Mahotas: Python Image Processing Library
Project description
Image Processing Library for Python.
It includes a couple of algorithms implemented in C++ for speed while operating in numpy arrays.
- Notable algorithms:
watershed.
thresholding.
convex points calculations.
hit & miss. thinning.
Zernike & Haralick features.
freeimage based numpy image loading (requires freeimage libraries to be installed).
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 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) pylab.imshow(labeled)
Recent Changes
For version 0.6
Improve Local Binary patterns (faster and better interface)
Much faster erode() (10x faster)
Faster dilate() (2x faster)
TAS for 3D images
Haralick for 3D images
Support
Website: http://luispedro.org/software/mahotas
API Docs: http://packages.python.org/mahotas/
Mailing List: Use the pythonvision mailing list for questions, bug submissions, etc.
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