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Mahotas: Computer Vision Library

author Luis Pedro Coelho
author_email luis@luispedro.org
  • Development Status :: 5 - Production/Stable
  • Intended Audience :: Developers
  • Intended Audience :: Science/Research
  • Topic :: Scientific/Engineering :: Image Recognition
  • Topic :: Software Development :: Libraries
  • Programming Language :: Python
  • Programming Language :: Python :: 2
  • Programming Language :: Python :: 2.7
  • Programming Language :: Python :: 3
  • Programming Language :: Python :: 3.3
  • Programming Language :: Python :: 3.4
  • Programming Language :: Python :: 3.5
  • Programming Language :: Python :: 3.6
  • Programming Language :: Python :: 3.7
  • Programming Language :: C++
  • Operating System :: OS Independent
  • License :: OSI Approved :: MIT License
description_content_type text/markdown
license MIT
  • Any
  • numpy

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Python Computer Vision Library

Mahotas is a library of fast computer vision algorithms (all implemented in C++ for speed) operating over numpy arrays.

GH Actions Status Coverage Status License Downloads Install with Conda Install with Anaconda

Python versions 2.7, 3.4+, are supported.

Notable algorithms:

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.


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)

(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

As you can see, we rely on numpy/matplotlib for many operations.


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.


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 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.

Links & Contacts

Documentation: https://mahotas.readthedocs.io/

Issue Tracker: github mahotas issues

Mailing List: Use the pythonvision mailing list for questions, bug submissions, etc. Or ask on stackoverflow (tag mahotas)

Main Author & Maintainer: Luis Pedro Coelho (follow on twitter or github).

Mahotas also includes code by Zachary Pincus [from scikits.image], Peter J. Verveer [from scipy.ndimage], and Davis King [from dlib], Christoph Gohlke, as well as others.

Presentation about mahotas for bioimage informatics

For more general discussion of computer vision in Python, the pythonvision mailing list is a much better venue and generates a public discussion log for others in the future. You can use it for mahotas or general computer vision in Python questions.

Recent Changes

Version 1.4.13 (Jun 28 2022)

Version 1.4.12 (Oct 14 2021)

Version 1.4.11 (Aug 16 2020)

Version 1.4.10 (Jun 11 2020)

Version 1.4.9 (Nov 12 2019)

Version 1.4.8 (Oct 11 2019)

Version 1.4.7 (Jul 10 2019)

Version 1.4.6 (Jul 10 2019)

Version 1.4.5 (Oct 20 2018)

Version 1.4.4 (Nov 5 2017)

Version 1.4.3 (Oct 3 2016)

Version 1.4.2 (Oct 2 2016)

Version 1.4.1 (Dec 20 2015)

Version 1.4.0 (July 8 2015)

Version 1.3.0 (April 28 2015)

Version 1.2.4 (December 23 2014)

Version 1.2.3 (November 8 2014)

Version 1.2.2 (October 19 2014)

Version 1.2.1 (July 21 2014)

Version 1.2 (July 17 2014)

Version 1.1.1 (July 4 2014)

1.1.0 (February 12 2014)

See the ChangeLog for older version.


FOSSA Status