def testGenerateCollar(self):
self.assertEqual(self.sc1.generate_collar(2),
S({
3: 1,
7: 0,
8: 1,
12: 0
}))
# Input signal should be normalized in the generate_collar
# function
self.assertEqual(self.sc2.generate_collar(2),
S({
3: 1,
7: 0,
8: 1,
12: 0
}))
self.assertEqual(self.sc3.generate_collar(2),
S({
-2: 1,
2: 0,
8: 1,
12: 0
}))
# Output signal should be normalized
self.assertEqual(self.sc1.generate_collar(5), S({0: 1, 15: 0}))
def testSubtraction(self):
self.assertEqual(self.s1 - self.se, self.s1)
self.assertEqual(self.se - self.s1, self.se)
self.assertEqual(self.s1 - self.si1, S({30: 1, 45: 0}))
self.assertEqual(self.si1 - self.s1, S({60: 1, 100: 0}))
self.assertEqual(self.sb1 - self.s1, S({0: 1, 100: 0}))
self.assertEqual(self.sb1 - self.sb3, S({0: 1, 60: 0}))
self.assertEqual(self.sb3 - self.sb1, S({60: 1, 130: 0}))
self.assertEqual(self.sb1 - self.se, self.sb1)
def testUnion(self):
self.assertEqual(self.se | self.se, self.se)
self.assertEqual(self.s1 | self.s1, self.s1)
self.assertEqual(self.s1 | self.s2, S({0: 1, 10: 0, 30: 1, 60: 0}))
self.assertEqual(self.s2 | self.s1, S({0: 1, 10: 0, 30: 1, 60: 0}))
self.assertEqual(self.s1 | self.s5 | self.s6, self.s7)
self.assertEqual(self.s5 | self.s7, self.s7)
self.assertEqual(self.s7 | self.s5, self.s7)
self.assertEqual(
S({
'0': 1,
'20941': 0
}) | S({
'19337': 1,
'19352': 0
}), S({
'0': 1,
'20941': 0
}))
self.assertEqual(
S({
'0': 1,
'20941': 0
}) | S({
'4288': 1,
'4636': 0
}), S({
'0': 1,
'20941': 0
}))
def testSignalMultiAdd(self):
self.assertEqual(reduce(add, [self.s1, self.s1, self.s1]),
S({
30: 3,
60: 0
}))
self.assertEqual(reduce(add, [self.s1, self.s2, self.s3]),
S({
0: 1,
10: 0,
30: 2,
40: 1,
60: 0
}))
def build_ref_sig(ref, file_framedur_lookup
): #(aligned_pairs, missed_ref, file_framedur_lookup):
# Need to modify join, find ways to keep from doing full join each time.
# reference stays the same, calculated once. system, just need to include
# new false alarms
#
#print "Building Ref Sig"
#num_aligned = len(ref) #aligned_pairs) + len(missed_ref)
ref_temp_add = {}
not_ref = {}
nr_area = {}
if ref == {}: #num_aligned == 0:
return [{}, {}, {}]
for key, value in ref.items():
ref_temp = [temporal_single_signal(b) for b in value]
ref_temp_add[key] = reduce(add, [r[0] for r in ref_temp], S())
not_ref[key] = ref_temp_add[key].not_sig(file_framedur_lookup[key])
nr_area[key] = not_ref[key].area()
#ref_temp = [temporal_single_signal(b) for b in aligned_pairs ] + [ temporal_single_signal(m) for m in missed_ref ] #list(itertools.chain([temporal_single_signal(b) for b in aligned_pairs], [temporal_single_signal(m) for m in missed_ref])) #[temporal_single_signal(r) for r in combined_ref ]
#ref_temp_add=reduce(add, [r[0] for r in ref_temp], S())
#not_ref=ref_temp_add.not_sig(file_framedur_lookup.get(ref_temp[0][1]))
#nr_area=not_ref.area()
#print "ref_temp_add"
#print ref_temp_add
#print "not_ref"
#print not_ref
#print "nr_area"
#print nr_area
return [ref_temp_add, not_ref, nr_area]
def testIntersection(self):
self.assertEqual(self.s1 & self.se, self.se)
self.assertEqual(self.s1 & self.s1, self.s1)
self.assertEqual(self.s1 & self.s2, self.se)
self.assertEqual(self.s1 & self.s3, self.s3)
self.assertEqual(self.s3 & self.s1, self.s3)
self.assertEqual(self.s1 & self.sb1, self.s1)
self.assertEqual(self.s1 & self.si1, S({45: 1, 60: 0}))
def setUp(self):
super(TestNMIDE, self).setUp()
self.cost_fn = lambda x: 1 * x
self.r1 = S({5: 1, 15: 0})
self.s1 = S({5: 1, 15: 0})
self.s2 = S({2: 1, 10: 0})
self.r3 = S({5: 1, 15: 0, 30: 1, 35: 0})
self.s3 = S({10: 1, 15: 0, 40: 1, 50: 0})
self.r4 = S({30: 1, 35: 0})
self.c1 = [(A({ "f1": self.r1 }), A({ "f1": self.s1 })),
(A({ "f1": self.r3 }), A({ "f1": self.s3 }))]
self.cd = [(A({ "f1": self.r1, "f2": self.r1 }), A({ "f1": self.s1, "f2": self.s1 })),
(A({ "f1": self.r3, "f2": self.r3 }), A({ "f1": self.s3, "f2": self.s3 }))]
self.c3 = [(A({ "f1": self.r1 }), A({ "f2": self.s1 })),
(A({ "f1": self.r3 }), A({ "f2": self.s3 }))]
self.c4 = [(A({ "f1": self.r1 }), A({ "f1": self.s2 })),
(A({ "f1": self.r4 }), A({ "f1": self.s3 }))]
self.filedur_1 = { "f1": 80, "f2": 100 }
# Testing 0 FA denominator
self.filedur_2 = { "f1": 15 }
self.cost_0 = lambda x: 0 * x
def _sweep(alignment_records):
c, m, f = partition_alignment(alignment_records)
if file_framedur_lookup != 0:
ref_sigs = build_ref_sig([ (ar.ref) for ar in c ],
[(ar.ref) for ar in m],
file_framedur_lookup )
fa_func=measure_funcs.pop()
#sys_sig = build_sys_sig([ (ar.sys) for ar in c ])
#print "sys_sig"
#print sys_sig
sys_sig=[[],S()]
#sys_sig[0] = []
#sys_sig[1] = S()
total_c = len(c)
num_m = len(m)
out_points = []
current_c, current_f = [], []
# m records don't need to be sorted as they have None
# confidence scores
current_m = m + sorted(c, None, conf_key_func)
remaining_f = sorted(f, None, conf_key_func)
uniq_confs = sorted(set(map(conf_key_func, c + f)), reverse = True)
for conf in uniq_confs:
newsig = []
newsig_tem = []
while len(current_m) > 0 and current_m[-1].alignment != "MD" and conf_key_func(current_m[-1]) >= conf:
if file_framedur_lookup != 0:
newsig.append(current_m[-1])
current_c.append(current_m.pop())
while len(remaining_f) > 0 and conf_key_func(remaining_f[-1]) >= conf:
if file_framedur_lookup != 0:
newsig.append(remaining_f[-1])
current_f.append(remaining_f.pop())
if file_framedur_lookup != 0:
if len(newsig) != 0:
newsig_tem = add_sys_sig(sys_sig[0],[(ar.sys) for ar in newsig])
sys_sig[0] = sys_sig[0] + newsig_tem
sys_sig[1] = reduce(add, [s[0] for s in newsig_tem], sys_sig[1])
out_points.append((conf, reduce(merge_dicts, [ m(current_c, current_m, current_f) for m in measure_funcs ], fa_func(ref_sigs, sys_sig))))
else:
out_points.append((conf, reduce(merge_dicts, [ m(current_c, current_m, current_f) for m in measure_funcs ], {})))
#print "out_points"
#print out_points
#print "sys_sig"
#print sys_sig
#out_points.append((conf, reduce(merge_dicts, [ m(current_c, current_m, current_f) for m in measure_funcs ], fa_func(ref_sigs, sys_sig))))
return out_points
def build_ref_sig(ref, file_framedur_lookup):
# Need to modify join, find ways to keep from doing full join each time.
# reference stays the same, calculated once. system, just need to include
# new false alarms
ref_temp_add = {}
not_ref = {}
nr_area = {}
if ref == {}:
return [{}, {}, {}]
for key, value in ref.items():
ref_temp = [temporal_single_signal(b) for b in value]
ref_temp_add[key] = reduce(add, [r[0] for r in ref_temp], S())
not_ref[key] = ref_temp_add[key].not_sig(file_framedur_lookup[key])
nr_area[key] = not_ref[key].area()
return[ref_temp_add, not_ref, nr_area]
def build_ref_sig(aligned_pairs, missed_ref, file_framedur_lookup):
# Need to modify join, find ways to keep from doing full join each time.
# reference stays the same, calculated once. system, just need to include
# new false alarms
#
#print "Building Ref Sig"
num_aligned = len(aligned_pairs) + len(missed_ref)
if num_aligned == 0:
return [{},{},0]
#combined_ref = itertools.chain([temporal_single_signal(b) for b in aligned_pairs], [temporal_single_signal(m) for m in missed_ref]) #[b for b in aligned_pairs] + [m for m in missed_ref] #works
ref_temp = [temporal_single_signal(b) for b in aligned_pairs ] + [ temporal_single_signal(m) for m in missed_ref ] #list(itertools.chain([temporal_single_signal(b) for b in aligned_pairs], [temporal_single_signal(m) for m in missed_ref])) #[temporal_single_signal(r) for r in combined_ref ]
ref_temp_add=reduce(add, [r[0] for r in ref_temp], S())
#ref_temp_add = reduce(add,ref_temp) #special_join(ref_temp)
not_ref=ref_temp_add.not_sig(file_framedur_lookup.get(ref_temp[0][1]))
nr_area=not_ref.area()
return[ref_temp_add, not_ref, nr_area]
def test2D(self):
self.assertEqual(self.s2d_1.area(), 250)
self.assertEqual(self.s2d_2.area(), 250)
self.assertEqual(self.s2d_1.join(self.s2d_1.join(self.s2d_1, lambda a, b: a.join(b, min, 0), S()), lambda a, b: a.join(b, sub, 0), S()), S())
self.assertEqual(self.s2d_1.join(self.s2d_2, lambda a, b: a.join(b, min, 0), S()), S({10: S({15: 1, 20: 0}), 15: S(), 30: S({15: 1, 25: 0}), 35: S()}))
self.assertEqual(self.s2d_1.join_nd(self.s2d_2, 2, min), S({10: S({15: 1, 20: 0}), 15: S(), 30: S({15: 1, 25: 0}), 35: S()}))
self.assertEqual(self.s2d_1.join_nd(S(), 2, max), self.s2d_1)
self.assertEqual(self.s2d_1.join_nd(self.s2d_2, 2, lambda a, b: a - min(a, b)), S({10: S({10: 1, 15: 0}), 15: S(), 30: S({25: 1, 35: 0}), 35: S({15: 1, 35: 0}), 40: S()}))
self.assertEqual(self.s2d_1.join_nd(self.s2d_2, 2, lambda a, b: a - min(a, b)).area(), 175)
self.assertEqual(self.s2d_1.join_nd(self.s2d_2, 2, max), S({10: S({10: 1, 25: 0}), 15: S({15: 1, 25: 0}), 30: S({15: 1, 35: 0}), 40: S()}))
self.assertEqual(self.s2d_1.join_nd(self.s2d_2, 2, max).area(), 425)
def _temporal_signal_accessor(localization, k):
return S(localization.get(k, {}))
def setUp(self):
self.ae = A({})
self.a1 = A({"f1": O(S({10: S({10: 1, 20: 0}), 15: S()}))})
self.a2 = A({"f1": O(S({10: S({15: 1, 25: 0}), 35: S()}))})
self.a3 = A({"f1": O(S({30: S({15: 1, 35: 0}), 40: S()}))})
self.a4 = A({
"f1": O(S({
10: S({
10: 1,
20: 0
}),
15: S()
})),
"f2": O(S({
30: S({
15: 1,
35: 0
}),
40: S()
}))
})
self.a5 = A({
"f1": O(S({
10: S({
15: 1,
25: 0
}),
35: S()
})),
"f2": O(S({
10: S({
15: 1,
25: 0
}),
35: S()
}))
})
def _sweep(alignment_records):
c, m, f = partition_alignment(alignment_records)
sys_sig = {}
sys_sig_add = {}
ref_all = {}
if file_framedur_lookup != 0:
for key in file_framedur_lookup:
sys_sig[key] = []
sys_sig_add[key] = S()
ref_all[key] = []
for ar in c:
ref_all[ar.video_file].append(ar.ref)
for ar in m:
ref_all[ar.video_file].append((ar.ref))
ref_sigs = build_ref_sig(ref_all, file_framedur_lookup)
fa_func = measure_funcs.pop()
out_points = []
current_c, current_f = [], []
# m records don't need to be sorted as they have None
# confidence scores
current_m = m + sorted(c, key=conf_key_func)
remaining_f = sorted(f, key=conf_key_func)
uniq_confs = sorted(set(map(conf_key_func, c + f)), reverse=True)
if uniq_conf_limit != 0:
if (len(uniq_confs) > uniq_conf_limit):
le = len(uniq_confs)
indices = np.round(np.linspace(0, len(uniq_confs)-1,
min(len(uniq_confs), uniq_conf_limit))
).astype(int)
uniq_confs = list(np.array(uniq_confs)[indices])
print("""[Info] Reducing to {} unique confidence scores for"""
""" Sweeping to {} [{},{}] unique confidence scores"""
"""""".format(
le, len(uniq_confs), uniq_confs[0], uniq_confs[-1]))
for conf in uniq_confs:
newsig = {}
newsig_tem = {}
while len(current_m) > 0 and current_m[-1].alignment != "MD" and \
conf_key_func(current_m[-1]) >= conf:
if file_framedur_lookup != 0:
if not current_m[-1].video_file in newsig:
newsig[current_m[-1].video_file] = []
newsig[current_m[-1].video_file].append(current_m[-1])
current_c.append(current_m.pop())
while len(remaining_f) > 0 and \
conf_key_func(remaining_f[-1]) >= conf:
if file_framedur_lookup != 0:
if not remaining_f[-1].video_file in newsig:
newsig[remaining_f[-1].video_file] = []
newsig[remaining_f[-1].video_file].append(remaining_f[-1])
current_f.append(remaining_f.pop())
if file_framedur_lookup != 0:
if newsig != {}:
for key, value in newsig.items():
newsig_tem[key] = add_sys_sig(
sys_sig[key], [(ar.sys) for ar in newsig[key]])
sys_sig[key] = sys_sig[key] + newsig_tem[key]
sys_sig_add[key] = reduce(
add, [s[0] for s in newsig_tem[key]],
sys_sig_add[key])
out_points.append((
conf, reduce(merge_dicts, [
m(current_c, current_m, current_f) for
m in measure_funcs], fa_func(
ref_sigs, sys_sig, sys_sig_add))))
else:
out_points.append((
conf, reduce(merge_dicts, [
m(current_c, current_m, current_f) for
m in measure_funcs], {})))
return out_points
def _bounding_box_to_signal(bounding_box):
x, y, w, h = map(lambda e: bounding_box[e], ("x", "y", "w", "h"))
return S({x: S({y: 1, y + h: 0}), x + w: S()})
def testSignalAdditionPass3(self):
self.assertEqual(self.s1 + self.s5, S({0: 1, 60: 0}))
self.assertEqual(self.s1 + self.s1, S({30: 2, 60: 0}))
self.assertEqual(self.s1 + self.s6, S({30: 1, 100: 0}))
self.assertEqual(self.s1 + self.s7, S({0: 1, 30: 2, 60: 1, 100: 0}))
def testSignalAdditionPass2(self):
self.assertEqual(self.s1 + self.s2, S({0: 1, 10: 0, 30: 1, 60: 0}))
self.assertEqual(self.s1 + self.s3, S({30: 2, 40: 1, 60: 0}))
self.assertEqual(self.s1 + self.s4, S({30: 1, 80: 0}))
def testSignalEquivalence(self):
self.assertEqual(self.se, self.se)
self.assertEqual(self.s1, self.s1)
self.assertEqual(S({30: 1, 60: 0}), self.s1)
def setUp(self):
super(TestSparseSignal, self).setUp()
self.se = S()
self.s1 = S({30: 1, 60: 0})
self.s2 = S({0: 1, 10: 0})
self.s3 = S({30: 1, 40: 0})
self.s4 = S({60: 1, 80: 0})
self.s5 = S({0: 1, 30: 0})
self.s6 = S({60: 1, 100: 0})
self.s7 = S({0: 1, 100: 0})
self.sb1 = S({0: 1, 30: 2, 60: 1, 100: 0})
self.sb2 = S({30: 2, 40: 1, 60: 0})
self.sb3 = S({30: 1, 60: 2, 100: 1, 130: 0})
self.ss1 = S({0: 1, 30: 0, 60: 1, 100: 0})
self.si1 = S({45: 1, 100: 0})
self.sd1 = S({10: 1, 20: 0})
self.sd2 = S({10.45: 1, 20: 0})
self.sd3 = S({10.00: 1, 20.85: 0})
self.sd4 = S({10.90: 1, 20.35: 0})
self.sd5 = S({0.12: 1, 30.32: 0, 30.7: 1, 100.2: 0})
self.sd6 = S({0.12: 1, 30.32: 2, 60.7: 1, 100.2: 0})
self.sn1 = S({0: 1, 10: 1, 20: 0})
self.sc1 = S({5: 1, 10: 0})
self.sc2 = S({5: 1, 7:1, 10: 0})
self.sc3 = S({0: 1, 10: 0})
self.s2d_1 = S({10: S({10: 1, 20: 0}), 15: S(), 30: S({15: 1, 35: 0}), 40: S()})
self.s2d_2 = S({10: S({15: 1, 25: 0}), 35: S()})
self.s3d_1 = S({1: S({10: S({10: 1, 20: 0}), 20: S()}), 2: S({10: S({10: 1, 30: 0}), 30: S()}), 4: S()})
self.s3d_2 = S({1: S({10: S({10: 1, 20: 0}), 20: S()}), 4: S()})
self.s_iter1 = S({3: 1, 5: 2, 10: 0})
self.s_iter2 = S({3: 1, 5: 0, 7: 1, 10: 0})
def __init__(self, bounding_box, conf, obj_type, obj_id):
self.spatial_signal = _bounding_box_to_signal(
bounding_box) if bounding_box else S()
self.presenceConf = conf
self.objectType = obj_type
self.objectID = obj_id
def _spatial_signal_accessor(localization, k):
if k in localization:
return localization.get(k).spatial_signal
else:
return S()
def _temporal_signal_accessor(localization, k):
#print 'localization:'
#print localization
return S(localization.get(k, {}))
def _object_congruence(r, s, obj_kernel_builder, ref_filter, sys_filter, cmiss,
cfa, target_rfas):
ro, so = r.objects, s.objects
# For N_MODE computation, localizations spanning multiple files
# are treated independently
total_c, total_m, total_f, total_r = [], [], [], []
frame_alignment_records = []
# We need to report out the ref filter localization for aggregate
# PMiss@RFA measurements
ref_filter_localization = {}
for r, s, k in temporal_signal_pairs(r, s):
local_so_localizations = map(lambda o: o.localization.get(k, S()), so)
local_ro_localizations = map(lambda o: o.localization.get(k, S()), ro)
sos_lookup = _object_signals_to_lookup(sys_filter(r, s),
local_so_localizations)
ros_lookup = _object_signals_to_lookup(ref_filter(r, s),
local_ro_localizations)
ref_filter_localization[k] = ref_filter(r, s)
for frame in sos_lookup.viewkeys() | ros_lookup.viewkeys():
sys = sos_lookup.get(frame, [])
ref = ros_lookup.get(frame, [])
c, m, f = perform_alignment(ref, sys, obj_kernel_builder(sys))
total_c.extend(c)
total_m.extend(m)
total_f.extend(f)
total_r.extend(ref)
for ar in c + m + f:
frame_alignment_records.append((frame, ar))
num_miss = len(total_m)
num_correct = len(total_c)
ref_filter_area = sum([v.area() for v in ref_filter_localization.values()])
def _conf_sweep_reducer(init, conf):
mode_scores, det_points = init
num_filtered_c = len(
filter(lambda ar: ar.sys.presenceConf >= conf, total_c))
num_filtered_fa = len(
filter(lambda ar: ar.sys.presenceConf >= conf, total_f))
num_miss_w_filtered_c = num_miss + num_correct - num_filtered_c
mode_scores.append((conf,
mode(num_filtered_c, num_miss_w_filtered_c,
num_filtered_fa, cmiss, cfa)))
det_points.append((conf,
r_fa(num_filtered_c, num_miss_w_filtered_c,
num_filtered_fa, ref_filter_area),
p_miss(num_filtered_c, num_miss_w_filtered_c,
num_filtered_fa)))
return init
mode_scores, det_points = reduce(
_conf_sweep_reducer,
sorted(list({ar.sys.presenceConf
for ar in total_c + total_f})), ([], []))
min_mode = min(map(lambda x: x[1],
mode_scores)) if len(mode_scores) > 0 else None
pmiss_at_rfa_measures = {
"object-p_miss@{}rfa".format(target_rfa):
p_miss_at_r_fa(det_points, target_rfa)
for target_rfa in target_rfas
}
out_components = {
"object_congruence": 1 - min_mode if min_mode is not None else None,
"minMODE": min_mode,
"MODE_records": mode_scores,
"alignment_records": frame_alignment_records,
"det_points": det_points,
"ref_filter_localization": ref_filter_localization
}
return merge_dicts(out_components, pmiss_at_rfa_measures)
def _object_tracking_congruence(r, s, obj_kernel_builder, ref_filter,
sys_filter, object_types, cmiss, cfa, cid,
target_rfas):
# If object_types provided as non-empty array, only consider objects included in object_types
ro = r.objects if len(object_types) == 0 else filter(
lambda o: o.objectType in object_types, r.objects)
so = s.objects if len(object_types) == 0 else filter(
lambda o: o.objectType in object_types, s.objects)
# For N_MODE computation, localizations spanning multiple files
# are treated independently
total_c, total_m, total_f, total_r = [], [], [], []
frame_alignment_records = []
correct_frame_alignment_records = []
# We need to report out the ref filter localization for aggregate
# PMiss@RFA measurements
ref_filter_localization = {}
# obj_align={}
# FirstRun=True
for r, s, k in temporal_signal_pairs(r, s):
local_so_localizations = map(lambda o: o.localization.get(k, S()), so)
local_ro_localizations = map(lambda o: o.localization.get(k, S()), ro)
sos_lookup = _object_signals_to_lookup(sys_filter(r, s),
local_so_localizations)
ros_lookup = _object_signals_to_lookup(ref_filter(r, s),
local_ro_localizations)
ref_filter_localization[k] = ref_filter(r, s)
FirstRun = True
obj_align = {}
for frame in sos_lookup.viewkeys() | ros_lookup.viewkeys():
sys = sos_lookup.get(frame, [])
ref = ros_lookup.get(frame, [])
c, m, f = perform_alignment(ref, sys, obj_kernel_builder(sys))
#print "C: "
#print c
#print "C: "
#print c
#print "REFOBJECT ID?"
#print c[0].ref.objectID
#print "SYSOBJECT ID?"
#print c[0].sys.objectID
#print "FRAME: "
#print frame
#c.append(obj_switch)
total_c.extend(c)
total_m.extend(m)
total_f.extend(f)
total_r.extend(ref)
for ar in c:
correct_frame_alignment_records.append((frame, ar))
for ar in c + m + f:
frame_alignment_records.append((frame, ar))
#print "correct_frame_alignment"
#print correct_frame_alignment_records
ref_filter_area = sum([v.area() for v in ref_filter_localization.values()])
sweeper = build_sweeper(lambda r: r.sys.presenceConf, [
build_rfa_metric(ref_filter_area),
build_pmiss_metric(),
build_mode_metric(cmiss, cfa),
build_mote_metric(correct_frame_alignment_records,
lambda r: r.sys.presenceConf, cmiss, cfa, cid)
])
sweep_recs = sweeper(total_c + total_m + total_f)
# Filter out None mode scores (in the case of zero reference
# objects)
mode_scores = filter(lambda r: r[1] is not None,
flatten_sweeper_records(sweep_recs, ["mode"]))
mote_scores = filter(lambda r: r[1] is not None,
flatten_sweeper_records(sweep_recs, ["mote"]))
# print "mote_scores"
# print mote_scores
det_points = flatten_sweeper_records(sweep_recs, ["rfa", "p_miss"])
min_mode = min(map(lambda x: x[1],
mode_scores)) if len(mode_scores) > 0 else None
min_mote = min(map(lambda x: x[1],
mote_scores)) if len(mote_scores) > 0 else None
pmiss_at_rfa_measures = get_points_along_confidence_curve(
sweep_recs, "rfa", lambda r: r["rfa"], "object-p_miss",
lambda r: r["p_miss"], target_rfas)
out_components = {
"object_congruence": 1 - min_mode if min_mode is not None else None,
"minMODE": min_mode,
"MODE_records": mode_scores,
"object_tracking_congruence":
min_mote if min_mote is not None else None,
"minMOTE": min_mote,
"MOTE_records": mote_scores,
"alignment_records": frame_alignment_records,
"det_points": det_points,
"ref_filter_localization": ref_filter_localization
}
return merge_dicts(out_components, pmiss_at_rfa_measures)
def testNormalize(self):
self.assertEqual(self.sn1.normalize(), S({0: 1, 20: 0}))
