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dataset_info = dict(
dataset_name='mhp',
paper_info=dict(
author='Zhao, Jian and Li, Jianshu and Cheng, Yu and '
'Sim, Terence and Yan, Shuicheng and Feng, Jiashi',
title='Understanding humans in crowded scenes: '
'Deep nested adversarial learning and a '
'new benchmark for multi-human parsing',
container='Proceedings of the 26th ACM '
'international conference on Multimedia',
year='2018',
homepage='https://lv-mhp.github.io/dataset',
),
keypoint_info={
0:
dict(
name='right_ankle',
id=0,
color=[255, 128, 0],
type='lower',
swap='left_ankle'),
1:
dict(
name='right_knee',
id=1,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
2:
dict(
name='right_hip',
id=2,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
3:
dict(
name='left_hip',
id=3,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
4:
dict(
name='left_knee',
id=4,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
5:
dict(
name='left_ankle',
id=5,
color=[0, 255, 0],
type='lower',
swap='right_ankle'),
6:
dict(name='pelvis', id=6, color=[51, 153, 255], type='lower', swap=''),
7:
dict(name='thorax', id=7, color=[51, 153, 255], type='upper', swap=''),
8:
dict(
name='upper_neck',
id=8,
color=[51, 153, 255],
type='upper',
swap=''),
9:
dict(
name='head_top', id=9, color=[51, 153, 255], type='upper',
swap=''),
10:
dict(
name='right_wrist',
id=10,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
11:
dict(
name='right_elbow',
id=11,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
12:
dict(
name='right_shoulder',
id=12,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
13:
dict(
name='left_shoulder',
id=13,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
14:
dict(
name='left_elbow',
id=14,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
15:
dict(
name='left_wrist',
id=15,
color=[0, 255, 0],
type='upper',
swap='right_wrist')
},
skeleton_info={
0:
dict(link=('right_ankle', 'right_knee'), id=0, color=[255, 128, 0]),
1:
dict(link=('right_knee', 'right_hip'), id=1, color=[255, 128, 0]),
2:
dict(link=('right_hip', 'pelvis'), id=2, color=[255, 128, 0]),
3:
dict(link=('pelvis', 'left_hip'), id=3, color=[0, 255, 0]),
4:
dict(link=('left_hip', 'left_knee'), id=4, color=[0, 255, 0]),
5:
dict(link=('left_knee', 'left_ankle'), id=5, color=[0, 255, 0]),
6:
dict(link=('pelvis', 'thorax'), id=6, color=[51, 153, 255]),
7:
dict(link=('thorax', 'upper_neck'), id=7, color=[51, 153, 255]),
8:
dict(link=('upper_neck', 'head_top'), id=8, color=[51, 153, 255]),
9:
dict(link=('upper_neck', 'right_shoulder'), id=9, color=[255, 128, 0]),
10:
dict(
link=('right_shoulder', 'right_elbow'), id=10, color=[255, 128,
0]),
11:
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),
12:
dict(link=('upper_neck', 'left_shoulder'), id=12, color=[0, 255, 0]),
13:
dict(link=('left_shoulder', 'left_elbow'), id=13, color=[0, 255, 0]),
14:
dict(link=('left_elbow', 'left_wrist'), id=14, color=[0, 255, 0])
},
joint_weights=[
1.5, 1.2, 1., 1., 1.2, 1.5, 1., 1., 1., 1., 1.5, 1.2, 1., 1., 1.2, 1.5
],
# Adapted from COCO dataset.
sigmas=[
0.089, 0.083, 0.107, 0.107, 0.083, 0.089, 0.026, 0.026, 0.026, 0.026,
0.062, 0.072, 0.179, 0.179, 0.072, 0.062
])
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