def test_as_rotation_matrix(Rs):
def quat_mat(quat):
return np.array([(quat * v * quat.inverse()).vec for v in [quaternion.x, quaternion.y, quaternion.z]]).T
def quat_mat_vec(quats):
mat_vec = np.array([quaternion.as_float_array(quats * v * np.invert(quats))[..., 1:]
for v in [quaternion.x, quaternion.y, quaternion.z]])
return np.transpose(mat_vec, tuple(range(mat_vec.ndim))[1:-1]+(-1, 0))
with pytest.raises(ZeroDivisionError):
quaternion.as_rotation_matrix(quaternion.zero)
for R in Rs:
# Test correctly normalized rotors:
assert allclose(quat_mat(R), quaternion.as_rotation_matrix(R), atol=2*eps)
# Test incorrectly normalized rotors:
assert allclose(quat_mat(R), quaternion.as_rotation_matrix(1.1*R), atol=2*eps)
Rs0 = Rs.copy()
Rs0[Rs.shape[0]//2] = quaternion.zero
with pytest.raises(ZeroDivisionError):
quaternion.as_rotation_matrix(Rs0)
# Test correctly normalized rotors:
assert allclose(quat_mat_vec(Rs), quaternion.as_rotation_matrix(Rs), atol=2*eps)
# Test incorrectly normalized rotors:
assert allclose(quat_mat_vec(Rs), quaternion.as_rotation_matrix(1.1*Rs), atol=2*eps)
def test_from_rotation_matrix(Rs):
rot_mat_eps = 10*eps
try:
from scipy import linalg
except ImportError:
rot_mat_eps = 400*eps
for i, R1 in enumerate(Rs):
R2 = quaternion.from_rotation_matrix(quaternion.as_rotation_matrix(R1))
d = quaternion.rotation_intrinsic_distance(R1, R2)
assert d < rot_mat_eps, (i, R1, R2, d) # Can't use allclose here; we don't care about rotor sign
Rs2 = quaternion.from_rotation_matrix(quaternion.as_rotation_matrix(Rs))
for R1, R2 in zip(Rs, Rs2):
d = quaternion.rotation_intrinsic_distance(R1, R2)
assert d < rot_mat_eps, (R1, R2, d) # Can't use allclose here; we don't care about rotor sign
def _apply_acceleration(self, orientation, props):
thrust = 0
self._acceleration = np.array([0, 0, 0], dtype='float32').T
accel_rotation = np.array([0,0,0], dtype='float32').T
for i, p in enumerate(props):
response = Quad._accels[i]
accel_rotation += p * response
thrust += p
rotate = quaternion.as_rotation_matrix(orientation)
thrust_vector = rotate.dot(np.array([0, 1, 0], dtype='float32').T)
self._acceleration += thrust * thrust_vector
gravity_vector = np.array([0, -10, 0], dtype='float32').T
self._acceleration += gravity_vector
def get_plane_params_in_local(planes, camera_info):
"""
input:
@planes: plane params
@camera_info: plane params from camera info, type = dict, must contain 'position' and 'rotation' as keys
output:
plane parameters in global frame.
"""
tran = camera_info['position']
rot = camera_info['rotation']
b = planes
a = np.ones((len(planes),3))*tran
planes_world = a + b - ((a*b).sum(axis=1) / np.linalg.norm(b, axis=1)**2).reshape(-1,1)*b
end = (quaternion.as_rotation_matrix(rot.inverse())@(planes_world - tran).T).T #world2cam
planes_local = end*np.array([1, -1, -1])# habitat2suncg
return planes_local
def calc_rotation_matrix(self): # DONE
"""Calculates the euler rotation matrix.
R(phi,theta) = Ry(theta)Rx(phi)
See https://en.wikipedia.org/wiki/Davenport_chained_rotations.
"""
'''
sp,cp = self.sin_phi,self.cos_phi
st,ct = self.sin_theta,self.cos_theta
return np.array([[ct,sp*st,-st*cp],
[0,cp,sp],
[st,-sp*ct,cp*ct]])'''
## BEGIN CHANGE
q = np.quaternion(self.q0, self.q1, self.q2, self.q3)
return quaternion.as_rotation_matrix(q)
def publish_bearings(self):
#print("calculating bearings")
self._msg.header.stamp = rospy.Time.now()
self._msg.drones = self._drones
self._msg.links = []
for i in range(len(self._drones)): # loop through list of drones
self._msg.links.append(FormationLink())
self._msg.links[i].drone_name = self._drones[i]
for j in range(len(
self._from)): # loop through list of drones observing
if self._from[j] == self._drones[i]:
self._msg.links[i].targets.append(self._to[j])
target_index = self._drones.index(self._to[j])
from_index = self._drones.index(self._from[j])
bearing = Vector3()
dx = self._poses[target_index].position.x - self._poses[
from_index].position.x
dy = self._poses[target_index].position.y - self._poses[
from_index].position.y
dz = self._poses[target_index].position.z - self._poses[
from_index].position.z
vect = np.array([[dx], [dy], [dz]])
distance = Float64()
distance.data = np.linalg.norm(vect)
self._msg.links[i].distances.append(distance)
vect = vect / np.linalg.norm(vect)
# find world frame bearing vector
w = self._poses[from_index].orientation.w
x = self._poses[from_index].orientation.x
y = self._poses[from_index].orientation.y
z = self._poses[from_index].orientation.z
# find local frame bearing vector
q = np.array([quaternion.quaternion(w, x, y, z)])
R = quaternion.as_rotation_matrix(q)
RT = np.transpose(R).reshape(1, 3, 3)
vect_rotatated = np.matmul(RT, vect)
bearing.x = vect_rotatated[0][0]
bearing.y = vect_rotatated[0][1]
bearing.z = vect_rotatated[0][2]
self._msg.links[i].bearings.append(bearing)
self._pub.publish(self._msg)
def check_box_collision(self, joints, box):
'''
Arguments: joints represents the current location of the robot
box contains the position of the center of the box [x, y, z, r, p, y] and the length, width, and height [l, w, h]
Returns: A boolean where True means the box is in collision with the arm and false means that there are no collisions.
'''
box_pos, box_rpy, box_hsizes = box[:3], box[3:6], box[6:]/2
box_q = quaternion.from_euler_angles(box_rpy)
box_axes = quaternion.as_rotation_matrix(box_q)
self._box_vertices_offset[:,:] = self._vertex_offset_signs * box_hsizes
box_vertices = (box_axes.dot(self._box_vertices_offset.T) + np.expand_dims(box_pos, 1)).T
box_hdiag = np.linalg.norm(box_hsizes)
min_col_dists = box_hdiag + self._collision_box_hdiags
franka_box_poses = self.get_collision_boxes_poses(joints)
for i, franka_box_pose in enumerate(franka_box_poses):
fbox_pos = franka_box_pose[:3, 3]
fbox_axes = franka_box_pose[:3, :3]
# coarse collision check
if np.linalg.norm(fbox_pos - box_pos) > min_col_dists[i]:
continue
fbox_vertex_offsets = self._collision_box_vertices_offset[i]
fbox_vertices = fbox_vertex_offsets.dot(fbox_axes.T) + fbox_pos
# construct axes
cross_product_pairs = np.array(list(product(box_axes.T, fbox_axes.T)))
cross_axes = np.cross(cross_product_pairs[:,0], cross_product_pairs[:,1]).T
self._collision_proj_axes[:, :3] = box_axes
self._collision_proj_axes[:, 3:6] = fbox_axes
self._collision_proj_axes[:, 6:] = cross_axes
# projection
box_projs = box_vertices.dot(self._collision_proj_axes)
fbox_projs = fbox_vertices.dot(self._collision_proj_axes)
min_box_projs, max_box_projs = box_projs.min(axis=0), box_projs.max(axis=0)
min_fbox_projs, max_fbox_projs = fbox_projs.min(axis=0), fbox_projs.max(axis=0)
# check if no separating planes exist
if np.all([min_box_projs <= max_fbox_projs, max_box_projs >= min_fbox_projs]):
return True
return False
def generate_pose(self, min_z=0.3, max_z=1.5, edge_offset=3):
z = min_z + np.random.rand() * (max_z - min_z)
## determine x and y by z value
min_x = (edge_offset - self.K[0, 2]) * z / self.K[0, 0]
max_x = (
(self.img_width - edge_offset) - self.K[0, 2]) * z / self.K[0, 0]
min_y = (edge_offset - self.K[1, 2]) * z / self.K[1, 1]
max_y = (
(self.img_height - edge_offset) - self.K[1, 2]) * z / self.K[1, 1]
x = min_x + np.random.rand() * (max_x - min_x)
y = min_y + np.random.rand() * (max_y - min_y)
pos = np.array([x, y, z])
quat = quaternion.from_euler_angles(np.random.rand() * 360,
np.random.rand() * 360,
np.random.rand() * 360)
rot = quaternion.as_rotation_matrix(quat)
return pos, rot
def transform_points(self, points3d: np.ndarray) -> np.ndarray:
"""
Applies the self transformation to the given 3d points.
:param points3d: input 3d points, stored row wise (one point per row)
:return: another array with the 3d points transformed using the PoseTransform.
"""
assert self._r is not None
assert self._t is not None
assert isinstance(points3d, np.ndarray)
if points3d.shape[1] == 6: # expunge RGB
points3d = points3d[:, 0:3]
points3d = points3d.transpose()
rotation_matrix = quaternion.as_rotation_matrix(self.r)
points3d = np.add(np.matmul(rotation_matrix, points3d), self.t)
return points3d.transpose()
def generate_image(self, pose, grid=5):
if self._pts4d is None:
x, y, z = pose.loc
if 0:
xx, yy = np.meshgrid(
np.linspace(x - z * 3, x + z * 3,
math.ceil(z * 3 / grid) * 2 + 1),
np.linspace(y - z * 3, y + z * 3,
math.ceil(z * 3 / grid) * 2 + 1))
else:
xx = np.random.uniform(x - z * 3, x + z * 3,
2 * (math.ceil(z * 3 / grid) * 2 + 1, ))
yy = np.random.uniform(x - z * 3, x + z * 3,
2 * (math.ceil(z * 3 / grid) * 2 + 1, ))
if 0:
zz = np.zeros_like(xx)
else:
zz = np.random.uniform(0, -40, xx.shape)
pts3d = np.stack((xx.flatten(), yy.flatten(), zz.flatten()),
axis=1).reshape((-1, 3))
self._pts4d = np.hstack((pts3d, np.ones((len(pts3d), 1))))
T = np.hstack(
(quaternion.as_rotation_matrix(pose.quat), pose.loc.reshape(
(-1, 1))))
proj_pts2d = self.real_cam.cam_mx.dot(T).dot(self._pts4d.T).T
uvp = proj_pts2d[:, :2] / proj_pts2d[:, 2:]
I = np.logical_and.reduce((
uvp[:, 0] >= 0,
uvp[:, 1] >= 0,
uvp[:, 0] < self.real_cam.width,
uvp[:, 1] < self.real_cam.height,
))
uvp = self.real_cam.distort(uvp[I, :])
uvp = (uvp + 0.5).astype(int)
img = np.ones(
(self.real_cam.height, self.real_cam.width), dtype=np.uint8) * 64
for i in range(5):
for j in range(5):
img[np.clip(uvp[:, 1] + i - 2, 0, self.real_cam.height - 1),
np.clip(uvp[:, 0] + j - 2, 0, self.real_cam.width -
1)] = 224
return img
def getGradientRespectToTranslation(agent, targetPositionW, NBV):
R_w_s = quaternion.as_rotation_matrix(
agent.get_state().sensor_states["left_sensor"].rotation)
t_w_s = agent.state.sensor_states["left_sensor"].position
R = R_w_s.dot(R_s_c)
sum_delta = np.zeros(3)
delta = 2 * (R.dot(NBV) - targetPositionW + t_w_s)
while (np.linalg.norm(sum_delta) < 0.25):
sum_delta += delta
return sum_delta
def compute_rotation(ground_point):
x_plus = np.array([1, 0, 0])
z_plus = np.array([0, 0, 1])
look_vector = -ground_point / np.linalg.norm(ground_point)
# First compute the rotation that takes +Z to the look vector
look_rotation = rotation_between(z_plus, look_vector)
# Next compute the rotation that aligns North up, the rotation that takes
# the rotated +X to the component of +Z that is orthogonal the look vector.
rotated_x = quaternion.as_rotation_matrix(look_rotation) @ x_plus
north_up = z_plus - np.dot(z_plus, look_vector) * look_vector
# We need to make sure that if the rotated x and north up vector are
# opposites, then we use a 180 rotation about the look vector so as
# to not screw it up
north_rotation = rotation_between(rotated_x, north_up, look_vector)
return north_rotation * look_rotation
def updateEyeX(self):
global gEye, gAt, gUp
wp = gEye - gAt
w = wp / np.sqrt(np.dot(wp, wp))
upt = np.cross(gUp, w)
u = upt / np.sqrt(np.dot(upt, upt))
v = np.cross(w, u)
print("vvvvv", v)
print("xsubrad", self.xsubrad)
quat = quaternion.quaternion(np.cos(self.xsubrad),
np.sin(self.xsubrad) * gUp[0],
np.sin(self.xsubrad) * gUp[1],
np.sin(self.xsubrad) * gUp[2])
qrm = quaternion.as_rotation_matrix(quat)
gEye = qrm @ wp + gAt
def drawCoord():
rot = quaternion.as_rotation_matrix(quaternion.one)
arrow_prop_dict = dict(mutation_scale=20,
arrowstyle='->',
shrinkA=0,
shrinkB=0)
a = Arrow3D(rot[:, 0], **arrow_prop_dict, color='r')
ax.add_artist(a)
b = Arrow3D(rot[:, 1], **arrow_prop_dict, color='g')
ax.add_artist(b)
c = Arrow3D(rot[:, 2], **arrow_prop_dict, color='b')
ax.add_artist(c)
cap1 = ax.text(0.0, 0.0, -0.15, r'$o$')
cap2 = ax.text(rot[:, 0][0], rot[:, 0][1], rot[:, 0][2] + 0.1, r'$x$')
cap3 = ax.text(rot[:, 1][0], rot[:, 1][1], rot[:, 1][2] + 0.1, r'$y$')
cap4 = ax.text(rot[:, 2][0], rot[:, 2][1], rot[:, 2][2] + 0.1, r'$z$')
title0 = ax.set_title('Current Step: 0.0')
return a, b, c, cap1, cap2, cap3, cap4, title0
def get_plane_params_in_global(planes, camera_info):
"""
input:
@planes: plane params
@camera_info: plane params from camera info, type = dict, must contain 'position' and 'rotation' as keys
output:
plane parameters in global frame.
"""
tran = camera_info["position"]
rot = camera_info["rotation"]
start = np.ones((len(planes), 3)) * tran
end = planes * np.array([1, -1, -1]) # suncg2habitat
end = (quaternion.as_rotation_matrix(rot) @ (end).T).T + tran # cam2world
a = end
b = end - start
planes_world = (
(a * b).sum(axis=1) / np.linalg.norm(b, axis=1)**2).reshape(-1, 1) * b
return planes_world
def computeObsCovariance(agent, depth_pair, targetPositionW):
pixels = targetPixelInCurrentCamera(agent, targetPositionW)
pixels_withoutOffset = np.array(
[pixels[0] - cx, pixels[1] - cx, pixels[2] - cy])
J = makeJacobian(pixels_withoutOffset)
Q = makeQ(depth_pair[int(pixels[2]), int(pixels[0])])
R_w_s = quaternion.as_rotation_matrix(
agent.get_state().sensor_states["left_sensor"].rotation)
R = R_w_s.dot(R_s_c)
U_obs = np.linalg.multi_dot([R, J, Q, J.T, R.T])
return U_obs
def setAgentPosition(agent, nextBestCameraPostionIncy):
R_w_s = quaternion.as_rotation_matrix(
agent.get_state().sensor_states["left_sensor"].rotation)
R_w_c = R_w_s.dot(R_s_c) # R_w_c
nextBestCameraPosition = nextBestCameraPostionIncy + R_w_c.dot(
tcyclopean_leftcamera)
agent_state = agent.get_state()
y = agent_state.position[1]
# y = nextBestCameraPostionIncy[1]-1.5
agent_state.position = np.array([
nextBestCameraPosition[0] + cam_baseline / 2, y,
nextBestCameraPosition[2]
])
agent.set_state(agent_state)
return agent.state # agent.scene_node.transformation_matrix()
def IMU_double_integration(t,
rotation,
acceleration,
no_transform=False,
only_xy=False):
"""
Compute position and orientation by integrating angular velocity and double integrating acceleration
Expect the drift to be as large as hell
:param t: time sequence, Nx1 array
:param rotation: device orientation as quaternion, Nx4 array
:param acceleration: acceleration data, Nx3 array
:param no_transform: if set to true, assume acceleration vectors to be inside the global frame
:return: position: Nx3 array
"""
# Sanity check
assert t.shape[0] == rotation.shape[0], "{}, {}".format(
t.shape[0], rotation.shape[0])
assert t.shape[0] == acceleration.shape[0]
if not no_transform:
assert rotation.shape[1] == 4
# quats = quaternion.as_quat_array(rotation)
# convert the acceleration vector to world coordinate frame
if no_transform:
result = acceleration
else:
result = np.empty([acceleration.shape[0], 3], dtype=float)
for i in range(acceleration.shape[0]):
q = quaternion.quaternion(*rotation[i])
result[i, :] = np.dot(quaternion.as_rotation_matrix(q),
acceleration[i, :].reshape([3,
1])).flatten()
# double integration with trapz rule
position = integrate.cumtrapz(integrate.cumtrapz(result,
t,
axis=0,
initial=0),
t,
axis=0,
initial=0)
if only_xy:
position[:, 2] = 0
return position
def project(pts_3d, camera_params, mobile_captures, pts_2d_3d_indices, pts_2d_cam_indices, viz=0):
PTS_2D = np.empty((0, 2))
n_cams = len(mobile_captures.keys())
for c in range(n_cams):
fx, fy = camera_params[c, :2]
px, py = camera_params[c, 2:4]
R = quat.as_rotation_matrix(quat.quaternion(*camera_params[c, 4:8]))
C = camera_params[c, 8:]
# select the indices of only the 3d points triangulated from the current camera
pts_3d_idx = pts_2d_3d_indices[pts_2d_cam_indices == c]
_pts_3d = pts_3d[:, pts_3d_idx]
K = np.asarray([[fx, 0, px],
[0, fy, py],
[0, 0, 1]])
P = np.dot(K, np.c_[R.T, - np.dot(R.T, C)])
pts_2d = np.dot(P, np.vstack((_pts_3d, np.ones((1, _pts_3d.shape[1])))))
pts_2d = pts_2d[:2, :] / pts_2d[2, :]
PTS_2D = np.vstack((PTS_2D, pts_2d.T))
cam_key = c + 1 # todo: substitute this by taking the key directly from the key list
if viz == cam_key or viz == -1:
plt.figure()
_img = copy.deepcopy(mobile_captures[cam_key]['im'])
_img[:, :, 0] = mobile_captures[cam_key]['im'][:, :, 2]
_img[:, :, 2] = mobile_captures[cam_key]['im'][:, :, 0]
plt.imshow(_img)
plt.plot(mobile_captures[cam_key]['person_pose'][::3], mobile_captures[cam_key]['person_pose'][1::3],
'o', color='green', markersize=4)
plt.plot(pts_2d[0, :], pts_2d[1, :],
'o', color='red', markersize=4)
plt.draw()
plt.pause(.001)
return PTS_2D.T
def get_camera_pose(tf_buffer,
map_frame_id,
camera_frame_id,
target_time=rospy.Time()):
trans = get_tf_pose(tf_buffer, map_frame_id, camera_frame_id, target_time)
if trans is None:
return None, None
translation = np.asarray([
trans.transform.translation.x, trans.transform.translation.y,
trans.transform.translation.z
]).reshape(-1, 1)
rotation = quaternion.as_rotation_matrix(
quaternion.quaternion(trans.transform.rotation.w,
trans.transform.rotation.x,
trans.transform.rotation.y,
trans.transform.rotation.z))
return translation, rotation
def __init__(self,x,y,z,vx,vy,vz,phi,theta,gamma,
phidot,thetadot,gammadot,debug=False):
"""
Constructor
x,y,z: euclidean positions
vx,vy,vz: velocities
phi,theta,gamma: euler angles
phidot,thetadot,gammadot: euler angle time derivatives
debug: default False; for printing
Note: all kinematic variables are collectively
referred to as 'coordinates' since they are the
variables that change in the equations of motion (EOM).
Calls update_data_fields to calculate quantities
that depend only on positions.
"""
self.x=x
self.y=y
self.z=z
self.vx=vx
self.vy=vy
self.vz=vz
self.phi=phi
self.theta=theta
self.gamma=gamma
q = toQuaternion.euler_to_quaternion(phi,theta,gamma) # CHANGE
self.q0, self.q1, self.q2, self.q3 = q.w, q.x, q.y, q.z
# Convert from angle_dots to wxb,wyb,wzb
self.rotation_matrix = quaternion.as_rotation_matrix(q)
self.calc_trig_functions()
st,ct = self.sin_theta,self.cos_theta
angular_velocity_B = np.array([phidot*ct,
thetadot,
phidot*st + gammadot])
self.wxl,self.wyl,self.wzl = np.dot(angular_velocity_B,self.rotation_matrix)
# CHANGE
self.debug=debug
self.has_model=False
self.update_data_fields()
def as_matrix(self) -> np.ndarray:
"""Return the transform as a 4x4 transform matrix.
Returns:
A 4x4 numpy array represents the transform matrix.
Examples:
>>> transform = Transform3D([1, 2, 3], [0, 1, 0, 0])
>>> transform.as_matrix()
array([[ 1., 0., 0., 1.],
[ 0., -1., 0., 2.],
[ 0., 0., -1., 3.],
[ 0., 0., 0., 1.]])
"""
matrix: np.ndarray = np.eye(4)
matrix[:3, 3] = self._translation
matrix[:3, :3] = as_rotation_matrix(self._rotation)
return matrix
def calc_Mbbox(model):
trs_obj = model["trs"]
bbox_obj = np.asarray(model["bbox"], dtype=np.float64)
center_obj = np.asarray(model["center"], dtype=np.float64)
trans_obj = np.asarray(trs_obj["translation"], dtype=np.float64)
rot_obj = np.asarray(trs_obj["rotation"], dtype=np.float64)
q_obj = np.quaternion(rot_obj[0], rot_obj[1], rot_obj[2], rot_obj[3])
scale_obj = np.asarray(trs_obj["scale"], dtype=np.float64)
tcenter1 = np.eye(4)
tcenter1[0:3, 3] = center_obj
trans1 = np.eye(4)
trans1[0:3, 3] = trans_obj
rot1 = np.eye(4)
rot1[0:3, 0:3] = quaternion.as_rotation_matrix(q_obj)
scale1 = np.eye(4)
scale1[0:3, 0:3] = np.diag(scale_obj) * np.diag(bbox_obj)
M = trans1.dot(rot1).dot(scale1).dot(tcenter1)
return M
def compose(pose_list: List['PoseTransform']) -> 'PoseTransform':
"""
Merges multiple pose transformation into a single one.
:param pose_list: the list of pose transformation to be merged. They are merged from right to left.
:return: the pose transformation being the composition of the given ones.
"""
assert isinstance(pose_list, list)
pose_composed = pose_list[0]
for pose_current in pose_list[1:]:
# shrink the poses from the left
assert pose_current._r is not None
assert pose_current._t is not None
new_r = pose_composed.r * pose_current.r
new_t = np.add(
np.matmul(quaternion.as_rotation_matrix(pose_composed.r),
pose_current.t), pose_composed.t)
pose_composed = PoseTransform(new_r, new_t)
return pose_composed
def forwardKinematics(self, y_data, tag):
hip_data = self.dataset[tag]['test_hip']
hip_quart = quaternion.as_quat_array(hip_data[:, :4].copy())
hip_position = hip_data[:, 4:].copy()
y_data = deepcopy(y_data)
outs = np.zeros_like(y_data)
marker_idxs = np.array(range(y_data.shape[1])).reshape(-1, 3)
for idx in range(y_data.shape[0]):
rtMat = quaternion.as_rotation_matrix(hip_quart[idx])
pos = hip_position[idx]
for marker_idx in marker_idxs:
outs[idx,
marker_idx] = np.matmul(rtMat.T,
(y_data[idx, marker_idx])) + pos
return outs
def _get_transform_mtrx(self, idx):
c_idx = idx
n_idx = idx+1
c_pose = self.poses[c_idx]
n_pose = self.poses[n_idx]
local_dtrans = np.linalg.inv(c_pose) @ n_pose[:, 3]
quat_c = quat.from_rotation_matrix(c_pose[:3,:3])
quat_n = quat.from_rotation_matrix(n_pose[:3,:3])
quat_t = quat_c.inverse() * quat_n
transform = np.eye(4)
transform[:3, :3] = quat.as_rotation_matrix(quat_t)
transform[:3, 3] = local_dtrans[:3]
return transform
def set_K_frame_to_link(self, frame_name, timeout=5.0):
"""
Set new K frame to the same frame as the link frame given by 'frame_name'
Motion controllers are stopped for switching
@type frame_name: str
@param frame_name: desired tf frame name in the tf tree
@rtype: [bool, str]
@return: [success status of service request, error msg if any]
"""
trans = False
listener = tf.TransformListener()
err = "FrankaFramesInterface: Error while looking up transform from EE frame to link frame %s" % frame_name
def body():
try:
listener.lookupTransform('/panda_EE', frame_name,
rospy.Time(0))
except (tf.LookupException, tf.ConnectivityException,
tf.ExtrapolationException) as e:
err = e
return False
return True
franka_dataflow.wait_for(lambda: body(),
timeout=timeout,
raise_on_error=True,
timeout_msg=err)
t, rot = listener.lookupTransform('/panda_EE', frame_name,
rospy.Time(0))
rot = np.quaternion(rot[3], rot[0], rot[1], rot[2])
rot = quaternion.as_rotation_matrix(rot)
trans_mat = np.eye(4)
trans_mat[:3, :3] = rot
trans_mat[:3, 3] = np.array(t)
return self.set_K_frame(trans_mat)
def save_parts(cube_params, output_file, level='1'):
n_part = np.shape(cube_params)[0]
mtl_filename = output_file.replace('.obj', '.mtl')
with open(mtl_filename, 'w') as f:
palette = color_palette['L{}'.format(level)]
n_color = len(palette)
for i in range(n_part):
color_index = i
if level == '1':
step = n_color/float(n_part)
color_index = int(i*step)
part_color = palette[color_index]
f.write('newmtl m{}\nKd {} {} {}\nKa 0 0 0\n'.format(i, float(part_color[0])/256, float(part_color[1])/256, float(part_color[2])/256))
with open(output_file, 'w') as f:
vert_offset = 0
n_vert = np.shape(cube_vert)[0]
f.write('mtllib {}\n'.format(mtl_filename.split('/')[-1]))
for i in range(n_part):
verts = cube_vert
scale = cube_params[i][0:3]
rot = cube_params[i][3:7]
translation = cube_params[i][7:10]
verts = verts * scale
quat = np.quaternion(rot[0], rot[1], rot[2], rot[3])
rotation = quaternion.as_rotation_matrix(quat)
verts = np.transpose(np.matmul(rotation, np.transpose(verts)))
verts = verts + translation
faces = cube_face + vert_offset
f.write('# {} {} {} {} {} {} {} {} {} {}\n'.format(
scale[0], scale[1], scale[2],
rot[0], rot[1], rot[2], rot[3],
translation[0], translation[1], translation[2])
)
f.write('usemtl m{}\n'.format(i))
for j in range(n_vert):
sigma = 0.1
f.write('v {} {} {}\n'.format(verts[j][0], verts[j][1], verts[j][2]))
for j in range(np.shape(cube_face)[0]):
f.write('f {} {} {} {}\n'.format(faces[j][0], faces[j][1], faces[j][2], faces[j][3]))
vert_offset += n_vert
def nextBestCameraPosition(agent, delta):
#need Rwc twc=tws
R_c_s = R_s_c.T
R_w_s = quaternion.as_rotation_matrix(
agent.get_state().sensor_states["left_sensor"].rotation)
R_w_c = R_w_s.dot(R_s_c) # R_w_c
R_s_w = R_w_s.T
t_w_s = agent.state.sensor_states["left_sensor"].position
t_s_w = -R_s_w.dot(t_w_s)
t_c_w = R_c_s.dot(t_s_w)
sum_delta = 0.25 * (delta / np.linalg.norm(delta))
print("sum_delta {0}".format(sum_delta))
NBCameraPosition = -R_w_c.dot(t_c_w - sum_delta)
return NBCameraPosition
def computeObsCovariance(agent, depth_pair, targetPositionW):
pixels = targetPixelInCurrentCamera(agent, targetPositionW)
isValid = isObserved(pixels)
if isValid:
J = makeJacobian(pixels, cam_baseline)
Q = makeQ(depth_pair[pixels[2], pixels[0]])
R_w_s = quaternion.as_rotation_matrix(
agent.get_state().sensor_states["left_sensor"].rotation)
R = R_w_s.dot(R_s_c)
U_obs = np.linalg.multi_dot([R, J, Q, J.T, R.T])
return True, U_obs
else:
return False, np.identity(3)
def computeCameraPostion(agent, targetPositionNext_, targetPositionW_):
R_w_s = quaternion.as_rotation_matrix(
agent.state.sensor_states["left_sensor"].rotation)
tws = agent.state.sensor_states["left_sensor"].position
targetPositionNextS_ = R_s_c.dot(targetPositionNext_)
delta_tws = 2 * (R_w_s.dot(
(targetPositionNextS_)) + tws - targetPositionW_)
delta_tws = delta_tws / np.linalg.norm(delta_tws)
nextBestCameraPosition = tws - 0.25 * delta_tws
print("delta_tws: {0}, norm of delta_tws: {1}".format(
delta_tws, np.linalg.norm(delta_tws)))
print("nextBestCameraPostion: {0}".format(nextBestCameraPosition))
return nextBestCameraPosition
def _imuCallback(data):
self.prev_time = time.time()
self.func_data[theme_name].setdefault('value', [])
self.func_data[theme_name].setdefault('ros_value', [])
try:
frame_id = data.header.frame_id
value = data.linear_acceleration
self.func_data[theme_name]['value'].append(value)
if (self.tfBuffer.can_transform('camera_link', frame_id, rclpy.time.Time(), rclpy.time.Duration(nanoseconds=3e6))):
msg = self.tfBuffer.lookup_transform('camera_link', frame_id, rclpy.time.Time(), rclpy.time.Duration(nanoseconds=3e6)).transform
quat = np.quaternion(msg.rotation.x, msg.rotation.y, msg.rotation.z, msg.rotation.w)
point = np.matrix([value.x, value.y, value.z], dtype='float32')
point.resize((3, 1))
rotated = quaternion.as_rotation_matrix(quat) * point
rotated.resize(1,3)
self.func_data[theme_name]['ros_value'].append(rotated)
except Exception as e:
print(e)
return
def state_dot(self, t, state):
velocity = state[3:6]
orientation = quaternion.from_float_array(state[6:10])
omega = state[10:]
drag = 0.5 * self.FLUID_DENSITY * self.controller.DRAG_COEFF * self.controller.AREA * (velocity - self.WIND) * np.absolute(velocity - self.WIND)
force = np.array([0, 0, -self.GRAVITY]) - drag + self.control_force
torque = self.control_torque
state_dot = np.empty((13)) # [x, y, z, vx, vy, vz, qw, qx, qy, qz, ox, oy, oz]
state_dot[0:3] = velocity
state_dot[3:6] = force / self.controller.MASS
state_dot[6:10] = quaternion.as_float_array(0.5 * np.quaternion(0, *omega) * orientation)
rotation_matrix = quaternion.as_rotation_matrix(orientation)
rot_inertia_tensor = np.asarray(rotation_matrix * self.controller.INERTIA_TENSOR * np.transpose(rotation_matrix))
state_dot[10:] = np.matmul(np.linalg.inv(rot_inertia_tensor), (torque - np.cross(omega, np.matmul(rot_inertia_tensor, omega))))
return state_dot
def run(self):
t = threading.Thread(target=runQuad, args=(self.quad,))
t.start()
while 1:
for event in pygame.event.get():
if event.type == pygame.QUIT:
self.quad.quit()
pygame.quit()
sys.exit()
self.clock.tick(50)
self.screen.fill((0, 32, 0))
position, orientation = self.quad.params
rotation = quaternion.as_rotation_matrix(orientation)
t = self.vertices.dot(rotation) + position
factors = 256 / (4 + t[:,2]) # fov / viewer_distance + z
t[:, 0] = t[:, 0] * factors + self.screen.get_width() / 2
t[:, 1] = -t[:, 1] * factors + self.screen.get_height() / 2
avg_z = []
for i, f in enumerate(self.faces):
z = (t[f[0],2] + t[f[1],2] + t[f[2],2] + t[f[3],2]) / 4.0
avg_z.append([i, z])
for tmp in sorted(avg_z, key=itemgetter(1), reverse=True):
face_index = tmp[0]
f = self.faces[face_index]
pointlist = [(t[f[0],0], t[f[0],1]), (t[f[1],0], t[f[1],1]),
(t[f[1],0], t[f[1],1]), (t[f[2],0], t[f[2],1]),
(t[f[2],0], t[f[2],1]), (t[f[3],0], t[f[3],1]),
(t[f[3],0], t[f[3],1]), (t[f[0],0], t[f[0],1])]
pygame.draw.polygon(self.screen, self.colors[face_index], pointlist)
self.angle += 1
pygame.display.flip()
#!/usr/bin/env python from __future__ import print_function, division, absolute_import import sys import numpy as np import quaternion from numpy import * eps = np.finfo(float).eps rot_mat_eps = 200*eps R1 = np.array([quaternion.quaternion(0, -math.sqrt(0.5), 0, -math.sqrt(0.5))]) print(R1) print(quaternion.as_rotation_matrix(R1)) R2 = quaternion.from_rotation_matrix(quaternion.as_rotation_matrix(R1)) print(R2) d = quaternion.rotation_intrinsic_distance(R1[0], R2[0]) print(d) print() sys.stdout.flush() sys.stderr.flush() assert d < rot_mat_eps, (R1, R2, d) # Can't use allclose here; we don't care about rotor sign