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https://github.com/uploadcare/pillow-simd

The friendly PIL fork
https://github.com/uploadcare/pillow-simd

Last synced: 2 months ago
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The friendly PIL fork

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README 

        
# Pillow-SIMD



Pillow-SIMD is highly optimized version of [Pillow library][original-docs]

for x86 architecture (mainly Intel and AMD CPUs).



Pillow-SIMD is "following" Pillow which means it is

drop-in replacements for Pillow of the same version.

For example, `Pillow-SIMD 3.2.0.post3` is a drop-in replacement for

`Pillow 3.2.0`, and  `Pillow-SIMD 3.3.3.post0` — for `Pillow 3.3.3`.



For more information on the original Pillow, please refer to:

[read the documentation][original-docs],

[check the changelog][original-changelog] and

[find out how to contribute][original-contribute].



## Why SIMD



There are multiple ways to tweak image processing performance.

To name a few, such ways can be: utilizing better algorithms, optimizing existing implementations, 

using more processing power and/or resources. 

One of the great examples of using a more efficient algorithm is [replacing][gaussian-blur-changes] 

a convolution-based Gaussian blur with a sequential-box one.



Such examples are rather rare, though. It is also known, that certain processes might be optimized 

by using parallel processing to run the respective routines.

But a more practical key to optimizations might be making things work faster 

using the resources at hand. For instance, SIMD computing might be the case.



SIMD stands for "single instruction, multiple data" and its essence is 

in performing the same operation on multiple data points simultaneously 

by using multiple processing elements. 

Common CPU SIMD instruction sets are MMX, SSE-SSE4, AVX, AVX2, AVX512, NEON.



Currently, Pillow-SIMD can be [compiled](#installation) with SSE4 (default) or AVX2 support.



## Status



Pillow-SIMD project is production-ready.

The project is supported by Uploadcare, a SAAS for cloud-based image storing and processing.



[![Uploadcare][uploadcare.logo]][uploadcare.com]



In fact, Uploadcare has been running Pillow-SIMD since 2015.



The following image operations are currently SIMD-accelerated:



- Resize (convolution-based resampling): SSE4, AVX2

- Gaussian and box blur: SSE4

- Alpha composition: SSE4, AVX2

- RGBA → RGBa (alpha premultiplication): SSE4, AVX2

- RGBa → RGBA (division by alpha): SSE4, AVX2

- RGB → L (grayscale): SSE4

- 3x3 and 5x5 kernel filters: SSE4, AVX2

- Split and get_channel: SSE4



## Benchmarks



Tons of tests can be found on the [Pillow Performance][pillow-perf-page] page.

There are benchmarks against different versions of Pillow and Pillow-SIMD

as well as ImageMagick, Skia, OpenCV and IPP.



The results show that for resizing Pillow is always faster than ImageMagick, 

Pillow-SIMD, in turn, is even faster than the original Pillow by the factor of 4-6. 

In general, Pillow-SIMD with AVX2 is always **16 to 40 times faster** than 

ImageMagick and outperforms Skia, the high-speed graphics library used in Chromium.



## Why Pillow itself is so fast



No cheats involved. We've used identical high-quality resize and blur methods for the benchmark. 

Outcomes produced by different libraries are in almost pixel-perfect agreement. 

The difference in measured rates is only provided with the performance of every involved algorithm. 



## Why Pillow-SIMD is even faster



Because of the SIMD computing, of course. But there's more to it: 

heavy loops unrolling, specific instructions, which aren't available for scalar data types.



## Why do not contribute SIMD to the original Pillow



Well, it's not that simple. First of all, the original Pillow supports 

a large number of architectures, not just x86.

But even for x86 platforms, Pillow is often distributed via precompiled binaries.

In order for us to integrate SIMD into the precompiled binaries 

we'd need to execute runtime CPU capabilities checks.

To compile the code this way we need to pass the `-mavx2` option to the compiler.

But with the option included, a compiler will inject AVX instructions even

for SSE functions (i.e. interchange them) since every SSE instruction has its AVX equivalent.

So there is no easy way to compile such library, especially with setuptools.



## Installation



If there's a copy of the original Pillow installed, it has to be removed first

with `$ pip uninstall -y pillow`.

Please install [prerequisites](https://pillow.readthedocs.io/en/stable/installation.html#building-from-source) for your platform.

The installation itself is simple just as running `$ pip install pillow-simd`, 

and if you're using SSE4-capable CPU everything should run smoothly.

If you'd like to install the AVX2-enabled version, 

you need to pass the additional flag to a C compiler. 

The easiest way to do so is to define the `CC` variable during the compilation.



```bash

$ pip uninstall pillow

$ CC="cc -mavx2" pip install -U --force-reinstall pillow-simd

```



## Contributing to Pillow-SIMD



Please be aware that Pillow-SIMD and Pillow are two separate projects.

Please submit bugs and improvements not related to SIMD to the [original Pillow][original-issues].

All bugfixes to the original Pillow will then be transferred to the next Pillow-SIMD version automatically.



  [original-homepage]: https://python-pillow.org/

  [original-docs]: https://pillow.readthedocs.io/

  [original-issues]: https://github.com/python-pillow/Pillow/issues/new

  [original-changelog]: https://github.com/python-pillow/Pillow/blob/master/CHANGES.rst

  [original-contribute]: https://github.com/python-pillow/Pillow/blob/master/.github/CONTRIBUTING.md

  [gaussian-blur-changes]: https://pillow.readthedocs.io/en/stable/releasenotes/2.7.0.html#gaussian-blur-and-unsharp-mask

  [pillow-perf-page]: https://python-pillow.github.io/pillow-perf/

  [pillow-perf-repo]: https://github.com/python-pillow/pillow-perf

  [uploadcare.com]: https://uploadcare.com/?utm_source=github&utm_medium=description&utm_campaign=pillow-simd

  [uploadcare.logo]: https://ucarecdn.com/8eca784b-bbe5-4f7e-8cdf-98d75aab8cec/logotransparent.svg