# Mahotas
## Python Computer Vision Library
Mahotas is a library of fast computer vision algorithms (all implemented
in C++ for speed) operating over numpy arrays.
[](https://coveralls.io/github/luispedro/mahotas?branch=master)
[](https://opensource.org/licenses/MIT)
[](https://pepy.tech/project/mahotas/month)
[](https://anaconda.org/conda-forge/mahotas)
[](https://anaconda.org/conda-forge/mahotas)
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 1.4.18 (Jul 18 2024)
- Fix bug in Haralick features and NumPy 2 (thanks to @Czaki, see [#150](https://github.com/luispedro/mahotas/pull/150))
### Version 1.4.17 (Jul 13 2024)
- Fix bug that stopped mahotas from working on Windows
### Version 1.4.16 (Jul 3 2024)
- update for NumPy 2
- Add deprecated warning for freeimage
### 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
[](https://app.fossa.io/projects/git%2Bgithub.com%2Fluispedro%2Fmahotas?ref=badge_large)
Raw data
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"description": "# Mahotas\n\n## Python Computer Vision Library\n\nMahotas is a library of fast computer vision algorithms (all implemented\nin C++ for speed) operating over numpy arrays.\n\n\n[](https://coveralls.io/github/luispedro/mahotas?branch=master)\n[](https://opensource.org/licenses/MIT)\n[](https://pepy.tech/project/mahotas/month)\n[](https://anaconda.org/conda-forge/mahotas)\n[](https://anaconda.org/conda-forge/mahotas)\n\nPython versions 2.7, 3.4+, are supported.\n\nNotable algorithms:\n\n- [watershed](https://mahotas.readthedocs.io/en/latest/distance.html)\n- [convex points calculations](https://mahotas.readthedocs.io/en/latest/polygon.html).\n- hit & miss, thinning.\n- Zernike & Haralick, LBP, and TAS features.\n- [Speeded-Up Robust Features\n (SURF)](https://mahotas.readthedocs.io/en/latest/surf.html), a form of local\n features.\n- [thresholding](https://mahotas.readthedocs.io/en/latest/thresholding.html).\n- convolution.\n- Sobel edge detection.\n- spline interpolation\n- SLIC super pixels.\n\nMahotas currently has over 100 functions for image processing and\ncomputer vision and it keeps growing.\n\nThe release schedule is roughly one release a month and each release\nbrings new functionality and improved performance. The interface is very\nstable, though, and code written using a version of mahotas from years\nback will work just fine in the current version, except it will be\nfaster (some interfaces are deprecated and will be removed after a few\nyears, but in the meanwhile, you only get a warning). In a few\nunfortunate cases, there was a bug in the old code and your results will\nchange for the better.\n\nPlease cite [the mahotas paper](https://dx.doi.org/10.5334/jors.ac) (see\ndetails below under [Citation](#Citation)) if you use it in a publication.\n\n## Examples\n\nThis is a simple example (using an example file that is shipped with\nmahotas) of calling watershed using above threshold regions as a seed\n(we use Otsu to define threshold).\n\n```python\n# import using ``mh`` abbreviation which is common:\nimport mahotas as mh\n\n# Load one of the demo images\nim = mh.demos.load('nuclear')\n\n# Automatically compute a threshold\nT_otsu = mh.thresholding.otsu(im)\n\n# Label the thresholded image (thresholding is done with numpy operations\nseeds,nr_regions = mh.label(im > T_otsu)\n\n# Call seeded watershed to expand the threshold\nlabeled = mh.cwatershed(im.max() - im, seeds)\n```\n\nHere is a very simple example of using `mahotas.distance` (which\ncomputes a distance map):\n\n```python\nimport pylab as p\nimport numpy as np\nimport mahotas as mh\n\nf = np.ones((256,256), bool)\nf[200:,240:] = False\nf[128:144,32:48] = False\n# f is basically True with the exception of two islands: one in the lower-right\n# corner, another, middle-left\n\ndmap = mh.distance(f)\np.imshow(dmap)\np.show()\n```\n\n(This is under [mahotas/demos/distance.py](https://github.com/luispedro/mahotas/blob/master/mahotas/demos/distance.py).)\n\nHow to invoke thresholding functions:\n\n```python\nimport mahotas as mh\nimport numpy as np\nfrom pylab import imshow, gray, show, subplot\nfrom os import path\n\n# Load photo of mahotas' author in greyscale\nphoto = mh.demos.load('luispedro', as_grey=True)\n\n# Convert to integer values (using numpy operations)\nphoto = photo.astype(np.uint8)\n\n# Compute Otsu threshold\nT_otsu = mh.otsu(photo)\nthresholded_otsu = (photo > T_otsu)\n\n# Compute Riddler-Calvard threshold\nT_rc = mh.rc(photo)\nthresholded_rc = (photo > T_rc)\n\n# Now call pylab functions to display the image\ngray()\nsubplot(2,1,1)\nimshow(thresholded_otsu)\nsubplot(2,1,2)\nimshow(thresholded_rc)\nshow()\n```\n\nAs you can see, we rely on numpy/matplotlib for many operations.\n\n## Install\n\nIf you are using [conda](https://anaconda.org/), you can install mahotas from\n[conda-forge](https://conda-forge.github.io/) using the following commands:\n\n```bash\nconda config --add channels conda-forge\nconda install mahotas\n```\n\n### Compilation from source\n\nYou will need python (naturally), numpy, and a C++ compiler. Then you\nshould be able to use:\n\n```bash\npip install mahotas\n```\n\nYou can test your installation by running:\n\n```bash\npython -c \"import mahotas as mh; mh.test()\"\n```\n\nIf you run into issues, the manual has more [extensive documentation on\nmahotas\ninstallation](https://mahotas.readthedocs.io/en/latest/install.html),\nincluding how to find pre-built for several platforms.\n\n## Citation\n\nIf you use mahotas on a published publication, please cite:\n\n> **Luis Pedro Coelho** Mahotas: Open source software for scriptable\n> computer vision in Journal of Open Research Software, vol 1, 2013.\n> [[DOI](https://dx.doi.org/10.5334/jors.ac)]\n\nIn Bibtex format:\n\n> @article{mahotas,\n> author = {Luis Pedro Coelho},\n> title = {Mahotas: Open source software for scriptable computer vision},\n> journal = {Journal of Open Research Software},\n> year = {2013},\n> doi = {https://dx.doi.org/10.5334/jors.ac},\n> month = {July},\n> volume = {1}\n> }\n\nYou can access this information using the `mahotas.citation()` function.\n\n## Development\n\nDevelopment happens on github\n([https://github.com/luispedro/mahotas](https://github.com/luispedro/mahotas)).\n\nYou can set the `DEBUG` environment variable before compilation to get a\ndebug version:\n\n```bash\nexport DEBUG=1\npython setup.py test\n```\n\nYou can set it to the value `2` to get extra checks:\n\n```bash\nexport DEBUG=2\npython setup.py test\n```\n\nBe careful not to use this in production unless you are chasing a bug.\nDebug level 2 is very slow as it adds many runtime checks.\n\nThe `Makefile` that is shipped with the source of mahotas can be useful\ntoo. `make debug` will create a debug build. `make fast` will create a\nnon-debug build (you need to `make clean` in between). `make test` will\nrun the test suite.\n\n## Links & Contacts\n\n*Documentation*:\n[https://mahotas.readthedocs.io/](https://mahotas.readthedocs.io/)\n\n*Issue Tracker*: [github mahotas\nissues](https://github.com/luispedro/mahotas/issues)\n\n*Mailing List*: Use the [pythonvision mailing\nlist](https://groups.google.com/group/pythonvision?pli=1) for questions,\nbug submissions, etc. Or ask on [stackoverflow (tag\nmahotas)](https://stackoverflow.com/questions/tagged/mahotas)\n\n*Main Author & Maintainer*: [Luis Pedro Coelho](https://luispedro.org)\n(follow on [twitter](https://twitter.com/luispedrocoelho) or\n[github](https://github.com/luispedro)).\n\nMahotas also includes code by Zachary Pincus [from scikits.image], Peter\nJ. Verveer [from scipy.ndimage], and Davis King [from dlib], Christoph\nGohlke, as well as\n[others](https://github.com/luispedro/mahotas/graphs/contributors).\n\n[Presentation about mahotas for bioimage\ninformatics](https://luispedro.org/files/talks/2013/EuBIAS/mahotas.html)\n\nFor more general discussion of computer vision in Python, the\n[pythonvision mailing\nlist](https://groups.google.com/group/pythonvision?pli=1) is a much\nbetter venue and generates a public discussion log for others in the\nfuture. You can use it for mahotas or general computer vision in Python\nquestions.\n\n## Recent Changes\n\n### Version 1.4.18 (Jul 18 2024)\n\n- Fix bug in Haralick features and NumPy 2 (thanks to @Czaki, see [#150](https://github.com/luispedro/mahotas/pull/150))\n\n### Version 1.4.17 (Jul 13 2024)\n\n- Fix bug that stopped mahotas from working on Windows\n\n### Version 1.4.16 (Jul 3 2024)\n\n- update for NumPy 2\n- Add deprecated warning for freeimage\n\n\n### Version 1.4.15 (Mar 24 2024)\n\n- Update build system (thanks to @Czaki, see #147)\n\n### Version 1.4.14 (Mar 24 2024)\n\n- Fix code for C++17 (issue #146)\n\n\n### Version 1.4.13 (Jun 28 2022)\n\n- Fix freeimage testing (and make freeimage loading more robust, see #129)\n- Add GIL fixed (which triggered crashes in newer NumPy versions)\n\n### Version 1.4.12 (Oct 14 2021)\n\n- Update to newer NumPy\n- Build wheels for Python 3.9 & 3.10\n\n### Version 1.4.11 (Aug 16 2020)\n\n- Convert tests to pytest\n- Fix testing for PyPy\n\n### Version 1.4.10 (Jun 11 2020)\n\n- Build wheels automatically (PR #114 by [nathanhillyer](https://github.com/nathanhillyer))\n\n### Version 1.4.9 (Nov 12 2019)\n\n- Fix FreeImage detection (issue #108)\n\n### Version 1.4.8 (Oct 11 2019)\n\n- Fix co-occurrence matrix computation (patch by @databaaz)\n\n### Version 1.4.7 (Jul 10 2019)\n\n- Fix compilation on Windows\n\n### Version 1.4.6 (Jul 10 2019)\n\n- Make watershed work for >2\u00b3\u00b9 voxels (issue #102)\n- Remove milk from demos\n- Improve performance by avoid unnecessary array copies in `cwatershed()`,\n `majority_filter()`, and color conversions\n- Fix bug in interpolation\n\n### Version 1.4.5 (Oct 20 2018)\n- Upgrade code to newer NumPy API (issue #95)\n\n### Version 1.4.4 (Nov 5 2017)\n- Fix bug in Bernsen thresholding (issue #84)\n\n### Version 1.4.3 (Oct 3 2016)\n- Fix distribution (add missing `README.md` file)\n\n### Version 1.4.2 (Oct 2 2016)\n\n- Fix `resize\\_to` return exactly the requested size\n- Fix hard crash when computing texture on arrays with negative values (issue #72)\n- Added `distance` argument to haralick features (pull request #76, by\n Guillaume Lemaitre)\n\n### Version 1.4.1 (Dec 20 2015)\n\n- Add `filter\\_labeled` function\n- Fix tests on 32 bit platforms and older versions of numpy\n\n### Version 1.4.0 (July 8 2015)\n\n- Added `mahotas-features.py` script\n- Add short argument to citation() function\n- Add max\\_iter argument to thin() function\n- Fixed labeled.bbox when there is no background (issue \\#61, reported\n by Daniel Haehn)\n- bbox now allows dimensions greater than 2 (including when using the\n `as_slice` and `border` arguments)\n- Extended croptobbox for dimensions greater than 2\n- Added use\\_x\\_minus\\_y\\_variance option to haralick features\n- Add function `lbp_names`\n\n### Version 1.3.0 (April 28 2015)\n\n- Improve memory handling in freeimage.write\\_multipage\n- Fix moments parameter swap\n- Add labeled.bbox function\n- Add return\\_mean and return\\_mean\\_ptp arguments to haralick\n function\n- Add difference of Gaussians filter (by Jianyu Wang)\n- Add Laplacian filter (by Jianyu Wang)\n- Fix crash in median\\_filter when mismatched arguments are passed\n- Fix gaussian\\_filter1d for ndim \\> 2\n\n### Version 1.2.4 (December 23 2014)\n\n- Add PIL based IO\n\n### Version 1.2.3 (November 8 2014)\n\n- Export mean\\_filter at top level\n- Fix to Zernike moments computation (reported by Sergey Demurin)\n- Fix compilation in platforms without npy\\_float128 (patch by Gabi\n Davar)\n\n### Version 1.2.2 (October 19 2014)\n\n- Add minlength argument to labeled\\_sum\n- Generalize regmax/regmin to work with floating point images\n- Allow floating point inputs to `cwatershed()`\n- Correctly check for float16 & float128 inputs\n- Make sobel into a pure function (i.e., do not normalize its input)\n- Fix sobel filtering\n\n### Version 1.2.1 (July 21 2014)\n\n- Explicitly set numpy.include\\_dirs() in setup.py [patch by Andrew\n Stromnov]\n\n### Version 1.2 (July 17 2014)\n\n- Export locmax|locmin at the mahotas namespace level\n- Break away ellipse\\_axes from eccentricity code as it can be useful\n on its own\n- Add `find()` function\n- Add `mean_filter()` function\n- Fix `cwatershed()` overflow possibility\n- Make labeled functions more flexible in accepting more types\n- Fix crash in `close_holes()` with nD images (for n \\> 2)\n- Remove matplotlibwrap\n- Use standard setuptools for building (instead of numpy.distutils)\n- Add `overlay()` function\n\n### Version 1.1.1 (July 4 2014)\n\n- Fix crash in close\\_holes() with nD images (for n \\> 2)\n\n### 1.1.0 (February 12 2014)\n\n- Better error checking\n- Fix interpolation of integer images using order 1\n- Add resize\\_to & resize\\_rgb\\_to\n- Add coveralls coverage\n- Fix SLIC superpixels connectivity\n- Add remove\\_regions\\_where function\n- Fix hard crash in convolution\n- Fix axis handling in convolve1d\n- Add normalization to moments calculation\n\nSee the\n[ChangeLog](https://github.com/luispedro/mahotas/blob/master/ChangeLog)\nfor older version.\n\n\n## License\n[](https://app.fossa.io/projects/git%2Bgithub.com%2Fluispedro%2Fmahotas?ref=badge_large)\n",
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"license": "MIT",
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