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ReStructuredText
110 lines
4.0 KiB
ReStructuredText
Concepts
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========
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The Python Imaging Library handles *raster images*; that is, rectangles of
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pixel data.
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Bands
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-----
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An image can consist of one or more bands of data. The Python Imaging Library
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allows you to store several bands in a single image, provided they all have the
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same dimensions and depth.
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To get the number and names of bands in an image, use the
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:py:meth:`~PIL.Image.Image.getbands` method.
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Mode
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----
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The :term:`mode` of an image defines the type and depth of a pixel in the
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image. The current release supports the following standard modes:
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* ``1`` (1-bit pixels, black and white, stored with one pixel per byte)
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* ``L`` (8-bit pixels, black and white)
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* ``P`` (8-bit pixels, mapped to any other mode using a color palette)
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* ``RGB`` (3x8-bit pixels, true color)
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* ``RGBA`` (4x8-bit pixels, true color with transparency mask)
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* ``CMYK`` (4x8-bit pixels, color separation)
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* ``YCbCr`` (3x8-bit pixels, color video format)
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* ``I`` (32-bit signed integer pixels)
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* ``F`` (32-bit floating point pixels)
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PIL also provides limited support for a few special modes, including ``LA`` (L
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with alpha), ``RGBX`` (true color with padding) and ``RGBa`` (true color with
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premultiplied alpha). However, PIL doesn’t support user-defined modes; if you
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to handle band combinations that are not listed above, use a sequence of Image
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objects.
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You can read the mode of an image through the :py:attr:`~PIL.Image.Image.mode`
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attribute. This is a string containing one of the above values.
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Size
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----
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You can read the image size through the :py:attr:`~PIL.Image.Image.size`
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attribute. This is a 2-tuple, containing the horizontal and vertical size in
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pixels.
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Coordinate System
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-----------------
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The Python Imaging Library uses a Cartesian pixel coordinate system, with (0,0)
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in the upper left corner. Note that the coordinates refer to the implied pixel
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corners; the centre of a pixel addressed as (0, 0) actually lies at (0.5, 0.5).
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Coordinates are usually passed to the library as 2-tuples (x, y). Rectangles
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are represented as 4-tuples, with the upper left corner given first. For
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example, a rectangle covering all of an 800x600 pixel image is written as (0,
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0, 800, 600).
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Palette
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-------
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The palette mode (``P``) uses a color palette to define the actual color for
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each pixel.
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Info
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----
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You can attach auxiliary information to an image using the
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:py:attr:`~PIL.Image.Image.info` attribute. This is a dictionary object.
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How such information is handled when loading and saving image files is up to
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the file format handler (see the chapter on :ref:`image-file-formats`). Most
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handlers add properties to the :py:attr:`~PIL.Image.Image.info` attribute when
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loading an image, but ignore it when saving images.
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Filters
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-------
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For geometry operations that may map multiple input pixels to a single output
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pixel, the Python Imaging Library provides four different resampling *filters*.
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``NEAREST``
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Pick the nearest pixel from the input image. Ignore all other input pixels.
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``BILINEAR``
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Use linear interpolation over a 2x2 environment in the input image. Note
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that in the current version of PIL, this filter uses a fixed input
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environment when downsampling.
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``BICUBIC``
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Use cubic interpolation over a 4x4 environment in the input image. Note
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that in the current version of PIL, this filter uses a fixed input
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environment when downsampling.
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``ANTIALIAS``
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Calculate the output pixel value using a high-quality resampling filter (a
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truncated sinc) on all pixels that may contribute to the output value. In
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the current version of PIL, this filter can only be used with the resize
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and thumbnail methods.
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.. versionadded:: 1.1.3
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Note that in the current version of PIL, the ``ANTIALIAS`` filter is the only
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filter that behaves properly when downsampling (that is, when converting a
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large image to a small one). The ``BILINEAR`` and ``BICUBIC`` filters use a
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fixed input environment, and are best used for scale-preserving geometric
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transforms and upsamping.
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