forked from Idein/chainer-pose-proposal-net
-
Notifications
You must be signed in to change notification settings - Fork 0
/
mpii_dataset.py
162 lines (140 loc) · 5.04 KB
/
mpii_dataset.py
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
import numpy as np
import json
from dataset import KeypointDataset2D
from chainer.datasets import split_dataset_random
from utils import pairwise
KEYPOINT_NAMES = [
'head_top',
'upper_neck',
'l_shoulder',
'r_shoulder',
'l_elbow',
'r_elbow',
'l_wrist',
'r_wrist',
'l_hip',
'r_hip',
'l_knee',
'r_knee',
'l_ankle',
'r_ankle',
]
FLIP_CONVERTER = {'head_top': 'head_top',
'upper_neck': 'upper_neck',
'l_shoulder': 'r_shoulder',
'r_shoulder': 'l_shoulder',
'l_elbow': 'r_elbow',
'r_elbow': 'l_elbow',
'l_wrist': 'r_wrist',
'r_wrist': 'l_wrist',
'l_hip': 'r_hip',
'r_hip': 'l_hip',
'l_knee': 'r_knee',
'r_knee': 'l_knee',
'l_ankle': 'r_ankle',
'r_ankle': 'l_ankle',
}
FLIP_INDICES = [KEYPOINT_NAMES.index(FLIP_CONVERTER[k]) for k in KEYPOINT_NAMES]
KEYPOINT_NAMES = ['instance'] + KEYPOINT_NAMES
COLOR_MAP = {
'instance': (225, 225, 225),
'head_top': (255, 0, 0),
'upper_neck': (255, 85, 0),
'r_shoulder': (255, 170, 0),
'r_elbow': (255, 255, 0),
'r_wrist': (170, 255, 0),
'l_shoulder': (85, 255, 0),
'l_elbow': (0, 127, 0),
'l_wrist': (0, 255, 85),
'r_hip': (0, 170, 170),
'r_knee': (0, 255, 255),
'r_ankle': (0, 170, 255),
'l_hip': (0, 85, 255),
'l_knee': (0, 0, 255),
'l_ankle': (85, 0, 255),
'r_eye': (170, 0, 255),
'l_eye': (255, 0, 255),
'r_ear': (255, 0, 170),
'l_ear': (255, 0, 85),
}
EDGES_BY_NAME = [
['instance', 'upper_neck'],
['upper_neck', 'head_top'],
['upper_neck', 'l_shoulder'],
['upper_neck', 'r_shoulder'],
['upper_neck', 'l_hip'],
['upper_neck', 'r_hip'],
['l_shoulder', 'l_elbow'],
['l_elbow', 'l_wrist'],
['r_shoulder', 'r_elbow'],
['r_elbow', 'r_wrist'],
['l_hip', 'l_knee'],
['l_knee', 'l_ankle'],
['r_hip', 'r_knee'],
['r_knee', 'r_ankle'],
]
EDGES = [[KEYPOINT_NAMES.index(s), KEYPOINT_NAMES.index(d)] for s, d in EDGES_BY_NAME]
TRACK_ORDER_0 = ['instance', 'upper_neck', 'head_top']
TRACK_ORDER_1 = ['instance', 'upper_neck', 'l_shoulder', 'l_elbow', 'l_wrist']
TRACK_ORDER_2 = ['instance', 'upper_neck', 'r_shoulder', 'r_elbow', 'r_wrist']
TRACK_ORDER_3 = ['instance', 'upper_neck', 'l_hip', 'l_knee', 'l_ankle']
TRACK_ORDER_4 = ['instance', 'upper_neck', 'r_hip', 'r_knee', 'r_ankle']
TRACK_ORDERS = [TRACK_ORDER_0, TRACK_ORDER_1, TRACK_ORDER_2, TRACK_ORDER_3, TRACK_ORDER_4]
DIRECTED_GRAPHS = []
for keypoints in TRACK_ORDERS:
es = [EDGES_BY_NAME.index([a, b]) for a, b in pairwise(keypoints)]
ts = [KEYPOINT_NAMES.index(b) for a, b in pairwise(keypoints)]
DIRECTED_GRAPHS.append([es, ts])
def get_mpii_dataset(insize, image_root, annotations,
train_size=0.5, min_num_keypoints=1, use_cache=False, seed=0):
dataset_type = 'mpii'
annotations = json.load(open(annotations, 'r'))
# filename => keypoints, bbox, is_visible, is_labeled
images = {}
for filename in np.unique([anno['filename'] for anno in annotations]):
images[filename] = [], [], [], []
for anno in annotations:
is_visible = [anno['is_visible'][k] for k in KEYPOINT_NAMES[1:]]
if sum(is_visible) < min_num_keypoints:
continue
keypoints = [anno['joint_pos'][k][::-1] for k in KEYPOINT_NAMES[1:]]
x1, y1, x2, y2 = anno['head_rect']
entry = images[anno['filename']]
entry[0].append(np.array(keypoints)) # array of y,x
entry[1].append(np.array([x1, y1, x2 - x1, y2 - y1])) # x, y, w, h
entry[2].append(np.array(is_visible, dtype=np.bool))
entry[3].append(np.ones(len(is_visible), dtype=np.bool))
# split dataset
train_images, test_images = split_dataset_random(
list(images.keys()), int(len(images) * train_size), seed=seed)
train_set = KeypointDataset2D(
dataset_type=dataset_type,
insize=insize,
keypoint_names=KEYPOINT_NAMES,
edges=np.array(EDGES),
flip_indices=FLIP_INDICES,
keypoints=[images[i][0] for i in train_images],
bbox=[images[i][1] for i in train_images],
is_visible=[images[i][2] for i in train_images],
is_labeled=[images[i][3] for i in train_images],
image_paths=train_images,
image_root=image_root,
use_cache=use_cache,
do_augmentation=True
)
test_set = KeypointDataset2D(
dataset_type=dataset_type,
insize=insize,
keypoint_names=KEYPOINT_NAMES,
edges=np.array(EDGES),
flip_indices=FLIP_INDICES,
keypoints=[images[i][0] for i in test_images],
bbox=[images[i][1] for i in test_images],
is_visible=[images[i][2] for i in test_images],
is_labeled=[images[i][3] for i in test_images],
image_paths=test_images,
image_root=image_root,
use_cache=use_cache,
do_augmentation=False
)
return train_set, test_set