def preprocessResult(res, gt, inifile):
"""Preprocesses data for utils.CLEAR_MOT_M.
Returns a subset of the predictions.
"""
# pylint: disable=too-many-locals
st = time.time()
labels = [
'ped', # 1
'person_on_vhcl', # 2
'car', # 3
'bicycle', # 4
'mbike', # 5
'non_mot_vhcl', # 6
'static_person', # 7
'distractor', # 8
'occluder', # 9
'occluder_on_grnd', # 10
'occluder_full', # 11
'reflection', # 12
'crowd', # 13
]
distractors = [
'person_on_vhcl', 'static_person', 'distractor', 'reflection'
]
is_distractor = {i + 1: x in distractors for i, x in enumerate(labels)}
for i in distractors:
is_distractor[i] = 1
seqIni = ConfigParser()
seqIni.read(inifile, encoding='utf8')
F = int(seqIni['Sequence']['seqLength'])
todrop = []
for t in range(1, F + 1):
if t not in res.index or t not in gt.index:
continue
resInFrame = res.loc[t]
GTInFrame = gt.loc[t]
A = GTInFrame[['X', 'Y', 'Width', 'Height']].values
B = resInFrame[['X', 'Y', 'Width', 'Height']].values
disM = mmd.iou_matrix(A, B, max_iou=0.5)
le, ri = linear_sum_assignment(disM)
flags = [
1 if is_distractor[it['ClassId']] or it['Visibility'] < 0. else 0
for i, (k, it) in enumerate(GTInFrame.iterrows())
]
hid = [k for k, it in resInFrame.iterrows()]
for i, j in zip(le, ri):
if not np.isfinite(disM[i, j]):
continue
if flags[i]:
todrop.append((t, hid[j]))
ret = res.drop(labels=todrop)
logging.info('Preprocess take %.3f seconds and remove %d boxes.',
time.time() - st, len(todrop))
return ret
def preprocessResult(res, gt, inifile):
st = time.time()
labels = ['ped', # 1
'person_on_vhcl', # 2
'car', # 3
'bicycle', # 4
'mbike', # 5
'non_mot_vhcl', # 6
'static_person', # 7
'distractor', # 8
'occluder', # 9
'occluder_on_grnd', #10
'occluder_full', # 11
'reflection', # 12
'crowd' # 13
]
distractors_ = ['person_on_vhcl','static_person','distractor','reflection']
distractors = {i+1 : x in distractors_ for i,x in enumerate(labels)}
for i in distractors_:
distractors[i] = 1
seqIni = ConfigParser()
seqIni.read(inifile, encoding='utf8')
F = int(seqIni['Sequence']['seqLength'])
todrop = []
for t in range(1,F+1):
if t not in res.index or t not in gt.index: continue
#st = time.time()
resInFrame = res.loc[t]
N = len(resInFrame)
GTInFrame = gt.loc[t]
Ngt = len(GTInFrame)
A = GTInFrame[['X','Y','Width','Height']].values
B = resInFrame[['X','Y','Width','Height']].values
disM = mmd.iou_matrix(A, B, max_iou = 0.5)
#en = time.time()
#print('----', 'disM', en - st)
le, ri = linear_sum_assignment(disM)
flags = [1 if distractors[it['ClassId']] or it['Visibility']<0. else 0 for i,(k,it) in enumerate(GTInFrame.iterrows())]
hid = [k for k,it in resInFrame.iterrows()]
for i, j in zip(le, ri):
if not np.isfinite(disM[i, j]):
continue
if flags[i]:
todrop.append((t, hid[j]))
#en = time.time()
#print('Frame %d: '%t, en - st)
ret = res.drop(labels=todrop)
logging.info('Preprocess take %.3f seconds and remove %d boxes.'%(time.time() - st, len(todrop)))
return ret
def compute_iou(a, b):
return iou_matrix(a, b, max_iou=distth)