https://github.com/khrynczenko/compimg

compimg - python package for computing similarity between the images
https://github.com/khrynczenko/compimg

computer-vision image-analysis image-processing image-quality-assessment image-similarity psnr python python-library python3 quality-assessment similarity similarity-measures ssim

Last synced: 12 days ago
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compimg - python package for computing similarity between the images

• Host: GitHub
• URL: https://github.com/khrynczenko/compimg
• Owner: khrynczenko
• License: apache-2.0
• Created: 2019-02-20T23:24:43.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-11-27T19:33:27.000Z (almost 4 years ago)
• Last Synced: 2024-10-01T11:08:40.891Z (about 2 months ago)
• Topics: computer-vision, image-analysis, image-processing, image-quality-assessment, image-similarity, psnr, python, python-library, python3, quality-assessment, similarity, similarity-measures, ssim
• Language: Python
• Homepage:
• Size: 83 KB
• Stars: 15
• Watchers: 3
• Forks: 0
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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README

        
# compimg

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[![Documentation Status](https://readthedocs.org/projects/compimg/badge/?version=stable)](https://compimg.readthedocs.io/en/stable/?badge=stable)

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Branches:  

master: [![CircleCI](https://circleci.com/gh/khrynczenko/compimg/tree/master.svg?style=svg)](https://circleci.com/gh/khrynczenko/compimg/tree/master)  

develop: [![CircleCI](https://circleci.com/gh/khrynczenko/compimg/tree/develop.svg?style=svg)](https://circleci.com/gh/khrynczenko/compimg/tree/develop)

## Introduction

**_For full documentation visit [documentation site](https://compimg.readthedocs.io)._**  

Image similarity metrics are often used in image quality assessment for performance

evaluation of image restoration and reconstruction algorithms. They require two images:

- test image (image of interest)

- reference image (image we compare against)  

Such metrics produce numerical values and are widely called full/reduced-reference methods for 

assessing image quality.

`compimg` package is all about calculating similarity between images. 

It provides image similarity metrics (PSNR, SSIM etc.) that are widely used 

to asses image quality.

```python

import numpy as np

from compimg.similarity import SSIM

some_grayscale_image = np.ones((20,20), dtype=np.uint8)

identical_image = np.ones((20,20), dtype=np.uint8)

result = SSIM().compare(some_grayscale_image, identical_image)

assert result == 1.0 # SSIM returns 1.0 when images are identical

```

## Features  

- common metrics for calculating similarity of one image to another 

- images are treated as `numpy` arrays which makes `compimg` compatible 

with most image processing packages

- only `scipy` (and inherently `numpy`) as a dependency

## Installation

`compimg` is available on *PyPI*. You can install it using pip:  

`pip install compimg`

## Note 

Keep in mind that metrics are not aware of what kind of image you are passing. 

If metric relies on intensity values and you have YCbCr image you should probably 

pass only the first channel to the computing subroutine.

## Help

If you have any problems or questions please post an issue.