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Phasepack

Python ports of some of Peter Kovesi's MATLAB functions for image analysis using local phase information

Install / Use

/learn @alimuldal/Phasepack
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

============================================================ Phasepack: a toolkit for phase-based image feature detection

This toolkit consists of a set of functions which use information contained within the phase of a Fourier-transformed image to detect localised features such as edges, blobs and corners. These methods have the key advantage that the properties they measure are invariant with respect to image brightness and contrast.

:phasecong: Phase congruency using oriented filters

:phasecongmono: Fast phase congruency using monogenic filters

:phasesym: Phase symmetry using oriented filters

:phasesymmono: Fast phase symmetry using monogenic filters

For more information on a particular function, see the associated docstring and the references therein.

Installation

::

$ python setup.py install

Fast(er) Fourier Transforms

All of the functions in this module make use of the Fast Fourier Transform (FFT), and their speed significantly depends on the module used to provide FFT functions. If it is available, the pyFFTW <http://hgomersall.github.io/pyFFTW/>_ module will be used. This provides Python bindings to the FFTW C library, and is substantially faster than fftpack, the default for scipy.

To install pyFFTW:

::

$ pip install pyfftw

Authorship

These functions were originally written for MATLAB by Peter Kovesi, and were ported to Python by Alistair Muldal. The original MATLAB code, as well as further explanatory information and references are available from Peter Kovesi's website <http://www.csse.uwa.edu.au/~pk/Research/MatlabFns/index.html#phasecong>_.

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

The software is provided "as is", without warranty of any kind.

Related Skills

View on GitHub
GitHub Stars61
CategoryDevelopment
Updated1mo ago
Forks20

Languages

Python

Security Score

80/100

Audited on Feb 26, 2026

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