FFT based image registration
============================
Imreg is a Python library that implements an FFT-based technique for
translation, rotation and scale-invariant image registration [1].
:Author: `Christoph Gohlke <https://www.cgohlke.com>`_
:License: BSD 3-Clause
:Version: 2024.5.24
Quickstart
----------
Install the imreg package and all dependencies from the
`Python Package Index <https://pypi.org/project/imreg/>`_::
python -m pip install -U imreg
See `Examples`_ for using the programming interface.
Source code and support are available on
`GitHub <https://github.com/cgohlke/imreg>`_.
Requirements
------------
This revision was tested with the following requirements and dependencies
(other versions may work):
- `CPython <https://www.python.org>`_ 3.9.13, 3.10.11, 3.11.9, 3.12.3
- `NumPy <https://pypi.org/project/numpy/>`_ 1.26.4
- `Scipy <https://pypi.org/project/scipy>`_ 1.12.0
- `Matplotlib 3.8.4 <https://pypi.org/project/matplotlib>`_
(optional for plotting)
Revisions
---------
2024.5.24
- Support NumPy 2.
- Fix docstring examples not correctly rendered on GitHub.
2024.1.2
- Add type hints.
- Drop support for Python 3.8 and numpy < 1.23 (NEP29).
2022.9.27
- Fix scipy.ndimage DeprecationWarning.
Notes
-----
Imreg is no longer being actively developed.
This implementation is mainly for educational purposes.
An improved version is being developed at https://github.com/matejak/imreg_dft.
References
----------
1. An FFT-based technique for translation, rotation and scale-invariant
image registration. BS Reddy, BN Chatterji.
IEEE Transactions on Image Processing, 5, 1266-1271, 1996
2. An IDL/ENVI implementation of the FFT-based algorithm for automatic
image registration. H Xiea, N Hicksa, GR Kellera, H Huangb, V Kreinovich.
Computers & Geosciences, 29, 1045-1055, 2003.
3. Image Registration Using Adaptive Polar Transform. R Matungka, YF Zheng,
RL Ewing. IEEE Transactions on Image Processing, 18(10), 2009.
Examples
--------
>>> im0 = imread('t400')
>>> im1 = imread('Tr19s1.3')
>>> im2, scale, angle, (t0, t1) = similarity(im0, im1)
>>> imshow(im0, im1, im2)
>>> im0 = imread('t350380ori')
>>> im1 = imread('t350380shf')
>>> t0, t1 = translation(im0, im1)
>>> t0, t1
(20, 50)
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