semvec/pypi-packages huggingface dataset:
compimg 0.2.2
Image similarity metrics.
Requires Python: >=3.6

# compimg
![PyPI](https://img.shields.io/pypi/v/compimg.svg)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/compimg.svg)
![PyPI - Wheel](https://img.shields.io/pypi/wheel/compimg.svg)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Documentation Status](https://readthedocs.org/projects/compimg/badge/?version=stable)](https://compimg.readthedocs.io/en/stable/?badge=stable)
![PyPI - Downloads](https://img.shields.io/pypi/dm/compimg)

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.