mahotas 1.0.2

Mahotas: Computer Vision Library

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.

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 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)
p.imshow(dmap)
p.show()

(This is under mahotas/demos/distance.py).

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('luispedro.org', 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)

Install

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

Download the source and then run:

python setup.py 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

Development happens on github (http://github.com/luispedro/mahotas).

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 setup.py 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

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, 2013 (in press).

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},
    note = {in press},
    volume = {1}
}

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

Contacts

For bug reports and fixes, feel free to use my email: luis@luispedro.org

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.

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