PILKit is a collection of utilities for working with PIL (the Python Imaging Library).

One of its main features is a set of processors which expose a simple interface for performing manipulations on PIL images.

Looking for more advanced processors? Check out Instakit!

For the complete documentation on the latest stable version of PILKit, see PILKit on RTD.

1. Install PIL or Pillow.
2. Run pip install pilkit (or clone the source and put the pilkit module on your path)

The "pilkit.processors" module contains several classes for processing PIL images, which provide an easy to understand API:

from pilkit.processors import ResizeToFit
img = Image.open('/path/to/my/image.png')
processor = ResizeToFit(100, 100)
new_img = processor.process(img)

A few of the included processors are:

• ResizeToFit
• ResizeToFill
• SmartResize
• Adjust
• TrimBorderColor
• Transpose

There's also a ProcessorPipeline class for executing processors sequentially:

from pilkit.processors import ProcessorPipeline, ResizeToFit, Adjust
img = Image.open('/path/to/my/image.png')
processor = ProcessorPipeline([Adjust(color=0), ResizeToFit(100, 100)])
new_image = processor.process(img)

In addition to the processors, PILKit contains a few utilities to ease the pain of working with PIL. Some examples:

prepare_image

Prepares the image for saving to the provided format by doing some common-sense conversions, including preserving transparency and quantizing.

save_image

Wraps PIL's Image.save() method in order to gracefully handle PIL's "Suspension not allowed here" errors, and (optionally) prepares the image using prepare_image

Utilities are also included for converting between formats, extensions, and mimetypes.

Please use the GitHub issue tracker to report bugs. A mailing list also exists to discuss the project and ask questions, as well as the official #imagekit channel on Freenode. (Both of these are shared with the django-imagekit project—from which PILKit spun off.)