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Computer Vision in Python

README.rst

Mahotas

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 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 you compiled from source, you need to do this in another directory (or compile locally, which can be accomplished with python setup.py build --build-lib=.).

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

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

Visual Studio

For compiling from source in Visual Studio, use:

python setup.py build_ext -c msvc
python setup.py install

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 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.

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.

Travis Build Status

https://travis-ci.org/luispedro/mahotas.png

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

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

1.0.4 (2013-12-15)

  • Add mahotas.demos.load()
  • Add stretch_rgb() function
  • Add demos to mahotas namespace
  • Fix SLIC superpixels

1.0.3 (2013-10-06)

  • 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)

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)

See the ChangeLog for older version.

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