def auto_proofread(body2gtbody, rag_file, size_threshold, master_logger, test_stack, session_location):
nomerge_hist = []
tot_hist = []
nomerge_hist2 = []
tot_hist2 = []
dirtybodies = set()
for iter1 in range(0, 101):
nomerge_hist.append(0)
tot_hist.append(0)
nomerge_hist2.append(0)
tot_hist2.append(0)
neuroproof.initialize_priority_scheduler(rag_file, 0.1, 0.9, 0.1)
bodyremap = {}
num_body = 0
neuroproof.set_body_mode(size_threshold, 0)
while neuroproof.get_estimated_num_remaining_edges() > 0:
process_edge(body2gtbody, nomerge_hist, tot_hist, nomerge_hist2, tot_hist2, dirtybodies, bodyremap)
num_body += 1
num_synapse = 0
neuroproof.set_synapse_mode(0.1)
while neuroproof.get_estimated_num_remaining_edges() > 0:
process_edge(body2gtbody, nomerge_hist, tot_hist, nomerge_hist2, tot_hist2, dirtybodies, bodyremap)
num_synapse += 1
num_orphan = 0
neuroproof.set_orphan_mode(size_threshold, size_threshold, size_threshold)
while neuroproof.get_estimated_num_remaining_edges() > 0:
process_edge(body2gtbody, nomerge_hist, tot_hist, nomerge_hist2, tot_hist2, dirtybodies, bodyremap)
num_orphan += 1
master_logger.info("Probability Actual Agreement with Groundtruth Flat")
for iter1 in range(0, 101):
if tot_hist[iter1] == 0:
per = 0
else:
per = (float(nomerge_hist[iter1])/float(tot_hist[iter1]) * 100)
print iter1, ", ", per , ", " , tot_hist[iter1]
master_logger.info("Probability Actual Agreement with Groundtruth Est")
for iter1 in range(0, 101):
if tot_hist2[iter1] == 0:
per = 0
else:
per = (float(nomerge_hist2[iter1])/float(tot_hist2[iter1]) * 100)
print iter1, ", ", per , ", " , tot_hist2[iter1]
body2body = {}
for key, vallist in bodyremap.items():
for body in vallist:
body2body[body] = key
os.system("cp -R " + test_stack + "/superpixel_maps " + session_location + "/")
os.system("cp " + test_stack + "/superpixel_to_segment_map.txt " + session_location + "/")
mapping_file = open(test_stack + "/segment_to_body_map.txt")
outfile = open(session_location + "/segment_to_body_map.txt", 'w')
for line in mapping_file.readlines():
vals = line.split(' ')
seg = int(vals[0])
body = int(vals[1])
if body in body2body:
body = body2body[body]
outfile.write(str(seg) + " " + str(body) + "\n")
master_logger.info("Num body: " + str(num_body))
master_logger.info("Num synapse: " + str(num_synapse))
master_logger.info("Num orphan: " + str(num_orphan))
master_logger.info("Num total: " + str(num_body + num_synapse + num_orphan))
import libNeuroProofPriority as npp
import sys
import random
# read file
status = npp.initialize_priority_scheduler(sys.argv[1], .1, .9, .5)
print(status)
# body mode
#npp.set_edge_mode(0.1, 0.9, 0.5)
num_examined = 0
npp.set_body_mode(25000, 0)
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
#!/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")
import libNeuroProofPriority as npp
import sys
status = npp.initialize_priority_scheduler(sys.argv[1], .1, .9, .5)
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()
import libNeuroProofPriority as npp
import sys
graph_json = sys.argv[1]
output_path = sys.argv[2]
groundtruth_path = sys.argv[3]
status = npp.initialize_priority_scheduler(graph_json, .1, .9, .5)
assert status, "initialize_priority_scheduler() failed."
npp.set_edge_mode(0.1, 0.9, 0.5)
num_examined = 0
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
assert num_examined == 610
npp.export_priority_scheduler(output_path)
tempout = open(output_path).read()
groundout = open(groundtruth_path).read()
assert tempout == groundout, \
