Mahotas: Computer Vision in Python

Mahotas is a computer vision and image processing library for Python.

It includes many algorithms implemented in C++ for speed while operating in numpy arrays and with a very clean Python interface.

Notable algorithms:

• watershed.
• 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.
• thresholding.
• convolution.
• Sobel edge detection.

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.

Examples

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
import mahotas
import pylab

img = mahotas.imread('test.jpeg')
T_otsu = mahotas.thresholding.otsu(img)
seeds,_ = mahotas.label(img > T_otsu)
labeled = mahotas.cwatershed(img.max() - img, seeds)

pylab.imshow(labeled)

Computing a distance transform is easy too:

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()

(Source code, png, hires.png, pdf)

Full Documentation Contents

Jump to detailed API Documentation

• How To Install Mahotas
  • From source
  • On Windows
  • Packaged Versions
• Finding Wally
• Labeled Image Functions
  • Labeling Images
  • Filtering Regions
  • Borders
  • API Documentation
• Thresholding
  • API Documentation
• Wavelet Transforms
  • What About the Borders?
  • API Documentation
• Distance Transform
  • Distance Transform and Watershed
  • API Documentation
• Polygon Utitilities
  • Drawing
  • Convex Hull
  • API Documentation
• Features
  • Global features
  • Local features
• Local Binary Patterns
  • API Documentation
• Speeded-Up Robust Features
  • API Documentation
• Classification Using Mahotas
• Morphological Operators
  • Dilation & Erosion
  • Close & Open
• Input/Output with Mahotas
• Frequently Asked Questions
  • Why did you not simply contribute to scipy.ndimage or scikits.image?
  • What are the parameters to Local Binary Patterns?
  • I am using mahotas in a scientific publication, is there a citation?
• Mahotas Internals
  • Philosophy
  • C++/Python Division
  • C++ Templates
• Principles of Mahotas
  • The Why of mahotas
  • Just work
  • Well documented
  • Fast code
  • Simple code
  • Minimal dependencies
• Contributing
  • Debug Mode
• Possible Tasks
  • New Features
  • Small Improvements
  • Internals
  • Tutorials
• History
  • 0.9.7 (February 03 2013)
  • 0.9.6 (December 02 2012)
  • 0.9.5 (November 05 2012)
  • 0.9.4 (October 10 2012)
  • 0.9.3 (October 9 2012)
  • 0.9.2 (September 1 2012)
  • 0.9.1 (August 28 2012)
  • 0.9 (July 16 2012)
  • 0.8.1 (June 6 2012)
  • 0.8 (May 7 2012)
  • 0.7.3 (March 14 2012)
  • 0.7.2 (February 13 2012)
  • 0.7.1 (January 6 2012)
• Full API Documentation
  • Main Features

Indices and tables

• Index
• Search Page