Working with transformations


In image analysis one commonly wants to transform images. When putting an image through several transforms it can be really useful to save out the intermediate images to disk. Creating a visual audit track of the image processing.

To make it painless to set-up such an audit trail jicbioimage provides the function decorator jicbioimage.core.transform.transformation(). When applied to a transformation function the decorator adds both “autowriting” of the tranformed image as well as a log in the history of the image.

Pre-built transformations

The jicbioimage.transform package constains a number of standard image transformations that have had the jicbioimage.core.transform.transformation() function decorator applied to them.

For more information see

Creating a custom transform

Suppose that we wanted to create a transformation to invert our image. We can achieve this by importing the jicbioimage.core.transform.transformation() decorator.

>>> import numpy as np
>>> from jicbioimage.core.transform import transformation
>>> @transformation
... def invert(image):
...     """Return an inverted image."""
...     maximum = np.iinfo(image.dtype).max
...     maximum_array = np.ones(image.shape, dtype=image.dtype) * maximum
...     return maximum_array - image

Let us create an image to apply our tranformation to.

>>> from jicbioimage.core.image import Image
>>> ar = np.zeros((3,3), dtype=np.uint8)
>>> im = Image.from_array(ar)

We can now apply the transformation to our image.

>>> invert(im)  
<Image object at 0x..., dtype=uint8>

Specifying dtype contracts

Sometimes one want to be able to ensure that the input/output image(s) are of a particular dtype. This can be achieved using the function decorator jicbioimage.core.util.array.dtype_contract().

>>> from jicbioimage.core.util.array import dtype_contract
>>> @transformation
... @dtype_contract(input_dtype=bool, output_dtype=bool)
... def bool_invert(image):
...     """Return an inverted image."""
...     return np.logical_not(image)

If we try to apply this transform to an image of the wrong dtype we get an informative error message.

>>> bool_invert(im)  
Traceback (most recent call last):
TypeError: Invalid dtype uint8. Allowed dtype(s): [<type 'bool'>]

Customising the behaviour of the visual audit trail

By default the audit trail images are written to the working directory. The location can be customised using attribute.

The generation of the audit trail images can be turned off by setting attribute to False.