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https://github.com/fluiddyn/fluidimage

:sparkler: A libre Python framework for scientific treatments of large series of images (publish-only mirror)
https://github.com/fluiddyn/fluidimage

image-processing particle-image-velocimetry piv postprocessing

Last synced: about 1 month ago
JSON representation

:sparkler: A libre Python framework for scientific treatments of large series of images (publish-only mirror)

Host: GitHub
URL: https://github.com/fluiddyn/fluidimage
Owner: fluiddyn
License: other
Created: 2017-07-04T15:41:41.000Z (over 7 years ago)
Default Branch: branch/default
Last Pushed: 2024-05-22T07:18:53.000Z (8 months ago)
Last Synced: 2024-05-22T08:35:02.965Z (8 months ago)
Topics: image-processing, particle-image-velocimetry, piv, postprocessing
Language: Python
Homepage: https://foss.heptapod.net/fluiddyn/fluidimage
Size: 25.6 MB
Stars: 12
Watchers: 5
Forks: 4
Open Issues: 0
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
- Authors: AUTHORS.md

Awesome Lists containing this project

README

        # FluidImage

[![Latest version](https://img.shields.io/pypi/v/fluidimage.svg)](https://pypi.python.org/pypi/fluidimage/)

![Supported Python versions](https://img.shields.io/pypi/pyversions/fluidimage.svg)

[![Documentation status](https://readthedocs.org/projects/fluidimage/badge/?version=latest)](http://fluidimage.readthedocs.org)

[![Code coverage](https://codecov.io/gh/fluiddyn/fluidimage/branch/branch%2Fdefault/graph/badge.svg)](https://codecov.io/gh/fluiddyn/fluidimage/branch/branch%2Fdefault/)

[![Heptapod CI](https://foss.heptapod.net/fluiddyn/fluidimage/badges/branch/default/pipeline.svg)](https://foss.heptapod.net/fluiddyn/fluidimage/-/pipelines)

[![Github Actions Linux](https://github.com/fluiddyn/fluidimage/actions/workflows/ci-linux.yml/badge.svg?branch=branch/default)](https://github.com/fluiddyn/fluidimage/actions/workflows/ci-linux.yml)

[![Github Actions Pixi](https://github.com/fluiddyn/fluidimage/actions/workflows/ci-pixi.yml/badge.svg?branch=branch/default)](https://github.com/fluiddyn/fluidimage/actions/workflows/ci-pixi.yml)

FluidImage is a free and open-source Python framework to process images of fluids (in

particular with [PIV]), and analyse the resulting fields.

**Documentation:** 

Image processing for fluid mechanics is still dominated by proprietary tools. Such tools

are not ideal when you want to understand and tweak the algorithms and/or to use

clusters. There are also good and useful PIV software ([PIVlab], [UVmat]) written in

Matlab, which is itself proprietary.

With the improvement of the Python numerical ecosystem and of tools for collaborative

development, one can think it is possible to build together a good community-driven

library/toolkit specialized in image processing for fluid mechanics. This is our project

with FluidImage.

Fluidimage has now grown into a clean software reimplementing in modern Python algorithms

and ideas taken from [UVmat], [OpenPIV], [PIVlab] and [PIVmat] with a focus on

performance, usability and maintanability. However, Fluidimage is not restricted to

Particle Image Velocimetry computations ([PIV], i.e. displacements of pattern obtained by

correlations of cropped images) and can be used to

- display and pre-process images,

- compute displacement or velocity fields with PIV, Background-Oriented Schlieren

  ([BOS](https://en.wikipedia.org/wiki/Background-oriented_schlieren_technique)) and

  [optical flow](https://en.wikipedia.org/wiki/Optical_flow),

- analyze and display vector and scalar fields.

We want to make FluidImage easy (useful documentation, easy installation, nice API,

usable with simple scripts and few simple graphical user interfaces), reliable (with good

[unittests](https://codecov.io/gh/fluiddyn/fluidimage/)) and very efficient, in

particular when the number of images is large. Thus we want FluidImage to be able to run

efficiently and easily on a personal computer and on big clusters. The efficiency is

achieved by using

- a framework for asynchronous computations (currently, we use

  [Trio](https://trio.readthedocs.io)) and an associated API to define "topologies" of

  parallel computations.

- parallelism to efficiently use the available cores of the Central Processing Units

  (CPU),

- good profiling and efficient and specialized algorithms,

- cutting-edge tools for fast computations with Python (in particular

  [Pythran](https://pythran.readthedocs.io) through

  [Transonic](https://transonic.readthedocs.io)).

[openpiv]: http://www.openpiv.net/

[piv]: https://en.wikipedia.org/wiki/Particle_image_velocimetry

[pivlab]: https://pivlab.blogspot.com/p/what-is-pivlab.html

[pivmat]: http://www.fast.u-psud.fr/pivmat/

[uvmat]: http://servforge.legi.grenoble-inp.fr/projects/soft-uvmat/wiki/UvmatHelp