jpegtran-cffi

jpegtran-cffi is a Python package for fast JPEG transformations. Compared to other, more general purpose image processing libraries like wand-py or PIL/Pillow, transformations are generally more than twice as fast (see Benchmarks). In addition, all operations except for scaling are lossless, since the image is not being re-compressed in the process. This is due to the fact that all transformation operations work directly with the JPEG data.

This is achieved by using multiple C routines from the Enlightenment project's epeg library (for scaling) and the `turbojpeg`_ library from the libjpeg-turbo project (for all other operations). These routines are called from Python through the CFFI module, i.e. no external processes are launched.

The package also includes rudimentary support for getting and setting the EXIF orientation tag, automatically transforming the image according to it and obtaining the JFIF thumbnail image.

jpegtran-cffi was developed as part of a web interface for the spreads project, where a large number of images from digital cameras had to be prepared for display by a Raspberry Pi. With the Pi's rather slow ARMv6 processor, both Wand and PIL were too slow to be usable.

Requirements

CPython >=2.7 or >=3.5 or PyPy
cffi >= 1.0
libturbojpeg with headers

Installation

$ pip install jpegtran-cffi

Note

There is a bug in some Ubuntu versions of the libturbojpeg package that prevents the package from installing correctly. If you get the error relocation R_X86_64_32 against .data can not be used..., please perform the following command:

sudo ln -s /usr/lib/x86_64-linux-gnu/libturbojpeg.so.0.1.0 /usr/lib/x86_64-linux-gnu/libturbojpeg.so

Usage

from jpegtran import JPEGImage

img = JPEGImage('image.jpg')

# JPEGImage can also be initialized from a bytestring
blob = requests.get("http://example.com/image.jpg").content
from_blob = JPEGImage(blob=blob)

# Reading various image parameters
print img.width, img.height  # "640 480"
print img.exif_orientation  # "1" (= "normal")

# If present, the JFIF thumbnail can be obtained as a bytestring
thumb = img.exif_thumbnail

# Transforming the image
img.downscale(320, 240).save('scaled.jpg')
img.rotate(90).save('rotated.jpg')
img.crop(0, 0, 100, 100).save('cropped.jpg')

# Transformations can be chained
data = (img.downscale(320, 240)
            .rotate(90)
            .flip('horizontal')
            .as_blob())

# jpegtran can transform the image automatically according to the EXIF
# orientation tag
photo = JPEGImage(blob=requests.get("http://example.com/photo.jpg").content)
print photo.exif_orientation  # "6" (= 270°)
print photo.width, photo.height # "4320 3240"
corrected = photo.exif_autotransform()
print corrected.exif_orientation  # "1" (= "normal")
print corrected.width, corrected.height  # "3240 4320"

For more details, refer to the API Reference.

Benchmarks

All operations were done on a 3.4GHz i7-3770 with 16GiB of RAM and a 7200rpm HDD with the following 2560x1920 8bit RGB JPEG:

http://upload.wikimedia.org/wikipedia/commons/8/82/Mandel_zoom_05_tail_part.jpg

https://jpegtran-cffi.readthedocs.io/en/latest/_images/benchmark.png

Both wand-py and PIL were run with the fastest scaling algorithm available, for wand-py this meant using Image.sample instead of Image.resize and for PIL the nearest-neighbour filter was used for the Image.resize call.

Benchmark source: https://gist.github.com/jbaiter/8596064

License

The MIT License (MIT)

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.