Metadata-Version: 2.1
Name: image-quality
Version: 1.2.7
Summary: Image quality is an open source software library for Automatic Image Quality Assessment (IQA).
Home-page: https://github.com/ocampor/image-quality
Author: Ricardo Ocampo
Author-email: me@ocampor.ai
License: Apache 2.0
Project-URL: Bug Tracker, https://github.com/ocampor/image-quality/issues
Project-URL: Source Code, https://github.com/ocampor/image-quality
Description: .. -*- mode: rst -*-
        
        |Travis|_ |PyPi|_
        
        .. |Travis| image:: https://travis-ci.com/ocampor/image-quality.svg?branch=master
        .. _Travis: https://travis-ci.com/ocampor/image-quality
        
        .. |PyPi| image:: https://img.shields.io/pypi/dm/image-quality?color=blue   :alt: PyPI - Downloads
        .. _PyPi: https://pypi.org/project/image-quality/
        
        Image Quality
        =============
        
        Description
        -----------
        
        Image quality is an open source software library for Automatic Image
        Quality Assessment (IQA).
        
        Dependencies
        ------------
        
        -  Python 3.8
        -  (Development) Docker
        
        Installation
        ------------
        
        The package is public and is hosted in PyPi repository. To install it in
        your machine run
        
        ::
        
           pip install image-quality
        
        Example
        -------
        
        After installing ``image-quality`` package, you can test that it was
        successfully installed running the following commands in a python
        terminal.
        
        ::
        
           >>> import imquality.brisque as brisque
           >>> import PIL.Image
        
           >>> path = 'path/to/image'
           >>> img = PIL.Image.open(path)
           >>> brisque.score(img)
           4.9541572815704455
        
        
        Development
        -----------
        
        In case of adding a new tensorflow dataset or modifying the location of a zip file, it is
        necessary to update the url checksums. You can find the instructions in the following
        `tensorflow documentation <https://www.tensorflow.org/datasets/add_dataset#1_adjust_the_checksums_directory>`_.
        
        The steps to create the url checksums are the following:
        
        1. Take the file with the dataset configuration (e.g. live_iqa.py) an place it in the ``tensorflow_datasets``
        folder. The folder is commonly placed in ``${HOME}/.local/lib/python3.8/site-packages`` if you
        install the python packages using the ``user`` flag.
        
        2. Modify the ``__init__.py`` of the ``tensorflow_datasets`` to import your new dataset.
        For example ``from .image.live_iqa import LiveIQA`` at the top of the file.
        
        3. In your terminal run the commands:
        ::
        
           touch url_checksums/live_iqa.txt
           python -m tensorflow_datasets.scripts.download_and_prepare  \
              --register_checksums  \
              --datasets=live_iqa
        
        4. The file ``live_iqa.txt`` is going to contain the checksum. Now you can copy and paste it to your
        project's ``url_checksums`` folder.
        
        Sponsor
        -------
        
        .. image:: https://github.com/antonreshetov/mysigmail/raw/master/jetbrains.svg?sanitize=true
           :target: <https://www.jetbrains.com/?from=mysigmail>_
        
        Maintainer
        ----------
        
        - `Ricardo Ocampo <https://ocampor.com>`_
        
Keywords: image,quality,reference,reference-less
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: Microsoft :: Windows :: Windows 10
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX :: Linux
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
Provides-Extra: dev
Provides-Extra: dataset
