I believe that pretty generic connected-component finding is already available with scipy.ndimage.label, as David suggested at the beginning of the thread...
This function takes a binary array (e.g. zeros where the background is, non-zero where foreground is) and outputs an array where each connected component of non-background pixels has a unique non-zero "label" value. ndimage.find_objects will then give slices (e.g. bounding boxes) for each labeled object (or a subset of them as specified). There are also a ton of statistics you can calculate based on the labeled objects -- look at the entire ndimage.measurements namespace. Zach On Sep 21, 2009, at 1:45 PM, Gökhan Sever wrote: > I asked this question at > http://stackoverflow.com/questions/1449139/simple-object-recognition > and get lots of nice feedback, and finally I have managed to > implement what I wanted. > > What I was looking for is named "connected component labelling or > analysis" for my "connected component extraction" > > I have put the code (lab2.py) and the image (particles.png) under: > http://code.google.com/p/ccnworks/source/browse/#svn/trunk/AtSc450/ > labs > > What do you think of improving that code and adding into scipy's > ndimage library (like connected_components()) ? > > Comments and suggestions are welcome :) > > > On Wed, Sep 16, 2009 at 7:22 PM, Gökhan Sever > <[email protected]> wrote: > Hello all, > > I want to be able to count predefined simple rectangle shapes on an > image as shown like in this one: > http://img7.imageshack.us/img7/2327/particles.png > > Which is in my case to count all the blue pixels (they are ice-snow > flake shadows in reality) in one of the column. > > What is the way to automate this task, which library or technique > should I study to tackle it. > > Thanks. > > -- > Gökhan > > > > -- > Gökhan > _______________________________________________ > SciPy-User mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/scipy-user _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
