def distVarCost(a, b, c):
#np.seterr(all='warn')
'''prefers lines with less variance in their spacing'''
abDist = cluster_util.findDistance(a, b)
bcDist = cluster_util.findDistance(b, c)
if bcDist == 0:
#shouldn't ever occur, but prevents undefined data while debugging
return 0
return np.abs(np.log2((1 / abDist) * bcDist))
def distVarCost(a, b, c):
#np.seterr(all='warn')
'''prefers lines with less variance in their spacing'''
abDist = cluster_util.findDistance(a, b)
bcDist = cluster_util.findDistance(b, c)
if bcDist==0:
#shouldn't ever occur, but prevents undefined data while debugging
return 0
return np.abs(np.log2((1/abDist)*bcDist))
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
def distCost(current, step, start, goal):
'''prefers dense lines to sparse ones'''
stepdist = cluster_util.findDistance(current, step)
totaldist = cluster_util.findDistance(start, goal)
return stepdist**2 / totaldist**2
def distCost(current,step,start,goal):
'''prefers dense lines to sparse ones'''
stepdist = cluster_util.findDistance(current, step)
totaldist= cluster_util.findDistance(start, goal)
return stepdist**2/totaldist**2