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Pillow 2.7
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==========
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Pillow 2.7.0
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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: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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: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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Almost all of them were changed in this version.
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Bicubic and Bilinear downscaling
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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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 affine 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 flexible kernel
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was used for :py:attr:`~PIL.Image.ANTIALIAS` filter).
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From the beginning :py:attr:`~PIL.Image.BILINEAR` and
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:py:attr:`~PIL.Image.BICUBIC` filters were based on affine transformations
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and used a fixed number of pixels from the source image for every destination
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pixel (2x2 pixels for :py:attr:`~PIL.Image.BILINEAR` and 4x4 for
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:py:attr:`~PIL.Image.BICUBIC`). This gave an unsatisfactory result for
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downscaling. At the same time, a high quality convolutions-based algorithm with
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flexible kernel 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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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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: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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(for example, reducing within several steps), they are 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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A 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-quality 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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this. Starting from Pillow 2.7.0 all resize method are based on convolutions.
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All of them are antialias from now on. And the real name of the
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:py:attr:`~PIL.Image.ANTIALIAS` filter is Lanczos filter.
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:py:attr:`~PIL.Image.ANTIALIAS` constant is leaved for backward compatibility
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The :py:attr:`~PIL.Image.ANTIALIAS` constant is left 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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The image upscaling quality with :py:attr:`~PIL.Image.LANCZOS` filter was
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almost the same as :py:attr:`~PIL.Image.BILINEAR` due to bug. This has been fixed.
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Bicubic upscaling quality
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^^^^^^^^^^^^^^^^^^^^^^^^^
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:py:attr:`~PIL.Image.BICUBIC` filter for affine transformations produced
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The :py:attr:`~PIL.Image.BICUBIC` filter for affine transformations produced
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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 takes 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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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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: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 the 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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:py:attr:`~PIL.Image.NEAREST`. So if such quality is suitable for your tasks
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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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which will give a huge improvement 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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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 :py:attr:`~PIL.Image.LANCZOS` filter has
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remained the same. For :py:attr:`~PIL.Image.BILINEAR` filter it has improved by
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1.5 times and for :py:attr:`~PIL.Image.BICUBIC` by four 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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In Pillow 2.5 the 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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Antialias was chosen because all the other filters gave poor quality for
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reduction. Starting from Pillow 2.7.0, :py:attr:`~PIL.Image.ANTIALIAS` has been
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replaced with :py:attr:`~PIL.Image.BICUBIC`, because it's 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 internally, not convolutions.
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downscaling with libjpeg, which uses supersampling internally, not convolutions.
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Image transposing
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-----------------
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Image transposition
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-------------------
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New method :py:attr:`PIL.Image.TRANSPOSE` was added for
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A new method :py:attr:`PIL.Image.TRANSPOSE` has been added for the
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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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:py:attr:`~PIL.Image.ROTATE_270`. :py:attr:`~PIL.Image.TRANSPOSE` is an algebra
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transpose, with an image reflected across 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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The speed of :py:attr:`~PIL.Image.ROTATE_90`, :py:attr:`~PIL.Image.ROTATE_270`
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and :py:attr:`~PIL.Image.TRANSPOSE` has been significantly improved for large
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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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:py:meth:`~PIL.ImageFilter.GaussianBlur` implementation was replaced with
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sequential applying of series of box filters. New implementation is based on
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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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Mathematical Image Analysis Group. As :py:meth:`~PIL.ImageFilter.UnsharpMask`
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implementations uses Gaussian blur internally, all changes from this chapter
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also applicable to it.
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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 previous version of PIL, when blur radius (the standard
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deviation of Gaussian) is actually meant blur diameter.
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For example for blurring image with actual radius 5 you were forced
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to use value 10. This was fixed. For now the meaning of the radius
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is the same as in other software.
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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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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 new Gaussian blur implementation
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is based on box filter, it's computation time is also doesn't depends on blur
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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 depends on the blur
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radius.
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If before execution time for the same test image was 1 second for radius 1,
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3.6 seconds for radius 10, 17 seconds for 50. Now blur with any radius on same
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image is executed for 0.2 seconds.
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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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Previous implementation takes in account only source pixels within
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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 quality was worse compared to other Gaussian blur software.
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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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