A simple usage example¶
Suppose we have two images of about the same portion of the sky, and we would like to transform one of them to fit on top of the other one. Suppose we do not have WCS information, but we are confident that we could do it by eye, by matching some obvious asterisms on the two images.
In this particular use case, astroalign can be of great help to automatize the process.
After we load our images into numpy arrays, we simple choose one to be the source image and the other to be the target.
The usage for this simple most common case would be as follows:
>>> import astroalign as aa >>> registered_image = aa.register(source, target)
registered_image is now a transformed (numpy array) image of
source that will match pixel to pixel to
source is a masked array,
registered_image will have a mask transformed
source with pixels outside the boundary masked with True
(read more in Working with masks).
Finding the transformation¶
In some cases it may be necessary to inspect first the transformation parameters before applying it,
or we may be interested only in a star to star correspondance between the images.
For those cases, we can use
find_transform will return a scikit-image SimilarityTransform object that encapsulates the matrix transformation,
and the transformation parameters.
It will also return a tuple with two lists of star positions of
source and its corresponding ordered star postions on
>>> transf, (source_list, target_list) = aa.find_transform(source, target)
source and target here can be either numpy arrays of the image pixels, or any iterable (x, y) pair, corresponding to a star position.
The transformation parameters can be found in
and the transformation matrix in
If the transformation is satisfactory we can apply it to the image with
Continuing our example:
>>> if transf.rotation > MIN_ROT: ... registered_image = aa.apply_transform(transf, source, target)
As a convenience,
matrix_transform from scikit-image are imported in astroalign as well.
See Module Methods for more information.