| Name | fluidimage JSON |
| Version |
0.5.0
JSON |
| download |
| home_page | None |
| Summary | Fluid image processing with Python. |
| upload_time | 2024-05-02 10:48:05 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.13,>=3.9 |
| license | CeCILL |
| keywords |
piv
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
FluidImage
==========
[](https://pypi.python.org/pypi/fluidimage/)
[](http://fluidimage.readthedocs.org)
[](https://codecov.io/gh/fluiddyn/fluidimage/branch/branch%2Fdefault/)
[](https://foss.heptapod.net/fluiddyn/fluidimage/-/pipelines)
[](https://github.com/fluiddyn/fluidimage/actions/workflows/ci-linux.yml)
[](https://github.com/fluiddyn/fluidimage/actions/workflows/ci-pixi.yml)
FluidImage is a libre Python framework for scientific processing of
large series of images.
**Documentation:** <http://fluidimage.readthedocs.org>
Image processing for fluid mechanics is highly dominated by proprietary
tools. Such tools are not ideal when you want to understand and tweak
the processes and/or to use clusters. With the improvement of the
open-source tools for scientific computing and collaborative
development, one can think it is possible to build together a good
library/toolkit specialized in image processing for fluid mechanics.
This is our project with FluidImage.
This package is young but already good enough to be used "in
production" to
- display and pre-process images,
- compute displacement or velocity fields with [Particle Image
Velocimetry](https://en.wikipedia.org/wiki/Particle_image_velocimetry%20(PIV))
(PIV, i.e. displacements of pattern obtained by correlations of
cropped images) and [optical
flow](https://en.wikipedia.org/wiki/Optical_flow),
- analyze and display PIV fields.
We want to make FluidImage easy (useful documentation, easy
installation, usable with scripts and GUI in Qt), reliable (with good
[unittests](https://codecov.io/gh/fluiddyn/fluidimage/)) and very
efficient, in particular when the number of images to process becomes
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) + multiprocessing, and in the
long term we want to be able to plug FluidImage to distributed
computational systems like [Dask](http://dask.pydata.org),
[Spark](https://spark.apache.org/) or
[Storm](http://storm.apache.org/)),
- the available cores of the central processing units (CPU) and the
available graphics processing units (GPU),
- good profiling and efficient and specialized algorithms,
- cutting-edge tools for fast computations with Python (in particular
[Pythran](https://pythonhosted.org/pythran/)).
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