libvips/doc/using-python.xml

<?xml version="1.0"?>
<!DOCTYPE refentry PUBLIC "-//OASIS//DTD DocBook XML V4.5//EN"
               "http://www.oasis-open.org/docbook/xml/4.5/docbookx.dtd" [
]>
<!-- vim: set ts=2 sw=2 expandtab: --> 
<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>

  <refsect1 id="python-intro">
    <title>Introduction</title>
    <para>
      VIPS comes with a convenient, high-level Python API based 
      on <code>gobject-introspection</code>. As long as you can get GOI 
      for your platform, you should be able to use vips. The 
      <code>Vips.py</code> file 
      needs to be copied to the overrides directory of your GOI install,
      and you need to have the vips typelib on your 
      <code>GI_TYPELIB_PATH</code>. This may already have happened, depending 
      on your platform. 
    </para>

    <para>
<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>

      Reading this example, the first line loads the input file. 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 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. <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 <code>similarity</code> line 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>

    </para>

    <para>
      <code>.new_from_array()</code> makes an image from a 2D array. 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, <code>.write_to_file()</code> 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>
  </refsect1>

  <refsect1 id="python-basics">
    <title><code>pyvips8</code> basics</title>
    <para>
      The Python interface comes in two main parts. First, the C source code 
      to libvips has been marked up with special comments describing the 
      interface in a standard way. These comments are read by 
      gobject-introspection when libvips is compiled and used to generate a 
      typelib, a description of how to call the library. When your Python 
      program starts, the import line:

<programlisting language="Python">
from gi.repository import Vips
</programlisting>

      loads the typelib and creates Python classes for all the objects and 
      all the functions in the library. You can then call these functions 
      from your code, and they will call into libvips for you. C functions 
      become Python functions in an obvious way: vips_operation_new(), 
      for example, the constructor for the class #VipsOperation, becomes 
      <code>Vips.Operation.new()</code>. See the C API docs for details. 
    </para>

    <para>
      Using libvips like this is possible, but a bit painful. To make the API 
      seem more pythonesque, vips includes a set of overrides which form a 
      layer over the bare functions created by gobject-introspection.
    </para>
  </refsect1>

  <refsect1 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 = Vips.Image.bandjoin([image1, image2, image3])
result_image = image1.bandjoin([image2, 255])
</programlisting>
      
      and so on. 
    </para>
  </refsect1>

  <refsect1 id="python-doc">
    <title>Automatic docstrings</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.new_from_file("x.jpg")
help(image.add)
</programlisting>

      And you'll get a summary of the operator's behaviour and how the
      arguments are represented in Python. Use the C API docs for more detail.
    </para>
  </refsect1>

  <refsect1 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>
  </refsect1>

  <refsect1 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>
  </refsect1>

  <refsect1 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() &lt; 128) | 4
</programlisting>
    </para>
  </refsect1>

  <refsect1 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>
  </refsect1>

  <refsect1 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>
  </refsect1>

  <refsect1 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>

</refsect1>

</refentry>