Hi Jeff, On 9/27/07, Jeffrey Wise <[EMAIL PROTECTED]> wrote: > I've gotten to the point in my image analysis that I need to find all the > groups of connected pixels in an image. These groups form arbitrary shapes > in the image. I want to preserve the shape information for each group, so I > can compare/analyze/categorize... it later.
This is usually done with chain codes. Use a filter (and morphology?) and a threshold to turn your image into a set of edges, then search for part of the edge and trace around it, recording up/down/left/right movements. Once you have the boundary as a set of lines it's easy to calculate things like area and principal axis. > check in every direction about it for a neighbor that is 255 too. The > algorithm would presumably recurse to find neighbors of the neighbor, etc. > I suspect the trick in this algorithm is the data structures needed to keep > track of the discovered adjacencies. I have loaded the image into the 2D This sounds very like a flood-fill. You'll find if you recurse for each connection you will use huge amounts of stack. It's usually better to keep a list of candidates for later testing and iterate rather then recurse. You can also do it a line at a time rather than a point at a time. Write a function to fill to the right or left until it hits an edge, then add the point below to your undone list. You need much less memory and it'll run a lot quicker. But chain codes are probably a better bet. John _______________________________________________ Image-SIG maillist - Image-SIG@python.org http://mail.python.org/mailman/listinfo/image-sig
