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.
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
import mahotas
import pylab
img = mahotas.imread('test.jpeg')
T_otsu = mahotas.thresholding.otsu(img)
seeds,_ = mahotas.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)
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)"
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 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.
For bug reports and fixes use the github issue tracker.
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.
- Add border & as_slice arguments to bbox()
- Better error message in gaussian_filter
- Allow as_rgb() to take integer arguments
- Extend distance() to n-dimensions
- Update to newer Numpy APIs (remove direct access to PyArray members)
- Fix requirements filename
- 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
- 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)
- Make matplotlib a soft dependency
- Add demos.image_path() function
- Add citation() function
This version is 1.0 beta.
- 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 mahotas.tests.run() anywhere)
- Add labeled.is_same_labeling function
- Post filter SLIC for smoother regions
- Fix compilation warnings on several platforms
- Add
haralick_featuresfunction - Add
outparameter 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
verboseargument to tests.run() - Add
circle_setomorph - Allow
loc(max|min)to take floating point inputs - Add Bernsen local thresholding (
bernsenandgbernsenfunctions)
See the ChangeLog for older version.
Website: http://luispedro.org/software/mahotas
API Docs: 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)
Author: Luis Pedro Coelho (with code by Zachary Pincus [from scikits.image], Peter J. Verveer [from scipy.ndimage], and Davis King [from dlib])