mahotas 1.2.2

Mahotas: Computer Vision Library

Python Computer Vision Library

Mahotas is a 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.

Examples

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 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

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

pip install mahotas

You can test your instalation by running:

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

If you run into issues, the manual has more extensive documentation on mahotas intallation

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.

Links & Contacts

Documentation: http://mahotas.readthedocs.org/

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 1.2.2 (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.