Pillow/docs/handbook/concepts.rst

Concepts
========

The Python Imaging Library handles *raster images*; that is, rectangles of
pixel data.

.. _concept-bands:

Bands
-----

An image can consist of one or more bands of data. The Python Imaging Library
allows you to store several bands in a single image, provided they all have the
same dimensions and depth.  For example, a PNG image might have 'R', 'G', 'B',
and 'A' bands for the red, green, blue, and alpha transparency values.  Many
operations act on each band separately, e.g., histograms.  It is often useful to
think of each pixel as having one value per band.

To get the number and names of bands in an image, use the
:py:meth:`~PIL.Image.Image.getbands` method.

.. _concept-modes:

Modes
-----

The ``mode`` of an image defines the type and depth of a pixel in the
image. The current release supports the following standard modes:

    * ``1`` (1-bit pixels, black and white, stored with one pixel per byte)
    * ``L`` (8-bit pixels, black and white)
    * ``P`` (8-bit pixels, mapped to any other mode using a color palette)
    * ``RGB`` (3x8-bit pixels, true color)
    * ``RGBA`` (4x8-bit pixels, true color with transparency mask)
    * ``CMYK`` (4x8-bit pixels, color separation)
    * ``YCbCr`` (3x8-bit pixels, color video format)

      * Note that this refers to the JPEG, and not the ITU-R BT.2020, standard

    * ``LAB`` (3x8-bit pixels, the L*a*b color space)
    * ``HSV`` (3x8-bit pixels, Hue, Saturation, Value color space)
    * ``I`` (32-bit signed integer pixels)
    * ``F`` (32-bit floating point pixels)

PIL also provides limited support for a few special modes, including ``LA`` (L
with alpha), ``RGBX`` (true color with padding) and ``RGBa`` (true color with
premultiplied alpha). However, PIL doesn’t support user-defined modes; if you
need to handle band combinations that are not listed above, use a sequence of
Image objects.

You can read the mode of an image through the :py:attr:`~PIL.Image.Image.mode`
attribute. This is a string containing one of the above values.

Size
----

You can read the image size through the :py:attr:`~PIL.Image.Image.size`
attribute. This is a 2-tuple, containing the horizontal and vertical size in
pixels.

Coordinate System
-----------------

The Python Imaging Library uses a Cartesian pixel coordinate system, with (0,0)
in the upper left corner. Note that the coordinates refer to the implied pixel
corners; the centre of a pixel addressed as (0, 0) actually lies at (0.5, 0.5).

Coordinates are usually passed to the library as 2-tuples (x, y). Rectangles
are represented as 4-tuples, with the upper left corner given first. For
example, a rectangle covering all of an 800x600 pixel image is written as (0,
0, 800, 600).

Palette
-------

The palette mode (``P``) uses a color palette to define the actual color for
each pixel.

Info
----

You can attach auxiliary information to an image using the
:py:attr:`~PIL.Image.Image.info` attribute. This is a dictionary object.

How such information is handled when loading and saving image files is up to
the file format handler (see the chapter on :ref:`image-file-formats`). Most
handlers add properties to the :py:attr:`~PIL.Image.Image.info` attribute when
loading an image, but ignore it when saving images.

.. _concept-filters:

Filters
-------

For geometry operations that may map multiple input pixels to a single output
pixel, the Python Imaging Library provides different resampling *filters*.

``NEAREST``
    Pick one nearest pixel from the input image. Ignore all other input pixels.

``BOX``
    Each pixel of source image contributes to one pixel of the
    destination image with identical weights.
    For upscaling is equivalent of ``NEAREST``.
    This filter can only be used with the :py:meth:`~PIL.Image.Image.resize`
    and :py:meth:`~PIL.Image.Image.thumbnail` methods.

    .. versionadded:: 3.4.0

``BILINEAR``
    For resize calculate the output pixel value using linear interpolation
    on all pixels that may contribute to the output value.
    For other transformations linear interpolation over a 2x2 environment
    in the input image is used.

``HAMMING``
    Produces a sharper image than ``BILINEAR``, doesn't have dislocations
    on local level like with ``BOX``.
    This filter can only be used with the :py:meth:`~PIL.Image.Image.resize`
    and :py:meth:`~PIL.Image.Image.thumbnail` methods.

    .. versionadded:: 3.4.0

``BICUBIC``
    For resize calculate the output pixel value using cubic interpolation
    on all pixels that may contribute to the output value.
    For other transformations cubic interpolation over a 4x4 environment
    in the input image is used.

``LANCZOS``
    Calculate the output pixel value using a high-quality Lanczos filter (a
    truncated sinc) on all pixels that may contribute to the output value.
    This filter can only be used with the :py:meth:`~PIL.Image.Image.resize`
    and :py:meth:`~PIL.Image.Image.thumbnail` methods.

    .. versionadded:: 1.1.3


Filters comparison table
~~~~~~~~~~~~~~~~~~~~~~~~

+------------+-------------+-----------+-------------+
| Filter     | Downscaling | Upscaling | Performance |
|            | quality     | quality   |             |
+============+=============+===========+=============+
|``NEAREST`` |             |           | ⭐⭐⭐⭐⭐       |
+------------+-------------+-----------+-------------+
|``BOX``     | ⭐           |           | ⭐⭐⭐⭐        |
+------------+-------------+-----------+-------------+
|``BILINEAR``| ⭐           | ⭐         | ⭐⭐⭐         |
+------------+-------------+-----------+-------------+
|``HAMMING`` | ⭐⭐          |           | ⭐⭐⭐         |
+------------+-------------+-----------+-------------+
|``BICUBIC`` | ⭐⭐⭐         | ⭐⭐⭐       | ⭐⭐          |
+------------+-------------+-----------+-------------+
|``LANCZOS`` | ⭐⭐⭐⭐        | ⭐⭐⭐⭐      | ⭐           |
+------------+-------------+-----------+-------------+