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        "description": "# compimg\n![PyPI](https://img.shields.io/pypi/v/compimg.svg)\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/compimg.svg)\n![PyPI - Wheel](https://img.shields.io/pypi/wheel/compimg.svg)\n[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)\n[![Documentation Status](https://readthedocs.org/projects/compimg/badge/?version=stable)](https://compimg.readthedocs.io/en/stable/?badge=stable)\n\nBranches:  \nmaster: [![CircleCI](https://circleci.com/gh/khrynczenko/compimg/tree/master.svg?style=svg)](https://circleci.com/gh/khrynczenko/compimg/tree/master)  \ndevelop: [![CircleCI](https://circleci.com/gh/khrynczenko/compimg/tree/develop.svg?style=svg)](https://circleci.com/gh/khrynczenko/compimg/tree/develop)\n\n\n## Introduction\n**_For full documentation visit [documentation site](https://compimg.readthedocs.io)._**  \n\nImage similarity metrics are often used in image quality assessment for performance\nevaluation of image restoration and reconstruction algorithms. They require two images:\n- test image (image of interest)\n- reference image (image we compare against)  \n\nSuch metrics produce numerical values.\n\nSuch methods are are widely called full/reduced-reference methods for \nassessing image quality.\n\n`compimg` package is all about calculating similarity between images. \nIt provides image similarity metrics (PSNR, SSIM etc.) that are widely used \nto asses image quality.\n\n```python\nimport numpy as np\nfrom compimg.similarity import SSIM\nsome_grayscale_image = np.ones((20,20), dtype=np.uint8)\nidentical_image = np.ones((20,20), dtype=np.uint8)\nresult = SSIM().compare(some_grayscale_image, identical_image)\nassert result == 1.0\n```\n\n## Features  \n- common metrics for calculating similarity of one image to another \n- images are treated as `numpy` arrays which makes `compimg` compatible \nwith most image processing packages\n- only `scipy` (and inherently `numpy`) as a dependency\n\n## Installation\n`compimg` is available on *PyPI*. You can install it using pip:  \n`pip install compimg`\n\n## Note \nKeep in mind that metrics are not aware of what kind of image you are passing. \nIf metric relies on intensity values and you have YCbCr image you should pass\nonly the first channel to the computing routine.\n\n## Help\nIf you have any problems or questions please post an issue.\n\n\n",
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