Mahotas: Computer Vision Library
Project description
Mahotas
Python Computer Vision Library
Mahotas is a library of fast computer vision algorithms (all implemented in C++ for speed) operating over numpy arrays.
Python versions 2.7, 3.4+, are supported.
Notable algorithms:
- watershed
- convex points calculations.
- hit & miss, thinning.
- Zernike & Haralick, LBP, and TAS features.
- Speeded-Up Robust Features (SURF), a form of local features.
- thresholding.
- 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 (see details below under 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.)
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, you can install mahotas from conda-forge 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, 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]
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).
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/
Issue Tracker: github mahotas issues
Mailing List: Use the pythonvision mailing list for questions, bug submissions, etc. Or ask on stackoverflow (tag mahotas)
Main Author & Maintainer: Luis Pedro Coelho (follow on twitter or github).
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.
Presentation about mahotas for bioimage informatics
For more general discussion of computer vision in Python, the pythonvision mailing list 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)
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
andborder
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 for older version.
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BLAKE2b-256 | d703097ea67211aeb9272703131fa64118f2a423541ecb94d7da7cd3ec164f5d |
Hashes for mahotas-1.4.15-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 7c2960dc910d1d651c4fa459d799c980c8a6df58004981493c6dee26d371f25b |
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MD5 | ad148ae95fb3ff27ba855f529d45eeb1 |
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BLAKE2b-256 | d338d87347dcc5f98337bcc1a6c85def44df03b9ddcc8cdf03062065191a538f |
Hashes for mahotas-1.4.15-cp38-cp38-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
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SHA256 | 97ad53668d9b5ae812d1eca94c75db9d68602a8fb6569e0d2a246d117aa9a01a |
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MD5 | b89c431b135c0ea91ce6a824573ecaa9 |
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BLAKE2b-256 | 6bec0083800e5a0185ff51bfb12f70c7245c7feb121f833924fdac3546252c66 |
Hashes for mahotas-1.4.15-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9aeec0144b39f1f4a67bff286ed2e342b58e85deef239e81d02e9c82a81e5a69 |
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MD5 | 3a0a46b7536d600a86cebefbbb2922a6 |
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BLAKE2b-256 | 79b777cf4dbe06febbd5a38e5297222dd9de83b451316fe9c29cec234dc805d9 |
Hashes for mahotas-1.4.15-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0cc709b3939a9b626b452c85df3c28b1d04aa1b6b296851b5fba3e6516b34d59 |
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MD5 | 4561db71019fb7f9bdc808799c2e497c |
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BLAKE2b-256 | c35769ec0d76f9bf88b9391ad4a79ede7acff82a489f8d133a873c99927db745 |
Hashes for mahotas-1.4.15-cp37-cp37m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c6fcf1e1dde09a9b93280e914132ea40fd16b19870ad9ad3ef53174502bcb6bf |
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MD5 | 17a5351d586cbb6b49a40136666f0e83 |
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BLAKE2b-256 | 1068b351a50806accdb8eebcad2bbd6c6fad56fa6cf3c576d7334174341c659f |
Hashes for mahotas-1.4.15-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 05fde90de229062b276f0c4caf220c84b27fb046a81b4aa6818330630a612c5b |
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MD5 | 5cb838888f813da670b0ff879a6b9f7a |
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BLAKE2b-256 | b9ca4242d1f0969e9b83c2c5e67d7ba395cdb72c91d6362ea29c1388e85cc236 |
Hashes for mahotas-1.4.15-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f1743f099ff2badc59e9e694c3e6acb355d034883e692877b29071e0b5cc572f |
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MD5 | abefe5595c5d364bb03b519aae9b524c |
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BLAKE2b-256 | 9e667b8526bb2ad3e8f8948528f5fcc3e10d60c4229590f777505d1901a3353a |
Hashes for mahotas-1.4.15-cp36-cp36m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b793fb70c9c62d662d32a95225131009210c29a5222afc4ef8dcbeda7c5f6bae |
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MD5 | 014b6e1bbdff7992b4cea6b355e2a15e |
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BLAKE2b-256 | f4fe4f74ae67cee87acba3bd74167e25fab12fbaf6761be1eee21b5d4b3d59b6 |
Hashes for mahotas-1.4.15-cp36-cp36m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e429d0bdfa8c131a368a7d79789edbcf5d18bfa495669bfbe66a33caf8b3c422 |
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MD5 | a61d180e7edbd9f7f9443c7ea0a09ba3 |
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BLAKE2b-256 | 048af40c14f62c2a84f27f8d9f4184814e3782d115c863fcd7bb71cd3b9fcac9 |
Hashes for mahotas-1.4.15-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 42abacbcd59f4f1ae1458cf5bbc5334c3234ff647f02392a0da2d98d4d6e0a56 |
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MD5 | 9516d6c268a088014f01165c75cada0f |
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BLAKE2b-256 | a1cf99f367da3dd4ce4a8a424b25db712256ab6dc0934900c9571c64a7f194ac |
Hashes for mahotas-1.4.15-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5d98898f1e5cc93ec9e701c619999691707d41f7c777de1d446edbbe4dd1ad26 |
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MD5 | 899b403a1e9d76c4692e95e89f1154dc |
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BLAKE2b-256 | bc3303e9f3ea55f14d60a46620b6892de300061a145b48c6b5d6eaf5e7205589 |