Am 07.03.2012 20:49, schrieb Wanderer:
I have a list of defective CCD pixels and I need to find clusters
where a cluster is a group of adjacent defective pixels. This seems to
me to be a classic linked list tree search.I take a pixel from the
defective list and check if an adjacent pixel is in the list. If it is
I add the pixel to the cluster and remove it from the defective list.
I then check an adjacent pixel of the new pixel and so on down the
branch until I don't find a defective pixel. The I move up to the
previous pixel and check the next adjacent pixel and so on until I'm
back at the head I can't find any more defective adjacent pixels. How
do you handle this sort of thing in Python?
I'd do something like (code not tested):
defective_list = [(x1, y1), (x2, y2), ...] #list of coordinates of
#defective pixel
#build one cluster:
cluster_start = defective_list.pop() #starting point
buf = [] #buffer for added pixels
buf.push(cluster_start)
cluster = []
cluster.push(cluster_start)
while len(buf)>0:
i = buf.pop()
for b, d in itertools.product(xrange(2), [-1,1]): #4 neighbours
j = list(i)
j[b] += d
j = tuple(j)
if outside_lcd(j) or j in cluster or j not in defective_list:
continue
defective_list.remove(j)
cluster.push(j)
buf.push(j)
return cluster
and repeat it until defective_list is empty.
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