def test_matrices_from_quaternions():
random_state = np.random.RandomState(83)
for _ in range(5):
q = pr.random_quaternion(random_state)
R = pbr.matrices_from_quaternions([q], normalize_quaternions=False)[0]
q2 = pr.quaternion_from_matrix(R)
pr.assert_quaternion_equal(q, q2)
for _ in range(5):
q = random_state.randn(4)
R = pbr.matrices_from_quaternions([q], normalize_quaternions=True)[0]
q2 = pr.quaternion_from_matrix(R)
pr.assert_quaternion_equal(q / np.linalg.norm(q), q2)
def test_matrices_from_pos_quat():
"""Test conversion from positions and quaternions to matrices."""
P = np.empty((10, 7))
random_state = np.random.RandomState(0)
P[:, :3] = random_state.randn(10, 3)
for t in range(10):
P[t, 3:] = random_quaternion(random_state)
H = matrices_from_pos_quat(P)
for t in range(10):
assert_array_almost_equal(P[t, :3], H[t, :3, 3])
assert_quaternion_equal(P[t, 3:], quaternion_from_matrix(H[t, :3, :3]))
def test_transforms_from_pqs_1dim():
P = np.empty((10, 7))
random_state = np.random.RandomState(0)
P[:, :3] = random_state.randn(len(P), 3)
P[:, 3:] = norm_vectors(random_state.randn(len(P), 4))
H = transforms_from_pqs(P)
P2 = pqs_from_transforms(H)
assert_array_almost_equal(P[:, :3], H[:, :3, 3])
assert_array_almost_equal(P[:, :3], P2[:, :3])
for t in range(len(P)):
assert_quaternion_equal(P[t, 3:], quaternion_from_matrix(H[t, :3, :3]))
assert_quaternion_equal(P[t, 3:], P2[t, 3:])
def calc_inverse_kinematics(self,
guess,
target_position,
target_orientation,
tcp_config=None,
orientation_type=None):
"""
End-effector orientation (target_orientation) can be provided in several standard formats,
by specifying the orinetation_type (default is None):
- 'matrix': rotation matrix -> 3x3 array, in row major order
- 'axis_angle': axis angle -> {'angle': float, 'x': float, 'y': float, 'z': float}
- 'euler_zyx': {yaw, pitch, roll} Euler (Tait-Bryan) angles -> {'yaw': float, 'pitch': float, 'roll': float}
- 'quat': quaternion -> {'w': float, 'x': float, 'y': float, 'z': float}
- None: defaults to quaternion
Conversion relies on pytransform3d library
"""
quat_not_normed = None
if orientation_type == 'matrix':
self.__ensure_pyt3d()
quat_not_normed = pyrot.quaternion_from_matrix(
pyrot.check_matrix(target_orientation))
elif orientation_type == 'axis_angle':
self.__ensure_pyt3d()
axis_angle = [
target_orientation['x'], target_orientation['y'],
target_orientation['z'], target_orientation['angle']
]
quat_not_normed = pyrot.quaternion_from_axis_angle(
pyrot.check_axis_angle(axis_angle))
elif orientation_type == 'euler_zyx':
self.__ensure_pyt3d()
euler_zyx = [
target_orientation['yaw'], target_orientation['pitch'],
target_orientation['roll']
]
matrix = pyrot.matrix_from_euler_zyx(euler_zyx)
quat_not_normed = pyrot.quaternion_from_matrix(
pyrot.check_matrix(matrix))
elif orientation_type == 'quat' or orientation_type is None:
quat_not_normed = [
target_orientation['w'], target_orientation['x'],
target_orientation['y'], target_orientation['z']
]
else:
eva_error(
f'calc_inverse_kinematics invalid "{orientation_type}" orientation_type'
)
quat_normed = self.__normalize(quat_not_normed)
quaternion = {
'w': quat_normed[0],
'x': quat_normed[1],
'y': quat_normed[2],
'z': quat_normed[3]
}
body = {
'guess': guess,
'position': target_position,
'orientation': quaternion
}
if tcp_config is not None:
body['tcp_config'] = tcp_config
r = self.api_call_with_auth('PUT', 'calc/inverse_kinematics',
json.dumps(body))
if r.status_code != 200:
eva_error('inverse_kinematics error', r)
return self.__check_calculation(r, 'calc_inverse_kinematics',
'ik')['joints']
def run(vid, viz=False, all_case=None):
# ------------------------------------------------------------------------------------------------------------------
# load data
vid_data = np.load(vid, allow_pickle=True)
assert (vid_data['poses'].shape[-1] == 156), 'poses are of dim ' + str(
len(vid_data['poses'])
) # expecting SMPL-H format, otherwise retargeting isn't guaranteed to work
data = vid_data['poses'][:, :66]
T = data.shape[0]
data = data.reshape(T, -1, 3)
with open('../DeepMimic/data/characters/humanoid3d.txt') as f:
dm_model = json.load(f)
with open(
'../human_dynamics/models/neutral_smpl_with_cocoplustoesankles_reg.pkl',
'rb') as f:
smpl_model = pickle.load(f, encoding='latin1')
# get SMPL model info and orient to DeepMimic
base = smpl_model['J'][:-2]
smpl_parents = smpl_model['kintree_table'][
0][:-2] # dont need last two palm joints
# convert data to 3d pose
Rs = np.array([Rot.from_rotvec(data[i]).as_matrix() for i in range(T)])
posed, _ = batch_global_rigid_transformation(
Rs, np.tile(np.expand_dims(base, 0), (T, 1, 1)), smpl_parents)
# get DeepMimic joints
joints = dm_model['Skeleton']['Joints']
joint_coords = []
dm_parents = []
for joint_dict in joints:
delta = np.array([
joint_dict['AttachX'], joint_dict['AttachY'], joint_dict['AttachZ']
])
if joint_dict['Parent'] != -1:
coords = joint_coords[joint_dict['Parent']] + delta
else:
coords = delta
joint_coords.append(coords)
dm_parents.append(joint_dict['Parent'])
joint_coords = np.array(joint_coords)
euler = [0, -np.pi / 2, 0]
dm_to_smpl = Rot.from_rotvec(euler).as_matrix()
euler = [0, np.pi / 2, 0]
smpl_to_dm = Rot.from_rotvec(euler).as_matrix()
# sanity = joint_coords.copy()
# sanity = np.concatenate([sanity, [[1,0,0]]], axis=0)
# sanity = np.concatenate([[sanity[0]], (dm_to_smpl@sanity[1:].T).T], axis=0)
# compare_single(sanity, base)
# ---------------------------------------------------retarget---------------------------------------------------------------
dm_parents += [2, 5, 11
] # need the extra joints to stabilize ankle/head rotations
# first calculate root offset
corrected = posed.copy()
corrected[:, 0] = (corrected[:, 1] + corrected[:, 2]) / 2
corrected = corrected[:, smpl_to_dm_map]
# then just adjust the relevant limb lengths
adjusted = []
for i in range(1, 15):
limb_length = np.linalg.norm(joint_coords[i] -
joint_coords[dm_parents[i]])
current = corrected[:, i] - corrected[:, dm_parents[i]]
adjusted.append(current /
np.linalg.norm(current, axis=1).reshape(-1, 1) *
limb_length)
adjusted.append(corrected[:, 15] - corrected[:, dm_parents[15]])
adjusted.append(corrected[:, 16] - corrected[:, dm_parents[16]])
adjusted.append(corrected[:, 17] - corrected[:, dm_parents[17]])
for i in range(1, 18):
corrected[:, i] = corrected[:, dm_parents[i]] + adjusted[i - 1]
# corrected[:, :, 1] += 1 # set character higher on y axis
corrected = corrected - corrected[:, 0:1] + joint_coords[0:1]
np.set_printoptions(suppress=True)
# orientation correction
if all_case is None:
animate_single(posed)
root_adjust = get_root_correction(
int(input('which case? (0/1/2/any number)')))
else:
root_adjust = get_root_correction(all_case)
# ------------------------------------------------- visualize retarget-----------------------------------------------------------------
# posed[:, :, 0] += 1
corrected2 = corrected.copy()
corrected2 += np.expand_dims(vid_data['trans'], 1) - np.expand_dims(
corrected2[:, 0], 1)
corrected2 = np.array([(root_adjust @ corrected2[i].T).T
for i in range(T)])
posed += np.expand_dims(vid_data['trans'], 1) - np.expand_dims(
posed[:, 0], 1)
if viz:
compare_animate(posed, corrected2, interval=12)
# sys.exit(0)
# ------------------------------------------------------get and save retargeted rotation matrices-----------------------------------------------------------
head_coords = (smpl_to_dm @ (
base[smpl_to_dm_map[15]] - base[smpl_to_dm_map[dm_parents[15]]] +
dm_to_smpl @ joint_coords[dm_parents[15]]).T).T.reshape(1, 3)
rtoe_coords = (smpl_to_dm @ (
base[smpl_to_dm_map[16]] - base[smpl_to_dm_map[dm_parents[16]]] +
dm_to_smpl @ joint_coords[dm_parents[16]])).T.reshape(1, 3)
ltoe_coords = (smpl_to_dm @ (
base[smpl_to_dm_map[17]] - base[smpl_to_dm_map[dm_parents[17]]] +
dm_to_smpl @ joint_coords[dm_parents[17]])).T.reshape(1, 3)
joint_coords_retargeting = np.concatenate(
[joint_coords, head_coords, rtoe_coords, ltoe_coords], axis=0)
# compare_single(sanity, joint_coords_retargeting)
# sys.exit(0)
corrected_body = np.array([(root_adjust @ corrected[i, 1:].T).T
for i in range(T)])
corrected = np.concatenate([corrected[:, 0:1], corrected_body], axis=1)
quarts = []
terminal = [8, 14, 15, 16, 17]
all_Rs = []
# # TODO: can we batch this somehow?
for t in tqdm(range(T), 'processing frames...'):
root_orient = root_adjust @ Rs[t, 0] @ dm_to_smpl
retargeted_Rs, retargeted_As = inv_kin(
root_orient, joint_coords_retargeting, corrected[t], dm_parents,
terminal) # retargeted_Rs = (1,22,3,3)
quart = []
for i in range(len(retargeted_Rs[0])):
R = retargeted_Rs[0, i]
if i == 0:
q = quaternion_from_matrix(R, strict_check=False)
quart.append(q)
elif i not in terminal:
try:
q = quaternion_from_matrix(R)
if i in one_d_rotations:
end_orient = np.linalg.inv(
retargeted_As[0, dm_parents[i]]) @ (
corrected[t, dm_parents.index(i)] -
corrected[t, i])
start_orient = (
joint_coords_retargeting[dm_parents.index(i)] -
joint_coords_retargeting[i])
if np.all(end_orient == -start_orient):
theta = np.pi
else:
theta = np.arccos(
np.dot(end_orient, start_orient) /
(np.linalg.norm(end_orient) *
np.linalg.norm(start_orient))
) # so far seems like a good enough approx
if i not in elbows: # intuitively, knees and elbows rotate w.r.t different axes
theta = 2 * np.pi - theta
quart.append(np.array([theta]))
else:
quart.append(q)
except ValueError as e: # strict check should only fail for the random rotation matrices of the terminal joints
print("unexpected bad R at joint %d, frame %d" % (i, t))
print(e)
quarts.append(np.concatenate(quart))
all_Rs.append(retargeted_Rs[0])
fps = vid_data['mocap_framerate']
spf = 1 / fps
root_motion = (root_adjust @ vid_data['trans'].T).T
if not os.path.exists(out_path):
os.makedirs(out_path)
outfile = os.path.join(out_path, vid.split('/')[-1].split('.')[0] + '.txt')
with open(outfile, 'w') as f:
f.write('{\n')
f.write('"Loop": "wrap",\n')
f.write('"Frames":\n')
f.write('[\n')
for i in range(len(quarts)):
frame = [spf] + list(root_motion[i]) + list(quarts[i])
f.write(str(frame))
if i < len(quarts) - 1:
f.write(',')
f.write("\n")
f.write(']\n')
f.write('}')
# ----------------------------------check final output one last time----------------------------------
if viz:
all_Rs = np.array(all_Rs)
check = batch_global_rigid_transformation(
all_Rs,
np.tile(np.expand_dims(joint_coords_retargeting, 0), (T, 1, 1)),
dm_parents)[0]
corrected[:, :, 0] += 1
compare_animate(corrected, check)
import numpy as np
import matplotlib.pyplot as plt
from pytransform3d.rotations import (matrix_from_axis_angle,
quaternion_from_matrix, quaternion_slerp)
from pytransform3d.trajectories import plot_trajectory
T = np.linspace(0, 1, 1001)
sigmoid = 0.5 * (np.tanh(1.5 * np.pi * (T - 0.5)) + 1.0)
radius = 0.5
start_angle = np.deg2rad(0.0)
end_angle = np.deg2rad(350.0)
R1 = matrix_from_axis_angle([0, 0, 1, 0.5 * np.pi])
R2_start = matrix_from_axis_angle([1, 0, 0, start_angle])
R2_end = matrix_from_axis_angle([1, 0, 0, end_angle])
q_start = quaternion_from_matrix(R1.dot(R2_start))
q_end = quaternion_from_matrix(R1.dot(R2_end))
Y = np.zeros((len(T), 7))
Y[:, 0] = radius * np.cos(np.deg2rad(90) - end_angle * sigmoid)
Y[:, 2] = radius * np.sin(np.deg2rad(90) - end_angle * sigmoid)
if end_angle > np.pi:
q_end *= -1.0
Y[:, 3:] = (1.0 - T)[:, np.newaxis] * q_start + T[:, np.newaxis] * q_end
Y_slerp = np.zeros((len(T), 7))
Y_slerp[:, 0] = 0.7 * radius * np.cos(np.deg2rad(90) - end_angle * sigmoid)
Y_slerp[:, 2] = 0.7 * radius * np.sin(np.deg2rad(90) - end_angle * sigmoid)
for i, t in enumerate(T):
Y_slerp[i, 3:] = quaternion_slerp(q_start, q_end, t)
canvas_y, canvas_x, _ = canvas.shape
# canvas = np.zeros((canvas_y, canvas_x, 3), dtype=np.uint8)
# 读取纸张
paper = cv2.imread(r'input\paper.png')
jmax, imax, _ = paper.shape
# 纸张放在画布的初始位置，这里定位中心，并建立起纸张坐标->画布坐标的转换关系
dx, dy = (canvas_x - imax)//2, (canvas_y-jmax)//2
def paper2canvas(x, y):
return x+dx, y+dy
# 通过欧拉角获得旋转的四元数
z_rot, y_rot, x_rot = 30, 30, 15
rot_matrix = active_matrix_from_extrinsic_euler_zyx(((z_rot/180*np.pi), (y_rot/180*np.pi), (x_rot/180*np.pi)))
qs = quaternion_slerp_batch(q_id, quaternion_from_matrix(rot_matrix), np.linspace(0, 1, n_steps))
def do_rot(v, t):
'''对t时刻时的向量（点）v做旋转变换'''
return q_prod_vector(qs[t], v)
# 除了旋转之外，还会有平移
trans_start, trans_max = (0, 0, 0), (0, 0, 1600)
trans = np.linspace(trans_start, trans_max, n_steps)
def do_trans(v, t):
return (v[0]+trans[t][0]), (v[1]+trans[t][1]), (v[2]+trans[t][2])
# 透视的话需要知道眼睛的位置
eye = (imax//2, jmax//2, -1600)
def proj(v):
'''将空间坐标映射到纸张初始平面'''
while True:
frames = pipeline.wait_for_frames()
f = frames.first_or_default(rs.stream.pose)
data = f.as_pose_frame().get_pose_data()
#angles = quaternion_to_euler_angle([data.rotation.w, data.rotation.x, data.rotation.y, data.rotation.z])
tm.add_transform(
"world", "slam_sensor",
pytr.transform_from_pq([
data.translation.x * 1000.0, -data.translation.z * 1000.0,
data.translation.y * 1000.0, data.rotation.w, data.rotation.x,
data.rotation.y, data.rotation.z
]))
t = tm.get_transform("world", "origin")
q = pyrot.quaternion_from_matrix(t[0:3, 0:3])
print(
pytr.transform(t, [0, 0, 0, 1])[0:3], quaternion_to_euler_angle(q),
data.mapper_confidence, data.tracker_confidence)
##print("\r Device Position: ", t[0][3], t[1][3], t[2][3], angles, end="\r")
# now = time.time()
# with open("back_side.txt", "w") as text_file:
# while (time.time() - now) < 5:
# frames = pipeline.wait_for_frames()
# f = frames.first_or_default(rs.stream.pose)
# data = f.as_pose_frame().get_pose_data()
# angles = quaternion_to_euler_angle(data.rotation.w, data.rotation.x, data.rotation.y,data.rotation.z)
# print("\r Device Position: ", -data.translation.x * 1000, data.translation.z * 1000, data.translation.y * 1000, angles, end="\r")
# print(str(data.translation), str(data.rotation), file=text_file)
