PYCAM : filtering, face tracking, object detection and feature detection

Using python in Computer Vision interesting project:

http://code.google.com/p/pycam/

SIFT Python Implementation

http://jesolem.blogspot.com/2009/02/sift-python-implementation.html

This is the text and the code from the link above:

I'd like to share a Python interface I wrote for David Lowe's Scale Invariant Feature Transform (SIFT) implementation. David, the inventor of SIFT, has since several years generously shared binaries with a Matlab interface on his website. Inspired by the Matlab files for reading keypoint descriptor files and for matching between images, I decided to write a Python version. Here it is: sift.py.

The file itself should be self-explanatory, especially together with the documentation that comes with Lowe's zip-file. Anyway, here's an example:

>>>from PIL import Image
>>>from numpy import *
>>>import sift

>>>sift.process_image('basmati.pgm', 'basmati.key')
>>>l1,d1 = sift.read_features_from_file('basmati.key')
>>>im = array(Image.open('basmati.pgm'))
>>>sift.plot_features(im,l1)

Py wrapper for Sift++

http://code.google.com/p/pysift/

Python wrapper for Sift ++, available on google code.

SIFT

http://www.cs.ubc.ca/~lowe/keypoints/

David Lowe's SIFT keypoint detector.

VLFeat is a MATLAB-compatible implementation common computer vision algorithms

http://www.vlfeat.org/

VLFeat is a MATLAB-compatible implementation common computer vision algorithms such as SIFT, MSER, k-means, hierarchical k-means, and agglomerative information bottleneck.

Photo Application by INRIA Alpes

http://lear.inrialpes.fr/src/yorg/doc/index.html

On the link above you can find the documentation and the link to the source code of a nice and very interesting application, implemented in Inria Alpes(oe of the most active research center in compute vision).
The application is implemented in Python and there are some examples of its capabilities.