def new_episode(self):
self.vehicle_collided = None
self.episode_started = True
self.timestamp = time.time()
self.timestamp0 = self.timestamp
max_tries = 100
for _ in range(max_tries):
episode = self.world.new_episode()
# TODO: change name
pose = episode.vehicle_state
SE3.belongs(pose)
primitives = self.world.get_primitives()
check_primitives(primitives)
self.vehicle.set_world_primitives(primitives)
collision = self.vehicle.colliding_pose(pose,
safety_margin=self.safety_margin)
if not collision.collided:
self.vehicle.set_pose(pose)
return episode
# print('Bad random: collision %s' % str(collision))
else:
msg = ('Cannot find a non-colliding state after %d tries.'
% max_tries)
raise Exception(msg)
def new_episode(self):
self.vehicle_collided = None
self.episode_started = True
self.timestamp = time.time()
self.timestamp0 = self.timestamp
max_tries = 100
for _ in range(max_tries):
episode = self.world.new_episode()
# TODO: change name
pose = episode.vehicle_state
SE3.belongs(pose)
primitives = self.world.get_primitives()
check_primitives(primitives)
self.vehicle.set_world_primitives(primitives)
collision = self.vehicle.colliding_pose(
pose, safety_margin=self.safety_margin)
if not collision.collided:
self.vehicle.set_pose(pose)
return episode
# print('Bad random: collision %s' % str(collision))
else:
msg = ('Cannot find a non-colliding state after %d tries.' %
max_tries)
raise Exception(msg)
def read_pose_data(bds):
for bd in bds:
extra = bd['extra'].item()
if not 'robot_pose' in extra:
msg = 'Could not find pose "odom" in extra.'
raise Exception(msg)
pose = np.array(extra['robot_pose'])
SE3.belongs(pose)
yield bd, pose
def servo_stats_summary(data_central, id_agent, id_robot, id_episode): # @UnusedVariable
from geometry import (SE2, SE2_from_SE3, translation_from_SE2,
angle_from_SE2, SE3)
log_index = data_central.get_log_index()
errors = []
timestamps = []
poses = []
dist_xy = []
dist_th = []
for observations in \
log_index.read_robot_episode(id_robot, id_episode,
read_extra=True):
extra = observations['extra'].item()
servoing = extra.get('servoing', None)
if servoing is None:
logger.error('Warning, no "servoing" in episode %r.' %
id_episode)
break
obs0 = np.array(servoing['obs0'])
pose0 = SE2_from_SE3(SE3.from_yaml(servoing['pose0']))
pose1 = SE2_from_SE3(SE3.from_yaml(servoing['pose1']))
poseK = SE2_from_SE3(SE3.from_yaml(servoing['poseK']))
pose1 = SE2.multiply(SE2.inverse(pose0), pose1)
poseK = SE2.multiply(SE2.inverse(pose0), poseK)
poses.append(poseK)
dist_xy.append(np.linalg.norm(translation_from_SE2(poseK)))
dist_th.append(np.abs(angle_from_SE2(poseK)))
# obs1 = np.array(servoing['obs1'])
obsK = np.array(servoing['obsK'])
err_L2 = np.linalg.norm(obs0 - obsK)
errors.append(err_L2)
timestamps.append(observations['timestamp'])
# last['time_from_episode_start'] = observations['time_from_episode_start']
initial_distance = np.linalg.norm(translation_from_SE2(pose1))
summary = {}
summary['pose0'] = pose0
summary['pose1'] = pose1
summary['poses'] = poses
summary['errors'] = errors
summary['timestamps'] = timestamps
summary['initial_translation'] = translation_from_SE2(pose1)
summary['initial_distance'] = initial_distance
summary['initial_rotation'] = angle_from_SE2(pose1)
summary['dist_xy'] = dist_xy
summary['dist_th'] = dist_th
return summary
def to_yaml(self):
if self.current_observations is None:
raise Exception('Observations not computed')
return {
'sensor': self.sensor.to_yaml(),
'pose': SE3.to_yaml(self.pose),
'joint': self.joint,
'extra': self.extra,
'current_observations': dict_to_yaml(self.current_observations),
'current_pose': SE3.to_yaml(self.current_pose),
}
def to_yaml(self):
if self.current_observations is None:
raise Exception('Observations not computed')
return {
'sensor': self.sensor.to_yaml(),
'pose': SE3.to_yaml(self.pose),
'joint': self.joint,
'extra': self.extra,
'current_observations':
dict_to_yaml(self.current_observations),
'current_pose': SE3.to_yaml(self.current_pose),
}
def update_(self, msg):
child_frame_id = msg.child_frame_id
if child_frame_id == '/base_footprint':
new_pose = pose_from_ROS_transform(msg.transform)
if False:
if self.pose is not None:
delta = SE3.multiply(SE3.inverse(self.pose), new_pose)
x, y = translation_from_SE2(SE2_from_SE3(delta))
interval = (msg.header.stamp - self.stamp).to_sec()
print('base_delta: delta %.3f seconds %.4f y %.4f' % (interval, x, y))
self.pose = new_pose
self.stamp = msg.header.stamp
def test_collisions():
L = 10
world = Box(L, L)
# id_vehicle = 'd_SE2_rb_v-rf360'
id_vehicle = 'd_SE2_rb_v-random_5'
get_vehicles_config().load('default')
vehicles = get_conftools_vehicles()
# vehicles.load('default')
vehicle = vehicles.instance(id_vehicle) # @UndefinedVariable
vehicle.set_world_primitives(world.get_primitives())
vehicle.set_pose(SE3.identity())
commands = np.array([1, 0, 0])
# go straight
simulation_length = 10
dt = 0.1
time = 0
steps = int(np.ceil(simulation_length / dt))
for _ in range(steps):
vehicle.simulate(commands, dt)
pose = SE2_from_SE3(vehicle.get_pose())
translation = translation_from_SE2(pose)
# print('t=%.3f %s' % (time, SE2.friendly(pose)))
time += dt
assert translation[0] <= 9.5
assert translation[0] >= 9.4
assert translation[1] == 0
def jac_function(theta):
body_to_c = SE3.group_from_algebra(
se3.algebra_from_vector(theta[:6]))
# tag_in_board_offset = theta[6:]
t_in_board = tag_in_board.copy()
# t_in_board[:3,3] += tag_in_board_offset
# t_in_board = np.dot(t_in_board, SE3.group_from_algebra(
# se3.algebra_from_vector(tag_in_board_offset)))
error = 0
img_count = 0
jacs = []
for measurement, body_to_world, board_to_world, tag_in_cam in data_tuples:
tag_pts = np.concatenate(
(objp, np.ones((objp.shape[0], 1))), axis=1).transpose()
tag_pts_in_world = np.dot(
board_to_world, np.dot(t_in_board, tag_pts))
tag_pts_in_body = np.dot(np.linalg.inv(
body_to_world), tag_pts_in_world)
tag_pts_in_cam = np.dot(body_to_c, tag_pts_in_body)
projections, jac = cv2.projectPoints(
tag_pts_in_body.T[:, :3], theta[:3], theta[3:6], K, np.zeros((1, 4)))
jacs.append(jac[:, :6])
return np.vstack(np.array(jacs))
def test_collisions():
L = 10
world = Box(L, L)
# id_vehicle = 'd_SE2_rb_v-rf360'
id_vehicle = 'd_SE2_rb_v-random_5'
get_vehicles_config().load('default')
vehicles = get_conftools_vehicles()
# vehicles.load('default')
vehicle = vehicles.instance(id_vehicle) # @UndefinedVariable
vehicle.set_world_primitives(world.get_primitives())
vehicle.set_pose(SE3.identity())
commands = np.array([1, 0, 0])
# go straight
simulation_length = 10
dt = 0.1
time = 0
steps = int(np.ceil(simulation_length / dt))
for _ in range(steps):
vehicle.simulate(commands, dt)
pose = SE2_from_SE3(vehicle.get_pose())
translation = translation_from_SE2(pose)
# print('t=%.3f %s' % (time, SE2.friendly(pose)))
time += dt
assert translation[0] <= 9.5
assert translation[0] >= 9.4
assert translation[1] == 0
def cost_function_tuple_offset( params):
cam_to_body = SE3.group_from_algebra(se3.algebra_from_vector(params[:6]))
tuple_offset = int(params[6]*100)
# Use a central region of data tuples +- 100
# The offset determines the start of the measurement offset
# pdb.set_trace()
# measurements_offset = data_tuples[buffer_size + tuple_offset: -buffer_size + tuple_offset, 0]
# bodys_to_world_tuple_offset = data_tuples[buffer_size:-buffer_size, 1]
# boards_to_world_tuple_offset = data_tuples[buffer_size:-buffer_size, 2]
# offset_tuples = np.concatenate(measurements_offset, bodys_to_world_offset, boards_to_world_offset, axis=1)
error = 0
for i in range(len(data_tuples) - buffer_size*2):
measurement = data_tuples[i + buffer_size + tuple_offset][0]
body_to_world = data_tuples[i + buffer_size][1]
board_to_world = data_tuples[i + buffer_size][2]
cam_to_world = np.dot(body_to_world, cam_to_body)
tag_pts = np.array([[-s, -s, 0, 1], [s, -s, 0, 1],
[s, s, 0, 1], [-s, s, 0, 1]]).transpose()
tag_in_board = np.array(
[[0, -1, 0, s], [1, 0, 0, s], [0, 0, 1, 0], [0, 0, 0, 1]])
tag_pts_in_world = np.dot(
board_to_world, np.dot(tag_in_board, tag_pts))
tag_pts_in_cam = np.dot(np.linalg.inv(cam_to_world), tag_pts_in_world)
projections = np.dot(K, tag_pts_in_cam[:3, :])
projections /= projections[2]
projections = projections[:2].transpose()
error += np.linalg.norm(measurement - projections)
return error
def Interpolate(a, b, time):
ratio = float(time - a.id) / (b.id - a.id)
M0, M1 = a.M, b.M
dm = SE3.algebra_from_group(M1.dot(invT(M0)))
dM = SE3.group_from_algebra(ratio * dm)
Mt = dM.dot(M0)
cov_time = (1 - ratio) * a.cov + ratio * b.cov
return AngleAxisPose.FromM(Mt, id=time, cov=cov_time)
def test_Interpolate():
p1 = AngleAxisPose(np.zeros(3), np.zeros(3), 0,
block_diag(np.eye(3), np.eye(3)))
p2 = AngleAxisPose(np.zeros(3), np.ones(3), 1,
block_diag(np.eye(3), np.eye(3)))
AngleAxisPose.Interpolate(p1, p2, 0)
AngleAxisPose.Interpolate(p1, p2, 0.5)
AngleAxisPose.Interpolate(p1, p2, 1)
def show_sensor_data(pylab, vehicle, robot_pose=None, col='r'):
if robot_pose is None:
robot_pose = SE3.from_yaml(vehicle['pose'])
for attached in vehicle['sensors']:
sensor_pose = from_yaml(attached['current_pose'])
sensor_t, sensor_theta = \
translation_angle_from_SE2(SE2_from_SE3(sensor_pose))
print('robot: %s' % SE2.friendly(SE2_from_SE3(robot_pose)))
print(' sens: %s' % SE2.friendly(SE2_from_SE3(sensor_pose)))
# sensor_theta = -sensor_theta
sensor = attached['sensor']
if sensor['type'] == 'Rangefinder':
directions = np.array(sensor['directions'])
observations = attached['current_observations']
readings = np.array(observations['readings'])
valid = np.array(observations['valid'])
# directions = directions[valid]
# readings = readings[valid]
x = []
y = []
rho_min = 0.05
for theta_i, rho_i in zip(directions, readings):
print('theta_i: %s' % theta_i)
x.append(sensor_t[0] +
np.cos(sensor_theta + theta_i) * rho_min)
y.append(sensor_t[1] +
np.sin(sensor_theta + theta_i) * rho_min)
x.append(sensor_t[0] +
np.cos(sensor_theta + theta_i) * rho_i)
y.append(sensor_t[1] +
np.sin(sensor_theta + theta_i) * rho_i)
x.append(None)
y.append(None)
pylab.plot(x, y, color=col, markersize=0.5, zorder=2000)
elif sensor['type'] == 'Photoreceptors':
directions = np.array(sensor['directions'])
observations = attached['current_observations']
readings = np.array(observations['readings'])
luminance = np.array(observations['luminance'])
valid = np.array(observations['valid'])
readings[np.logical_not(valid)] = 0.6
rho_min = 0.5
for theta_i, rho_i, lum in zip(directions, readings, luminance):
x = []
y = []
x.append(sensor_t[0] +
np.cos(sensor_theta + theta_i) * rho_min)
y.append(sensor_t[1] +
np.sin(sensor_theta + theta_i) * rho_min)
x.append(sensor_t[0] +
np.cos(sensor_theta + theta_i) * rho_i)
y.append(sensor_t[1] +
np.sin(sensor_theta + theta_i) * rho_i)
pylab.plot(x, y, color=(lum, lum, lum),
markersize=0.5, zorder=2000)
else:
print('Unknown sensor type %r' % sensor['type'])
def cairo_plot_sensor_data(cr, vehicle_state, scale=1.0, compact=True):
for attached in vehicle_state['sensors']:
sensor = attached['sensor']
observations = attached['current_observations']
sensor_pose = SE2_from_SE3(SE3.from_yaml(attached['current_pose']))
sensor_type = sensor['type']
if sensor_type == VehiclesConstants.SENSOR_TYPE_RANGEFINDER:
if compact:
with cairo_rototranslate(cr, sensor_pose):
cr.scale(scale, scale)
plot_ranges_compact(cr=cr,
directions=np.array(sensor['directions']),
valid=np.array(observations['valid']),
readings=np.array(observations['readings'])
)
else:
plot_ranges(cr=cr,
pose=sensor_pose,
directions=np.array(sensor['directions']),
valid=np.array(observations['valid']),
readings=np.array(observations['readings'])
)
elif sensor_type == VehiclesConstants.SENSOR_TYPE_PHOTORECEPTORS:
if compact:
with cairo_rototranslate(cr, sensor_pose):
cr.scale(scale, scale)
plot_photoreceptors_compact(cr=cr,
directions=np.array(sensor['directions']),
valid=np.array(observations['valid']),
luminance=np.array(observations['luminance']))
else:
plot_photoreceptors(cr=cr,
pose=sensor_pose,
directions=np.array(sensor['directions']),
valid=np.array(observations['valid']),
readings=np.array(observations['readings']),
luminance=np.array(observations['luminance']))
elif sensor_type == VehiclesConstants.SENSOR_TYPE_FIELDSAMPLER:
if True:
with cairo_rototranslate(cr, sensor_pose):
plot_fieldsampler_fancy(cr=cr,
positions=np.array(sensor['positions']),
sensels=np.array(observations['sensels']))
else:
plot_fieldsampler(cr=cr,
pose=sensor_pose,
positions=np.array(sensor['positions']),
sensels=np.array(observations['sensels']))
elif sensor_type == 'RandomSensor':
# XXX: how can we visualize this?
pass
else:
logger.warning('Unknown sensor type %r.' % sensor['type'])
pass
def plot_servonave(cr, servonav):
locations = servonav['locations']
# current_goal = servonav['current_goal']
for _, loc in enumerate(locations):
pose = SE2_from_SE3(SE3.from_yaml(loc['pose']))
with cairo_rototranslate(cr, pose):
# if current_goal == i:
# cairo_ref_frame(cr, l=0.5)
# else:
grey = [.6, .6, .6]
cairo_ref_frame(cr, l=0.5, x_color=grey, y_color=grey)
def compute_observations(self):
# TODO: add dynamics observations
sensel_values = []
for attached in self.sensors:
pose = self.dynamics.joint_state(self._get_state(), attached.joint)
j_pose, _ = pose
attached.current_pose = SE3.multiply(j_pose, attached.pose)
attached.current_observations = \
attached.sensor.compute_observations(attached.current_pose)
sensels = attached.current_observations['sensels']
sensel_values.extend(sensels.tolist())
return np.array(sensel_values, dtype='float32')
def compute_observations(self):
# TODO: add dynamics observations
sensel_values = []
for attached in self.sensors:
pose = self.dynamics.joint_state(self._get_state(), attached.joint)
j_pose, _ = pose
attached.current_pose = SE3.multiply(j_pose, attached.pose)
attached.current_observations = \
attached.sensor.compute_observations(attached.current_pose)
sensels = attached.current_observations['sensels']
sensel_values.extend(sensels.tolist())
return np.array(sensel_values, dtype='float32')
def sparse_sequence(data, min_th_dist, min_dist):
""" Yields a sequence """
# sp = Sparsifier(manifold=R2, min_dist=min_dist)
last_pose = None
for data, pose in data:
if last_pose is None:
ok = True
else:
distances = SE3.distances(pose, last_pose)
# print distances
ok = (distances[0] > min_dist) or (distances[1] > min_th_dist)
if ok:
last_pose = pose
yield data, pose
def cost_function(theta):
body_to_c = SE3.group_from_algebra(
se3.algebra_from_vector(theta[:6]))
# tag_in_board_offset = theta[6:]
t_in_board = tag_in_board.copy()
# t_in_board[:3,3] += tag_in_board_offset
# t_in_board = np.dot(t_in_board, SE3.group_from_algebra(
# se3.algebra_from_vector(tag_in_board_offset)))
error = 0
img_count = 0
residual_vectors = []
for measurement, body_to_world, board_to_world, tag_in_cam in data_tuples:
tag_pts = np.concatenate(
(objp, np.ones((objp.shape[0], 1))), axis=1).transpose()
tag_pts_in_world = np.dot(
board_to_world, np.dot(t_in_board, tag_pts))
tag_pts_in_body = np.dot(np.linalg.inv(
body_to_world), tag_pts_in_world)
tag_pts_in_cam = np.dot(body_to_c, tag_pts_in_body)
projections, jac = cv2.projectPoints(
tag_pts_in_body.T[:, :3], theta[:3], theta[3:6], K, np.zeros((1, 4)))
projections = projections.astype(np.float32)
# cv2.drawChessboardCorners(debug_img, (rows, cols), projections, True)
projections.shape = (projections.shape[0], projections.shape[-1])
measurement.shape = (measurement.shape[0], measurement.shape[-1])
if img_count == 0:
debug_img = img_tuples[img_count].copy()
for i in range(projections.shape[0]):
cv2.circle(
debug_img, (projections[i, 0], projections[i, 1]), 2, (255, 0, 0), 1)
cv2.circle(
debug_img, (measurement[i, 0], measurement[i, 1]), 2, (0, 0, 255), 1)
cv2.line(debug_img, (measurement[i, 0], measurement[i, 1]),
(projections[i, 0], projections[i, 1]), (0, 255, 0))
cv2.imshow('cost function img', debug_img)
cv2.waitKey(10)
# pdb.set_trace()
img_count += 1
residual_vectors.append((measurement - projections).ravel())
# np.linalg.norm(measurement - projections)
# error += np.sum((measurement - projections), axis=0)
return np.array(residual_vectors).ravel()
def joint_state(self, state, joint=0):
base_state = self.base_state_from_big_state(state)
top_state = self.top_state_from_big_state(state)
conf = self.base.joint_state(base_state, 0)
body_pose, body_vel = conf
if joint == 0:
# print('here: %s' % describe_value(conf))
return body_pose, body_vel
elif joint == 1:
# FIXME: velocity not computed
q, _ = self.top.joint_state(top_state)
j = SE3.multiply(body_pose, q)
jvel = se3.zero() # @UndefinedVariable
conf2 = j, jvel
# print('there: %s' % describe_value(conf2))
return conf2
else:
raise ValueError('No such joint %d.' % joint)
def joint_state(self, state, joint=0):
base_state = self.base_state_from_big_state(state)
top_state = self.top_state_from_big_state(state)
conf = self.base.joint_state(base_state, 0)
body_pose, body_vel = conf
if joint == 0:
# print('here: %s' % describe_value(conf))
return body_pose, body_vel
elif joint == 1:
# FIXME: velocity not computed
q, _ = self.top.joint_state(top_state)
j = SE3.multiply(body_pose, q)
jvel = se3.zero() # @UndefinedVariable
conf2 = j, jvel
# print('there: %s' % describe_value(conf2))
return conf2
else:
raise ValueError('No such joint %d.' % joint)
def joint_state(self, state, joint=0):
car_state = self.car_state_from_big_state(state)
steering = self.steering_from_big_state(state)
car_position = self.car.joint_state(car_state, joint=0)
if joint == 0:
return car_position
elif joint == 1:
p = [self.car.axis_dist, self.car.L]
elif joint == 2:
p = [self.car.axis_dist, -self.car.L]
else:
raise ValueError('Invalid joint ID %r' % joint)
pose, vel = car_position
rel_pose = SE3_from_SE2(SE2_from_translation_angle(p, steering))
wpose = SE3.multiply(pose, rel_pose)
return wpose, vel # XXX: vel
def joint_state(self, state, joint=0):
car_state = self.car_state_from_big_state(state)
steering = self.steering_from_big_state(state)
car_position = self.car.joint_state(car_state, joint=0)
if joint == 0:
return car_position
elif joint == 1:
p = [self.car.axis_dist, self.car.L]
elif joint == 2:
p = [self.car.axis_dist, -self.car.L]
else:
raise ValueError('Invalid joint ID %r' % joint)
pose, vel = car_position
rel_pose = SE3_from_SE2(SE2_from_translation_angle(p, steering))
wpose = SE3.multiply(pose, rel_pose)
return wpose, vel # XXX: vel
def cost_function( cam_to_body_log):
cam_to_body = SE3.group_from_algebra(se3.algebra_from_vector(cam_to_body_log))
error = 0
for measurement, body_to_world, board_to_world in data_tuples:
cam_to_world = np.dot(body_to_world, cam_to_body)
tag_pts = np.array([[-s, -s, 0, 1], [s, -s, 0, 1],
[s, s, 0, 1], [-s, s, 0, 1]]).transpose()
tag_in_board = np.array(
[[0, -1, 0, s], [1, 0, 0, s], [0, 0, 1, 0], [0, 0, 0, 1]])
tag_pts_in_world = np.dot(
board_to_world, np.dot(tag_in_board, tag_pts))
tag_pts_in_cam = np.dot(np.linalg.inv(cam_to_world), tag_pts_in_world)
projections = np.dot(K, tag_pts_in_cam[:3, :])
projections /= projections[2]
projections = projections[:2].transpose()
error += np.linalg.norm(measurement - projections)
return error
def cairo_plot_sensor_skins(cr, vehicle_state, scale=1.0):
for attached in vehicle_state['sensors']:
sensor = attached['sensor']
sensor_pose = SE2_from_SE3(SE3.from_yaml(attached['current_pose']))
extra = attached.get('extra', {})
sensor_skin = extra.get('skin', None)
if sensor_skin is None:
sensor_skin = sensor['type']
skins_library = get_conftools_skins()
if not sensor_skin in skins_library:
logger.warning('Could not find skin %r' % sensor_skin)
else:
skin = skins_library.instance(sensor_skin)
with cairo_rototranslate(cr, sensor_pose):
cr.scale(scale, scale)
skin.draw(cr)
def to_yaml(self):
# pose, velocity
configuration = self.get_configuration()
pose = configuration[0]
joints = []
for i in range(self.dynamics.num_joints()):
jc = self.dynamics.joint_state(self._get_state(), i)
joints.append(to_yaml("TSE3", jc))
data = {
'radius': self.radius,
'id_sensors': self.id_sensors,
'id_dynamics': self.id_dynamics,
'pose': SE3.to_yaml(pose),
'conf': to_yaml('TSE3', configuration),
'state': self.dynamics.state_to_yaml(self._get_state()),
'joints': joints,
'sensors': [s.to_yaml() for s in self.sensors],
'extra': self.extra
}
check_yaml_friendly(data)
return data
def to_yaml(self):
# pose, velocity
configuration = self.get_configuration()
pose = configuration[0]
joints = []
for i in range(self.dynamics.num_joints()):
jc = self.dynamics.joint_state(self._get_state(), i)
joints.append(to_yaml("TSE3", jc))
data = {
'radius': self.radius,
'id_sensors': self.id_sensors,
'id_dynamics': self.id_dynamics,
'pose': SE3.to_yaml(pose),
'conf': to_yaml('TSE3', configuration),
'state': self.dynamics.state_to_yaml(self._get_state()),
'joints': joints,
'sensors': [s.to_yaml() for s in self.sensors],
'extra': self.extra
}
check_yaml_friendly(data)
return data
np.set_printoptions(precision=3, suppress=True)
axes_to_plot = range(6)
for i in [cam_id]: # range(num_images):
pose_cpp = poses[i]
depth_img_cpp = depth_imgs[i]
sampled_poses = []
ray_likelihoods = [[]] * 6
for j in axes_to_plot:
print '!!Evaluating for axis ' + labels[j]
likes = []
for k in range(num_poses):
print "Axis::{0} Pose::{1}".format(j, k)
delta_vector = np.zeros(6)
delta_vector[j] = deltas[k]
T = SE3.group_from_algebra(
se3.algebra_from_vector(delta_vector))
pose = np.dot(pose_cpp, T)
sampled_poses.append(pose)
# print 'pose is '+str(pose)
mrfmap.inference.set_pose(i, pose.astype(dtype))
# pdb.set_trace()
sum = mrfmap.inference.compute_likelihood(i)
likes.append(sum)
ray_likelihoods[j] = np.array(likes)
# viewer.visualize_poses(sampled_poses, resolution, scaled_dims)
# viewer.show()
f, ax = plt.subplots(6, 1)
handles = []
def servoing_episode(id_robot, robot,
id_servo_agent, servo_agent,
writer, id_episode,
displacement,
max_episode_len,
save_robot_state,
converged_dist_t_m=0.1,
converged_dist_th_deg=1,
max_tries=10000):
'''
:arg:displacement: Time in seconds to displace the robot.
'''
from geometry import SE3
def mean_observations(n=10):
obss = []
for _ in range(n): # XXX: fixed threshold
obss.append(robot.get_observations().observations)
return np.mean(obss, axis=0)
def robot_pose():
return robot.get_observations().robot_pose
def timestamp():
return robot.get_observations().timestamp
def episode_ended():
return robot.get_observations().episode_end
def simulate_hold(cmd0, displacement):
t0 = timestamp()
nsteps = 0
while timestamp() < t0 + displacement:
nsteps += 1
source = BootOlympicsConstants.CMD_SOURCE_SERVO_DISPLACEMENT
robot.set_commands(cmd0, source)
if episode_ended():
logger.debug('Collision after %d steps' % ntries)
return False
logger.debug('%d steps of simulation to displace by %s' %
(nsteps, displacement))
return True
for ntries in xrange(max_tries):
# iterate until we can do this correctly
episode = robot.new_episode()
obs0 = mean_observations()
cmd0 = robot.get_spec().get_commands().get_random_value()
pose0 = robot_pose()
ok = simulate_hold(cmd0, displacement)
if ok:
pose1 = robot_pose()
logger.info('Displacement after %s tries.' % ntries)
break
else:
msg = 'Could not do the displacement (%d tries).' % max_tries
raise Exception(msg)
servo_agent.set_goal_observations(obs0)
for robot_observations, boot_observations in \
run_simulation_servo(id_robot, robot,
id_servo_agent, servo_agent,
100000, max_episode_len,
id_episode=id_episode,
id_environment=episode.id_environment):
def pose_to_yaml(x):
''' Converts to yaml, or sets None. '''
if x is None:
return None
else:
return SE3.to_yaml(x)
extra = {}
sensels_list = boot_observations['observations'].tolist()
extra['servoing_base'] = dict(goal=obs0.tolist(), current=sensels_list)
current_pose = robot_observations.robot_pose
has_pose = current_pose is not None
if has_pose:
# Add extra pose information
extra['servoing_poses'] = dict(goal=pose_to_yaml(pose0),
current=pose_to_yaml(current_pose))
delta = SE2_from_SE3(SE3.multiply(SE3.inverse(current_pose),
pose0))
dist_t_m = np.linalg.norm(translation_from_SE2(delta))
dist_th_deg = np.abs(angle_from_SE2(delta))
# TODO: make it not overlapping
extra['servoing'] = dict(obs0=obs0.tolist(),
pose0=pose_to_yaml(pose0),
poseK=pose_to_yaml(current_pose),
obsK=sensels_list,
displacement=displacement,
cmd0=cmd0.tolist(),
pose1=pose_to_yaml(pose1))
if save_robot_state:
extra['robot_state'] = robot.get_state()
writer.push_observations(observations=boot_observations,
extra=extra)
if has_pose:
if ((dist_t_m <= converged_dist_t_m) and
(dist_th_deg <= converged_dist_th_deg)):
print('Converged!')
break
else:
# TODO: write convergence criterion
# without pose information
pass
def get_observations(self, messages, queue):
"""
messages: topic -> last ROS Message
returns: an instance of RobotObservations, or None if the update is not ready.
"""
self.debug_nseen += 1
if self.use_tf:
for topic, msg, _ in queue:
if topic == '/tf':
pose = self.tfreader.update(msg)
if pose is not None:
robot_pose = pose
if self.is_ready(messages, queue):
observations = self.obs_adapter.observations_from_messages(messages)
cmds = self.cmd_adapter.commands_from_messages(messages)
if cmds is None:
# we couldn't interpret a meaningful message for use
# logger.info('Skipping because not interpretable by my cmd_adapter.')
self.debug_nskipped += 1
if self.debug_nskipped % 100 == 0:
perc = 100.0 * self.debug_nskipped / self.debug_nseen
msg = ('Skipped %6d/%6d = %.1f%% because cmd_adapter could not interpret.' %
(self.debug_nskipped, self.debug_nseen, perc))
logger.info(msg)
return None
commands_source, commands = cmds
episode_end = False
_, _, last_t = queue[-1]
timestamp = last_t.to_sec()
robot_pose = None
if self.use_odom_topic:
odometry = messages[self.odom_topic]
ros_pose = odometry.pose.pose
x = ros_pose.position.x
y = ros_pose.position.y
theta = ros_pose.orientation.z
from geometry import SE3_from_SE2, SE2_from_translation_angle
robot_pose = SE3_from_SE2(SE2_from_translation_angle([x, y], theta))
# print('odom: %s' % SE2.friendly(SE2_from_SE3(robot_pose)))
if self.prev_odom is not None:
delta = SE3.multiply(SE3.inverse(self.prev_odom), robot_pose)
# print('odom delta: %s' % SE2.friendly(SE2_from_SE3(delta)))
self.prev_odom = robot_pose
if self.use_tf:
robot_pose = self.tfreader.get_pose()
return RobotObservations(timestamp=timestamp,
observations=observations,
commands=commands, commands_source=commands_source,
episode_end=episode_end,
robot_pose=robot_pose)
else:
return None
def plot_servoing_poses(cr, servoing_poses):
# TODO
goal = SE3.from_yaml(servoing_poses['goal'])
with cairo_rototranslate(cr, goal):
cairo_ref_frame(cr, l=0.5)
def step_too_big(pose1, pose2, max_theta_diff_deg):
diff = SE3.multiply(SE3.inverse(pose1), pose2)
diff_theta = np.abs(angle_from_SE2(SE2_from_SE3(diff)))
return diff_theta > np.deg2rad(max_theta_diff_deg)
def regularly_spaced(data, min_dist):
sp = Sparsifier(manifold=R2, min_dist=min_dist)
for data, pose in data:
p = SE3.project_to(R2, pose)
if sp.accept(p):
yield data, pose
def pose_diff(a, b):
return SE3.multiply(SE3.inverse(a), b)
def vehicles_cairo_display_all(cr, width, height,
sim_state,
grid=1,
zoom=0,
bgcolor=[1, 1, 1],
zoom_scale_radius=False,
extra_draw_world=None,
first_person=True,
show_world=True,
# show_features='relevant',
show_sensor_data=True,
show_sensor_data_compact=False,
show_robot_body=True,
show_robot_sensors=True,
show_textures=True,
plot_sources=False):
'''
:param zoom_scale_radius: If true, scales the zoom by the robot radius.
'''
with cairo_save(cr):
# Paint white
if bgcolor is not None:
with cairo_save(cr):
cairo_set_color(cr, bgcolor)
cr.rectangle(0, 0, width, height)
cr.fill()
vehicle_state = sim_state['vehicle']
robot_pose = SE2_from_SE3(SE3.from_yaml(vehicle_state['pose']))
robot_radius = vehicle_state['radius']
world_state = sim_state['world']
bounds = world_state['bounds']
bx = bounds[0]
by = bounds[1]
if zoom_scale_radius and zoom != 0:
zoom = zoom * robot_radius
world_bounds_pad = 0 # CairoConstants.texture_width
vehicles_cairo_set_coordinates(cr, width, height,
bounds, robot_pose,
zoom=zoom,
world_bounds_pad=world_bounds_pad,
first_person=first_person)
if False:
cr.set_source_rgb(0, 1, 0)
cr.set_line_width(0.05)
cr.rectangle(bx[0], by[0], bx[1] - bx[0], by[1] - by[0])
cr.stroke()
if grid > 0:
show_grid(cr, bx, by, spacing=grid, margin=1)
if extra_draw_world is not None:
extra_draw_world(cr)
sensor_types = get_sensor_types(vehicle_state)
has_cameras = (VehiclesConstants.SENSOR_TYPE_PHOTORECEPTORS
in sensor_types)
has_field_sampler = (VehiclesConstants.SENSOR_TYPE_FIELDSAMPLER
in sensor_types)
if show_world:
if has_field_sampler:
cairo_plot_sources(cr, world_state)
plot_textures = has_cameras and show_textures
cairo_show_world_geometry(cr, world_state,
plot_textures=plot_textures,
plot_sources=plot_sources,
extra_pad=world_bounds_pad)
# XXX: tmp
if extra_draw_world is not None:
extra_draw_world(cr)
if show_robot_body:
joints = get_joints_as_TSE3(vehicle_state)
extra = vehicle_state.get('extra', {})
id_skin = extra.get('skin', 'default_skin')
skin = get_conftools_skins().instance(id_skin)
with cairo_rototranslate(cr, robot_pose):
cr.scale(robot_radius, robot_radius)
skin.draw(cr, joints=joints,
timestamp=sim_state['timestamp'])
if show_robot_sensors:
# don't like it cause it uses global "current_pose"
cairo_plot_sensor_skins(cr, vehicle_state,
scale=robot_radius)
if show_sensor_data:
cairo_plot_sensor_data(cr, vehicle_state,
scale=robot_radius,
compact=show_sensor_data_compact)
def got_tuple(img_msg, cam_odom, board_odom):
img = bridge.imgmsg_to_cv2(img_msg, "bgr8")
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
body_to_world = transform_matrix_from_odom(cam_odom)
board_to_world = transform_matrix_from_odom(board_odom)
# Get detection from tracker
pixels = []
debug_img = bridge.imgmsg_to_cv2(img_msg, "bgr8")
gray = cv2.cvtColor(debug_img, cv2.COLOR_BGR2GRAY)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
axis = np.float32([[0.5, 0, 0], [0, 0.5, 0], [0, 0, 0.5]]).reshape(-1, 3)
ret, corners = cv2.findChessboardCorners(
gray, (cols, rows), None, cv2.CALIB_CB_FILTER_QUADS)
cv2.drawChessboardCorners(debug_img, (cols, rows), corners, ret)
cv2.imshow('PreRefinement', debug_img)
if ret == True:
if K is None:
print 'K not initialized yet!'
return
corners2 = cv2.cornerSubPix(gray, corners, (5, 5), (-1, -1), criteria)
corners2.shape = (corners2.shape[0], corners2.shape[-1])
# Find the rotation and translation vectors.
ret, rvecs, tvecs = cv2.solvePnP(
objp, corners2, K, np.array([[0, 0, 0, 0]], dtype=np.float32))
if ret == True:
rodRotMat = cv2.Rodrigues(rvecs)
tag_in_cam = np.eye(4)
tag_in_cam[:3, :3] = rodRotMat[0]
tag_in_cam[:3, 3] = tvecs[:, 0]
# project 3D points to image plane
imgpts, jac = cv2.projectPoints(
axis, rvecs, tvecs, K, np.zeros((1, 4)))
if visualize:
cv2.drawChessboardCorners(
debug_img, (cols, rows), corners2, ret)
img_msg = bridge.cv2_to_imgmsg(debug_img)
img_msg.header.frame_id = 'cam'
img_pub.publish(img_msg)
K_msg.header.stamp = img_msg.header.stamp
cam_info_pub.publish(K_msg)
debug_img = draw(debug_img, corners2, imgpts)
print ret
cam_to_world = np.dot(body_to_world, cam_to_body)
broadcaster.sendTransform(body_to_world[:3, 3],
tf.transformations.quaternion_from_matrix(
body_to_world),
rospy.Time.now(),
'body',
"world")
broadcaster.sendTransform(board_to_world[:3, 3],
tf.transformations.quaternion_from_matrix(
board_to_world),
rospy.Time.now(),
'board',
"world")
broadcaster.sendTransform(cam_to_body[:3, 3],
tf.transformations.quaternion_from_matrix(
cam_to_body),
rospy.Time.now(),
'cam',
"body")
broadcaster.sendTransform(tag_in_cam[:3, 3],
tf.transformations.quaternion_from_matrix(
tag_in_cam),
rospy.Time.now(),
'tag',
"cam")
broadcaster.sendTransform(tag_in_board[:3, 3],
tf.transformations.quaternion_from_matrix(
tag_in_board),
rospy.Time.now(),
'tag_gt',
"board")
# tag_in_cam = np.eye(4).astype(datatype)
# # Now see if the 3D points projected make sense.
# tag_pts = np.concatenate((objp, np.ones((objp.shape[0], 1))), axis=1).transpose()
# tag_pts_in_world = np.dot(
# board_to_world, np.dot(tag_in_board, tag_pts))
# tag_pts_in_cam = np.dot(np.linalg.inv(cam_to_world), tag_pts_in_world)
# projections = np.dot(K, tag_pts_in_cam[:3, :])
# projections /= projections[2]
# projections = projections[:2].transpose()
# pixels = []
# Draw these pixels
# pdb.set_trace()
tag_in_cam_mocap_approx = np.dot(np.linalg.inv(
cam_to_world), np.dot(board_to_world, tag_in_board))
diff = np.dot(np.linalg.inv(tag_in_cam), tag_in_cam_mocap_approx)
diff = se3.vector_from_algebra(SE3.algebra_from_group(diff))
diffs_vector.append(diff)
print diff
print np.linalg.norm(diff[:3])
# I'm curious to see the projected mocap frame in the image too
pts = np.eye(4)
pts[3, :] = 1
origin_in_cam = np.dot(tag_in_cam_mocap_approx, pts)
projections = np.dot(K, origin_in_cam[:3, :])
projections /= projections[2]
projections = projections.astype(np.float32)
debug_img = cv2.line(debug_img, tuple(projections[:2, 3]), tuple(
projections[:2, 0]), (0, 0, 127), 1)
debug_img = cv2.line(debug_img, tuple(projections[:2, 3]), tuple(
projections[:2, 1]), (0, 127, 0), 1)
debug_img = cv2.line(debug_img, tuple(projections[:2, 3]), tuple(
projections[:2, 2]), (127, 0, 0), 1)
cv2.imshow('img', debug_img)
cv2.waitKey(10)
data_tuples.append(
[corners2, body_to_world, board_to_world, tag_in_cam])
img_tuples.append(cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR))
if use_bag:
with rosbag.Bag(path, 'r') as bag:
counter = 0
for topic, msg, t in bag.read_messages(topics_to_parse):
if topic in topics_to_parse:
index = topics_to_parse.index(topic)
subs[index].signalMessage(msg)
counter += 1
if counter%1000 == 0:
print 'Read {0} tuples'.format(counter)
# Try to use a black box optimizer
print 'Starting optimization...'
from scipy.optimize import minimize
initial_guess = np.array([0,0,0,0,0,0,-0.1]) # Since initial guess is pretty close to unity
result = minimize(cost_function_tuple_offset, initial_guess, bounds=np.array([[-1,1],[-1,1],[-1,1],[-1,1],[-1,1],[-1,1], [-1, 1]]))
print 'Done, results is'
print result
print SE3.group_from_algebra(se3.algebra_from_vector(result.x[:6]))
print result.x[6]
pdb.set_trace()
else:
rospy.Subscriber(topics_to_parse[0], Image,
lambda msg: subs[0].signalMessage(msg))
rospy.Subscriber(topics_to_parse[1], Odometry,
lambda msg: subs[1].signalMessage(msg))
rospy.Subscriber(topics_to_parse[2], Odometry,
lambda msg: subs[2].signalMessage(msg))
rospy.spin()
def interpolate(pose1, pose2):
return SE3.geodesic(pose1, pose2, 0.5)
def convert(loc):
loc = dict(**loc)
loc["pose"] = SE3.to_yaml(loc["pose"])
loc["observations"] = loc["observations"].tolist()
return loc
def get_joints_as_TSE3(vehicle_state):
joints = []
for js in vehicle_state['joints']:
joints.append(SE3.from_yaml(js))
return joints
def servonav_episode(
id_robot,
robot,
id_servo_agent,
servo_agent,
writer,
id_episode,
max_episode_len,
save_robot_state,
interval_write=1,
interval_print=5,
resolution=0.5, # grid resolution
delta_t_threshold=0.2, # when to switch
MIN_PATH_LENGTH=8,
MAX_TIME_FOR_SWITCH=20.0,
fail_if_not_working=False,
max_tries=10000,
):
"""
:arg:displacement: Time in seconds to displace the robot.
"""
from geometry import SE2_from_SE3, translation_from_SE2, angle_from_SE2, SE3
stats_write = InAWhile(interval_print)
# Access the vehicleSimulation interface
vsim = get_vsim_from_robot(robot)
for _ in xrange(max_tries):
# iterate until we can do this correctly
episode = robot.new_episode()
locations = get_grid(robot=robot, vsim=vsim, resolution=resolution)
if len(locations) < MIN_PATH_LENGTH:
logger.info("Path too short, trying again.")
else:
break
else:
msg = "Could not find path in %d tries." % max_tries
raise Exception(msg)
locations_yaml = convert_to_yaml(locations)
vsim.vehicle.set_pose(locations[0]["pose"])
current_goal = 1
current_goal_obs = locations[current_goal]["observations"]
servo_agent.set_goal_observations(current_goal_obs)
counter = 0
time_last_switch = None
num_written = 0
for robot_observations, boot_observations in run_simulation_servonav(
id_robot,
robot,
id_servo_agent,
servo_agent,
100000,
max_episode_len,
id_episode=id_episode,
id_environment=episode.id_environment,
raise_error_on_collision=fail_if_not_working,
):
current_time = boot_observations["timestamp"].item()
if time_last_switch is None:
time_last_switch = current_time
time_since_last_switch = float(current_time - time_last_switch)
def obs_distance(obs1, obs2):
return float(np.linalg.norm(obs1.flatten() - obs2.flatten()))
curr_pose = robot_observations.robot_pose
curr_obs = boot_observations["observations"]
curr_goal = locations[current_goal]["observations"]
prev_goal = locations[current_goal - 1]["observations"]
curr_err = obs_distance(curr_goal, curr_obs)
prev_err = obs_distance(prev_goal, curr_obs)
current_goal_pose = locations[current_goal]["pose"]
current_goal_obs = locations[current_goal]["observations"]
delta = SE2_from_SE3(SE3.multiply(SE3.inverse(curr_pose), current_goal_pose))
delta_t = np.linalg.norm(translation_from_SE2(delta))
delta_th = np.abs(angle_from_SE2(delta))
if stats_write.its_time():
msg = " deltaT: %.2fm deltaTh: %.1fdeg" % (delta_t, np.rad2deg(delta_th))
logger.debug(msg)
# If at the final goal, go closer
is_final_goal = current_goal == len(locations) - 1
if is_final_goal:
delta_t_threshold *= 0.3
# TODO: should we care also about delta_th?
time_to_switch = (delta_t < delta_t_threshold) or (time_since_last_switch > MAX_TIME_FOR_SWITCH)
# does not work: curr_err < SWITCH_THRESHOLD * prev_err:
if time_to_switch:
current_goal += 1
logger.info("Switched to goal %d." % current_goal)
time_last_switch = current_time
if current_goal >= len(locations):
# finished
logger.info("Finished :-)")
break
threshold_lost_m = 3
if delta_t > threshold_lost_m:
msg = "Breaking because too far away."
if not (fail_if_not_working):
logger.error(msg)
break
else:
raise Exception(msg)
servo_agent.set_goal_observations(current_goal_obs)
extra = {}
extra["servoing_base"] = dict(goal=curr_goal.tolist(), current=curr_obs.tolist())
extra["servoing_poses"] = dict(goal=SE3.to_yaml(current_goal_pose), current=SE3.to_yaml(curr_pose))
extra["servonav"] = dict(
poseK=SE3.to_yaml(curr_pose),
obsK=boot_observations["observations"].tolist(),
pose1=SE3.to_yaml(current_goal_pose),
locations=locations_yaml,
current_goal=current_goal,
curr_err=curr_err,
prev_err=prev_err,
time_last_switch=time_last_switch,
time_since_last_switch=time_since_last_switch,
)
if counter % interval_write == 0:
if save_robot_state:
extra["robot_state"] = robot.get_state()
writer.push_observations(observations=boot_observations, extra=extra)
num_written += 1
counter += 1
if num_written == 0:
msg = "This log was too short to be written (%d observations)" % counter
raise Exception(msg)
def pose_to_yaml(x):
''' Converts to yaml, or sets None. '''
if x is None:
return None
else:
return SE3.to_yaml(x)
