366 lines
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366 lines
13 KiB
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<?xml version="1.0"?>
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<!DOCTYPE refentry PUBLIC "-//OASIS//DTD DocBook XML V4.5//EN"
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"http://www.oasis-open.org/docbook/xml/4.5/docbookx.dtd" [
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]>
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<!-- vim: set ts=2 sw=2 expandtab: -->
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<refentry id="using-from-python">
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<refmeta>
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<refentrytitle>VIPS from Python</refentrytitle>
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<manvolnum>3</manvolnum>
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<refmiscinfo>VIPS Library</refmiscinfo>
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</refmeta>
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<refnamediv>
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<refname>Using VIPS</refname>
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<refpurpose>How to use the VIPS library from Python</refpurpose>
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</refnamediv>
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<refsect1 id="python-intro">
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<title>Introduction</title>
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<para>
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VIPS comes with a convenient, high-level Python API based
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on <code>gobject-introspection</code>. As long as you can get GOI
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for your platform, you should be able to use vips. The
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<code>Vips.py</code> file
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needs to be copied to the overrides directory of your GOI install,
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and you need to have the vips typelib on your
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<code>GI_TYPELIB_PATH</code>. This may already have happened, depending
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on your platform.
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</para>
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<para>
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<programlisting language="Python">
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#!/usr/bin/python
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import sys
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from gi.repository import Vips
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im = Vips.Image.new_from_file(sys.argv[1])
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im = im.extract_area(100, 100, im.width - 200, im.height - 200)
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im = im.similarity(scale = 0.9)
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mask = Vips.Image.new_from_array([[-1, -1, -1],
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[-1, 16, -1],
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[-1, -1, -1]], scale = 8)
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im = im.conv(mask)
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im.write_to_file(sys.argv[2])
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</programlisting>
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Reading this example, the first line loads the input file. You can append
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load options to the argument list as keyword arguments, for example:
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<programlisting language="Python">
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im = Vips.Image.new_from_file(sys.argv[1], access = Vips.Access.SEQUENTIAL)
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</programlisting>
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See the various loaders for a list of the available options
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for each file format. The C equivalent to this function,
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vips_image_new_from_file(), has more extensive documentation. Try
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<code>help(Vips.Image)</code> to see a list of all the image
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constructors --- you can load from memory, or create from an array,
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for example.
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</para>
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<para>
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The next line crops 100 pixels off every edge. Try
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<code>help(im.extract_area)</code> and the C API docs for
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vips_extract_area() for details. You can use <code>.crop()</code> as a
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synonym, if you like. <code>im.width</code> gets the image width in
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pixels, see <code>help(Vips.Image)</code> and vips_image_get_width()
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and friends for a list of the other getters.
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</para>
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<para>
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The <code>similarity</code> line shrinks by 10%. By default it uses
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bilinear interpolation, use <code>interpolate</code> to pick another
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interpolator, for example:
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<programlisting language="Python">
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im = im.similarity(scale = 0.9, interpolate = Vips.Interpolate.new("bicubic"))
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</programlisting>
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</para>
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<para>
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<code>.new_from_array()</code> makes an image from a 2D array. The
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<code>scale</code> keyword argument lets you set a divisor for
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convolution, handy for integer convolutions. You can set
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<code>offset</code> as well. See vips_conv() for details on the vips
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convolution operator.
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</para>
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<para>
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Finally, <code>.write_to_file()</code> sends the image back to the
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filesystem. There's also <code>.write_to_buffer()</code> to make a
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string containing the formatted image.
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</para>
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</refsect1>
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<refsect1 id="python-basics">
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<title><code>pyvips8</code> basics</title>
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<para>
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The Python interface comes in two main parts. First, the C source code
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to libvips has been marked up with special comments describing the
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interface in a standard way. These comments are read by
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gobject-introspection when libvips is compiled and used to generate a
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typelib, a description of how to call the library. When your Python
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program starts, the import line:
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<programlisting language="Python">
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from gi.repository import Vips
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</programlisting>
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loads the typelib and creates Python classes for all the objects and
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all the functions in the library. You can then call these functions
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from your code, and they will call into libvips for you. C functions
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become Python functions in an obvious way: vips_operation_new(),
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for example, the constructor for the class #VipsOperation, becomes
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<code>Vips.Operation.new()</code>. See the C API docs for details.
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</para>
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<para>
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Using libvips like this is possible, but a bit painful. To make the API
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seem more pythonesque, vips includes a set of overrides which form a
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layer over the bare functions created by gobject-introspection.
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</para>
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</refsect1>
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<refsect1 id="python-wrapping">
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<title>Automatic wrapping</title>
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<para>
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The overrides intercept member lookup
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on the <code>Vips.Image</code> class and look for vips operations
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with that name. So the vips operation "add", which appears in the
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C API as vips_add(), appears in Python as
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<code>image.add()</code>.
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</para>
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<para>
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The first input image argument becomes the <code>self</code>
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argument. If there are no input image arguments, the operation
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appears as a class member. Optional input arguments become
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keyword arguments. The result is a list of all the output
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arguments, or a single output if there is only one.
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</para>
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<para>
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Optional output arguments are enabled with a boolean keyword
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argument of that name. For example, "min" (the operation which
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appears in the C API as vips_min()), can be called like this:
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<programlisting language="Python">
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min_value = im.min()
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</programlisting>
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and <code>min_value</code> will be a floating point value giving
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the minimum value in the image. "min" can also find the position
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of the minimum value with the <code>x</code> and <code>y</code>
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optional output arguments. Call it like this:
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<programlisting language="Python">
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min_value, x_pos, y_pos = im.min(x = True, y = True)
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</programlisting>
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Although in this case, the <code>.minpos()</code> convenience
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function would be simpler.
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</para>
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<para>
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Because operations are member functions and return the result image,
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you can chain them. For example, you can write:
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<programlisting language="Python">
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result_image = image.sin().pow(2)
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</programlisting>
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to calculate the square of the sine for each pixel. There is also a
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full set of arithmetic operator overloads, see below.
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</para>
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<para>
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VIPS types are also automatically wrapped. The override looks
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at the type of argument required by the operation and converts
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the value you supply, when it can. For example, "linear" takes a
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#VipsArrayDouble as an argument for the set of constants to use for
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multiplication. You can supply this value as an integer, a float,
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or some kind of compound object and it will be converted for you.
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You can write:
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<programlisting language="Python">
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result_image = image.linear(1, 3)
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result_image = image.linear(12.4, 13.9)
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result_image = image.linear([1, 2, 3], [4, 5, 6])
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result_image = image.linear(1, [4, 5, 6])
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</programlisting>
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And so on. A set of overloads are defined for <code>.linear()</code>,
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see below.
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</para>
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<para>
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It does a couple of more ambitious conversions. It will
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automatically convert to and from the various vips types,
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like #VipsBlob and #VipsArrayImage. For example, you can read the
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ICC profile out of an image like this:
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<programlisting language="Python">
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profile = im.get_value("icc-profile-data")
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</programlisting>
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and <code>profile</code> will be a string.
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</para>
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<para>
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If an operation takes several input images, you can use a constant
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for all but one of them and the wrapper will expand the constant
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to an image for you. For example, <code>.ifthenelse()</code> uses
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a condition image to pick pixels between a then and an else image:
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<programlisting language="Python">
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result_image = condition_image.ifthenelse(then_image, else_image)
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</programlisting>
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You can use a constant instead of either the then or the else
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parts, and it will be expanded to an image for you. If you use a
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constant for both then and else, it will be expanded to match the
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condition image. For example:
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<programlisting language="Python">
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result_image = condition_image.ifthenelse([0, 255, 0], [255, 0, 0])
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</programlisting>
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Will make an image where true pixels are green and false pixels
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are red.
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</para>
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<para>
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This is useful for <code>.bandjoin()</code>, the thing to join
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two or more images up bandwise. You can write:
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<programlisting language="Python">
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rgba = rgb.bandjoin(255)
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</programlisting>
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to add a constant 255 band to an image, perhaps to add an alpha
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channel. Of course you can also write:
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<programlisting language="Python">
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result_image = image1.bandjoin(image2)
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result_image = image1.bandjoin([image2, image3])
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result_image = Vips.Image.bandjoin([image1, image2, image3])
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result_image = image1.bandjoin([image2, 255])
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</programlisting>
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and so on.
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</para>
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</refsect1>
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<refsect1 id="python-doc">
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<title>Automatic docstrings</title>
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<para>
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Try <code>help(Vips)</code> for everything,
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<code>help(Vips.Image)</code> for something slightly more digestible, or
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something like <code>help(Vips.Image.black)</code> for help on a
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specific class member.
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</para>
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<para>
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You can't get help on dynamically bound member functions like
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<code>.add()</code> this way. Instead, make an image and get help
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from that, for example:
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<programlisting language="Python">
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image = Vips.Image.new_from_file("x.jpg")
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help(image.add)
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</programlisting>
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And you'll get a summary of the operator's behaviour and how the
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arguments are represented in Python. Use the C API docs for more detail.
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</para>
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</refsect1>
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<refsect1 id="python-exceptions">
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<title>Exceptions</title>
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<para>
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The wrapper spots errors from vips operations and raises the
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<code>Vips.Error</code> exception. You can catch it in the
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usual way. The <code>.detail</code> member gives the detailed
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error message.
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</para>
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</refsect1>
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<refsect1 id="python-modify">
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<title>Draw operations</title>
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<para>
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Paint operations like <code>draw_circle</code> and <code>draw_line</code>
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modify their input image. This makes them hard to use with the rest of
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libvips: you need to be very careful about the order in which operations
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execute or you can get nasty crashes.
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</para>
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<para>
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The wrapper spots operations of this type and makes a private copy of
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the image in memory before calling the operation. This stops crashes,
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but it does make it inefficient. If you draw 100 lines on an image,
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for example, you'll copy the image 100 times. The wrapper does make sure
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that memory is recycled where possible, so you won't have 100 copies in
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memory. At least you can execute these operations.
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</para>
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<para>
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If you want to avoid the copies, you'll need to call drawing
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operations yourself.
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</para>
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</refsect1>
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<refsect1 id="python-overloads">
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<title>Overloads</title>
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<para>
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The wrapper defines the usual set of arithmetic, boolean and
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relational overloads on
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<code>image</code>. You can mix images, constants and lists of
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constants (almost) freely. For example, you can write:
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<programlisting language="Python">
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result_image = ((image * [1, 2, 3]).abs() < 128) | 4
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</programlisting>
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</para>
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</refsect1>
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<refsect1 id="python-expansions">
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<title>Expansions</title>
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<para>
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Some vips operators take an enum to select an action, for example
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<code>.math()</code> can be used to calculate sine of every pixel
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like this:
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<programlisting language="Python">
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result_image = image.math(Vips.OperationMath.SIN)
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</programlisting>
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This is annoying, so the wrapper expands all these enums into
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separate members named after the enum. So you can write:
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<programlisting language="Python">
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result_image = image.sin()
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</programlisting>
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See <code>help(Vips.Image)</code> for a list.
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</para>
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</refsect1>
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<refsect1 id="python-utility">
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<title>Convenience functions</title>
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<para>
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The wrapper defines a few extra useful utility functions:
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<code>.get_value()</code>, <code>.set_value()</code>
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<code>.bandsplit()</code>, <code>.maxpos()</code>
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<code>.minpos()</code>.
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Again, see <code>help(Vips.Image)</code> for a list.
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</para>
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</refsect1>
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</refentry>
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