{
    "info": {
        "author": "Matthias Kestenholz",
        "author_email": "mk@feinheit.ch",
        "bugtrack_url": null,
        "classifiers": [
            "Environment :: Web Environment",
            "Framework :: Django",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Internet :: WWW/HTTP :: Dynamic Content",
            "Topic :: Software Development",
            "Topic :: Software Development :: Libraries :: Application Frameworks"
        ],
        "description": "=================\ndjango-imagefield\n=================\n\n.. image:: https://travis-ci.org/matthiask/django-imagefield.svg?branch=master\n    :target: https://travis-ci.org/matthiask/django-imagefield\n\n.. image:: https://readthedocs.org/projects/django-imagefield/badge/?version=latest\n    :target: https://django-imagefield.readthedocs.io/en/latest/?badge=latest\n    :alt: Documentation Status\n\nHeavily based on `django-versatileimagefield\n<https://github.com/respondcreate/django-versatileimagefield>`_, but\nwith a few important differences:\n\n- The amount of code is kept at a minimum. django-versatileimagefield\n  has several times as much code (without tests).\n- Generating images on-demand inside rendering code is made hard on\n  purpose. Instead, images are generated when models are saved and also\n  by running the management command ``process_imagefields``.\n- django-imagefield does not depend on a fast storage or a cache to be\n  and stay fast, at least as long as the image width and height is saved\n  in the database. An important part of this is never determining\n  whether a processed image exists in the hot path at all (except if you\n  ``force`` it).\n- django-imagefield fails early when image data is incomplete or not\n  processable by Pillow_ for some reason.\n- django-imagefield allows adding width, height and PPOI (primary point\n  of interest) fields to the model by adding ``auto_add_fields=True`` to\n  the field instead of boringly and verbosingly adding them yourself.\n\nReplacing existing uses of django-versatileimagefield requires the\nfollowing steps:\n\n- ``from imagefield.fields import ImageField as VersatileImageField, PPOIField``\n- Specify the image sizes by either providing ``ImageField(formats=...)`` or\n  adding the ``IMAGEFIELD_FORMATS`` setting. The latter overrides the\n  former if given.\n- Convert template code to access the new properties (e.g.\n  ``instance.image.square`` instead of ``instance.image.crop.200x200``\n  when using the ``IMAGEFIELD_FORMATS`` setting below).\n- When using django-imagefield with a PPOI, make sure that the PPOI\n  field is also added to ``ModelAdmin`` or ``InlineModelAdmin``\n  fieldsets, otherwise you'll just see the image, but no PPOI picker.\n  Contrary to django-versatileimagefield the PPOI field is editable\n  itself, which avoids apart from other complexities a pitfall with\n  inline form change detection.\n- Add ``\"imagefield\"`` to ``INSTALLED_APPS``.\n\nIf you used e.g. ``instance.image.crop.200x200`` and\n``instance.image.thumbnail.800x500`` before, you should add the\nfollowing setting:\n\n.. code-block:: python\n\n    IMAGEFIELD_FORMATS = {\n        # image field path, lowercase\n        'yourapp.yourmodel.image': {\n            'square': ['default', ('crop', (200, 200))],\n            'full': ['default', ('thumbnail', (800, 500))],\n\n            # The 'full' spec is equivalent to the following format\n            # specification in terms of image file produced (the\n            # resulting file name is different though):\n            # 'full': [\n            #     'autorotate', 'process_jpeg', 'process_gif', 'autorotate',\n            #     ('thumbnail', (800, 500)),\n            # ],\n            # Note that the exact list of default processors may\n            # change in the future.\n        },\n    }\n\nAfter running ``./manage.py process_imagefields`` once you can now\nuse use ``instance.image.square`` and ``instance.image.thumbnail`` in\ntemplates instead. Note that the properties on the ``image`` file do by\ndesign not check whether thumbs exist.\n\n\nImage processors\n================\n\ndjango-imagefield uses an image processing pipeline modelled after\nDjango's middleware.\n\nThe following processors are available out of the box:\n\n- ``autorotate``: Autorotates an image by reading the EXIF data.\n- ``process_jpeg``: Converts non-RGB images to RGB, activates\n  progressive encoding and sets quality to a higher value of 90.\n- ``process_gif``: Preserves transparency and palette data in resized\n  images.\n- ``preserve_icc_profile``: As the name says.\n- ``thumbnail``: Resizes images to not exceed a bounding box.\n- ``crop``: Crops an image to the given dimensions, also takes the PPOI\n  (primary point of interest) information into account if provided.\n- ``default``: The combination of ``autorotate``, ``process_jpeg``,\n  ``process_gif`` and ``preserve_icc_profile``. Additional default\n  processors may be added in the future. It is recommended to use\n  ``default`` instead of adding the processors one-by-one.\n\nProcessors can be specified either using their name alone, or if they\ntake arguments, using a tuple where the first entry is the processors'\nname and the rest are positional arguments.\n\nYou can easily register your own processors or even override built-in\nprocessors if you want to:\n\n.. code-block:: python\n\n    from imagefield.processing import register\n\n    # You could also write a class with a __call__ method, but I really\n    # like the simplicity of functions.\n\n    @register\n    def my_processor(get_image, ...):\n        def processor(image, context):\n            # read some information from the image...\n            # or maybe modify it, but it's mostly recommended to modify\n            # the image after calling get_image\n\n            image = get_image(image, context)\n\n            # modify the image, and return it...\n            modified_image = ...\n            # maybe modify the context...\n            return modified_image\n        return processor\n\nThe processor's name is taken directly from the registered object.\n\nAn example processor which converts images to grayscale would look as\nfollows:\n\n.. code-block:: python\n\n    from PIL import ImageOps\n    from imagefield.processing import register\n\n    @register\n    def grayscale(get_image):\n        def processor(image, context):\n            image = get_image(image, context)\n            return ImageOps.grayscale(image)\n        return processor\n\nNow include ``\"grayscale\"`` in the processing spec for the image where\nyou want to use it.\n\n\nThe processing context\n======================\n\nThe ``context`` is a namespace with the following attributes (feel free\nto add your own):\n\n- ``processors``: The list of processors.\n- ``name``: The name of the resulting image relative to its storages'\n  root.\n- ``extension``: The extension of the source and target.\n- ``ppoi``: The primary point of interest as a list of two floats\n  between 0 and 1.\n- ``save_kwargs``: A dictionary of keyword arguments to pass to\n  ``PIL.Image.save``.\n\nThe ``ppoi``, ``extension``, ``processors`` and ``name`` attributes\ncannot be modified when running processors anymore. Under some\ncircumstances ``extension`` and ``name`` will not even be there.\n\nIf you want to modify the extension or file type, or create a different\nprocessing pipeline depending on facts not known when configuring\nsettings you can use a callable instead of the list of processors. The\ncallable will receive the fieldfile and the context instance and must at\nleast set the context's ``processors`` attribute to something sensible.\nJust as an example here's an image field which always returns JPEG\nthumbnails:\n\n.. code-block:: python\n\n    from imagefield.processing import register\n\n    @register\n    def force_jpeg(get_image):\n        def processor(image, context):\n            image = get_image(image, context)\n            context.save_kwargs[\"format\"] = \"JPEG\"\n            context.save_kwargs[\"quality\"] = 90\n            return image\n        return processor\n\n    def jpeg_processor_spec(fieldfile, context):\n        context.extension = \".jpg\"\n        context.processors = [\n            \"force_jpeg\",\n            \"autorotate\",\n            (\"thumbnail\", (200, 200)),\n        ]\n\n    class Model(...):\n        image = ImageField(..., formats={\"thumb\": jpeg_processor_spec})\n\nOf course you can also access the model instance through the field file\nby way of its ``fieldfile.instance`` attribute and use those\ninformations to customize the pipeline.\n\n\nDevelopment\n===========\n\ndjango-imagefield uses flake8 and black to keep the code clean and\nformatted. Run both using tox_:\n\n.. code-block:: bash\n\n    tox -e style\n\nThe easiest way to build the documentation and run the test suite is\nalso by using tox_:\n\n.. code-block:: bash\n\n    tox -e docs  # Open docs/build/html/index.html\n    tox -e tests\n\n\n.. _documentation: https://django-imagefield.readthedocs.io/en/latest/\n.. _Pillow: https://pillow.readthedocs.io/en/latest/\n.. _tox: https://tox.readthedocs.io/\n\n\n",
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