Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/rshkarin/quanfima

Quanfima (Quantitative Analysis of Fibrous Materials)
https://github.com/rshkarin/quanfima

data-analysis material-science morphological-analysis volumetric-data

Last synced: about 12 hours ago
JSON representation

Quanfima (Quantitative Analysis of Fibrous Materials)

Awesome Lists containing this project

README

        

.. image:: docs/source/_static/logo.png
:align: left

-----------

.. image:: https://travis-ci.org/rshkarin/quanfima.svg?branch=master
:target: https://travis-ci.org/rshkarin/quanfima

.. image:: https://readthedocs.org/projects/quanfima/badge/?version=latest
:target: http://quanfima.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status

.. image:: https://zenodo.org/badge/127795855.svg
:target: https://zenodo.org/badge/latestdoi/127795855

*Quanfima* (**qu**\ antitative **an**\ alysis of **fi**\ brous **ma**\ terials)
is a collection of useful functions for morphological analysis and visualization
of 2D/3D data from various areas of material science. The aim is to simplify
the analysis process by providing functionality for frequently required tasks
in the same place.

More examples of usage you can find in the documentation.

- Analysis of fibrous structures by tensor-based method in 2D / 3D datasets.
- Estimation of structure diameters in 2D / 3D by a ray-casting method.
- Counting of particles in 2D / 3D datasets and providing a detailed report in
pandas.DataFrame format.
- Calculation of porosity measure for each material in 2D / 3D datasets.
- Visualization in 2D / 3D using matplotlib, visvis packages.

Installation
------------

The easiest way to install the latest version is by using pip::

$ pip install quanfima

You may also use Git to clone the repository and install it manually::

$ git clone https://github.com/rshkarin/quanfima.git
$ cd quanfima
$ python setup.py install

Usage
-----
Open a grayscale image, perform segmentation, estimate porosity, analyze fiber
orientation and diameters, and plot the results.

.. code-block:: python

import numpy as np
from skimage import io, filters
from quanfima import morphology as mrph
from quanfima import visualization as vis
from quanfima import utils

img = io.imread('../data/polymer_slice.tif')

th_val = filters.threshold_otsu(img)
img_seg = (img > th_val).astype(np.uint8)

# estimate porosity
pr = mrph.calc_porosity(img_seg)
for k,v in pr.items():
print 'Porosity ({}): {}'.format(k, v)

# prepare data and analyze fibers
data, skeleton, skeleton_thick = utils.prepare_data(img_seg)
cskel, fskel, omap, dmap, ovals, dvals = \
mrph.estimate_fiber_properties(data, skeleton)

# plot results
vis.plot_orientation_map(omap, fskel, min_label=u'0°', max_label=u'180°',
figsize=(10,10),
name='2d_polymer',
output_dir='/path/to/output/dir')
vis.plot_diameter_map(dmap, cskel, figsize=(10,10), cmap='gist_rainbow',
name='2d_polymer',
output_dir='/path/to/output/dir')

.. code-block:: python

>> Porosity (Material 1): 0.845488888889

.. image:: docs/source/_static/2d_polymer_data.png
:align: center

.. image:: docs/source/_static/2d_polymer_orientation_map_600px.png
:align: center

.. image:: docs/source/_static/2d_polymer_diameter_map_600px.png
:align: center