dataset_info = dict(
dataset_name='animalpose',
paper_info=dict(
author='Cao, Jinkun and Tang, Hongyang and Fang, Hao-Shu and '
'Shen, Xiaoyong and Lu, Cewu and Tai, Yu-Wing',
title='Cross-Domain Adaptation for Animal Pose Estimation',
container='The IEEE International Conference on '
'Computer Vision (ICCV)',
year='2019',
homepage='https://sites.google.com/view/animal-pose/',
),
keypoint_info={
0:
dict(
name='L_Eye', id=0, color=[0, 255, 0], type='upper', swap='R_Eye'),
1:
dict(
name='R_Eye',
id=1,
color=[255, 128, 0],
type='upper',
swap='L_Eye'),
2:
dict(
name='L_EarBase',
id=2,
color=[0, 255, 0],
type='upper',
swap='R_EarBase'),
3:
dict(
name='R_EarBase',
id=3,
color=[255, 128, 0],
type='upper',
swap='L_EarBase'),
4:
dict(name='Nose', id=4, color=[51, 153, 255], type='upper', swap=''),
5:
dict(name='Throat', id=5, color=[51, 153, 255], type='upper', swap=''),
6:
dict(
name='TailBase', id=6, color=[51, 153, 255], type='lower',
swap=''),
7:
dict(
name='Withers', id=7, color=[51, 153, 255], type='upper', swap=''),
8:
dict(
name='L_F_Elbow',
id=8,
color=[0, 255, 0],
type='upper',
swap='R_F_Elbow'),
9:
dict(
name='R_F_Elbow',
id=9,
color=[255, 128, 0],
type='upper',
swap='L_F_Elbow'),
10:
dict(
name='L_B_Elbow',
id=10,
color=[0, 255, 0],
type='lower',
swap='R_B_Elbow'),
11:
dict(
name='R_B_Elbow',
id=11,
color=[255, 128, 0],
type='lower',
swap='L_B_Elbow'),
12:
dict(
name='L_F_Knee',
id=12,
color=[0, 255, 0],
type='upper',
swap='R_F_Knee'),
13:
dict(
name='R_F_Knee',
id=13,
color=[255, 128, 0],
type='upper',
swap='L_F_Knee'),
14:
dict(
name='L_B_Knee',
id=14,
color=[0, 255, 0],
type='lower',
swap='R_B_Knee'),
15:
dict(
name='R_B_Knee',
id=15,
color=[255, 128, 0],
type='lower',
swap='L_B_Knee'),
16:
dict(
name='L_F_Paw',
id=16,
color=[0, 255, 0],
type='upper',
swap='R_F_Paw'),
17:
dict(
name='R_F_Paw',
id=17,
color=[255, 128, 0],
type='upper',
swap='L_F_Paw'),
18:
dict(
name='L_B_Paw',
id=18,
color=[0, 255, 0],
type='lower',
swap='R_B_Paw'),
19:
dict(
name='R_B_Paw',
id=19,
color=[255, 128, 0],
type='lower',
swap='L_B_Paw')
},
skeleton_info={
0: dict(link=('L_Eye', 'R_Eye'), id=0, color=[51, 153, 255]),
1: dict(link=('L_Eye', 'L_EarBase'), id=1, color=[0, 255, 0]),
2: dict(link=('R_Eye', 'R_EarBase'), id=2, color=[255, 128, 0]),
3: dict(link=('L_Eye', 'Nose'), id=3, color=[0, 255, 0]),
4: dict(link=('R_Eye', 'Nose'), id=4, color=[255, 128, 0]),
5: dict(link=('Nose', 'Throat'), id=5, color=[51, 153, 255]),
6: dict(link=('Throat', 'Withers'), id=6, color=[51, 153, 255]),
7: dict(link=('TailBase', 'Withers'), id=7, color=[51, 153, 255]),
8: dict(link=('Throat', 'L_F_Elbow'), id=8, color=[0, 255, 0]),
9: dict(link=('L_F_Elbow', 'L_F_Knee'), id=9, color=[0, 255, 0]),
10: dict(link=('L_F_Knee', 'L_F_Paw'), id=10, color=[0, 255, 0]),
11: dict(link=('Throat', 'R_F_Elbow'), id=11, color=[255, 128, 0]),
12: dict(link=('R_F_Elbow', 'R_F_Knee'), id=12, color=[255, 128, 0]),
13: dict(link=('R_F_Knee', 'R_F_Paw'), id=13, color=[255, 128, 0]),
14: dict(link=('TailBase', 'L_B_Elbow'), id=14, color=[0, 255, 0]),
15: dict(link=('L_B_Elbow', 'L_B_Knee'), id=15, color=[0, 255, 0]),
16: dict(link=('L_B_Knee', 'L_B_Paw'), id=16, color=[0, 255, 0]),
17: dict(link=('TailBase', 'R_B_Elbow'), id=17, color=[255, 128, 0]),
18: dict(link=('R_B_Elbow', 'R_B_Knee'), id=18, color=[255, 128, 0]),
19: dict(link=('R_B_Knee', 'R_B_Paw'), id=19, color=[255, 128, 0])
},
joint_weights=[
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.2, 1.2,
1.5, 1.5, 1.5, 1.5
],
# Note: The original paper did not provide enough information about
# the sigmas. We modified from 'https://github.com/cocodataset/'
# 'cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py#L523'
sigmas=[
0.025, 0.025, 0.026, 0.035, 0.035, 0.10, 0.10, 0.10, 0.107, 0.107,
0.107, 0.107, 0.087, 0.087, 0.087, 0.087, 0.089, 0.089, 0.089, 0.089
])
