Install

Install libfftw3 with development headers (http://www.fftw.org), python dev headers, gcc, the Python Imaging Library (PIL) and numpy.

Build locally for testing:

% python setup.py buid_ext -i
% export PYTHONPATH=`pwd`/src

Build and install system wide:

% python setup.py build
% sudo python setup.py install

Usage

Here is a sample session in a python shell once the library is installed:

>>> from PIL import Image
>>> import leargist

>>> im = Image.open('lear_gist/ar.ppm')
>>> descriptors = leargist.color_gist(im)

>>> descriptors.shape
(960,)

>>> descriptors.dtype
dtype('float32')

>>> descriptors[:4]
array([ 0.05786307,  0.19255637,  0.09331483,  0.06622448], dtype=float32)

The GIST descriptors (fixed size, 960 by default) can then be used as an euclidian space to cluster images based on their content.

This dimension can then be reduced to a 32 or 64 bits semantic hash by using Locality Sensitive Hashing, Spectral Hashing or Stacked Denoising Autoencoders.

A sample implementation of picture semantic hashing with SDAs is showcased in the libsgd library: http://code.oliviergrisel.name/libsgd

Changes

• 1.1.0: 2010/03/25 - fix segmentation fault bug, thx to S. Campion

• 1.0.1: 2009/10/10 - added missing missing MANIFEST

• 1.0.0: 2009/10/10 - initial release