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167 lines
6.5 KiB
ReStructuredText
167 lines
6.5 KiB
ReStructuredText
2.7.0
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=====
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Sane Plugin
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-----------
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The Sane plugin has now been split into its own repo:
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https://github.com/python-pillow/Sane .
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Png text chunk size limits
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--------------------------
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To prevent potential denial of service attacks using compressed text
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chunks, there are now limits to the decompressed size of text chunks
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decoded from PNG images. If the limits are exceeded when opening a PNG
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image a :py:exc:`ValueError` will be raised.
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Individual text chunks are limited to
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:py:attr:`PIL.PngImagePlugin.MAX_TEXT_CHUNK`, set to 1MB by
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default. The total decompressed size of all text chunks is limited to
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:py:attr:`PIL.PngImagePlugin.MAX_TEXT_MEMORY`, which defaults to
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64MB. These values can be changed prior to opening PNG images if you
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know that there are large text blocks that are desired.
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Image resizing filters
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----------------------
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Image resizing methods :py:meth:`~PIL.Image.Image.resize` and
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:py:meth:`~PIL.Image.Image.thumbnail` take a ``resample`` argument, which tells
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which filter should be used for resampling. Possible values are:
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``NEAREST``, ``BILINEAR``, ``BICUBIC`` and ``ANTIALIAS``. Almost all of them
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were changed in this version.
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Bicubic and bilinear downscaling
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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From the beginning ``BILINEAR`` and ``BICUBIC`` filters were based on affine
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transformations and used a fixed number of pixels from the source image for
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every destination pixel (2x2 pixels for ``BILINEAR`` and 4x4 for ``BICUBIC``).
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This gave an unsatisfactory result for downscaling. At the same time, a high
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quality convolutions-based algorithm with flexible kernel was used for
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``ANTIALIAS`` filter.
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Starting from Pillow 2.7.0, a high quality convolutions-based algorithm is used
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for all of these three filters.
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If you have previously used any tricks to maintain quality when downscaling with
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``BILINEAR`` and ``BICUBIC`` filters (for example, reducing within several
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steps), they are unnecessary now.
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Antialias renamed to Lanczos
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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A new ``LANCZOS`` constant was added instead of ``ANTIALIAS``.
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When ``ANTIALIAS`` was initially added, it was the only high-quality filter
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based on convolutions. It's name was supposed to reflect this. Starting from
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Pillow 2.7.0 all resize method are based on convolutions. All of them are
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antialias from now on. And the real name of the ``ANTIALIAS`` filter is Lanczos
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filter.
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The ``ANTIALIAS`` constant is left for backward compatibility and is an alias
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for ``LANCZOS``.
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Lanczos upscaling quality
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^^^^^^^^^^^^^^^^^^^^^^^^^
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The image upscaling quality with ``LANCZOS`` filter was almost the same as
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``BILINEAR`` due to a bug. This has been fixed.
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Bicubic upscaling quality
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^^^^^^^^^^^^^^^^^^^^^^^^^
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The ``BICUBIC`` filter for affine transformations produced sharp, slightly
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pixelated image for upscaling. Bicubic for convolutions is more soft.
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Resize performance
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^^^^^^^^^^^^^^^^^^
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In most cases, convolution is more a expensive algorithm for downscaling
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because it takes into account all the pixels of source image. Therefore
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``BILINEAR`` and ``BICUBIC`` filters' performance can be lower than before.
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On the other hand the quality of ``BILINEAR`` and ``BICUBIC`` was close to
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``NEAREST``. So if such quality is suitable for your tasks you can switch to
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``NEAREST`` filter for downscaling, which will give a huge improvement in
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performance.
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At the same time performance of convolution resampling for downscaling has been
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improved by around a factor of two compared to the previous version.
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The upscaling performance of the ``LANCZOS`` filter has remained the same. For
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``BILINEAR`` filter it has improved by 1.5 times and for ``BICUBIC`` by four
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times.
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Default filter for thumbnails
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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In Pillow 2.5 the default filter for :py:meth:`~PIL.Image.Image.thumbnail` was
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changed from ``NEAREST`` to ``ANTIALIAS``. Antialias was chosen because all the
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other filters gave poor quality for reduction. Starting from Pillow 2.7.0,
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``ANTIALIAS`` has been replaced with ``BICUBIC``, because it's faster and
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``ANTIALIAS`` doesn't give any advantages after downscaling with libjpeg, which
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uses supersampling internally, not convolutions.
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Image transposition
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-------------------
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A new method ``TRANSPOSE`` has been added for the
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:py:meth:`~PIL.Image.Image.transpose` operation in addition to
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``FLIP_LEFT_RIGHT``, ``FLIP_TOP_BOTTOM``, ``ROTATE_90``, ``ROTATE_180``,
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``ROTATE_270``. ``TRANSPOSE`` is an algebra transpose, with an image reflected
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across its main diagonal.
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The speed of ``ROTATE_90``, ``ROTATE_270`` and ``TRANSPOSE`` has been significantly
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improved for large images which don't fit in the processor cache.
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Gaussian blur and unsharp mask
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------------------------------
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The :py:meth:`~PIL.ImageFilter.GaussianBlur` implementation has been replaced
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with a sequential application of box filters. The new implementation is based on
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"Theoretical foundations of Gaussian convolution by extended box filtering" from
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the Mathematical Image Analysis Group. As :py:meth:`~PIL.ImageFilter.UnsharpMask`
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implementations use Gaussian blur internally, all changes from this chapter
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are also applicable to it.
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Blur radius
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^^^^^^^^^^^
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There was an error in the previous version of Pillow, where blur radius (the
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standard deviation of Gaussian) actually meant blur diameter. For example, to
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blur an image with actual radius 5 you were forced to use value 10. This has
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been fixed. Now the meaning of the radius is the same as in other software.
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If you used a Gaussian blur with some radius value, you need to divide this
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value by two.
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Blur performance
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^^^^^^^^^^^^^^^^
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Box filter computation time is constant relative to the radius and depends
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on source image size only. Because the new Gaussian blur implementation
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is based on box filter, its computation time also doesn't depend on the blur
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radius.
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For example, previously, if the execution time for a given test image was 1
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second for radius 1, 3.6 seconds for radius 10 and 17 seconds for 50, now blur
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with any radius on same image is executed for 0.2 seconds.
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Blur quality
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^^^^^^^^^^^^
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The previous implementation takes into account only source pixels within
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2 * standard deviation radius for every destination pixel. This was not enough,
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so the quality was worse compared to other Gaussian blur software.
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The new implementation does not have this drawback.
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TIFF Parameter Changes
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----------------------
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Several kwarg parameters for saving TIFF images were previously
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specified as strings with included spaces (e.g. 'x resolution'). This
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was difficult to use as kwargs without constructing and passing a
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dictionary. These parameters now use the underscore character instead
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of space. (e.g. 'x_resolution')
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