Collection of tools to align series of images containing the same subject, such as astronomical data. 🌌
Clone the repository (or download the sources), and install with pip:
git clone https://github.com/gpelouze/align_images
cd align_images
pip install .
The alignment is done using two different functions in align
:
align.compute_shifts()
returns the spatial shift for all images in the series. The shifts are determined by searching for the maximum of the 2D cross-correlation of these these images, relatively to either a reference image, or to all other image in the series.align.align_cube()
builds a new series of aligned images using the output ofcompute_shifts()
. Input images are interpolated to build the aligned ones.
The slowest steps of both functions (ie. the determination cross-correlation
maximum location and the interpolations) can to be parallelised by using the
keyword processes
.
The function align.roll_cube()
is provided as a faster but much less accurate
alternative to align.align_cube()
. Make sure to understand its limitations
before using it!
See the functions documentation for more informations.
Example
from astropy.io import fits
from align_images import align, tools
cube = fits.open('data.fits')[0].data
shifts = align.compute_shifts(cube, ref_frame=cube[0])
aligned_cube = align.align_cube(cube, shifts, processes=4)
tools.save_fits_cube(aligned_cube, 'aligned_data.fits')
The shift between two images is determined computing their 2D
cross-correlation (CC), and finding the location its maximum. This is performed by
align.track()
, which calls the appropriate function from cc2d
, depending on
the required method.
Currently, 3 methods are provided by cc2d
for computing the (CC), each
implementing different boundary conditions:
explicit()
: multiplication in the real space.dft()
: multiplication in the real Fourier space.scipy()
: a wrapper aroundscipy.signal.correlate2d
.
While explicit(boundary='drop')
is far less sensitive to edge effects than
dft()
, it is also much slower. If a full CC map is not required,
explicit_minimize()
can instead be used to locate the CC maximum within a
reasonable computation time.
Normalization
For any method, let img1
and img2
the entry images. We first subtract their
respective averages:
I = img1
- avg(img1
),
J = img2
- avg(img2
).
Then compute the normalisation factor, which is the product of the standard
deviations of I and J:
norm = σ(I) × σ(J) = sqrt(sum(I²) × sum(J²)).
The cross-correlation returned by the method is normalized by this factor, such
that its maximum is 1:
cc = I ⋆ J / norm.
The submodule tools
contains various functions either required by align
and
cc2d
, or that can be used to manipulate the images before they are aligned.
Copyright (c) 2022 Gabriel Pelouze
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