mahotas 1.4.5

Mahotas: Computer Vision Library

# Mahotas

## Python Computer Vision Library

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

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Python versions 2.7, 3.4+, 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 1.4.5 (Oct 20 2018) - Upgrade code to newer NumPy API (issue #95)

### Version 1.4.4 (Nov 5 2017) - Fix bug in Bernsen thresholding (issue #84)

### 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.

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