compimg 0.1.0

Image similarity metrics.

Master: [](https://circleci.com/gh/JenioPY/compimg/tree/master) Develop: [](https://circleci.com/gh/JenioPY/compimg/tree/develop) [](https://opensource.org/licenses/Apache-2.0) [](https://compimg.readthedocs.io/en/latest/?badge=latest)

## 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 value.

Such methods are 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) # result == 1.0 `

## Features - common metrics for calculating similarity of one image to another - only numpy as a dependency

## Installation compimg is available on PyPI. You can install it using pip: pip install compimg

## Note Keep in mind that metric are not aware of what you are passing. If metric relies on intensity values and you have YCbCr image you should pass only the first channel to the computing routine.

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