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imreg_dft

Latest Version on PyPi Build Status Documentation Status

Project status

imreg_dft has reached a stable state. This means that although you won't see many commits, the project is not dead, there are just no outstanding issues and people are not complaining. Addition of some nice-to-have features is planned in Q3/2017. Until then, I will spend time on the Argbash project - you might want to check it out if you write shell scripts.

Overview

Image registration using discrete Fourier transform.

Given two images, imreg_dft can calculate difference between scale, rotation and position of imaged features. Given you have the requirements, you can start aligning images in about five minutes! Check the documentation on readthedocs.org (bleeding-edge) or pythonhosted.org (with images).

If you are a part of the education process that has something to do with phase correlation (doesn't matter whether you are a student or a teacher), the ird-show utility that is part of the imreg_dft project can help you understand (or explain) how the phase correlation process works. If you are a researcher and you are having problems with the method on your data, you can use ird-show to find out what causes your problems quite easily.

Features

  • Image pre-processing options (frequency filtration, image extension).
  • Under-the-hood options exposed (iterations, phase correlation filtration).
  • Visualization of various stages of the image registration (so you can more easily find out how it works or what went wrong).
  • Command-line interface for image registration (ird - text output and/or image output), for image transformation (ird-tform, cooperates with ird) and inspection (ird-show).
  • Documented Python API with examples.
  • Permissive open-source license (3-clause BSD).

Project facts

  • The project is written in pure Python.
  • Essentially requires only numpy and scipy (RHEL7-safe).
  • Includes quickstart documentation and example data files.
  • Per-commit tests and documentation (see badges under the heading).
  • Originally developed by Christoph Gohlke (University of California, Irvine, USA)
  • Currently developed by Matěj Týč (Brno University of Technology, CZ)