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<refentry id="using-from-python">
<refmeta>
<refentrytitle>VIPS from Python</refentrytitle>
<manvolnum>3</manvolnum>
<refmiscinfo>VIPS Library</refmiscinfo>
</refmeta>
<refnamediv>
<refname>Using VIPS</refname>
<refpurpose>How to use the VIPS library from Python</refpurpose>
</refnamediv>
<refsect3 id="python-intro">
<title>Introduction</title>
<para>
VIPS comes with a convenient, high-level Python API built on
on <code>gobject-introspection</code>. As long as you can get GOI
for your platform, you should be able to use libvips.
</para>
<para>
To test the binding, start up Python and at the console enter:
<programlisting language="Python">
>>> from gi.repository import Vips
>>> x = Vips.Image.new_from_file("/path/to/some/image/file.jpg")
>>> x.width
1450
>>>
</programlisting>
<orderedlist>
<listitem>
<para>
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 <code>GI_TYPELIB_PATH</code>.
</para>
</listitem>
<listitem>
<para>
If <code>.new_from_file()</code> fails, the vips overrides
have not been found. Make sure <code>Vips.py</code> is in
your system overrides area.
</para>
</listitem>
</orderedlist>
</para>
</refsect3>
<refsect3 id="python-example">
<title>Example program</title>
<para>
Here's a complete example program:
<programlisting language="Python">
#!/usr/bin/python
import sys
from gi.repository import Vips
im = Vips.Image.new_from_file(sys.argv[1])
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[2])
</programlisting>
</para>
<para>
Reading this code, the first interesting line is:
<programlisting language="Python">
from gi.repository import Vips
</programlisting>
When Python executes the import line it performs the following steps:
</para>
<orderedlist>
<listitem>
<para>
It searches for a file called <code>Vips-x.y.typelib</code>. 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 <code>/usr/lib/gi-repository-1.0</code> and along the path
in the environment variable <code>GI_TYPELIB_PATH</code>.
</para>
</listitem>
<listitem>
<para>
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 <code>VIPS_FORMAT_UCHAR</code>
of the enum <code>VipsBandFormat</code> becomes
<code>Vips.BandFormat.UCHAR</code>.
</para>
</listitem>
<listitem>
<para>
The binding you get can be rather unfriendly, so it also
loads a set of overrides from <code>Vips.py</code> in
<code>/usr/lib/python2.7/dist-packages/gi/overrides</code>
(on my system at least). If you're using python3, it's
<code>/usr/lib/python3/dist-packages/gi/overrides</code>.
Unfortunately, as far as I know, there is no way to extend
this search using environment variables. You MUST have
<code>Vips.py</code> in 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.
</para>
</listitem>
<listitem>
<para>
Finally, <code>Vips.py</code> makes the rest of the binding. In
fact, <code>Vips.py</code> makes almost all the binding: it
defines <code>__getattr__</code> on <code>Vips.Image</code>
and binds at runtime by searching libvips for an operation of
that name.
</para>
</listitem>
</orderedlist>
<para>
The next line is:
<programlisting language="Python">
im = Vips.Image.new_from_file(sys.argv[1])
</programlisting>
This loads the input image. You can append
load options to the argument list as keyword arguments, for example:
<programlisting language="Python">
im = Vips.Image.new_from_file(sys.argv[1], access = Vips.Access.SEQUENTIAL)
</programlisting>
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
<code>help(Vips.Image)</code> to see a list of all the image
constructors --- you can load from memory, or create from an array,
for example.
</para>
<para>
The next line is:
<programlisting language="Python">
im = im.extract_area(100, 100, im.width - 200, im.height - 200)
</programlisting>
The arguments are left, top, width, height, so this crops 100 pixels off
every edge. Try <code>help(im.extract_area)</code> and the C API docs
for vips_extract_area() for details. You can use <code>.crop()</code>
as a synonym, if you like.
</para>
<para>
<code>im.width</code> gets the image width
in pixels, see <code>help(Vips.Image)</code> and vips_image_get_width()
and friends for a list of the other getters.
</para>
<para>
The next line:
<programlisting language="Python">
im = im.similarity(scale = 0.9)
</programlisting>
shrinks by 10%. By default it uses
bilinear interpolation, use <code>interpolate</code> to pick another
interpolator, for example:
<programlisting language="Python">
im = im.similarity(scale = 0.9, interpolate = Vips.Interpolate.new("bicubic"))
</programlisting>
see vips_similarity() for full documentation. The similarity operator
will not give good results for large resizes (more than a factor of
two). See vips_resize() if you need to make a large change.
</para>
<para>
Next:
<programlisting language="Python">
mask = Vips.Image.new_from_array([[-1, -1, -1],
[-1, 16, -1],
[-1, -1, -1]], scale = 8)
im = im.conv(mask)
</programlisting>
makes an image from a 2D array, then convolves with that. The
<code>scale</code> keyword argument lets you set a divisor for
convolution, handy for integer convolutions. You can set
<code>offset</code> as well. See vips_conv() for details on the vips
convolution operator.
</para>
<para>
Finally,
<programlisting language="Python">
im.write_to_file(sys.argv[2])
</programlisting>
sends the image back to the
filesystem. There's also <code>.write_to_buffer()</code> to make a
string containing the formatted image, and <code>.write()</code> to
write to another image.
</para>
<para>
As with <code>.new_from_file()</code> you can append save options as
keyword arguments. For example:
<programlisting language="Python">
im.write_to_file("test.jpg", Q = 90)
</programlisting>
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
vips_jpegsave().
</para>
</refsect3>
<refsect3 id="python-doc">
<title>Getting help</title>
<para>
Try <code>help(Vips)</code> for everything,
<code>help(Vips.Image)</code> for something slightly more digestible, or
something like <code>help(Vips.Image.black)</code> for help on a
specific class member.
</para>
<para>
You can't get help on dynamically bound member functions like
<code>.add()</code> this way. Instead, make an image and get help
from that, for example:
<programlisting language="Python">
image = Vips.Image.black(1, 1)
help(image.add)
</programlisting>
And you'll get a summary of the operator's behaviour and how the
arguments are represented in Python.
</para>
<para>
The API docs have a <link linkend="function-list">handy table of all vips
operations</link>, if you want to find out how to do something, try
searching that.
</para>
<para>
The <command>vips</command> command can be useful too. For example, in a
terminal you can type <command>vips jpegsave</command> to get a
summary of an operation:
<programlisting language="Python">
$ 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
</programlisting>
</para>
</refsect3>
<refsect3 id="python-basics">
<title><code>pyvips8</code> basics</title>
<para>
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.
</para>
</refsect3>
<refsect3 id="python-wrapping">
<title>Automatic wrapping</title>
<para>
The overrides intercept member lookup
on the <code>Vips.Image</code> 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
<code>image.add()</code>.
</para>
<para>
The first input image argument becomes the <code>self</code>
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.
</para>
<para>
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:
<programlisting language="Python">
min_value = im.min()
</programlisting>
and <code>min_value</code> 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 <code>x</code> and <code>y</code>
optional output arguments. Call it like this:
<programlisting language="Python">
min_value, opts = im.min(x = True, y = True)
x = opts['x']
y = opts['y']
</programlisting>
In other words, if optional output args are requested, an extra
dictionary is returned containing those objects.
Of course in this case, the <code>.minpos()</code> convenience
function would be simpler, see below.
</para>
<para>
Because operations are member functions and return the result image,
you can chain them. For example, you can write:
<programlisting language="Python">
result_image = image.sin().pow(2)
</programlisting>
to calculate the square of the sine for each pixel. There is also a
full set of arithmetic operator overloads, see below.
</para>
<para>
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:
<programlisting language="Python">
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])
</programlisting>
And so on. A set of overloads are defined for <code>.linear()</code>,
see below.
</para>
<para>
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:
<programlisting language="Python">
profile = im.get_value("icc-profile-data")
</programlisting>
and <code>profile</code> will be a string.
</para>
<para>
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
<code>.erode()</code>, for example:
<programlisting language="Python">
im = im.erode([[128, 255, 128],
[255, 255, 255],
[128, 255, 128]])
</programlisting>
will erode an image with a 4-connected structuring element.
</para>
<para>
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, <code>.ifthenelse()</code> uses
a condition image to pick pixels between a then and an else image:
<programlisting language="Python">
result_image = condition_image.ifthenelse(then_image, else_image)
</programlisting>
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:
<programlisting language="Python">
result_image = condition_image.ifthenelse([0, 255, 0], [255, 0, 0])
</programlisting>
Will make an image where true pixels are green and false pixels
are red.
</para>
<para>
This is also useful for <code>.bandjoin()</code>, the thing to join
two or more images up bandwise. You can write:
<programlisting language="Python">
rgba = rgb.bandjoin(255)
</programlisting>
to add a constant 255 band to an image, perhaps to add an alpha
channel. Of course you can also write:
<programlisting language="Python">
result_image = image1.bandjoin(image2)
result_image = image1.bandjoin([image2, image3])
result_image = image1.bandjoin([image2, 255])
</programlisting>
and so on.
</para>
</refsect3>
<refsect3 id="python-exceptions">
<title>Exceptions</title>
<para>
The wrapper spots errors from vips operations and raises the
<code>Vips.Error</code> exception. You can catch it in the
usual way. The <code>.detail</code> member gives the detailed
error message.
</para>
</refsect3>
<refsect3 id="python-memory">
<title>Reading and writing areas of memory</title>
<para>
You can use the C API functions vips_image_new_from_memory(),
vips_image_new_from_memory_copy() and
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.
</para>
<para>
Use them from Python like this:
<programlisting language="Python">
image = Vips.Image.new_from_file("/path/to/some/image/file.jpg")
memory_area = image.write_to_memory()
</programlisting>
<code>memory_area</code> is now a string containing uncompressed binary
image data. For an RGB image, it will have bytes
<code>RGBRGBRGB...</code>, being
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.
</para>
<para>
Note that <code>.write_to_memory()</code> will make a copy of the image.
It would
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.
</para>
<para>
Going the other way, you can construct a vips image from a string of
binary data. For example:
<programlisting language="Python">
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)
</programlisting>
Now <code>image2</code> should be an identical copy of <code>image</code>.
</para>
<para>
Be careful: in this direction, vips does not make a copy of the memory
area, so if <code>memory_area</code> is freed by the Python garbage
collector and
you later try to use <code>image2</code>, you'll get a crash.
Make sure you keep a reference to <code>memory_area</code> around
for as long as you need it. A simple solution is to use
<code>new_from_memory_copy</code> instead. This will take a copy of the
memory area for vips. Of course this will raise memory usage.
</para>
</refsect3>
<refsect3 id="python-modify">
<title>Draw operations</title>
<para>
Paint operations like <code>draw_circle</code> and <code>draw_line</code>
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.
</para>
<para>
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.
</para>
<para>
If you want to avoid the copies, you'll need to call drawing
operations yourself.
</para>
</refsect3>
<refsect3 id="python-overloads">
<title>Overloads</title>
<para>
The wrapper defines the usual set of arithmetic, boolean and
relational overloads on
<code>image</code>. You can mix images, constants and lists of
constants (almost) freely. For example, you can write:
<programlisting language="Python">
result_image = ((image * [1, 2, 3]).abs() < 128) | 4
</programlisting>
</para>
<para>
The wrapper overloads <code>[]</code> to be vips_extract_band(). You
can write:
<programlisting language="Python">
result_image = image[2]
</programlisting>
to extract the third band of the image. It implements the usual
slicing and negative indexes, so you can write:
<programlisting language="Python">
result_image = image[1:]
result_image = image[:3]
result_image = image[-2:]
result_image = [x.avg() for x in image]
</programlisting>
and so on.
</para>
<para>
The wrapper overloads <code>()</code> to be vips_getpoint(). You can
write:
<programlisting language="Python">
r, g, b = image(10, 10)
</programlisting>
to read out the value of the pixel at coordinates (10, 10) from an RGB
image.
</para>
</refsect3>
<refsect3 id="python-expansions">
<title>Expansions</title>
<para>
Some vips operators take an enum to select an action, for example
<code>.math()</code> can be used to calculate sine of every pixel
like this:
<programlisting language="Python">
result_image = image.math(Vips.OperationMath.SIN)
</programlisting>
This is annoying, so the wrapper expands all these enums into
separate members named after the enum. So you can write:
<programlisting language="Python">
result_image = image.sin()
</programlisting>
See <code>help(Vips.Image)</code> for a list.
</para>
</refsect3>
<refsect3 id="python-utility">
<title>Convenience functions</title>
<para>
The wrapper defines a few extra useful utility functions:
<code>.get_value()</code>,
<code>.set_value()</code>,
<code>.bandsplit()</code>,
<code>.maxpos()</code>,
<code>.minpos()</code>,
<code>.median()</code>.
Again, see <code>help(Vips.Image)</code> for a list.
</para>
</refsect3>
<refsect3 id="python-args">
<title>Command-line option parsing</title>
<para>
GLib includes a command-line option parser, and Vips defines a set of
standard flags you can use with it. For example:
<programlisting language="Python">
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)
</programlisting>
</para>
</refsect3>
</refentry>