Pillow/docs/reference/ImageMath.rst

.. py:module:: PIL.ImageMath
.. py:currentmodule:: PIL.ImageMath

:py:mod:`~PIL.ImageMath` Module
===============================

The :py:mod:`~PIL.ImageMath` module can be used to evaluate “image expressions”, that
can take a number of images and generate a result.

:py:mod:`~PIL.ImageMath` only supports single-layer images. To process multi-band
images, use the :py:meth:`~PIL.Image.Image.split` method or :py:func:`~PIL.Image.merge`
function.

Example: Using the :py:mod:`~PIL.ImageMath` module
--------------------------------------------------

::

    from PIL import Image, ImageMath

    with Image.open("image1.jpg") as im1:
        with Image.open("image2.jpg") as im2:
            out = ImageMath.lambda_eval(
              lambda args: args["convert"](args["min"](args["a"], args["b"]), 'L'),
              a=im1,
              b=im2
            )
            out = ImageMath.unsafe_eval(
              "convert(min(a, b), 'L')",
              a=im1,
              b=im2
            )

.. py:function:: lambda_eval(expression, options)

    Returns the result of an image function.

    :param expression: A function that receives a dictionary.
    :param options: Values to add to the function's dictionary, mapping image
                    names to Image instances. You can use one or more keyword
                    arguments instead of a dictionary, as shown in the above
                    example. Note that the names must be valid Python
                    identifiers.
    :return: An image, an integer value, a floating point value,
             or a pixel tuple, depending on the expression.

.. py:function:: unsafe_eval(expression, options)

    Evaluates an image expression.

    .. danger::
        This uses Python's ``eval()`` function to process the expression string,
        and carries the security risks of doing so. It is not
        recommended to process expressions without considering this.
        :py:meth:`lambda_eval` is a more secure alternative.

    :py:mod:`~PIL.ImageMath` only supports single-layer images. To process multi-band
    images, use the :py:meth:`~PIL.Image.Image.split` method or
    :py:func:`~PIL.Image.merge` function.

    :param expression: A string which uses the standard Python expression
                       syntax. In addition to the standard operators, you can
                       also use the functions described below.
    :param options: Values to add to the function's dictionary, mapping image
                    names to Image instances. You can use one or more keyword
                    arguments instead of a dictionary, as shown in the above
                    example. Note that the names must be valid Python
                    identifiers.
    :return: An image, an integer value, a floating point value,
             or a pixel tuple, depending on the expression.

Expression syntax
-----------------

* :py:meth:`lambda_eval` expressions are functions that receive a dictionary
  containing images and operators.

* :py:meth:`unsafe_eval` expressions are standard Python expressions,
  but they’re evaluated in a non-standard environment.

.. danger::
  :py:meth:`unsafe_eval` uses Python's ``eval()`` function to process the
  expression string, and carries the security risks of doing so.
  It is not recommended to process expressions without considering this.
  :py:meth:`lambda_eval` is a more secure alternative.

Standard Operators
^^^^^^^^^^^^^^^^^^

You can use standard arithmetical operators for addition (+), subtraction (-),
multiplication (*), and division (/).

The module also supports unary minus (-), modulo (%), and power (**) operators.

Note that all operations are done with 32-bit integers or 32-bit floating
point values, as necessary. For example, if you add two 8-bit images, the
result will be a 32-bit integer image. If you add a floating point constant to
an 8-bit image, the result will be a 32-bit floating point image.

You can force conversion using the ``convert()``, ``float()``, and ``int()``
functions described below.

Bitwise Operators
^^^^^^^^^^^^^^^^^

The module also provides operations that operate on individual bits. This
includes and (&), or (|), and exclusive or (^). You can also invert (~) all
pixel bits.

Note that the operands are converted to 32-bit signed integers before the
bitwise operation is applied. This means that you’ll get negative values if
you invert an ordinary grayscale image. You can use the and (&) operator to
mask off unwanted bits.

Bitwise operators don’t work on floating point images.

Logical Operators
^^^^^^^^^^^^^^^^^

Logical operators like ``and``, ``or``, and ``not`` work
on entire images, rather than individual pixels.

An empty image (all pixels zero) is treated as false. All other images are
treated as true.

Note that ``and`` and ``or`` return the last evaluated operand,
while not always returns a boolean value.

Built-in Functions
^^^^^^^^^^^^^^^^^^

These functions are applied to each individual pixel.

.. py:currentmodule:: None

.. py:function:: abs(image)
    :noindex:

    Absolute value.

.. py:function:: convert(image, mode)
    :noindex:

    Convert image to the given mode. The mode must be given as a string
    constant.

.. py:function:: float(image)
    :noindex:

    Convert image to 32-bit floating point. This is equivalent to
    convert(image, “F”).

.. py:function:: int(image)
    :noindex:

    Convert image to 32-bit integer. This is equivalent to convert(image, “I”).

    Note that 1-bit and 8-bit images are automatically converted to 32-bit
    integers if necessary to get a correct result.

.. py:function:: max(image1, image2)
    :noindex:

    Maximum value.

.. py:function:: min(image1, image2)
    :noindex:

    Minimum value.