Note
If you are using mahotas in a scientific publication, please cite:
Coelho, L.P. 2013. Mahotas: Open source software for scriptable computer vision. Journal of Open Research Software 1(1):e3, DOI: http://dx.doi.org/10.5334/jors.ac
Mahotas is a computer vision and image processing library for Python.
It includes many algorithms implemented in C++ for speed while operating in numpy arrays and with a very clean Python interface.
Mahotas currently has over 100 functions for image processing and computer vision and it keeps growing. Some examples of mahotas functionality:
- watershed
- convex points calculations.
- hit & miss. thinning
- Zernike & Haralick, local binary patterns, and TAS features.
- morphological processing
- Speeded-Up Robust Features (SURF), a form of local features
- thresholding
- convolution.
- Sobel edge detection.
The release schedule is roughly one release every few months and each release brings new functionality and improved performance. The interface is very stable, though, and code written using a version of mahotas from years back will work just fine in the current version, except it will be faster (some interfaces are deprecated and will be removed after a few years, but in the meanwhile, you only get a warning).
Bug reports with test cases typically get fixed in 24 hours.
.. seealso::
`mahotas-imread <http://imread.readthedocs.io/en/latest/>`__ is side
project which includes code to read/write images to files
This is a simple example of loading a file (called test.jpeg) and calling watershed using above threshold regions as a seed (we use Otsu to define threshold).
import numpy as np
import mahotas
import pylab
img = mahotas.imread('test.jpeg')
T_otsu = mahotas.thresholding.otsu(img)
seeds,_ = mahotas.label(img > T_otsu)
labeled = mahotas.cwatershed(img.max() - img, seeds)
pylab.imshow(labeled)
Computing a distance transform is easy too:
.. plot::
:include-source:
import pylab as p
import numpy as np
import mahotas
f = np.ones((256,256), bool)
f[200:,240:] = False
f[128:144,32:48] = False
# f is basically True with the exception of two islands: one in the lower-right
# corner, another, middle-left
dmap = mahotas.distance(f)
p.imshow(dmap)
p.show()
Jump to detailed API Documentation
.. toctree:: :maxdepth: 2 install wally labeled thresholding wavelets distance polygon features lbp surf surfref morphology color io classification edf mahotas-features faq internals principles contributing tasks history api