install
image-similarity-measures on PyPI
Before you install
Low friction install with a pure-Python wheel. Package is abandoned (last release 2023-05-04, no activity for 1198 days), so expect no maintenance or bug fixes going forward.
Requires Python >=3.8; images must be in channel-last format; depends on numpy, scikit-image, opencv-python, and phasepack.
License in practice
MIT license is permissive; you can use this package freely in commercial and private projects with minimal restrictions.
Quickstart
from image_similarity_measures.evaluate import evaluation
evaluation(org_img_path="original.tif", pred_img_path="predicted.tif", metrics=["rmse", "psnr"])Verify before relying
- Whether the package works reliably with Python 3.10 and 3.11 given its abandoned status and lack of recent testing.
- Whether all eight metrics produce numerically correct results or if any have known issues that were never fixed.
- Performance characteristics of each metric on typical image sizes and whether optional speedups (pyfftw, rasterio) are still maintained.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesnumpyscikit-imageopencv-pythonphasepack |
| Maintenance | Abandoned 1,198 days since the last release |
| First released | |
| Downloads | 173,615 / month, #10,303 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: image_similarity_measures-0.3.6-py3-none-any.whl
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