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115 lines
4.5 KiB
ReStructuredText
115 lines
4.5 KiB
ReStructuredText
Pillow 2.7
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==========
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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` takes `resample` argument, which tells
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what filter should be used for resampling. Possible values are:
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:py:attr:`PIL.Image.NEAREST`, :py:attr:`PIL.Image.BILINEAR`,
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:py:attr:`PIL.Image.BICUBIC` and :py:attr:`PIL.Image.ANTIALIAS`.
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Almost all of them was changed in this version.
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Bicubic and Bilinear downscaling
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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From very begining :py:attr:`~PIL.Image.BILINEAR` and
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:py:attr:`~PIL.Image.BICUBIC` filters was based on afine transformations
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and uses fixed number of pixels from source image for every destination pixel
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(that was 2x2 pixels for :py:attr:`~PIL.Image.BILINEAR` and 4x4 for
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:py:attr:`~PIL.Image.BICUBIC`). This gave an unsatisfied result for downscaling.
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At the same time high quality convolutions-based algorithm with flexeible kernel
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was used for :py:attr:`~PIL.Image.ANTIALIAS` filter).
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Starting from 2.7 high quality convolutions-based algorithm is used for all of
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these three filters.
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If you have previously used any tricks to maintain quality when downscaling with
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:py:attr:`~PIL.Image.BILINEAR` and :py:attr:`~PIL.Image.BICUBIC` filters
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(for example, reducing within several steps), they a unnecessary now.
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Antialias renamed to Lanczos
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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New :py:attr:`PIL.Image.LANCZOS` constant was added instead of
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:py:attr:`~PIL.Image.ANTIALIAS`.
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When :py:attr:`~PIL.Image.ANTIALIAS` was initially added, it was the only
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high-qality filter based on convolutions. It's name was supposed to reflect
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this. Starting from 2.7 all resize method are based on convolutions. All of them
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are antialias from now. And the real name of :py:attr:`~PIL.Image.ANTIALIAS`
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filter is Lanczos filter.
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:py:attr:`~PIL.Image.ANTIALIAS` constant is leaved for backward compatibility
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and is an alias for :py:attr:`~PIL.Image.LANCZOS`.
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Lanczos upscaling quality
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^^^^^^^^^^^^^^^^^^^^^^^^^
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Image upscaling quality with :py:attr:`~PIL.Image.LANCZOS` filter was almost
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the same as :py:attr:`~PIL.Image.BILINEAR` due to bug. This was fixed.
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Bicubic upscaling quality
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^^^^^^^^^^^^^^^^^^^^^^^^^
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:py:attr:`~PIL.Image.BICUBIC` filter for affine trnsformations was producing
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sharp, slightly pixelated image for upscaling. Bicubic for convolutions is
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more soft.
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Resize performance
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^^^^^^^^^^^^^^^^^^
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In most cases convolution is more expensive algorithm for downscaling because
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it tekes in account all pixels of source image. Therefore
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:py:attr:`~PIL.Image.BILINEAR` and :py:attr:`~PIL.Image.BICUBIC` filters
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performance can be lower than before. On the other hand quality of
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:py:attr:`~PIL.Image.BILINEAR` and :py:attr:`~PIL.Image.BICUBIC` was close to
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:py:attr:`~PIL.Image.NEAREST`. So if such quality is suitable for your task
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you can switch to :py:attr:`~PIL.Image.NEAREST` filter for downscaling,
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that will give huge win in performance.
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At the same time performance of convolution resampling for downscaling was
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improved in about two times compared to previous version.
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Upscaling performance of :py:attr:`~PIL.Image.LANCZOS` filter remained the same.
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For :py:attr:`~PIL.Image.BILINEAR` filter it grew in 1.5 times and
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for :py:attr:`~PIL.Image.BICUBIC` in 4 times.
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Default filter for thumbnails
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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In Pillow 2.5 default filter for :py:meth:`~PIL.Image.Image.thumbnail` was
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changed from :py:attr:`~PIL.Image.NEAREST` to :py:attr:`~PIL.Image.ANTIALIAS`.
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Antialias was chosen because all other filters gave poor quality for reduction.
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Starting from Pillow 2.7 :py:attr:`~PIL.Image.ANTIALIAS` replaced with
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:py:attr:`~PIL.Image.BICUBIC`, because it faster and
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:py:attr:`~PIL.Image.ANTIALIAS` doesn't give any advantages after
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downscaling with libJPEG, which uses supersampling internaly, not convolutions.
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Image transposing
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-----------------
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New method :py:attr:`PIL.Image.TRANSPOSE` was added for
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:py:meth:`~PIL.Image.Image.transpose` operation in addition to
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:py:attr:`~PIL.Image.FLIP_LEFT_RIGHT`, :py:attr:`~PIL.Image.FLIP_TOP_BOTTOM`,
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:py:attr:`~PIL.Image.ROTATE_90`, :py:attr:`~PIL.Image.ROTATE_180`,
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:py:attr:`~PIL.Image.ROTATE_270`. :py:attr:`~PIL.Image.TRANSPOSE` is algebra
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transpose, when image reflected over its main diagonal.
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Speed of :py:attr:`~PIL.Image.ROTATE_90`, :py:attr:`~PIL.Image.ROTATE_270`
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and :py:attr:`~PIL.Image.TRANSPOSE` was significantly improved for large images,
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which doesn't fit in processor cache.
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Gaussian blur and unsharp mask
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------------------------------
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Blur radius
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^^^^^^^^^^^
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Blur Performance
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^^^^^^^^^^^^^^^^
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Blur quality
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^^^^^^^^^^^^
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