pyvips 2.0.5

binding for the libvips image processing library

PyPI package:

https://pypi.python.org/pypi/pyvips

This module wraps the libvips image processing library. It needs the libvips shared library on your library search path, version 8.2 or later.

https://jcupitt.github.io/libvips

This binding passes the vips test suite cleanly and with no leaks under python2.7 - python3.6, pypy and pypy3 on Windows, macOS and Linux.

We have formatted docs online here:

https://jcupitt.github.io/pyvips/

How it works

Programs that use pyvips don’t manipulate images directly, instead they create pipelines of image processing operations building on a source image. When the end of the pipe is connected to a destination, the whole pipeline executes at once, streaming the image in parallel from source to destination a section at a time.

Because pyvips is parallel, it’s quick, and because it doesn’t need to keep entire images in memory, it’s light. For example, the libvips speed and memory use benchmark:

https://github.com/jcupitt/libvips/wiki/Speed-and-memory-use

Loads a large tiff image, shrinks by 10%, sharpens, and saves again. On this test pyvips is typically 2x faster than ImageMagick and needs 5x less memory.

There’s a handy blog post explaining how libvips opens files, which gives some more background.

http://libvips.blogspot.co.uk/2012/06/how-libvips-opens-file.html

Install

You need the libvips shared library on your library search path, version 8.2 or later. On linux and macOS, you can install via your package manager; on Windows you can download a pre-compiled binary from the libvips website:

https://jcupitt.github.io/libvips/

Then just install this package, perhaps:

$ pip install --user pyvips

Example

This sample program loads a JPG image, doubles the value of every green pixel, sharpens, and then writes the image back to the filesystem again:

import pyvips

image = pyvips.Image.new_from_file('some-image.jpg', access='sequential')
image *= [1, 2, 1]
mask = pyvips.Image.new_from_array([[-1, -1, -1],
                                    [-1, 16, -1],
                                    [-1, -1, -1]
                                   ], scale=8)
image = image.conv(mask, precision='integer')
image.write_to_file('x.jpg')

Converting old code

To convert old code, replace the lines:

import gi
gi.require_version('Vips', '8.0')
from gi.repository import Vips

with:

import pyvips
Vips = pyvips

Instead of the pyvips = Vips, you can of course also swap all Vips for pyvips with eg.:

%s/Vips/pyvips/g

Background

The Python binding included in libvips works, but porting and installation are more difficult than they should be.

This new binding is:

• compatible with the current Python binding (it runs the same test suite, unmodified)

• easier to install, since the stack is much smaller, and there are no issues with the overrides directory

• faster, since we implement Buffer and save some copies

• faster, since it is “thinner”. The ffi Ruby binding is about twice as fast as the gobject-introspection one, when running the test suite

• portable across CPython, PyPy and others

• more simply portable to Windows

• easy to package for pip

Notes

Local user install:

$ pip install --user -e .
$ pip3 install --user -e .
$ pypy -m pip --user -e .

Run all tests:

$ tox

Run test suite:

$ tox test

Run a specific test:

$ pytest tests/test_conversion.py

Stylecheck:

$ tox qa

Generate HTML docs in doc/build/html:

$ cd doc; sphinx-build -bhtml . build/html

Regenerate autodocs:

$ cd doc; \
  python -c "import pyvips; pyvips.Operation.generate_sphinx_all()" > x

And copy-paste x into the obvious place in doc/vimage.rst.

Update version number:

$ vi pyvips/version.py
$ vi doc/conf.py

Update pypi package:

$ python setup.py bdist_wheel
$ twine upload dist/*