def clustercost(data,objectDict,baseline = 0.001):
# data is a tuple of two dictionaries: core cluster data first, then larger and more permissive clusters\
# this func needs to return a unified list of possible clusters using both dictionaries in the style of the chain finder function
# quick and dirty:
smallClusters = []
bigClusters = []
#cores
for i in data[0].values():
corePO = map(lambda x: objectDict.get(x),i)
coreHull = cluster_util.convex_hull(corePO)
try:
density = len(i)/cluster_util.area(coreHull)
except:
density = 0
if density>=1: print "groups are too dense, things may be weird."
cost = len(i)*(1-density) + baseline
corecluster=cluster_util.GroupBundle(i,cost)
smallClusters.append(corecluster)
#fringes
for i in data[1].values():
fringePO = map(lambda x: objectDict.get(x),i)
fringeHull = cluster_util.convex_hull(fringePO)
try:
density = len(i)/cluster_util.area(fringeHull)
except:
density = 0
if density>=1: print "groups are too dense, things may be weird."
cost = len(i)*(1-density) + baseline
bigcluster = cluster_util.GroupBundle(i,cost)
bigClusters.append(bigcluster)
return (smallClusters,bigClusters)
def clustercost(data, objectDict, baseline=0.001):
# data is a tuple of two dictionaries: core cluster data first, then larger and more permissive clusters\
# this func needs to return a unified list of possible clusters using both dictionaries in the style of the chain finder function
# quick and dirty:
smallClusters = []
bigClusters = []
#cores
for i in data[0].values():
corePO = map(lambda x: objectDict.get(x), i)
coreHull = cluster_util.convex_hull(corePO)
try:
density = len(i) / cluster_util.area(coreHull)
except:
density = 0
if density >= 1: print "groups are too dense, things may be weird."
cost = len(i) * (1 - density) + baseline
corecluster = cluster_util.GroupBundle(i, cost)
smallClusters.append(corecluster)
#fringes
for i in data[1].values():
fringePO = map(lambda x: objectDict.get(x), i)
fringeHull = cluster_util.convex_hull(fringePO)
try:
density = len(i) / cluster_util.area(fringeHull)
except:
density = 0
if density >= 1: print "groups are too dense, things may be weird."
cost = len(i) * (1 - density) + baseline
bigcluster = cluster_util.GroupBundle(i, cost)
bigClusters.append(bigcluster)
return (smallClusters, bigClusters)
def clusterCostWorker(data,objectDict,maxDiscountRatio,strong=False):
#maxDiscountRatio is the maximum amount of the groups cost that can be deducted
#for membership in a group. Higher values will make groups more favorable compared
# to lines.
output = []
for i in data:
clusterMembers = map(lambda x: objectDict.get(x),i)
hullAroundMembers = cluster_util.convex_hull(clusterMembers)
areaOfMembers = sum(cluster_util.bb_area(objectDict.get(p).representation.rect) for p in i)
try:
density = areaOfMembers/cluster_util.area(hullAroundMembers)
except:
density = 0
if density>=1: density = 1
cost = len(i)*(1-density**2)*maxDiscountRatio + len(i)*(1-maxDiscountRatio)
# corecluster=cluster_util.GroupBundle(i,cost,hullAroundMembers)
corecluster=landmark.GroupRectangleRepresentation([objectDict.get(j) for j in i],None,cost)
corecluster.density = density
corecluster.isStrong = strong
output.append(corecluster)
return output