The :pyImageMath
module can be used to evaluate “image expressions”. The module provides a single eval function, which takes an expression string and one or more images.
from PIL import Image, ImageMath
im1 = Image.open("image1.jpg")
im2 = Image.open("image2.jpg")
out = ImageMath.eval("convert(min(a, b), 'L')", a=im1, b=im2)
out.save("result.png")
Expressions are standard Python expressions, but they’re evaluated in a non-standard environment. You can use PIL methods as usual, plus the following set of operators and functions:
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 :py~PIL.ImageMath.convert
, :py~PIL.ImageMath.float
, and :py~PIL.ImageMath.int
functions described below.
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 greyscale image. You can use the and (&) operator to mask off unwanted bits.
Bitwise operators don’t work on floating point images.
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.
These functions are applied to each individual pixel.