mirror of
https://github.com/python-pillow/Pillow.git
synced 2024-12-25 17:36:18 +03:00
215 lines
8.3 KiB
ReStructuredText
215 lines
8.3 KiB
ReStructuredText
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 is a string which defines the type and depth of a pixel in the
|
||
image. Each pixel uses the full range of the bit depth. So a 1-bit pixel has a range of
|
||
0-1, an 8-bit pixel has a range of 0-255, a 32-signed integer pixel has the range of
|
||
INT32 and a 32-bit floating point pixel has the range of FLOAT32. 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, grayscale)
|
||
* ``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)
|
||
|
||
* Hue's range of 0-255 is a scaled version of 0 degrees <= Hue < 360 degrees
|
||
|
||
* ``I`` (32-bit signed integer pixels)
|
||
* ``F`` (32-bit floating point pixels)
|
||
|
||
Pillow also provides limited support for a few additional modes, including:
|
||
|
||
* ``LA`` (L with alpha)
|
||
* ``PA`` (P with alpha)
|
||
* ``RGBX`` (true color with padding)
|
||
* ``RGBa`` (true color with premultiplied alpha)
|
||
* ``La`` (L with premultiplied alpha)
|
||
* ``I;16`` (16-bit unsigned integer pixels)
|
||
* ``I;16L`` (16-bit little endian unsigned integer pixels)
|
||
* ``I;16B`` (16-bit big endian unsigned integer pixels)
|
||
* ``I;16N`` (16-bit native endian unsigned integer pixels)
|
||
* ``BGR;15`` (15-bit reversed true colour)
|
||
* ``BGR;16`` (16-bit reversed true colour)
|
||
* ``BGR;24`` (24-bit reversed true colour)
|
||
|
||
Premultiplied alpha is where the values for each other channel have been
|
||
multiplied by the alpha. For example, an RGBA pixel of ``(10, 20, 30, 127)``
|
||
would convert to an RGBa pixel of ``(5, 10, 15, 127)``. The values of the R,
|
||
G and B channels are halved as a result of the half transparency in the alpha
|
||
channel.
|
||
|
||
Apart from these additional modes, Pillow doesn't yet support multichannel
|
||
images with a depth of more than 8 bits per channel.
|
||
|
||
Pillow also 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:
|
||
|
||
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, (x1, y1, x2, y2), with the upper left corner given
|
||
first.
|
||
|
||
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.
|
||
|
||
Transparency
|
||
------------
|
||
|
||
If an image does not have an alpha band, transparency may be specified in the
|
||
:py:attr:`~PIL.Image.Image.info` attribute with a "transparency" key.
|
||
|
||
Most of the time, the "transparency" value is a single integer, describing
|
||
which pixel value is transparent in a "1", "L", "I" or "P" mode image.
|
||
However, PNG images may have three values, one for each channel in an "RGB"
|
||
mode image, or can have a byte string for a "P" mode image, to specify the
|
||
alpha value for each palette entry.
|
||
|
||
Orientation
|
||
-----------
|
||
|
||
A common element of the :py:attr:`~PIL.Image.Image.info` attribute for JPG and
|
||
TIFF images is the EXIF orientation tag. This is an instruction for how the
|
||
image data should be oriented. For example, it may instruct an image to be
|
||
rotated by 90 degrees, or to be mirrored. To apply this information to an
|
||
image, :py:meth:`~PIL.ImageOps.exif_transpose` can be used.
|
||
|
||
.. _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*.
|
||
|
||
.. py:currentmodule:: PIL.Image
|
||
|
||
.. data:: Resampling.NEAREST
|
||
|
||
Pick one nearest pixel from the input image. Ignore all other input pixels.
|
||
|
||
.. data:: Resampling.BOX
|
||
|
||
Each pixel of source image contributes to one pixel of the
|
||
destination image with identical weights.
|
||
For upscaling is equivalent of :data:`Resampling.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
|
||
|
||
.. data:: Resampling.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.
|
||
|
||
.. data:: Resampling.HAMMING
|
||
|
||
Produces a sharper image than :data:`Resampling.BILINEAR`, doesn't have
|
||
dislocations on local level like with :data:`Resampling.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
|
||
|
||
.. data:: Resampling.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.
|
||
|
||
.. data:: Resampling.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 | |
|
||
+===========================+=============+===========+=============+
|
||
|:data:`Resampling.NEAREST` | | | ⭐⭐⭐⭐⭐ |
|
||
+---------------------------+-------------+-----------+-------------+
|
||
|:data:`Resampling.BOX` | ⭐ | | ⭐⭐⭐⭐ |
|
||
+---------------------------+-------------+-----------+-------------+
|
||
|:data:`Resampling.BILINEAR`| ⭐ | ⭐ | ⭐⭐⭐ |
|
||
+---------------------------+-------------+-----------+-------------+
|
||
|:data:`Resampling.HAMMING` | ⭐⭐ | | ⭐⭐⭐ |
|
||
+---------------------------+-------------+-----------+-------------+
|
||
|:data:`Resampling.BICUBIC` | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐ |
|
||
+---------------------------+-------------+-----------+-------------+
|
||
|:data:`Resampling.LANCZOS` | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐ |
|
||
+---------------------------+-------------+-----------+-------------+
|