def findChains(inputObjectSet, params,objectDict):
'''finds all the chains, then returns the ones that satisfy constraints, sorted from best to worst.'''
bestlines = []
explored = set()
pairwise = cluster_util.find_pairs(inputObjectSet)
pairwise.sort(key=lambda p: p[0].distance_to(p[1].representation),reverse=False)
for pair in pairwise:
start,finish = pair[0],pair[1]
if frozenset([start.uuid,finish.uuid]) not in explored:
result = chainSearch(start, finish, inputObjectSet,params)
if result != None:
bestlines.append(result)
s = map(frozenset,cluster_util.find_pairs(result[0:len(result)-1]))
map(explored.add,s)
verybest = []
costSum = 0
for line in bestlines:
if len(line)>params.min_line_length:
verybest.append(line)
verybest.sort(key=lambda l: len(l),reverse=True)
costs = map(lambda l: l.pop()+1.5,verybest)
data = np.array(map(lambda x: (x.representation.middle,x.uuid),inputObjectSet))
output = []
for i in zip(costs,verybest):
output.append(landmark.GroupLineRepresentation([objectDict.get(j) for j in i[1]],i[0]))
return output
def findChains(inputObjectSet, params ):
'''finds all the chains, then returns the ones that satisfy constraints, sorted from best to worst.'''
bestlines = []
explored = set()
pairwise = cluster_util.find_pairs(inputObjectSet)
pairwise.sort(key=lambda p: cluster_util.findDistance(p[0].position, p[1].position),reverse=False)
for pair in pairwise:
start,finish = pair[0],pair[1]
if frozenset([start.id,finish.id]) not in explored:
result = chainSearch(start, finish, inputObjectSet,params)
if result != None:
bestlines.append(result)
s = map(frozenset,cluster_util.find_pairs(result[0:len(result)-1]))
map(explored.add,s)
verybest = []
costSum = 0
for line in bestlines:
if len(line)>params.min_line_length:
verybest.append(line)
verybest.sort(key=lambda l: len(l),reverse=True)
costs = map(lambda l: l.pop()+2,verybest)
listOfTheWordLine = ["line"]*len(costs)
data = np.array(map(lambda x: (x.position,x.id),inputObjectSet))
return zip(costs,verybest,listOfTheWordLine)
def findChains(inputObjectSet, params, distanceMatrix=-1):
'''finds all the chains, then returns the ones that satisfy constraints, sorted from best to worst.'''
if distanceMatrix == -1:
distanceMatrix = cluster_util.create_distance_matrix(inputObjectSet)
bestlines = []
explored = set()
pairwise = cluster_util.find_pairs(inputObjectSet)
pairwise.sort(
key=lambda p: cluster_util.findDistance(p[0].position, p[1].position),
reverse=False)
for pair in pairwise:
start, finish = pair[0], pair[1]
if frozenset([start.id, finish.id]) not in explored:
result = chainSearch(start, finish, inputObjectSet, params,
distanceMatrix)
if result != None:
bestlines.append(result)
s = map(frozenset,
cluster_util.find_pairs(result[0:len(result) - 1]))
map(explored.add, s)
verybest = []
costSum = 0
for line in bestlines:
if len(line) > params.min_line_length:
verybest.append(line)
verybest.sort(key=lambda l: len(l), reverse=True)
costs = map(lambda l: l.pop() + 2, verybest)
data = np.array(map(lambda x: (x.position, x.id), inputObjectSet))
output = []
for i in zip(costs, verybest):
output.append(cluster_util.LineBundle(i[1], i[0]))
return output