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 evaluateTracking(gtFramesAll, prFramesAll, outputDir, saveAll=True, saveSeq=False):
distThresh = 0.5
# assign predicted poses to GT poses
_, _, _, motAll = eval_helpers.assignGTmulti(gtFramesAll, prFramesAll, distThresh)
# compute MOT metrics per part
metricsAll = computeMetrics(gtFramesAll, motAll, outputDir, saveAll, saveSeq)
return metricsAll
def evaluateTracking(gtFramesAll, prFramesAll, trackUpperBound):
distThresh = 0.5
# assign predicted poses to GT poses
_, _, _, motAll = eval_helpers.assignGTmulti(gtFramesAll, prFramesAll,
distThresh, trackUpperBound)
# compute MOT metrics per part
metricsAll = computeMetrics(gtFramesAll, motAll)
return metricsAll
def evaluateTracking(gtFramesAll, prFramesAll, trackUpperBound):
distThresh = 0.5
# assign predicted poses to GT poses
_, _, _, motAll = eval_helpers.assignGTmulti(
gtFramesAll, prFramesAll, distThresh, trackUpperBound)
# compute MOT metrics per part
metricsAll = computeMetrics(gtFramesAll, motAll)
return metricsAll
def evaluateAP(gtFramesAll, prFramesAll):
distThresh = 0.5
# 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)
return apAll, preAll, recAll
def evaluateAP(gtFramesAll, prFramesAll):
distThresh = 0.5
# 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)
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 evaluateTracking(gtFramesAll, prFramesAll, trackUpperBound):
distThresh = 0.5
# assign predicted poses to GT poses
# #############debug
# flag=False
# for imgidx_ in range(len(gtFramesAll)):
# name_=gtFramesAll[imgidx_]["image_name"]
# if "145.jpg" in name_ and "00522" in name_:
# flag=True
# print("assign before Catch!!!!!!")
# break
# if not flag:
# print("assign before Catch bad!!!!!!")
# assert(False)
# ##################
_, _, _, motAll = eval_helpers.assignGTmulti(gtFramesAll, prFramesAll,
distThresh, trackUpperBound)
# _, _, _, motAll = eval_helpers.assignGT_pr_sub_one_multi(
# gtFramesAll, prFramesAll, distThresh, trackUpperBound)
# #############debug
# flag=False
# for imgidx_ in range(len(motAll)):
# name_=motAll[imgidx_]["image_name"]
# if "145.jpg" in name_ and "00522" in name_:
# flag=True
# print("assign after Catch!!!!!!")
# break
# if not flag:
# print("assign after Catch bad!!!!!!")
# assert(False)
# ##################
# compute MOT metrics per part
###########jianbo
metricsAll = computeMetrics(gtFramesAll, prFramesAll, motAll)
###########jianbo
return metricsAll
