def evaluateAP(gtFramesAll, prFramesAll, outputDir, bSaveAll=True, bSaveSeq=False):
distThresh = 0.5
seqidxs = []
for imgidx in range(len(gtFramesAll)):
seqidxs += [gtFramesAll[imgidx]["seq_id"]]
seqidxs = np.array(seqidxs)
seqidxsUniq = np.unique(seqidxs)
nSeq = len(seqidxsUniq)
names = Joint().name
names['15'] = 'total'
if (bSaveSeq):
for si in range(nSeq):
print("seqidx: %d/%d" % (si+1,nSeq))
# extract frames IDs for the sequence
imgidxs = np.argwhere(seqidxs == seqidxsUniq[si])
seqName = gtFramesAll[imgidxs[0,0]]["seq_name"]
gtFrames = [gtFramesAll[imgidx] for imgidx in imgidxs.flatten().tolist()]
prFrames = [prFramesAll[imgidx] for imgidx in imgidxs.flatten().tolist()]
# assign predicted poses to GT poses
scores, labels, nGT, _ = eval_helpers.assignGTmulti(gtFrames, prFrames, distThresh)
# compute average precision (AP), precision and recall per part
ap, pre, rec = computeMetrics(scores, labels, nGT)
metricsSeq = {'ap': ap.flatten().tolist(), 'pre': pre.flatten().tolist(), 'rec': rec.flatten().tolist(), 'names': names}
filename = outputDir + '/' + seqName + '_AP_metrics.json'
print('saving results to', filename)
eval_helpers.writeJson(metricsSeq,filename)
# assign predicted poses to GT poses
scoresAll, labelsAll, nGTall, _ = eval_helpers.assignGTmulti(gtFramesAll, prFramesAll, distThresh)
# compute average precision (AP), precision and recall per part
apAll, preAll, recAll = computeMetrics(scoresAll, labelsAll, nGTall)
if (bSaveAll):
metrics = {'ap': apAll.flatten().tolist(), 'pre': preAll.flatten().tolist(), 'rec': recAll.flatten().tolist(), 'names': names}
filename = outputDir + '/total_AP_metrics.json'
print('saving results to', filename)
eval_helpers.writeJson(metrics,filename)
return apAll, preAll, recAll
def evaluateAP(gtFramesAll, prFramesAll, bSaveAll=True, bSaveSeq=False):
distThresh = 0.5
names = Joint().name
names['17'] = 'total'
# assign predicted poses to GT poses
scoresAll, labelsAll, nGTall = eval_helpers.assignGTmulti(gtFramesAll, prFramesAll, distThresh)
# compute average precision (AP), precision and recall per part
apAll, preAll, recAll = computeMetrics(scoresAll, labelsAll, nGTall)
if (bSaveAll):
metrics = {'ap': apAll.flatten().tolist(), 'pre': preAll.flatten().tolist(), 'rec': recAll.flatten().tolist(), 'names': names}
filename = './total_AP_metrics.json'
eval_helpers.writeJson(metrics,filename)
return apAll, preAll, recAll
def computeMetrics(gtFramesAll, motAll, outputDir, bSaveAll, bSaveSeq):
assert (len(gtFramesAll) == len(motAll))
nJoints = Joint().count
seqidxs = []
for imgidx in range(len(gtFramesAll)):
seqidxs += [gtFramesAll[imgidx]["seq_id"]]
seqidxs = np.array(seqidxs)
seqidxsUniq = np.unique(seqidxs)
# intermediate metrics
metricsMidNames = [
'num_misses', 'num_switches', 'num_false_positives', 'num_objects',
'num_detections'
]
# final metrics computed from intermediate metrics
metricsFinNames = ['mota', 'motp', 'pre', 'rec']
# initialize intermediate metrics
metricsMidAll = {}
for name in metricsMidNames:
metricsMidAll[name] = np.zeros([1, nJoints])
metricsMidAll['sumD'] = np.zeros([1, nJoints])
# initialize final metrics
metricsFinAll = {}
for name in metricsFinNames:
metricsFinAll[name] = np.zeros([1, nJoints + 1])
# create metrics
mh = mm.metrics.create()
imgidxfirst = 0
# iterate over tracking sequences
# seqidxsUniq = seqidxsUniq[:20]
nSeq = len(seqidxsUniq)
# initialize per-sequence metrics
metricsSeqAll = {}
for si in range(nSeq):
metricsSeqAll[si] = {}
for name in metricsFinNames:
metricsSeqAll[si][name] = np.zeros([1, nJoints + 1])
names = Joint().name
names['15'] = 'total'
for si in range(nSeq):
print("seqidx: %d/%d" % (si + 1, nSeq))
# init per-joint metrics accumulator
accAll = {}
for i in range(nJoints):
accAll[i] = mm.MOTAccumulator(auto_id=True)
# extract frames IDs for the sequence
imgidxs = np.argwhere(seqidxs == seqidxsUniq[si])
imgidxs = imgidxs[:-1].copy()
seqName = gtFramesAll[imgidxs[0, 0]]["seq_name"]
print(seqName)
# create an accumulator that will be updated during each frame
# iterate over frames
for j in range(len(imgidxs)):
imgidx = imgidxs[j, 0]
# iterate over joints
for i in range(nJoints):
# GT tracking ID
trackidxGT = motAll[imgidx][i]["trackidxGT"]
# prediction tracking ID
trackidxPr = motAll[imgidx][i]["trackidxPr"]
# distance GT <-> pred part to compute MOT metrics
# 'NaN' means force no match
dist = motAll[imgidx][i]["dist"]
# Call update once per frame
accAll[i].update(
trackidxGT, # Ground truth objects in this frame
trackidxPr, # Detector hypotheses in this frame
dist # Distances from objects to hypotheses
)
# compute intermediate metrics per joint per sequence
for i in range(nJoints):
metricsMid = mh.compute(accAll[i],
metrics=metricsMidNames,
return_dataframe=False,
name='acc')
for name in metricsMidNames:
metricsMidAll[name][0, i] += metricsMid[name]
s = accAll[i].events['D'].sum()
if (np.isnan(s)):
s = 0
metricsMidAll['sumD'][0, i] += s
if (bSaveSeq):
# compute metrics per joint per sequence
for i in range(nJoints):
metricsMid = mh.compute(accAll[i],
metrics=metricsMidNames,
return_dataframe=False,
name='acc')
# compute final metrics per sequence
if (metricsMid['num_objects'] > 0):
numObj = metricsMid['num_objects']
else:
numObj = np.nan
numFP = metricsMid['num_false_positives']
metricsSeqAll[si]['mota'][0, i] = 100 * (
1. - 1. * (metricsMid['num_misses'] +
metricsMid['num_switches'] + numFP) / numObj)
numDet = metricsMid['num_detections']
s = accAll[i].events['D'].sum()
if (numDet == 0 or np.isnan(s)):
metricsSeqAll[si]['motp'][0, i] = 0.0
else:
metricsSeqAll[si]['motp'][0, i] = 100 * (1. -
(1. * s / numDet))
if (numFP + numDet > 0):
totalDet = numFP + numDet
else:
totalDet = np.nan
metricsSeqAll[si]['pre'][0, i] = 100 * (1. * numDet / totalDet)
metricsSeqAll[si]['rec'][0, i] = 100 * (1. * numDet / numObj)
# average metrics over all joints per sequence
idxs = np.argwhere(
~np.isnan(metricsSeqAll[si]['mota'][0, :nJoints]))
metricsSeqAll[si]['mota'][0, nJoints] = metricsSeqAll[si]['mota'][
0, idxs].mean()
idxs = np.argwhere(
~np.isnan(metricsSeqAll[si]['motp'][0, :nJoints]))
metricsSeqAll[si]['motp'][0, nJoints] = metricsSeqAll[si]['motp'][
0, idxs].mean()
idxs = np.argwhere(
~np.isnan(metricsSeqAll[si]['pre'][0, :nJoints]))
metricsSeqAll[si]['pre'][0, nJoints] = metricsSeqAll[si]['pre'][
0, idxs].mean()
idxs = np.argwhere(
~np.isnan(metricsSeqAll[si]['rec'][0, :nJoints]))
metricsSeqAll[si]['rec'][0, nJoints] = metricsSeqAll[si]['rec'][
0, idxs].mean()
metricsSeq = metricsSeqAll[si].copy()
metricsSeq['mota'] = metricsSeq['mota'].flatten().tolist()
metricsSeq['motp'] = metricsSeq['motp'].flatten().tolist()
metricsSeq['pre'] = metricsSeq['pre'].flatten().tolist()
metricsSeq['rec'] = metricsSeq['rec'].flatten().tolist()
metricsSeq['names'] = names
filename = outputDir + '/' + seqName + '_MOT_metrics.json'
print('saving results to', filename)
eval_helpers.writeJson(metricsSeq, filename)
# compute final metrics per joint for all sequences
for i in range(nJoints):
if (metricsMidAll['num_objects'][0, i] > 0):
numObj = metricsMidAll['num_objects'][0, i]
else:
numObj = np.nan
numFP = metricsMidAll['num_false_positives'][0, i]
metricsFinAll['mota'][0, i] = 100 * (
1. - (metricsMidAll['num_misses'][0, i] +
metricsMidAll['num_switches'][0, i] + numFP) / numObj)
numDet = metricsMidAll['num_detections'][0, i]
s = metricsMidAll['sumD'][0, i]
if (numDet == 0 or np.isnan(s)):
metricsFinAll['motp'][0, i] = 0.0
else:
metricsFinAll['motp'][0, i] = 100 * (1. - (s / numDet))
if (numFP + numDet > 0):
totalDet = numFP + numDet
else:
totalDet = np.nan
metricsFinAll['pre'][0, i] = 100 * (1. * numDet / totalDet)
metricsFinAll['rec'][0, i] = 100 * (1. * numDet / numObj)
# average metrics over all joints over all sequences
idxs = np.argwhere(~np.isnan(metricsFinAll['mota'][0, :nJoints]))
metricsFinAll['mota'][0, nJoints] = metricsFinAll['mota'][0, idxs].mean()
idxs = np.argwhere(~np.isnan(metricsFinAll['motp'][0, :nJoints]))
metricsFinAll['motp'][0, nJoints] = metricsFinAll['motp'][0, idxs].mean()
idxs = np.argwhere(~np.isnan(metricsFinAll['pre'][0, :nJoints]))
metricsFinAll['pre'][0, nJoints] = metricsFinAll['pre'][0, idxs].mean()
idxs = np.argwhere(~np.isnan(metricsFinAll['rec'][0, :nJoints]))
metricsFinAll['rec'][0, nJoints] = metricsFinAll['rec'][0, idxs].mean()
if (bSaveAll):
metricsFin = metricsFinAll.copy()
metricsFin['mota'] = metricsFin['mota'].flatten().tolist()
metricsFin['motp'] = metricsFin['motp'].flatten().tolist()
metricsFin['pre'] = metricsFin['pre'].flatten().tolist()
metricsFin['rec'] = metricsFin['rec'].flatten().tolist()
metricsFin['names'] = names
filename = outputDir + '/total_MOT_metrics.json'
print('saving results to', filename)
eval_helpers.writeJson(metricsFin, filename)
return metricsFinAll