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mahotas 1.0.2

Mahotas: Computer Vision Library

Package Documentation

Latest Version: 1.4.4

Python Computer Vision Library

This library of fast computer vision algorithms (all implemented in C++) operates over numpy arrays for convenience.

Notable algorithms:
  • watershed.
  • convex points calculations.
  • hit & miss. thinning.
  • Zernike & Haralick, LBP, and TAS features.
  • freeimage based numpy image loading (requires freeimage libraries to be installed).
  • Speeded-Up Robust Features (SURF), a form of local features.
  • thresholding.
  • convolution.
  • Sobel edge detection.
  • spline interpolation

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 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
from scipy import ndimage
import mahotas
import pylab

img = mahotas.imread('test.jpeg')
T_otsu = mahotas.thresholding.otsu(img)
seeds,_ = ndimage.label(img > T_otsu)
labeled = mahotas.cwatershed(img.max() - img, 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

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)

(This is under mahotas/demos/

How to invoke thresholding functions:

import mahotas
import numpy as np
from pylab import imshow, gray, show, subplot
from os import path

photo = mahotas.imread('', as_grey=True)
photo = photo.astype(np.uint8)

T_otsu = mahotas.otsu(photo)
thresholded_otsu = (photo > T_otsu)

T_rc = mahotas.rc(photo)
thresholded_rc = (photo > T_rc)


You will need python (naturally), numpy, and a C++ compiler. Then you should be able to either

Download the source and then run:

python install

or use one of:

pip install mahotas
easy_install mahotas

You can test your instalation by running:

python -c "import mahotas; mahotas.test()"

If something fails, you can obtain more detail by running it again in verbose mode:

python -c "import mahotas; mahotas.test(verbose=True)"


Development happens on github (

You can set the DEBUG environment variable before compilation to get a debug compile. You can set it to the value 2 to get extra checks:

export DEBUG=2
python test

Be careful not to use this in production unless you are chasing a bug. The debug modes are pretty slow as they add many runtime checks.

Travis Build Status


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, 2013 (in press).

In Bibtex format:

    author = {Luis Pedro Coelho},
    title = {Mahotas: Open source software for scriptable computer vision},
    journal = {Journal of Open Research Software},
    year = {2013},
    note = {in press},
    volume = {1}

You can access this information using the mahotas.citation() function.


For bug reports and fixes, feel free to use my email:

For more general with achieving certain tasks 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

1.0.2 (July 10 2013)

  • Fix requirements filename

1.0.1 (July 9 2013)

  • Add lbp_transform() function
  • Add rgb2sepia function
  • Add mahotas.demos.nuclear_image() function
  • Work around matplotlib.imsave’s implementation of greyscale
  • Fix Haralick bug (report & patch by Tony S Yu)
  • Add count_binary1s() function

1.0 (May 21 2013)

  • Make matplotlib a soft dependency
  • Add demos.image_path() function
  • Add citation() function
  • Fix a few corner cases in texture analysis
  • Integrate with travis
  • Update citation (include DOI)

0.99 (May 4 2013)

  • Make matplotlib a soft dependency
  • Add demos.image_path() function
  • Add citation() function

This version is 1.0 beta.

0.9.8 (April 22 2013)

  • Use matplotlib as IO backend (fallback only)
  • Compute dense SURF features
  • Fix sobel edge filtering (post-processing)
  • Faster 1D convultions (including faster Gaussian filtering)
  • Location independent tests (run anywhere)
  • Add labeled.is_same_labeling function
  • Post filter SLIC for smoother regions
  • Fix compilation warnings on several platforms

0.9.7 (February 03 2013)

  • Add haralick_features function
  • Add out parameter to morph functions which were missing it
  • Fix erode() & dilate() with empty structuring elements
  • Special case binary erosion/dilation in C-Arrays
  • Fix long-standing warning in TAS on zero inputs
  • Add verbose argument to
  • Add circle_se to morph
  • Allow loc(max|min) to take floating point inputs
  • Add Bernsen local thresholding (bernsen and gbernsen functions)

See the ChangeLog for older version.


API Docs:

Mailing List: Use the pythonvision mailing list for questions, bug submissions, etc.

Author: Luis Pedro Coelho (with code by Zachary Pincus [from scikits.image], Peter J. Verveer [from scipy.ndimage], and Davis King [from dlib])

File Type Py Version Uploaded on Size
mahotas-1.0.2.tar.gz (md5) Source 2013-07-10 1MB