Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
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
Last synced: 24 days ago
JSON representation
Computer Vision in Python
- Host: GitHub
- URL: https://github.com/luispedro/mahotas
- Owner: luispedro
- License: other
- Created: 2010-01-31T00:13:06.000Z (almost 15 years ago)
- Default Branch: main
- Last Pushed: 2024-07-17T19:01:24.000Z (4 months ago)
- Last Synced: 2024-10-07T21:30:49.446Z (about 1 month ago)
- Topics: c-plus-plus, computer-vision, numpy, python, python-2, python-3
- Language: Python
- Homepage: https://mahotas.rtfd.io
- Size: 3.82 MB
- Stars: 839
- Watchers: 50
- Forks: 147
- Open Issues: 24
-
Metadata Files:
- Readme: README.md
- Changelog: ChangeLog
- License: COPYING
- Citation: CITATION
- Authors: AUTHORS
Awesome Lists containing this project
- my-awesome-awesomeness - mahotas
- awesome-python-machine-learning-resources - GitHub - 20% open · ⏱️ 28.06.2022): (图像数据与CV)
- awesome-cv - Mahotas
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.[![Test mahotas](https://github.com/luispedro/mahotas/actions/workflows/test-python-package-with-conda.yml/badge.svg)](https://github.com/luispedro/mahotas/actions/workflows/test-python-package-with-conda.yml)
[![Coverage Status](https://coveralls.io/repos/github/luispedro/mahotas/badge.svg?branch=master)](https://coveralls.io/github/luispedro/mahotas?branch=master)
[![License](https://img.shields.io/badge/License-MIT-blue)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/mahotas)](https://pepy.tech/project/mahotas)
[![Conda Downloads](https://anaconda.org/conda-forge/mahotas/badges/downloads.svg)](https://anaconda.org/conda-forge/mahotas)
[![Install with conda](https://img.shields.io/badge/install%20with-conda-brightgreen.svg?style=flat)](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 mhf = 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-leftdmap = 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 calculationSee the
[ChangeLog](https://github.com/luispedro/mahotas/blob/master/ChangeLog)
for older version.## License
[![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fluispedro%2Fmahotas.svg?type=large)](https://app.fossa.io/projects/git%2Bgithub.com%2Fluispedro%2Fmahotas?ref=badge_large)