Mahotas: Computer Vision Library
Python Computer Vision Library
This library of fast computer vision algorithms (all implemented in C++) operates over numpy arrays for convenience.
- Notable algorithms:
- convex points calculations.
- hit & miss. thinning.
- Zernike & Haralick, LBP, and TAS features.
- freeimage based numpy image loading (requires freeimage libraries to be installed).
- Speeded-Up Robust Features (SURF), a form of local features.
- Sobel edge detection.
- spline interpolation
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.
There is a manuscript about mahotas, which will hopefully evolve into a journal publication later.
This is a simple example of loading a file (called test.jpeg) and calling watershed using above threshold regions as a seed (we use Otsu to define threshold).
import numpy as np from scipy import ndimage import mahotas import pylab img = mahotas.imread('test.jpeg') T_otsu = mahotas.thresholding.otsu(img) seeds,_ = ndimage.label(img > T_otsu) labeled = mahotas.cwatershed(img.max() - img, 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 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 = mahotas.distance(f) p.imshow(dmap) p.show()
(This is under mahotas/demos/distance.py).
How to invoke thresholding functions:
import mahotas import numpy as np from pylab import imshow, gray, show, subplot from os import path photo = mahotas.imread('luispedro.org', as_grey=True) photo = photo.astype(np.uint8) T_otsu = mahotas.otsu(photo) thresholded_otsu = (photo > T_otsu) T_rc = mahotas.rc(photo) thresholded_rc = (photo > T_rc)
You will need python (naturally), numpy, and a C++ compiler. Then you should be able to either
Download the source and then run:
python setup.py install
or use one of:
pip install mahotas easy_install mahotas
You can test your instalation by running:
python -c "import mahotas; mahotas.test()"
If something fails, you can obtain more detail by running it again in verbose mode:
python -c "import mahotas; mahotas.test(verbose=True)"
Development happens on github (http://github.com/luispedro/mahotas).
You can set the DEBUG environment variable before compilation to get a debug compile. 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. The debug modes are pretty slow as they add many runtime checks.
For bugfixes, feel free to use my email: email@example.com
For more general with achieving certain tasks in Python, the pythonvision mailing list is a much better venue and generates a public discussion log for others in the future.
0.99 (May 4 2013)
- Make matplotlib a soft dependency
- Add demos.image_path() function
- Add citation() function
This version is 1.0 beta.
0.9.8 (April 22 2013)
- Use matplotlib as IO backend (fallback only)
- Compute dense SURF features
- Fix sobel edge filtering (post-processing)
- Faster 1D convultions (including faster Gaussian filtering)
- Location independent tests (run mahotas.tests.run() anywhere)
- Add labeled.is_same_labeling function
- Post filter SLIC for smoother regions
- Fix compilation warnings on several platforms
0.9.7 (February 03 2013)
- Add haralick_features function
- Add out parameter to morph functions which were missing it
- Fix erode() & dilate() with empty structuring elements
- Special case binary erosion/dilation in C-Arrays
- Fix long-standing warning in TAS on zero inputs
- Add verbose argument to tests.run()
- Add circle_se to morph
- Allow loc(max|min) to take floating point inputs
- Add Bernsen local thresholding (bernsen and gbernsen functions)
0.9.6 (December 02 2012)
- Fix distance() of non-boolean images (issue #24 on github)
- Fix encoding issue on PY3 on Mac OS (issue #25 on github)
- Add relabel() function
- Add remove_regions() function in labeled module
- Fix median_filter() on the borders (respect the mode argument)
- Add mahotas.color module for conversion between colour spaces
- Add SLIC Superpixels
- Many improvements to the documentation
0.9.5 (November 05 2012)
- Fix compilation in older G++
- Faster Otsu thresholding
- Python 3 support without 2to3
- Add cdilate function
- Add subm function
- Add tophat transforms (functions tophat_close and tophat_open)
- Add mode argument to euler() (patch by Karol M. Langner)
- Add mode argument to bwperim() & borders() (patch by Karol M. Langner)
0.9.4 (October 10 2012)
- Fix compilation on 32-bit machines (Patch by Christoph Gohlke)
0.9.3 (October 9 2012)
- Fix interpolation (Report by Christoph Gohlke)
- Fix second interpolation bug (Report and patch by Christoph Gohlke)
- Update tests to newer numpy
- Enhanced debug mode (compile with DEBUG=2 in environment)
- Faster morph.dilate()
- Add labeled.labeled_max & labeled.labeled_min (This also led to a refactoring of the labeled_* code)
- Many documentation fixes
0.9.2 (September 1 2012)
- Fix compilation on Mac OS X 10.8 (reported by Davide Cittaro)
- Freeimage fixes on Windows by Christoph Gohlke
- Slightly faster _filter implementaiton
0.9.1 (August 28 2012)
- Python 3 support (you need to use 2to3)
- Haar wavelets (forward and inverse transform)
- Daubechies wavelets (forward and inverse transform)
- Corner case fix in Otsu thresholding
- Add soft_threshold function
- Have polygon.convexhull return an ndarray (instead of a list)
- Memory usage improvements in regmin/regmax/close_holes (first reported as issue #9 by thanasi)
0.9 (July 16 2012)
- Auto-convert integer to double on gaussian_filter (previously, integer values would result in zero-valued outputs).
- Check for integer types in (reg|loc)(max|min)
- Use name out instead of output for output arguments. This matches Numpy better
- Switched to MIT License
See the ChangeLog for older version.
API Docs: http://packages.python.org/mahotas/
Mailing List: Use the pythonvision mailing list for questions, bug submissions, etc.
Author: Luis Pedro Coelho (with code by Zachary Pincus [from scikits.image], Peter J. Verveer [from scipy.ndimage], and Davis King [from dlib])