Python Wrapper for the LEAR image descriptor implementation
Library to compute GIST global image descriptors to be used to compare pictures based on their content (to be used global scene recognition and categorization).
The GIST image descriptor theoritical definition can be found on A. Torralba's page: http://people.csail.mit.edu/torralba/code/spatialenvelope/
The source code of the C implementation is included in the lear_gist subfolder. See http://lear.inrialpes.fr/software for the original project information.
pyleargist is licensed under the GPL, the same license as the original C project.
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
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
- 2.0.5: 2012/11/21 - fix memory leak by using cython instead of ctypes
- 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