def track_edge(im,start=None):
edge = np.zeros(im.shape)
im2 = pad_image(im)
chain = []
dir = 6
# if it exists, find first 'on' point in image
# row,col point encoding
try:
on = np.nonzero(im2)
i,j =on[0][0],on[1][0]
chain = [i-1,j-1]
p0 = (i,j)
point = np.array(p0)
except:
print 'No figure found'
return edge, np.array(chain)
# unless a start point is specified, use the first 'on' point as the start
if start!=None:
p0 = start
point = np.array(p0)
iter = 0
# loop until break or too many iterations reached
while True:
iter+=1
if iter >= len(im2.flatten()):
print '***track_edge: max iterations reached with no end in sight...'
break
# set next direction to look, 1 step counterclockwise from where we came
checkdir = (dir+7) % 8
i,j = point + map8[checkdir]
# if we hit a pixel in the region, move there and add the direction to the list
if im2[i,j]==1:
dir = checkdir
point = point+map8[checkdir]
edge[i-1,j-1]=1
chain.append(dir)
# if we've hit the start, then end the loop
if (i,j) == p0:
break
# otherwise, check the next dir
else:
dir+=1
return edge,np.array(chain)
def label_components(im):
# initially pad label image so that we can the first row without error
l_im = pad_image(np.zeros(im.shape))
equiv = {0:0.0}
# raster through image
for i in range(im.shape[0]):
for j in range(im.shape[1]):
if im[i,j]==1:
# find if any neighbors have already been labeled
# if so, then take the smalles of all neighboring labels
# otherwise, create a new label
nearby = []
for dir in [1,2,3,4]:
dirval = l_im[i+map8[dir][0]+1,j+map8[dir][1]+1]
if dirval > 0:
nearby.append(dirval)
if nearby==[]:
newval = max(equiv)+1
nearby.append(newval)
else:
newval = min(nearby)
l_im[i+1,j+1] = newval
# adjust equivalence table, but maintain previous corrections to the table
for n in nearby:
if n in equiv:
equiv[n] = min(newval,equiv[n])
else:
equiv[n] = newval
# adjust image according to equivalence table
# go backwards through the keys so that chains of equivalences are handled properly
for key in equiv.keys()[::-1]:
l_im[l_im==key] = equiv[key]
return l_im[1:-1,1:-1],equiv # return without padding
return l_im[1:-1,1:-1],equiv # return without padding
