def process_edge(body2gtbody, nomerge_hist, tot_hist, nomerge_hist2, tot_hist2, dirtybodies, bodyremap):
priority = neuroproof.get_next_edge()
(body1, body2) = priority.body_pair
weight = neuroproof.get_edge_val(priority)
if body1 not in dirtybodies and body2 not in dirtybodies:
tot_hist[int(weight*100)] += 1
tot_hist2[int(weight*100)] += 1
link = True
if body2gtbody[body1] != body2gtbody[body2]:
if body1 not in dirtybodies and body2 not in dirtybodies:
nomerge_hist[int(weight*100)] += 1
nomerge_hist2[int(weight*100)] += 1
link = False
else:
if body2 not in bodyremap:
bodyremap[body2] = [body2]
if body1 not in bodyremap:
bodyremap[body1] = [body1]
dirtybodies.add(body1)
bodyremap[body1].extend(bodyremap[body2])
del bodyremap[body2]
neuroproof.set_edge_result(priority.body_pair, link)
decision = 0
undo = 0
num_undo = 0
npp.estimate_work()
estimated = npp.get_estimated_num_remaining_edges()
print(("Num estimated edges: ", estimated))
while npp.get_estimated_num_remaining_edges() > 0:
priority_info = npp.get_next_edge()
xyzlocation = priority_info.location
(body1, body2) = priority_info.body_pair
# proofread body pair at location
npp.set_edge_result(priority_info.body_pair, decision%2)
decision+=1
num_examined += 1
undo += 1
if not undo%5:
num_examined -= 1
decision -= 1
status = npp.undo()
if not status:
print("Won't undo")
decision += 1
num_examined += 1
else:
num_undo += 1
#npp.set_body_mode(25000, 0)
print(("Num body: ", num_examined))
decision = 0
undo = 0
num_undo = 0
npp.estimate_work()
estimated = npp.get_estimated_num_remaining_edges()
print(("Num estimated edges: ", estimated))
while npp.get_estimated_num_remaining_edges() > 0:
priority_info = npp.get_next_edge()
xyzlocation = priority_info.location
(body1, body2) = priority_info.body_pair
# proofread body pair at location
npp.set_edge_result(priority_info.body_pair, decision % 2)
decision += 1
num_examined += 1
undo += 1
if not undo % 5:
num_examined -= 1
decision -= 1
status = npp.undo()
if not status:
print("Won't undo")
decision += 1
num_examined += 1
else:
num_undo += 1
#npp.set_body_mode(25000, 0)
print(("Num body: ", num_examined))
num_examined = 0
if not status:
exit(1)
npp.set_edge_mode(0.1, 0.9, 0.5)
while npp.get_estimated_num_remaining_edges() > 0:
priority_info = npp.get_next_edge()
xyzlocation = priority_info.location
(body1, body2) = priority_info.body_pair
# proofread body pair at location
npp.set_edge_result(priority_info.body_pair, False)
num_examined += 1
if num_examined != 610:
exit(1)
npp.export_priority_scheduler(sys.argv[2])
tempfile = open(sys.argv[2])
groundfile = open(sys.argv[3])
tempout = tempfile.read()
groundout = groundfile.read()
if tempout != groundout:
exit(1)
#!/usr/bin/python
import libNeuroProofPriority as npp
status = npp.initialize_priority_scheduler("examples/graph.json", .1, .9, .5)
num_examined = 0
if status:
while npp.get_estimated_num_remaining_edges() > 0:
priority_info = npp.get_next_edge()
xyzlocation = priority_info.location
(body1, body2) = priority_info.body_pair
# proofread body pair at location
npp.set_edge_result(priority_info.body_pair, 1.0)
num_examined += 1
print "Num examined: " , num_examined
npp.export_priority_scheduler("output.json")
