def main(data):
forest = []
for item in data:
forest.append(node(item))
disjoin_set.union(forest[1],forest[2])
disjoin_set.union(forest[3],forest[4])
disjoin_set.union(forest[1],forest[4])
for item in forest:
if item==disjoin_set.find(item):
print "item is:"+str(item)+" value is:"+str(item.value)+" parent is:"+str(item.parent)
def main(data):
forest = []
for item in data:
forest.append(node(item))
disjoin_set.union(forest[1], forest[2])
disjoin_set.union(forest[3], forest[4])
disjoin_set.union(forest[1], forest[4])
for item in forest:
if item == disjoin_set.find(item):
print "item is:" + str(item) + " value is:" + str(
item.value) + " parent is:" + str(item.parent)
def grap_base_seg(e_set,k):
'''
Here we will implimention the graph based iamge segment method
which proposed by Pedro F.Felzenszwalb and Daniel P.Huttenlocher
in the paper "Efficient Graph-Based Image Segmentation"
This method based on Kruskal's algorithm and use the disjoin set
as the data structure
The interpretation of arguments:
e_set : is the list of edge's weight, and it saved in a
symmetric matrix. And the index is the position of pixel
which ordered by column first, raw second.As the matrix will
be very sparse, so here use link chain to store
k : is the coefficent multiplier
The result will stored in the disjoin set
'''
#Zero: order the edge in e_set by weight in Ascending order
ord_e = []
for i in range(0,len(e_set)):
for j in range(0,len(e_set[i])):
ord_e.append(edge_node(i,e_set[i][j][0],e_set[i][j][1]))
ord_e.sort(cmp_e)
#First: initialize the segment,v_set will maintains the result
v_set = []
for raw in range(0,len(e_set)):
v_set.append(vertex_node(0))
#Second, split loop, for each edge do the follow
for e in ord_e:
region1 = disjoin_set.find(v_set[e.v1])
region2 = disjoin_set.find(v_set[e.v2])
if region1!=region2:
Int1 = region1.value + k/region1.coefficient
Int2 = region2.value + k/region2.coefficient
Int = Int1
if Int1 > Int2:
Int = Int2
if e.w <= Int: #then the two region can be merged
region = disjoin_set.union(region1,region2)
region.value = e.w #the max w in MST of region
region.coefficient = region1.coefficient + region2.coefficient
else:
continue
#Third, return the result
return v_set
def grap_base_seg(e_set, k):
"""
Here we will implimention the graph based iamge segment method
which proposed by Pedro F.Felzenszwalb and Daniel P.Huttenlocher
in the paper "Efficient Graph-Based Image Segmentation"
This method based on Kruskal's algorithm and use the disjoin set
as the data structure
The interpretation of arguments:
e_set : is the list of edge's weight, and it saved in a
symmetric matrix. And the index is the position of pixel
which ordered by column first, raw second.As the matrix will
be very sparse, so here use link chain to store
k : is the coefficent multiplier
The result will stored in the disjoin set
"""
# Zero: order the edge in e_set by weight in Ascending order
ord_e = []
for i in range(0, len(e_set)):
for j in range(0, len(e_set[i])):
ord_e.append(edge_node(i, e_set[i][j][0], e_set[i][j][1]))
ord_e.sort(cmp_e)
# First: initialize the segment,v_set will maintains the result
v_set = []
for raw in range(0, len(e_set)):
v_set.append(vertex_node(0))
# Second, split loop, for each edge do the follow
for e in ord_e:
region1 = disjoin_set.find(v_set[e.v1])
region2 = disjoin_set.find(v_set[e.v2])
if region1 != region2:
Int1 = region1.value + k / region1.coefficient
Int2 = region2.value + k / region2.coefficient
Int = Int1
if Int1 > Int2:
Int = Int2
if e.w <= Int: # then the two region can be merged
region = disjoin_set.union(region1, region2)
region.value = e.w # the max w in MST of region
region.coefficient = region1.coefficient + region2.coefficient
else:
continue
# Third, return the result
return v_set
