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)
txt = open('result.txt', 'a')
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)
print("Mean Precision (Pre) metric:")
eval_helpers.printTable(preAll)
print("Mean Recall (Rec) metric:")
eval_helpers.printTable(recAll)
total_AP, total_Pre, total_Rec = apAll[15][0], preAll[15][0], recAll[
15][0]
txt.write('AP:{} Pre:{} Rec:{}\n'.format(total_AP, total_Pre,
total_Rec))
txt.close()
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.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)
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 = 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)
def _do_posetrack_keypoint_eval(self, gt_folder, pred_folder):
argv = ['', gt_folder, pred_folder]
outputDir = 'lib/poseval/py/out'
print("Loading data")
gtFramesAll, prFramesAll = eval_helpers.load_data_dir(argv)
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, outputDir,
True, False)
# print AP
print("Average Precision (AP) metric:")
eval_helpers.printTable(apAll)
def evaluate(gtdir,
preddir,
eval_pose=True,
eval_track=True,
eval_upper_bound=False):
gtFramesAll, prFramesAll = load_data_dir(['', gtdir, preddir])
print('# gt frames :', len(gtFramesAll))
print('# pred frames:', len(prFramesAll))
apAll = np.full((Joint().count + 1, 1), np.nan)
preAll = np.full((Joint().count + 1, 1), np.nan)
recAll = np.full((Joint().count + 1, 1), np.nan)
if eval_pose:
apAll, preAll, recAll = evaluateAP(gtFramesAll, prFramesAll)
print('Average Precision (AP) metric:')
#printTable(apAll)
cum = printTable(apAll)
metrics = np.full((Joint().count + 4, 1), np.nan)
#print(eval_track)
if eval_track:
#print(xy)
metricsAll = evaluateTracking(gtFramesAll, prFramesAll,
eval_upper_bound)
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('Multiple Object Tracking (MOT) metrics:')
track_cum = printTable(metrics, motHeader=True)
#return (apAll, preAll, recAll), metrics
#print(xy)
return cum