Metadata-Version: 2.4
Name: pycirclemedianfilter
Version: 0.1.7
Summary: Fast median filtering for circle-valued (phase / orientation) data — Python bindings for the C++ reference implementation
Author-email: Martin Storath <martin.storath@thws.de>, Andreas Weinmann <andreas.weinmann@thws.de>
License: MIT License
        
        Copyright (c) 2017 Martin Storath, Andreas Weinmann
        
        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, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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        The above copyright notice and this permission notice shall be included in all
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Project-URL: Homepage, https://github.com/mstorath/CircleMedianFilter
Project-URL: Repository, https://github.com/mstorath/CircleMedianFilter
Project-URL: Bug Tracker, https://github.com/mstorath/CircleMedianFilter/issues
Keywords: circle-median-filter,phase-data,orientation,median-filter,image-processing,signal-processing,variational-methods
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: C++
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.20
Provides-Extra: demos
Requires-Dist: matplotlib>=3.6; extra == "demos"
Requires-Dist: Pillow>=9; extra == "demos"
Provides-Extra: test
Requires-Dist: pytest>=7; extra == "test"
Requires-Dist: matplotlib>=3.6; extra == "test"
Requires-Dist: Pillow>=9; extra == "test"
Dynamic: license-file

# Circle median filter toolbox (CMF)
 
This toolbox contains a fast algorithm for median filtering of signals and images with values on the unit circle, for example phase or orientation data.
The (arc distance) median filter for an image y with values on the unit circle is given by 

<img src="docs/eqArcDistanceMedian.png" width="40%">

where d denotes the arc distance length of two angles, and r, t are the horizontal and vertical "radii" of the filter mask.

The code is a reference implementation (in C++ with Matlab wrappers) of the algorithms described in the paper:

Martin Storath, Andreas Weinmann.
[Fast median filtering for phase or orientation data.](https://doi.org/10.1109/TPAMI.2017.2692779)
IEEE Transactions on Pattern Analysis and Machine Intelligence, 	40(3):639-652, 2018  ([preprint](https://hci.iwr.uni-heidelberg.de/sites/default/files/profiles/mstorath/files/storath2017fast.pdf))

See also [![View Circle Median Filter on File Exchange](https://www.mathworks.com/matlabcentral/images/matlab-file-exchange.svg)](https://de.mathworks.com/matlabcentral/fileexchange/62509-circle-median-filter)

## Updates
- 2025/02/18: Added Python bindings for the core C++ filtering code. See installation notes below.



### Example 


![alt tag](https://hci.iwr.uni-heidelberg.de/sites/default/files/publications/teaserimages/1908951751/mediancircularrevision_teaser_small.png)

*Left:* A circle-valued image, i.e. every pixel takes its value on the unit circle (or in angular representation a value in (-pi, pi]). The values are visualized as hue component in the HSV color space.
*Right:* Effect of the circle-median filter using a filter mask of size 7 × 7. 

### Runtime comparison

The time complexity w.r.t. the size of the filter mask is
- linear for non-quantized data
- constant for quantized data

<img src="docs/runtime.png" width="80%">

### Applications

- Smoothing of phase data, e.g. interferometric SAR images
   ![alt tag](docs/InSAR.png)
- Smoothing of orientation data, e.g. wind directions

   <img src="docs/windDirections.png" width="60%">
   
- Smoothing of vector fields in polar coordinates, e.g. optical flow images

### Contents
- demos:     some demos, self explanatory (implemented in Matlab)
- auxiliary: some useful helper functions (implemented in Matlab)
- filters:   the fast algorithms for median filtering of circle valued data 
(implemented in C++ with Matlab wrappers)

## Installation and usage 

## Python
- Installation via ```pip install pycirclemedianfilter```.
- **Important:** The underlying implementation expects *column-major order* arrays. So convert data by ```data = np.asfortranarray(data)``` if necessary before calling the filter function. See the demos_python folder for examples.

## Matlab
- From Matlab: Run CMF_install.m in the Matlab console and follow the demos 
- From C++: Compile CMF_library.cpp. The relevant functions are medfiltCirc2D and medfiltCirc2DQuant. Their usage is described as comment in the CMF_library.cpp file.



## References
### How to cite
- M. Storath, A. Weinmann. [Fast median filtering for phase or orientation data.](https://doi.org/10.1109/TPAMI.2017.2692779) IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(3):639-652, 2018
### Selected user applications
- S. Quan et al. Derivation of the Orientation Parameters in Built-Up Areas: With Application to Model-Based Decomposition. IEEE Transactions on Geoscience and Remote Sensing, 2018
- H. Salmane et al. A method for the automated detection of solar radio bursts in dynamic spectra. J. Space Weather Space Clim. 2018
- S. Quan et al., Derivation of the Orientation Parameters in Built-Up Areas: With Application to Model-Based Decomposition. IEEE Transactions on Geoscience and Remote Sensing. 2018
- B. Guo, J. Wen, Y. Han. Deep Material Recognition in Light-Fields via Disentanglement of Spatial and Angular Information. ECCV 2020
