VIPS from Python
Using VIPS — How to use the VIPS library from Python
VIPS comes with a convenient, high-level Python API built on
gobject-introspection. As long as you can get GOI
for your platform, you should be able to use libvips.
To test the binding, start up Python and at the console enter:
>>> from gi.repository import Vips >>> x = Vips.Image.new_from_file("/path/to/some/image/file.jpg") >>> x.width 1450 >>>
If import fails, check you have the Python gobject-introspection packages installed, that you have the libvips typelib installed, and that the typelib is either in the system area or on your
.new_from_file()fails, the vips overrides have not been found. Make sure
Vips.pyis in your system overrides area.
Here's a complete example program:
#!/usr/bin/python import sys from gi.repository import Vips im = Vips.Image.new_from_file(sys.argv) im = im.extract_area(100, 100, im.width - 200, im.height - 200) im = im.similarity(scale = 0.9) mask = Vips.Image.new_from_array([[-1, -1, -1], [-1, 16, -1], [-1, -1, -1]], scale = 8) im = im.conv(mask) im.write_to_file(sys.argv)
Reading this code, the first interesting line is:
from gi.repository import Vips
When Python executes the import line it performs the following steps:
It searches for a file called
Vips-x.y.typelib. This is a binary file generated automatically during libvips build by introspection of the libvips shared library plus scanning of the C headers. It lists all the API entry points, all the types the library uses, and has an extra set of hints for object ownership and reference counting. The typelib is searched for in
/usr/lib/gi-repository-1.0and along the path in the environment variable
It uses the typelib to make a basic binding for libvips. It's just the C API with a little very light mangling, so for example the enum member
VIPS_FORMAT_UCHARof the enum
The binding you get can be rather unfriendly, so it also loads a set of overrides from
/usr/lib/python2.7/dist-packages/gi/overrides(on my system at least). If you're using python3, it's
/usr/lib/python3/dist-packages/gi/overrides. Unfortunately, as far as I know, there is no way to extend this search using environment variables. You MUST have
Vips.pyin exactly this directory. If you install vips via a package manager this will happen automatically, since vips and pygobject will have been built to the same prefix, but if you are installing vips from source and the prefix does not match, it will not be installed for you, you will need to copy it.
Vips.pymakes the rest of the binding. In fact,
Vips.pymakes almost all the binding: it defines
Vips.Imageand binds at runtime by searching libvips for an operation of that name.
The next line is:
im = Vips.Image.new_from_file(sys.argv)
This loads the input image. You can append load options to the argument list as keyword arguments, for example:
im = Vips.Image.new_from_file(sys.argv, access = Vips.Access.SEQUENTIAL)
See the various loaders for a list of the available options
for each file format. The C equivalent to this function,
vips_image_new_from_file(), has more extensive documentation. Try
help(Vips.Image) to see a list of all the image
constructors --- you can load from memory, or create from an array,
The next line is:
im = im.extract_area(100, 100, im.width - 200, im.height - 200)
The arguments are left, top, width, height, so this crops 100 pixels off
every edge. Try
help(im.extract_area) and the C API docs
vips_extract_area() for details. You can use
as a synonym, if you like.
im.width gets the image width
in pixels, see
and friends for a list of the other getters.
The next line:
im = im.similarity(scale = 0.9)
shrinks by 10%. By default it uses
bilinear interpolation, use
interpolate to pick another
interpolator, for example:
im = im.similarity(scale = 0.9, interpolate = Vips.Interpolate.new("bicubic"))
mask = Vips.Image.new_from_array([[-1, -1, -1], [-1, 16, -1], [-1, -1, -1]], scale = 8) im = im.conv(mask)
makes an image from a 2D array, then convolves with that. The
scale keyword argument lets you set a divisor for
convolution, handy for integer convolutions. You can set
offset as well. See
vips_conv() for details on the vips
sends the image back to the
filesystem. There's also
.write_to_buffer() to make a
string containing the formatted image, and
write to another image.
.new_from_file() you can append save options as
keyword arguments. For example:
im.write_to_file("test.jpg", Q = 90)
will write a JPEG image with quality set to 90. See the various save
operations for a list of all the save options, for example
help(Vips) for everything,
help(Vips.Image) for something slightly more digestible, or
help(Vips.Image.black) for help on a
specific class member.
You can't get help on dynamically bound member functions like
.add() this way. Instead, make an image and get help
from that, for example:
image = Vips.Image.black(1, 1) help(image.add)
And you'll get a summary of the operator's behaviour and how the arguments are represented in Python.
The API docs have a handy table of all vips operations, if you want to find out how to do something, try searching that.
The vips command can be useful too. For example, in a terminal you can type vips jpegsave to get a summary of an operation:
$ vips jpegsave save image to jpeg file usage: jpegsave in filename where: in - Image to save, input VipsImage filename - Filename to save to, input gchararray optional arguments: Q - Q factor, input gint default: 75 min: 1, max: 100 profile - ICC profile to embed, input gchararray optimize-coding - Compute optimal Huffman coding tables, input gboolean default: false interlace - Generate an interlaced (progressive) jpeg, input gboolean default: false no-subsample - Disable chroma subsample, input gboolean default: false trellis-quant - Apply trellis quantisation to each 8x8 block, input gboolean default: false overshoot-deringing - Apply overshooting to samples with extreme values, input gboolean default: false optimize-scans - Split the spectrum of DCT coefficients into separate scans, input gboolean default: false strip - Strip all metadata from image, input gboolean default: false background - Background value, input VipsArrayDouble operation flags: sequential-unbuffered nocache
As noted above, the Python interface comes in two main parts, an automatically generated binding based on the vips typelib, plus a set of extra features provided by overrides. The rest of this chapter runs through the features provided by the overrides.
The overrides intercept member lookup
Vips.Image class and look for vips operations
with that name. So the vips operation "add", which appears in the
C API as
vips_add(), appears in Python as
The first input image argument becomes the
argument. If there are no input image arguments, the operation
appears as a class member. Optional input arguments become
keyword arguments. The result is a list of all the output
arguments, or a single output if there is only one.
Optional output arguments are enabled with a boolean keyword
argument of that name. For example, "min" (the operation which
appears in the C API as
vips_min()), can be called like this:
min_value = im.min()
min_value will be a floating point value giving
the minimum value in the image. "min" can also find the position
of the minimum value with the
optional output arguments. Call it like this:
min_value, opts = im.min(x = True, y = True) x = opts['x'] y = opts['y']
In other words, if optional output args are requested, an extra
dictionary is returned containing those objects.
Of course in this case, the
function would be simpler, see below.
Because operations are member functions and return the result image, you can chain them. For example, you can write:
result_image = image.sin().pow(2)
to calculate the square of the sine for each pixel. There is also a full set of arithmetic operator overloads, see below.
VIPS types are also automatically wrapped. The override looks at the type of argument required by the operation and converts the value you supply, when it can. For example, "linear" takes a VipsArrayDouble as an argument for the set of constants to use for multiplication. You can supply this value as an integer, a float, or some kind of compound object and it will be converted for you. You can write:
result_image = image.linear(1, 3) result_image = image.linear(12.4, 13.9) result_image = image.linear([1, 2, 3], [4, 5, 6]) result_image = image.linear(1, [4, 5, 6])
And so on. A set of overloads are defined for
It does a couple of more ambitious conversions. It will automatically convert to and from the various vips types, like VipsBlob and VipsArrayImage. For example, you can read the ICC profile out of an image like this:
profile = im.get_value("icc-profile-data")
profile will be a string.
You can use array constants instead of images. A 2D array is simply
changed into a one-band double image. This is handy for things like
.erode(), for example:
im = im.erode([[128, 255, 128], [255, 255, 255], [128, 255, 128]])
will erode an image with a 4-connected structuring element.
If an operation takes several input images, you can use a 1D array
constant or a number constant
for all but one of them and the wrapper will expand it
to an image for you. For example,
a condition image to pick pixels between a then and an else image:
result_image = condition_image.ifthenelse(then_image, else_image)
You can use a constant instead of either the then or the else parts, and it will be expanded to an image for you. If you use a constant for both then and else, it will be expanded to match the condition image. For example:
result_image = condition_image.ifthenelse([0, 255, 0], [255, 0, 0])
Will make an image where true pixels are green and false pixels are red.
This is also useful for
.bandjoin(), the thing to join
two or more images up bandwise. You can write:
rgba = rgb.bandjoin(255)
to add a constant 255 band to an image, perhaps to add an alpha channel. Of course you can also write:
result_image = image1.bandjoin(image2) result_image = image1.bandjoin([image2, image3]) result_image = image1.bandjoin([image2, 255])
and so on.
The wrapper spots errors from vips operations and raises the
Vips.Error exception. You can catch it in the
usual way. The
.detail member gives the detailed
Reading and writing areas of memory
You can use the C API functions
vips_image_write_to_memory() directly from Python to read and write
areas of memory. This can be useful if you need to get images to and
from other other image processing libraries, like PIL or numpy.
Use them from Python like this:
image = Vips.Image.new_from_file("/path/to/some/image/file.jpg") memory_area = image.write_to_memory()
memory_area is now a string containing uncompressed binary
image data. For an RGB image, it will have bytes
the first three pixels of the first scanline of the image. You can pass
this string to the numpy or PIL constructors and make an image there.
.write_to_memory() will make a copy of the image.
be better to use a Python buffer to pass the data, but sadly this isn't
possible with gobject-introspection, as far as I know.
Going the other way, you can construct a vips image from a string of binary data. For example:
image = Vips.Image.new_from_file("/path/to/some/image/file.jpg") memory_area = image.write_to_memory() image2 = Vips.Image.new_from_memory(memory_area, image.width, image.height, image.bands, Vips.BandFormat.UCHAR)
image2 should be an identical copy of
Be careful: in this direction, vips does not make a copy of the memory
area, so if
memory_area is freed by the Python garbage
you later try to use
image2, you'll get a crash.
Make sure you keep a reference to
for as long as you need it. A simple solution is to use
new_from_memory_copy instead. This will take a copy of the
memory area for vips. Of course this will raise memory usage.
Paint operations like
modify their input image. This makes them hard to use with the rest of
libvips: you need to be very careful about the order in which operations
execute or you can get nasty crashes.
The wrapper spots operations of this type and makes a private copy of the image in memory before calling the operation. This stops crashes, but it does make it inefficient. If you draw 100 lines on an image, for example, you'll copy the image 100 times. The wrapper does make sure that memory is recycled where possible, so you won't have 100 copies in memory. At least you can execute these operations.
If you want to avoid the copies, you'll need to call drawing operations yourself.
The wrapper defines the usual set of arithmetic, boolean and
relational overloads on
image. You can mix images, constants and lists of
constants (almost) freely. For example, you can write:
result_image = ((image * [1, 2, 3]).abs() < 128) | 4
The wrapper overloads
 to be
result_image = image
to extract the third band of the image. It implements the usual slicing and negative indexes, so you can write:
result_image = image[1:] result_image = image[:3] result_image = image[-2:] result_image = [x.avg() for x in image]
and so on.
The wrapper overloads
() to be
vips_getpoint(). You can
r, g, b = image(10, 10)
to read out the value of the pixel at coordinates (10, 10) from an RGB image.
Some vips operators take an enum to select an action, for example
.math() can be used to calculate sine of every pixel
result_image = image.math(Vips.OperationMath.SIN)
This is annoying, so the wrapper expands all these enums into separate members named after the enum. So you can write:
result_image = image.sin()
help(Vips.Image) for a list.
The wrapper defines a few extra useful utility functions:
help(Vips.Image) for a list.
Command-line option parsing
GLib includes a command-line option parser, and Vips defines a set of standard flags you can use with it. For example:
import sys from gi.repository import GLib, Vips context = GLib.OptionContext(" - test stuff") main_group = GLib.OptionGroup("main", "Main options", "Main options for this program", None) context.set_main_group(main_group) Vips.add_option_entries(main_group) context.parse(sys.argv)