Metadata-Version: 2.1
Name: compimg
Version: 0.2.0
Summary: Image similarity metrics.
Home-page: http://github.com/khrynczenko/compimg
Author: khrynczenko
Author-email: jeniopy@gmail.com
License: UNKNOWN
Description: # 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)
          
        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 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)
        assert 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 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 pass
        only the first channel to the computing routine.
        
        ## Help
        If you have any problems or questions please post an issue.
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
