binding for the libvips image processing library
Project description
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/*
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