Tuesday, May 10, 2016

Mahotas for Python Computer Vision Library

Mahotas is a library of fast computer vision algorithms (all implemented in C++) operating over numpy arrays.

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.
  • 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.

you can see :
  1. https://pypi.python.org/pypi/mahotas#id1
  2. http://mahotas.readthedocs.io/en/latest/wally.html

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