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https://github.com/luispedro/mahotas

Computer Vision in Python
https://github.com/luispedro/mahotas

c-plus-plus computer-vision numpy python python-2 python-3

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Computer Vision in Python

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README 

        
# Mahotas



## Python Computer Vision Library



Mahotas is a library of fast computer vision algorithms (all implemented

in C++ for speed) operating over numpy arrays.



![GH Actions Status](https://github.com/luispedro/mahotas/workflows/Python%20Package%20using%20Conda/badge.svg)

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[![Downloads](https://pepy.tech/badge/mahotas/month)](https://pepy.tech/project/mahotas/month)

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Python versions 2.7, 3.4+, are supported.



Notable algorithms:



- [watershed](https://mahotas.readthedocs.io/en/latest/distance.html)

- [convex points calculations](https://mahotas.readthedocs.io/en/latest/polygon.html).

- hit & miss, thinning.

- Zernike & Haralick, LBP, and TAS features.

- [Speeded-Up Robust Features

  (SURF)](https://mahotas.readthedocs.io/en/latest/surf.html), a form of local

  features.

- [thresholding](https://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](https://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).



```python

# 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):



```python

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:



```python

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](https://anaconda.org/), you can install mahotas from

[conda-forge](https://conda-forge.github.io/) using the following commands:



```bash

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:



```bash

pip install mahotas

```



You can test your installation by running:



```bash

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](https://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 = {https://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

([https://github.com/luispedro/mahotas](https://github.com/luispedro/mahotas)).



You can set the `DEBUG` environment variable before compilation to get a

debug version:



```bash

export DEBUG=1

python setup.py test

```



You can set it to the value `2` to get extra checks:



```bash

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](https://groups.google.com/group/pythonvision?pli=1) for questions,

bug submissions, etc. Or ask on [stackoverflow (tag

mahotas)](https://stackoverflow.com/questions/tagged/mahotas)



*Main Author & Maintainer*: [Luis Pedro Coelho](https://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](https://luispedro.org/files/talks/2013/EuBIAS/mahotas.html)



For more general discussion of computer vision in Python, the

[pythonvision mailing

list](https://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 1.4.15 (Mar 24 2024)



- Update build system (thanks to @Czaki, see #147)



### Version 1.4.14 (Mar 24 2024)



- Fix code for C++17 (issue #146)



### Version 1.4.13 (Jun 28 2022)



- Fix freeimage testing (and make freeimage loading more robust, see #129)

- Add GIL fixed (which triggered crashes in newer NumPy versions)



### Version 1.4.12 (Oct 14 2021)



- Update to newer NumPy

- Build wheels for Python 3.9 & 3.10



### Version 1.4.11 (Aug 16 2020)



- Convert tests to pytest

- Fix testing for PyPy



### Version 1.4.10 (Jun 11 2020)



- Build wheels automatically (PR #114 by [nathanhillyer](https://github.com/nathanhillyer))



### Version 1.4.9 (Nov 12 2019)



- Fix FreeImage detection (issue #108)



### Version 1.4.8 (Oct 11 2019)



- Fix co-occurrence matrix computation (patch by @databaaz)



### Version 1.4.7 (Jul 10 2019)



- Fix compilation on Windows



### Version 1.4.6 (Jul 10 2019)



- Make watershed work for >2³¹ voxels (issue #102)

- Remove milk from demos

- Improve performance by avoid unnecessary array copies in `cwatershed()`,

  `majority_filter()`, and color conversions

- Fix bug in interpolation



### 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.



## License

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