Metadata-Version: 2.1
Name: fluidimage
Version: 0.1.3.post0
Summary: fluid image processing with Python.
Home-page: https://foss.heptapod.net/fluiddyn/fluidimage
Author: Pierre Augier
Author-email: pierre.augier@legi.cnrs.fr
License: CeCILL
Keywords: PIV
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.6
License-File: LICENSE.txt
License-File: AUTHORS.rst
Requires-Dist: fluiddyn (>=0.3.4)
Requires-Dist: transonic (>=0.2.2)
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.. |heptapod_ci| image:: https://foss.heptapod.net/fluiddyn/fluidimage/badges/branch/default/pipeline.svg
   :target: https://foss.heptapod.net/fluiddyn/fluidimage/-/pipelines
   :alt: Heptapod CI

.. |travis| image:: https://travis-ci.org/fluiddyn/fluidimage.svg?branch=branch%2Fdefault
    :target: https://travis-ci.org/fluiddyn/fluidimage

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 (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/>`_ and `Theano
  <http://deeplearning.net/software/theano>`_).


