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 = {https://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
(https://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 1.4.18 (Jul 18 2024)

• Fix bug in Haralick features and NumPy 2 (thanks to @Czaki, see #150)

Version 1.4.17 (Jul 13 2024)

• Fix bug that stopped mahotas from working on Windows

Version 1.4.16 (Jul 3 2024)

• update for NumPy 2
• Add deprecated warning for freeimage

Version 1.4.15 (Mar 24 2024)

• Update build system (thanks to @Czaki, see #147)

Version 1.4.14 (Mar 24 2024)

• Fix code for C++17 (issue #146)

Version 1.4.13 (Jun 28 2022)

• Fix freeimage testing (and make freeimage loading more robust, see #129)
• Add GIL fixed (which triggered crashes in newer NumPy versions)

Version 1.4.12 (Oct 14 2021)

• Update to newer NumPy
• Build wheels for Python 3.9 & 3.10

Version 1.4.11 (Aug 16 2020)

• Convert tests to pytest
• Fix testing for PyPy

Version 1.4.10 (Jun 11 2020)

• Build wheels automatically (PR #114 by nathanhillyer)

Version 1.4.9 (Nov 12 2019)

• Fix FreeImage detection (issue #108)

Version 1.4.8 (Oct 11 2019)

• Fix co-occurrence matrix computation (patch by @databaaz)

Version 1.4.7 (Jul 10 2019)

• Fix compilation on Windows

Version 1.4.6 (Jul 10 2019)

• Make watershed work for >2³¹ voxels (issue #102)
• Remove milk from demos
• Improve performance by avoid unnecessary array copies in cwatershed(),
  majority_filter(), and color conversions
• Fix bug in interpolation

Version 1.4.5 (Oct 20 2018)

• Upgrade code to newer NumPy API (issue #95)

Version 1.4.4 (Nov 5 2017)

• Fix bug in Bernsen thresholding (issue #84)

Version 1.4.3 (Oct 3 2016)

• Fix distribution (add missing README.md file)

Version 1.4.2 (Oct 2 2016)

• Fix resize\_to return exactly the requested size
• Fix hard crash when computing texture on arrays with negative values (issue #72)
• Added distance argument to haralick features (pull request #76, by
  Guillaume Lemaitre)

Version 1.4.1 (Dec 20 2015)

• Add filter\_labeled function
• Fix tests on 32 bit platforms and older versions of numpy

Version 1.4.0 (July 8 2015)

• Added mahotas-features.py script
• Add short argument to citation() function
• Add max_iter argument to thin() function
• Fixed labeled.bbox when there is no background (issue #61, reported
  by Daniel Haehn)
• bbox now allows dimensions greater than 2 (including when using the
  as_slice and border arguments)
• Extended croptobbox for dimensions greater than 2
• Added use_x_minus_y_variance option to haralick features
• Add function lbp_names

Version 1.3.0 (April 28 2015)

• Improve memory handling in freeimage.write_multipage
• Fix moments parameter swap
• Add labeled.bbox function
• Add return_mean and return_mean_ptp arguments to haralick
  function
• Add difference of Gaussians filter (by Jianyu Wang)
• Add Laplacian filter (by Jianyu Wang)
• Fix crash in median_filter when mismatched arguments are passed
• Fix gaussian_filter1d for ndim > 2

Version 1.2.4 (December 23 2014)

• Add PIL based IO

Version 1.2.3 (November 8 2014)

• Export mean_filter at top level
• Fix to Zernike moments computation (reported by Sergey Demurin)
• Fix compilation in platforms without npy_float128 (patch by Gabi
  Davar)

Version 1.2.2 (October 19 2014)

• Add minlength argument to labeled_sum
• Generalize regmax/regmin to work with floating point images
• Allow floating point inputs to cwatershed()
• Correctly check for float16 & float128 inputs
• Make sobel into a pure function (i.e., do not normalize its input)
• Fix sobel filtering

Version 1.2.1 (July 21 2014)

• Explicitly set numpy.include_dirs() in setup.py [patch by Andrew
  Stromnov]

Version 1.2 (July 17 2014)

• Export locmax|locmin at the mahotas namespace level
• Break away ellipse_axes from eccentricity code as it can be useful
  on its own
• Add find() function
• Add mean_filter() function
• Fix cwatershed() overflow possibility
• Make labeled functions more flexible in accepting more types
• Fix crash in close_holes() with nD images (for n > 2)
• Remove matplotlibwrap
• Use standard setuptools for building (instead of numpy.distutils)
• Add overlay() function

Version 1.1.1 (July 4 2014)

• Fix crash in close_holes() with nD images (for n > 2)

1.1.0 (February 12 2014)

• Better error checking
• Fix interpolation of integer images using order 1
• Add resize_to & resize_rgb_to
• Add coveralls coverage
• Fix SLIC superpixels connectivity
• Add remove_regions_where function
• Fix hard crash in convolution
• Fix axis handling in convolve1d
• Add normalization to moments calculation

See the
ChangeLog
for older version.

License