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217 lines
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ReStructuredText
217 lines
8.4 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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.. _concept-bands:
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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. For example, a PNG image might have 'R', 'G', 'B',
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and 'A' bands for the red, green, blue, and alpha transparency values. Many
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operations act on each band separately, e.g., histograms. It is often useful to
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think of each pixel as having one value per band.
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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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.. _concept-modes:
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Modes
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-----
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The ``mode`` of an image is a string which defines the type and depth of a pixel in the
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image. Each pixel uses the full range of the bit depth. So a 1-bit pixel has a range of
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0-1, an 8-bit pixel has a range of 0-255, a 32-signed integer pixel has the range of
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INT32 and a 32-bit floating point pixel has the range of FLOAT32. The current release
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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, grayscale)
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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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* Note that this refers to the JPEG, and not the ITU-R BT.2020, standard
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* ``LAB`` (3x8-bit pixels, the L*a*b color space)
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* ``HSV`` (3x8-bit pixels, Hue, Saturation, Value color space)
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* Hue's range of 0-255 is a scaled version of 0 degrees <= Hue < 360 degrees
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* ``I`` (32-bit signed integer pixels)
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* ``F`` (32-bit floating point pixels)
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Pillow also provides limited support for a few additional modes, including:
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* ``LA`` (L with alpha)
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* ``PA`` (P with alpha)
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* ``RGBX`` (true color with padding)
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* ``RGBa`` (true color with premultiplied alpha)
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* ``La`` (L with premultiplied alpha)
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* ``I;16`` (16-bit unsigned integer pixels)
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* ``I;16L`` (16-bit little endian unsigned integer pixels)
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* ``I;16B`` (16-bit big endian unsigned integer pixels)
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* ``I;16N`` (16-bit native endian unsigned integer pixels)
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* ``BGR;15`` (15-bit reversed true colour)
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* ``BGR;16`` (16-bit reversed true colour)
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* ``BGR;24`` (24-bit reversed true colour)
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* ``BGR;32`` (32-bit reversed true colour)
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Premultiplied alpha is where the values for each other channel have been
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multiplied by the alpha. For example, an RGBA pixel of ``(10, 20, 30, 127)``
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would convert to an RGBa pixel of ``(5, 10, 15, 127)``. The values of the R,
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G and B channels are halved as a result of the half transparency in the alpha
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channel.
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Apart from these additional modes, Pillow doesn't yet support multichannel
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images with a depth of more than 8 bits per channel.
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Pillow also doesn’t support user-defined modes; if you need to handle band
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combinations that are not listed above, use a sequence of Image 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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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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Transparency
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------------
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If an image does not have an alpha band, transparency may be specified in the
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:py:attr:`~PIL.Image.Image.info` attribute with a "transparency" key.
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Most of the time, the "transparency" value is a single integer, describing
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which pixel value is transparent in a "1", "L", "I" or "P" mode image.
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However, PNG images may have three values, one for each channel in an "RGB"
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mode image, or can have a byte string for a "P" mode image, to specify the
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alpha value for each palette entry.
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Orientation
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-----------
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A common element of the :py:attr:`~PIL.Image.Image.info` attribute for JPG and
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TIFF images is the EXIF orientation tag. This is an instruction for how the
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image data should be oriented. For example, it may instruct an image to be
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rotated by 90 degrees, or to be mirrored. To apply this information to an
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image, :py:meth:`~PIL.ImageOps.exif_transpose` can be used.
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.. _concept-filters:
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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 different resampling *filters*.
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.. py:currentmodule:: PIL.Image
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.. data:: Resampling.NEAREST
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Pick one nearest pixel from the input image. Ignore all other input pixels.
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.. data:: Resampling.BOX
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Each pixel of source image contributes to one pixel of the
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destination image with identical weights.
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For upscaling is equivalent of :data:`Resampling.NEAREST`.
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This filter can only be used with the :py:meth:`~PIL.Image.Image.resize`
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and :py:meth:`~PIL.Image.Image.thumbnail` methods.
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.. versionadded:: 3.4.0
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.. data:: Resampling.BILINEAR
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For resize calculate the output pixel value using linear interpolation
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on all pixels that may contribute to the output value.
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For other transformations linear interpolation over a 2x2 environment
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in the input image is used.
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.. data:: Resampling.HAMMING
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Produces a sharper image than :data:`Resampling.BILINEAR`, doesn't have
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dislocations on local level like with :data:`Resampling.BOX`.
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This filter can only be used with the :py:meth:`~PIL.Image.Image.resize`
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and :py:meth:`~PIL.Image.Image.thumbnail` methods.
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.. versionadded:: 3.4.0
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.. data:: Resampling.BICUBIC
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For resize calculate the output pixel value using cubic interpolation
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on all pixels that may contribute to the output value.
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For other transformations cubic interpolation over a 4x4 environment
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in the input image is used.
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.. data:: Resampling.LANCZOS
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Calculate the output pixel value using a high-quality Lanczos filter (a
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truncated sinc) on all pixels that may contribute to the output value.
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This filter can only be used with the :py:meth:`~PIL.Image.Image.resize`
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and :py:meth:`~PIL.Image.Image.thumbnail` methods.
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.. versionadded:: 1.1.3
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Filters comparison table
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~~~~~~~~~~~~~~~~~~~~~~~~
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+---------------------------+-------------+-----------+-------------+
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| Filter | Downscaling | Upscaling | Performance |
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| | quality | quality | |
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+===========================+=============+===========+=============+
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|:data:`Resampling.NEAREST` | | | ⭐⭐⭐⭐⭐ |
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+---------------------------+-------------+-----------+-------------+
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|:data:`Resampling.BOX` | ⭐ | | ⭐⭐⭐⭐ |
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+---------------------------+-------------+-----------+-------------+
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|:data:`Resampling.BILINEAR`| ⭐ | ⭐ | ⭐⭐⭐ |
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+---------------------------+-------------+-----------+-------------+
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|:data:`Resampling.HAMMING` | ⭐⭐ | | ⭐⭐⭐ |
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+---------------------------+-------------+-----------+-------------+
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|:data:`Resampling.BICUBIC` | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐ |
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+---------------------------+-------------+-----------+-------------+
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|:data:`Resampling.LANCZOS` | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐ |
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+---------------------------+-------------+-----------+-------------+
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