File size: 5,415 Bytes
2cd560a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 |
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
])
|