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Project Description
# Mahotas

## Python Computer Vision Library

[![Travis](https://api.travis-ci.org/luispedro/mahotas.png)](https://travis-ci.org/luispedro/mahotas)
[![Downloads](https://pypip.in/d/mahotas/badge.png)](https://pypi.python.org/pypi/mahotas)
[![License](https://pypip.in/license/mahotas/badge.png)](http://opensource.org/licenses/MIT)
[![Install with Anaconda](https://anaconda.org/conda-forge/mahotas/badges/installer/conda.svg)](https://conda.anaconda.org/conda-forge)
[![Join the chat at https://gitter.im/luispedro/mahotas](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/luispedro/mahotas)

Mahotas is a library of fast computer vision algorithms (all implemented
in C++) operating over numpy arrays.

Python versions 2.7, 3.3, 3.4, and 3.5 are supported.

Notable algorithms:

- [watershed](http://mahotas.readthedocs.io/en/latest/distance.html)
- [convex points calculations](http://mahotas.readthedocs.io/en/latest/polygon.html)
- hit & miss, thinning.
- Zernike & Haralick, LBP, and TAS features.
- [Speeded-Up Robust Features
(SURF)](http://mahotas.readthedocs.io/en/latest/surf.html) a form of local
features.
- [thresholding](http://mahotas.readthedocs.io/en/latest/thresholding.html)
- convolution.
- Sobel edge detection.
- spline interpolation
- SLIC super pixels.

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](http://dx.doi.org/10.5334/jors.ac) (see
details below under [Citation](#Citation)) if you use it in a publication.

## Examples

This is a simple example (using an example file that is shipped with
mahotas) of 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](https://github.com/luispedro/mahotas/blob/master/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

If you are using [conda](http://anaconda.org/) you can install mahotas from
[conda-forge](https://conda-forge.github.io/) using the following commands:

conda config --add channels conda-forge
conda install mahotas

### Compilation from source

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

pip install mahotas

You can test your installation by running:

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

If you run into issues, the manual has more [extensive documentation on
mahotas
installation](https://mahotas.readthedocs.io/en/latest/install.html)
including how to find pre-built for several platforms.

## 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](http://dx.doi.org/10.5334/jors.ac)]

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](https://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*:
[https://mahotas.readthedocs.io/](https://mahotas.readthedocs.io/)

*Issue Tracker*: [github mahotas
issues](https://github.com/luispedro/mahotas/issues)

*Mailing List*: Use the [pythonvision mailing
list](http://groups.google.com/group/pythonvision?pli=1) for questions,
bug submissions, etc. Or ask on [stackoverflow (tag
mahotas)](http://stackoverflow.com/questions/tagged/mahotas)

*Main Author & Maintainer*: [Luis Pedro Coelho](http://luispedro.org)
(follow on [twitter](https://twitter.com/luispedrocoelho) or
[github](https://github.com/luispedro))

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](https://github.com/luispedro/mahotas/graphs/contributors)

[Presentation about mahotas for bioimage
informatics](http://luispedro.org/files/talks/2013/EuBIAS/mahotas.html)

For more general discussion of computer vision in Python, the
[pythonvision mailing
list](http://groups.google.com/group/pythonvision?pli=1) 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 1.4.3 (Oct 3 2016)
- Fix distribution (add missing `README.md` file)

### Version 1.4.2 (Oct 2 2016)
- Fix `resize\_to` return exactly the requested size
- Fix hard crash when computing texture on arrays with negative values
(issue #72)
- Added `distance` argument to haralick features (pull request #76, by
Guillaume Lemaitre)

### Version 1.4.1 (Dec 20 2015)

- Add `filter\_labeled` function
- Fix tests on 32 bit platforms and older versions of numpy

### Version 1.4.0 (July 8 2015)

- Added `mahotas-features.py` script
- Add short argument to citation() function
- Add max\_iter argument to thin() function
- Fixed labeled.bbox when there is no background (issue \#61, reported
by Daniel Haehn)
- bbox now allows dimensions greater than 2 (including when using the
`as_slice` and `border` arguments)
- Extended croptobbox for dimensions greater than 2
- Added use\_x\_minus\_y\_variance option to haralick features
- Add function `lbp_names`

### Version 1.3.0 (April 28 2015)

- Improve memory handling in freeimage.write\_multipage
- Fix moments parameter swap
- Add labeled.bbox function
- Add return\_mean and return\_mean\_ptp arguments to haralick
function
- Add difference of Gaussians filter (by Jianyu Wang)
- Add Laplacian filter (by Jianyu Wang)
- Fix crash in median\_filter when mismatched arguments are passed
- Fix gaussian\_filter1d for ndim \> 2

### Version 1.2.4 (December 23 2014)

- Add PIL based IO

### Version 1.2.3 (November 8 2014)

- Export mean\_filter at top level
- Fix to Zernike moments computation (reported by Sergey Demurin)
- Fix compilation in platforms without npy\_float128 (patch by Gabi
Davar)

### 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](https://github.com/luispedro/mahotas/blob/master/ChangeLog)
for older version.
Release History

Release History

1.4.3

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0.9

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Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.6.4

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.6.3

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.6.2

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.6.1

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.6

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.5.3

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.5.2

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.5.1

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.5

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.4

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.3

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.2

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.1

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.0.1

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
mahotas-1.4.3.tar.gz (1.5 MB) Copy SHA256 Checksum SHA256 Source Oct 3, 2016

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