def main():
args = parseArgs()
print args
argv = ['', args.groundTruth, args.predictions]
print "Loading data"
gtFramesAll, prFramesAll = eval_helpers.load_data_dir(argv)
print "# gt frames :", len(gtFramesAll)
print "# pred frames:", len(prFramesAll)
if (not os.path.exists(args.outputDir)):
os.makedirs(args.outputDir)
if (args.evalPoseEstimation):
#####################################################
# evaluate per-frame multi-person pose estimation (AP)
# compute AP
print "Evaluation of per-frame multi-person pose estimation"
apAll, preAll, recAll = evaluateAP(gtFramesAll, prFramesAll,
args.outputDir, True,
args.saveEvalPerSequence)
# print AP
print "Average Precision (AP) metric:"
eval_helpers.printTable(apAll)
if (args.evalPoseTracking):
#####################################################
# evaluate multi-person pose tracking in video (MOTA)
# compute MOTA
print "Evaluation of video-based multi-person pose tracking"
metricsAll, metricsMidAll = evaluateTracking(gtFramesAll, prFramesAll,
args.outputDir, True,
args.saveEvalPerSequence)
metrics = np.zeros([Joint().count + 4, 1])
for i in range(Joint().count + 1):
metrics[i, 0] = metricsAll['mota'][0, i]
metrics[Joint().count + 1, 0] = metricsAll['motp'][0, Joint().count]
metrics[Joint().count + 2, 0] = metricsAll['pre'][0, Joint().count]
metrics[Joint().count + 3, 0] = metricsAll['rec'][0, Joint().count]
# print AP
print "Multiple Object Tracking (MOT) metrics:"
eval_helpers.printTable(metrics, motHeader=True)
eval_helpers.printStatistics(metricsMidAll)
def main():
args = parseArgs()
print args
argv = ['',args.groundTruth,args.predictions]
print "Loading data"
gtFramesAll,prFramesAll = eval_helpers.load_data_dir(argv)
print "# gt frames :", len(gtFramesAll)
print "# pred frames:", len(prFramesAll)
if (args.evalPoseEstimation):
#####################################################
# evaluate per-frame multi-person pose estimation (AP)
# compute AP
print "Evaluation of per-frame multi-person pose estimation"
apAll,preAll,recAll = evaluateAP(gtFramesAll,prFramesAll)
# print AP
print "Average Precision (AP) metric:"
eval_helpers.printTable(apAll)
if (args.evalPoseTracking):
#####################################################
# evaluate multi-person pose tracking in video (MOTA)
# compute MOTA
print "Evaluation of video-based multi-person pose tracking"
metricsAll = evaluateTracking(gtFramesAll,prFramesAll, args.trackUpperBound)
metrics = np.zeros([Joint().count + 4,1])
for i in range(Joint().count+1):
metrics[i,0] = metricsAll['mota'][0,i]
metrics[Joint().count+1,0] = metricsAll['motp'][0,Joint().count]
metrics[Joint().count+2,0] = metricsAll['pre'][0,Joint().count]
metrics[Joint().count+3,0] = metricsAll['rec'][0,Joint().count]
# print AP
print "Multiple Object Tracking (MOT) metrics:"
eval_helpers.printTable(metrics,motHeader=True)
def evaluation(list_):
# file_name
fname_list = list_[0]
# gt_key points
gt_kps_list = list_[1]
# humans
humans_list = list_[2]
# scores
scores_list = list_[3]
# gt bboxes
gt_bboxes_list = list_[4]
# is_visible
is_visible_list = list_[5]
# size
size_list = list_[6]
# Mean Average Precision
gtFramesAll = []
prFramesAll = []
for fname, humans, scores, gt_kps, gt_bboxes in zip(
fname_list, humans_list, scores_list, gt_kps_list, gt_bboxes_list):
pred_list = {"annolist": []}
gt_list = {"annolist": []}
pred_data = {"image": [], "annorect": []}
gt_data = {"image": [], "annorect": []}
pred_data["image"].append(fname)
gt_data["image"].append(fname)
for person, score in zip(humans, scores):
# head keypoints
y1, x1, y2, x2 = person[0]
pp = {
'x1': [x1],
'y1': [y1],
'x2': [x2],
'y2': [y2],
'score': [score[0]],
'annopoints': [{
"point": []
}]
}
for num in range(1, len(KEYPOINT_NAMES)): # others
if num in person:
# Restore center point
y = (person[num][0] + person[num][2]) / 2
x = (person[num][1] + person[num][3]) / 2
s = score[num]
else:
y, x, s = 0, 0, 0
p = {'id': [num - 1], 'x': [x], 'y': [y], 'score': [s]}
pp['annopoints'][0]['point'].append(p)
pred_data['annorect'].append(pp)
for person, keypoints in zip(gt_bboxes, gt_kps):
cx, cy, w, h = person
x1 = cx - w // 2
x2 = cx + w // 2
y1 = cy - h // 2
y2 = cy + h // 2
gtperson = {
'x1': [x1],
'y1': [y1],
'x2': [x2],
'y2': [y2],
'annopoints': [{
"point": []
}]
}
for idx, pt in enumerate(keypoints):
gtpt = {'id': [idx], 'x': [pt[0]], 'y': [pt[1]]}
gtperson['annopoints'][0]['point'].append(gtpt)
gt_data['annorect'].append(gtperson)
pred_list['annolist'].append(pred_data)
gt_list['annolist'].append(gt_data)
gtFramesAll += gt_list['annolist']
prFramesAll += pred_list['annolist']
# Poseeval code
gtFramesAll, prFramesAll = eval_helpers.load_data(gtFramesAll, prFramesAll)
print("# gt frames :", len(gtFramesAll))
print("# pred frames:", len(prFramesAll))
#####################################################
# evaluate per-frame multi-person pose estimation (AP)
# compute AP
print("Evaluation of per-frame multi-person pose estimation")
apAll, preAll, recAll = evaluateAP(gtFramesAll, prFramesAll, False)
# print AP
print("Average Precision (AP) metric:")
eval_helpers.printTable(apAll)
ap_vals = eval_helpers.getCum(apAll)
return ap_vals