Skip to content

Instantly share code, notes, and snippets.

Embed
What would you like to do?
Greedy clustering algorithm. No checks on simply connected are implemented. Probably could merge/eliminate really small clusters but I don't.
def GrowCluster ( intensity, cluster, mask, k ):
newcluster = cluster
#find boundary of current cluster
indexes = np.nonzero( np.logical_and(mask, (np.logical_not(newcluster))))
while indexes :
growingboundary = 0 * cluster
for kk in range ( len (indexes[0] ) ):
i = indexes[0][kk]
j = indexes[1][kk]
isConnected = False
isDecreasing = False
if ( i > 0 ):
if (newcluster[i-1,j] == k):
isConnected = True
if intensity[i-1,j] >= intensity[i,j]:
isDecreasing = True
if ( i < newcluster.shape[0]-1 ):
if (newcluster[i+1,j] == k):
isConnected = True
if intensity[i+1,j] >= intensity[i,j]:
isDecreasing = True
if ( j > 0 ):
if (newcluster[i,j-1] == k):
isConnected = True
if intensity[i,j-1] >= intensity[i,j]:
isDecreasing = True
if ( j < newcluster.shape[1]-1 ):
if (newcluster[i,j+1] == k):
isConnected = True
if intensity[i,j+1] >= intensity[i,j]:
isDecreasing = True
if ( i > 0 ) and ( j > 0 ):
if (newcluster[i-1,j-1] == k):
isConnected = True
if intensity[i-1,j-1] >= intensity[i,j]:
isDecreasing = True
if ( i < newcluster.shape[0]-1 ) and (j > 0):
if (newcluster[i+1,j-1] == k):
isConnected = True
if intensity[i+1,j-1] >= intensity[i,j]:
isDecreasing = True
if ( j < newcluster.shape[1]-1 ) and ( i < newcluster.shape[0]-1 ):
if (newcluster[i+1,j+1] == k):
isConnected = True
if intensity[i+1,j+1] >= intensity[i,j]:
isDecreasing = True
if isDecreasing and isConnected:
growingboundary[i,j] = k
if ( np.count_nonzero( growingboundary ) ):
newcluster = newcluster + growingboundary
indexes = np.nonzero( np.logical_and(mask, (np.logical_not(newcluster))))
else:
break
return newcluster
def FindLargestCluster( intensity, mask, k ):
peak = np.argmax(np.multiply( intensity, np.logical_and(intensity, mask)))
peaki = peak/intensity.shape[1]
peakj = peak - peaki * intensity.shape[1]
cluster = 0 * intensity
cluster[peaki,peakj] = k
return GrowCluster( intensity, cluster, mask, k )
def FindAllClusters( intensity ):
mask = (intensity >= 0.5).astype(int)
print np.amax(intensity)
cluster = 0 * intensity
k = 1
while (np.amax(mask) > 0 ):
k = k + 1
cluster += FindLargestCluster ( intensity, mask, k )
mask = np.logical_and(mask, np.logical_not(cluster))
return cluster
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
You can’t perform that action at this time.