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PyAFBF

PyAFBF: a Python library for sampling image textures from the anisotropic fractional Brownian field. - Published in JOSS (2022)

https://github.com/fjprichard/pyafbf

Science Score: 93.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 4 DOI reference(s) in README and JOSS metadata
  • ✓
    Academic publication links
    Links to: joss.theoj.org, zenodo.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ✓
    JOSS paper metadata
    Published in Journal of Open Source Software

Keywords

anisotropy fractional-brownian-motion fractional-gaussian-noise image-synthesis random-field random-fields texture-synthesis

Scientific Fields

Mathematics Computer Science - 84% confidence
Last synced: 6 months ago · JSON representation

Repository

A python library for sampling image textures from the anisotropic fractional Brownian field.

Basic Info
  • Host: GitHub
  • Owner: fjprichard
  • License: other
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 15.4 MB
Statistics
  • Stars: 4
  • Watchers: 2
  • Forks: 1
  • Open Issues: 0
  • Releases: 8
Topics
anisotropy fractional-brownian-motion fractional-gaussian-noise image-synthesis random-field random-fields texture-synthesis
Created almost 5 years ago · Last pushed almost 2 years ago
Metadata Files
Readme Changelog License Authors

README.rst 

.. image:: https://zenodo.org/badge/368267301.svg
   :target: https://zenodo.org/badge/latestdoi/368267301

The Package PyAFBF is intended for the simulation of rough anisotropic image textures. Textures are sampled from a mathematical model called the anisotropic fractional Brownian field. More details can be found on the `documentation `_.

Package features
================

- Simulation of rough anisotropic textures,

- Computation of field features (semi-variogram, regularity, anisotropy indices) that can serve as texture attributes,

- Random definition of simulated fields,

- Extensions to related fields (deformed fields, intrinsic fields, heterogeneous fields, binary patterns).


Installation from sources
=========================

The package source can be downloaded from the `repository `_. 

The package can be installed through PYPI with
 
 pip install PyAFBF
 
To install the package in a Google Collab environment, please type

 !pip install imgaug==0.2.6
 
 !pip install PyAFBF

Communication to the author
===========================

PyAFBF is developed and maintained by Frédéric Richard. For feed-back, contributions, bug reports, contact directly the `author `_, or use the `discussion `_ facility.


Licence
=======

PyAFBF is under licence GNU GPL, version 3.


Citation
========

When using PyAFBF, please cite the original paper

H. Biermé, M. Moisan, and F.J.P. Richard. A turning-band method for the simulation of anisotropic fractional Brownian field. J. Comput. Graph. Statist., 24(3):885–904, 2015.

and the JOSS paper:

F.J.P. Richard. PyAFBF: a Python library for sampling image textures from the anisotropic fractional Brownian field. Journal of Open Source Software, 7(75):3821, 2022.


.. image:: https://joss.theoj.org/papers/10.21105/joss.03821/status.svg
   :target: https://doi.org/10.21105/joss.03821


Contents
========

    - Quick start guide
       - Getting started
       - Customed models
       - Tuning model parameters
       - Model features
       - Simulating with turning-band fields
    - Example gallery
       - Basic examples
       - Extended anisotropic fields
       - Heterogeneous fields
       - Related anisotropic fields
    - API: main classes
       - AFBF (field)
       - Turning band field (tbfield)
    - API: auxiliary classes
       - Periodic functions (perfunction)
       - Coordinates (coordinates)
       - Spatial data (sdata)
       - Process (process)
       - Turning bands (tbparameters)
       - ndarray

Owner

  • Name: Frederic Richard
  • Login: fjprichard
  • Kind: user
  • Location: Marseille, France
  • Company: Aix-Marseille University

JOSS Publication

PyAFBF: a Python library for sampling image textures from the anisotropic fractional Brownian field.
Published
July 20, 2022
DOI
Volume 7, Issue 75, Page 3821
Authors
Frédéric J.p. Richard ORCID
Aix Marseille University, CNRS, Centrale Marseille, I2M, UMR 7373, Marseille, France.
Editor
Amy Roberts ORCID
Tags
mathematics texture synthesis image processing probability statistics fractional Brownian field
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GitHub Events

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Last synced: 7 months ago

All Time
  • Total Commits: 135
  • Total Committers: 1
  • Avg Commits per committer: 135.0
  • Development Distribution Score (DDS): 0.0
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  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
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Frederic Richard 8****d 135

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 0
  • Total pull requests: 8
  • Average time to close issues: N/A
  • Average time to close pull requests: about 21 hours
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.38
  • Merged pull requests: 7
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
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  • fjprichard (12)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 25 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 7
  • Total maintainers: 1
pypi.org: pyafbf

Sample image textures from anisotropic fractional Brownian fields

  • Versions: 7
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 25 Last month
Rankings
Dependent packages count: 10.0%
Average: 21.1%
Dependent repos count: 21.7%
Forks count: 22.6%
Stargazers count: 25.0%
Downloads: 26.1%
Maintainers (1)
fjprichard
Last synced: 6 months ago

Dependencies

PyAFBF.egg-info/requires.txt pypi
  • matplotlib >=3.3.2
  • numpy >=1.19.2
  • scipy >=1.5.2
requirements/defaults.txt pypi
  • matplotlib >=3.3.2
  • numpy >=1.19.2
  • scipy >=1.5.2
requirements/dev.txt pypi
  • doctest *
setup.py pypi
  • matplotlib >=3.3.2
  • numpy >=1.19.2
  • scipy >=1.5.2
.github/workflows/python-publish.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite