[Numpy-discussion] adaptive thresholding: get adacent cells for each pixel (original) (raw)
stephen emslie stephenemslie at gmail.com
Sat Jun 10 15:33:25 EDT 2006
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Thanks for all the help! Convolving looks like a great way to do this, and I think that mean will be just fine for my purposes.
That iterator also looks fantastic and is actually the sort of thing that I was looking for at first. I havn't tried it yet though. Any idea how fast it would be?
Stephen
On 6/10/06, Alex Liberzon <alex.liberzon at gmail.com> wrote:
Not sure, but my Google desktop search of "medfilt" (the name of Matlab function) brought me to: infosignal.py - N-dimensional order filter. medfilt -N-dimensional median filter If it's true, then it is the 2D median filter. Regarding the neighbouring cells, I found the iterator on 2D ranges on the O'Reily Cookbook by Simon Wittber very useful for my PyPIV (Particle Image Velocimetry, which works by correlation of 2D blocks of two successive images): http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/334971 def blocks(size, box=(1,1)): """ Iterate over a 2D range in 2D increments. Returns a 4 element tuple of top left and bottom right coordinates. """ box = list(box) pos = [0,0] yield tuple(pos + box) while True: if pos[0] >= size[0]-box[0]: pos[0] = 0 pos[1] += box[1] if pos[1] >= size[1]: raise StopIteration else: pos[0] += box[0] topleft = pos bottomright = [min(x[1]+x[0],x[2]) for x in zip(pos,box,size)] yield tuple(topleft + bottomright) if name == "main": for c in blocks((100,100),(99,10)): print c for c in blocks((10,10)): print c
HIH, Alex
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