Mahotas
Python Computer Vision Library
Mahotas is a library of fast computer vision algorithms (all implemented in C++ for speed) operating over numpy arrays.
Python versions 2.7, 3.4+, are supported.
Notable algorithms:
- watershed
- convex points calculations.
- hit & miss, thinning.
- Zernike & Haralick, LBP, and TAS features.
- Speeded-Up Robust Features
(SURF), a form of local
features. - thresholding.
- convolution.
- Sobel edge detection.
- spline interpolation
- SLIC super pixels.
Mahotas currently has over 100 functions for image processing and
computer vision and it keeps growing.
The release schedule is roughly one release a month 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). In a few
unfortunate cases, there was a bug in the old code and your results will
change for the better.
Please cite the mahotas paper (see
details below under Citation) if you use it in a publication.
Examples
This is a simple example (using an example file that is shipped with
mahotas) of calling watershed using above threshold regions as a seed
(we use Otsu to define threshold).
# import using ``mh`` abbreviation which is common:
import mahotas as mh
# Load one of the demo images
im = mh.demos.load('nuclear')
# Automatically compute a threshold
T_otsu = mh.thresholding.otsu(im)
# Label the thresholded image (thresholding is done with numpy operations
seeds,nr_regions = mh.label(im > T_otsu)
# Call seeded watershed to expand the threshold
labeled = mh.cwatershed(im.max() - im, seeds)
Here is a very simple example of using mahotas.distance (which
computes a distance map):
import pylab as p
import numpy as np
import mahotas as mh
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 = mh.distance(f)
p.imshow(dmap)
p.show()
(This is under mahotas/demos/distance.py.)
How to invoke thresholding functions:
import mahotas as mh
import numpy as np
from pylab import imshow, gray, show, subplot
from os import path
# Load photo of mahotas' author in greyscale
photo = mh.demos.load('luispedro', as_grey=True)
# Convert to integer values (using numpy operations)
photo = photo.astype(np.uint8)
# Compute Otsu threshold
T_otsu = mh.otsu(photo)
thresholded_otsu = (photo > T_otsu)
# Compute Riddler-Calvard threshold
T_rc = mh.rc(photo)
thresholded_rc = (photo > T_rc)
# Now call pylab functions to display the image
gray()
subplot(2,1,1)
imshow(thresholded_otsu)
subplot(2,1,2)
imshow(thresholded_rc)
show()
As you can see, we rely on numpy/matplotlib for many operations.
Install
If you are using conda, you can install mahotas from
conda-forge using the following commands:
conda config --add channels conda-forge
conda install mahotas
Compilation from source
You will need python (naturally), numpy, and a C++ compiler. Then you
should be able to use:
pip install mahotas
You can test your installation by running:
python -c "import mahotas as mh; mh.test()"
If you run into issues, the manual has more extensive documentation on
mahotas
installation,
including how to find pre-built for several platforms.
Citation
If you use mahotas on a published publication, please cite:
Luis Pedro Coelho Mahotas: Open source software for scriptable
computer vision in Journal of Open Research Software, vol 1, 2013.
[DOI]
In Bibtex format:
@article{mahotas,
author = {Luis Pedro Coelho},
title = {Mahotas: Open source software for scriptable computer vision},
journal = {Journal of Open Research Software},
year = {2013},
doi = {http://dx.doi.org/10.5334/jors.ac},
month = {July},
volume = {1}
}
You can access this information using the mahotas.citation() function.
Development
Development happens on github
(http://github.com/luispedro/mahotas).
You can set the DEBUG environment variable before compilation to get a
debug version:
export DEBUG=1
python setup.py test
You can set it to the value 2 to get extra checks:
export DEBUG=2
python setup.py test
Be careful not to use this in production unless you are chasing a bug.
Debug level 2 is very slow as it adds many runtime checks.
The Makefile that is shipped with the source of mahotas can be useful
too. make debug will create a debug build. make fast will create a
non-debug build (you need to make clean in between). make test will
run the test suite.
