forked from hassony2/obman_train
-
Notifications
You must be signed in to change notification settings - Fork 0
/
get_pose_m.py
177 lines (159 loc) · 5.24 KB
/
get_pose_m.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
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
import os
import pickle
import yaml
import numpy as np
import torch
import argparse
from scipy.spatial.transform import Rotation as Rot
from mano_pybullet.hand_model import HandModel45
# Parse arguments
parser = argparse.ArgumentParser()
parser.add_argument(
"--th_full_pose_path",
type=str,
help="Path to the numpy file storing th_full_pose parameters"
)
parser.add_argument(
"--hand_side",
type=str,
choices=["right", "left"],
help="Side of the hand",
default="right"
)
parser.add_argument(
"--pose_m_path",
type=str,
help="Path to store pose_m",
default=None
)
args = parser.parse_args()
# Get raw dataset dir.
raw_dir = "misc/dex-ycb"
# Load MANO model.
mano = {}
for k, name in zip(("right", "left"), ("RIGHT", "LEFT")):
mano_file = os.path.join(
os.path.dirname(__file__), "misc", "mano", "MANO_{}.pkl".format(name)
)
with open(mano_file, "rb") as f:
mano[k] = pickle.load(f, encoding="latin1")
# Load meta.
n = "20200709-subject-01"
if args.hand_side == "right":
s = "20200709_151032"
else:
s = "20200709_152624"
name = os.path.join(n, s)
meta_file = os.path.join(raw_dir, name, "meta.yml")
with open(meta_file, "r") as f:
meta = yaml.load(f, Loader=yaml.FullLoader)
print("The input th_full_pose parameters should be obtained from the {} hand.".format(meta["mano_sides"]))
# Load extrinsics.
extr_file = os.path.join(
raw_dir,
"calibration",
"extrinsics_{}".format(meta["extrinsics"]),
"extrinsics.yml",
)
with open(extr_file, "r") as f:
extr = yaml.load(f, Loader=yaml.FullLoader)
tag_T = np.array(extr["extrinsics"]["apriltag"], dtype=np.float32).reshape(3, 4)
tag_R = tag_T[:, :3]
tag_t = tag_T[:, 3]
tag_R_inv = tag_R.T
tag_t_inv = np.matmul(tag_R_inv, -tag_t)
# Process MANO pose.
mano_betas = []
root_trans = []
comp = []
mean = []
for s, c in zip(meta["mano_sides"], meta["mano_calib"]):
mano_calib_file = os.path.join(
raw_dir, "calibration", "mano_{}".format(c), "mano.yml"
)
with open(mano_calib_file, "r") as f:
mano_calib = yaml.load(f, Loader=yaml.FullLoader)
betas = mano_calib["betas"]
mano_betas.append(betas)
v = mano[s]["shapedirs"].dot(betas) + mano[s]["v_template"]
r = mano[s]["J_regressor"][0].dot(v)[0]
root_trans.append(r)
comp.append(mano[s]["hands_components"])
mean.append(mano[s]["hands_mean"])
root_trans = np.array(root_trans, dtype=np.float32)
comp = np.array(comp, dtype=np.float32)
mean = np.array(mean, dtype=np.float32)
pose_m = np.zeros((1, 1, 52))
pose_m[0, 0, :48] = np.load(args.th_full_pose_path)
q = pose_m[:, :, 0:3]
t = pose_m[:, :, 48:51]
def transform(q, t, tag_R_inv, tag_t_inv):
"""Transforms 6D pose to tag coordinates."""
q_trans = np.zeros((*q.shape[:2], 4), dtype=q.dtype)
t_trans = np.zeros(t.shape, dtype=t.dtype)
i = np.any(q != 0, axis=2) | np.any(t != 0, axis=2)
q = q[i]
t = t[i]
if q.shape[1] == 4:
R = Rot.from_quat(q).as_matrix().astype(np.float32)
if q.shape[1] == 3:
R = Rot.from_rotvec(q).as_matrix().astype(np.float32)
R = np.matmul(tag_R_inv, R)
t = np.matmul(tag_R_inv, t.T).T + tag_t_inv
q = Rot.from_matrix(R).as_quat().astype(np.float32)
q_trans[i] = q
t_trans[i] = t
return q_trans, t_trans
i = np.any(pose_m != 0.0, axis=2)
t[i] += root_trans[np.nonzero(i)[1]]
q, t = transform(q, t, tag_R_inv, tag_t_inv)
t[i] -= root_trans[np.nonzero(i)[1]]
p = pose_m[:, :, 3:48]
# Notes: We already done this in manopth
# p = np.einsum("abj,bjk->abk", p, comp) + mean
p[~i] = 0.0
q_i = q[i]
q_i = Rot.from_quat(q_i).as_rotvec().astype(np.float32)
q = np.zeros((*q.shape[:2], 3), dtype=q.dtype)
q[i] = q_i
q = np.dstack((q, p))
for o, (s, b) in enumerate(zip(meta["mano_sides"], mano_betas)):
model_dir = os.path.join(os.path.dirname(__file__), "misc", "mano")
model = HandModel45(
left_hand=s == "left", models_dir=model_dir, betas=b
)
origin = model.origins(b)[0]
sid = np.nonzero(np.any(q[:, o] != 0, axis=1))[0][0]
eid = np.nonzero(np.any(q[:, o] != 0, axis=1))[0][-1]
for f in range(sid, eid + 1):
mano_pose = q[f, o]
trans = t[f, o]
angles, basis = model.mano_to_angles(mano_pose)
trans = trans + origin - basis @ origin
q[f, o, 3:48] = angles
t[f, o] = trans
q_i = q[i]
q_i_base = q_i[:, 0:3]
q_i_pose = q_i[:, 3:48].reshape(-1, 3)
q_i_base = Rot.from_rotvec(q_i_base).as_quat().astype(np.float32)
q_i_pose = Rot.from_euler("XYZ", q_i_pose).as_quat().astype(np.float32)
q_i_pose = q_i_pose.reshape(-1, 60)
q_i = np.hstack((q_i_base, q_i_pose))
q = np.zeros((*q.shape[:2], 64), dtype=q.dtype)
q[i] = q_i
i = np.any(q != 0.0, axis=2)
q_i = q[i]
q = np.zeros((*q.shape[:2], 48), dtype=q.dtype)
for o in range(q.shape[1]):
q_i_o = q_i[np.nonzero(i)[1] == o]
q_i_o = q_i_o.reshape(-1, 4)
q_i_o = Rot.from_quat(q_i_o).as_euler("XYZ").astype(np.float32)
q_i_o = q_i_o.reshape(-1, 48)
# https://math.stackexchange.com/questions/463748/getting-cumulative-euler-angle-from-a-single-quaternion
q_i_o[:, 0:3] = np.unwrap(q_i_o[:, 0:3], axis=0)
q[i[:, o], o] = q_i_o
pose_m = np.dstack((t, q))[0, 0, 6:]
if args.pose_m_path is not None:
np.save(args.pose_m_path, pose_m)
else:
print("Parameters for the MANO hand : {}".format(pose_m))