# Overview FluidImage is a libre Python framework for scientific processing of large series of images. **Documentation:** 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/)).