def m1():
outdir = get_comptests_output_dir()
gm = load_map("udem1")
# dw = DuckietownWorld()
# for map_name, tm in gym_maps.items():
# DW.root.set_object(map_name, tm)
root = PlacedObject()
world = PlacedObject()
root.set_object("world", world)
origin = SE2Transform([1, 10], np.deg2rad(10))
world.set_object("tile_map",
gm,
ground_truth=Constant[SE2Transform](origin))
# d = dw.as_json_dict()
# print(json.dumps(d, indent=4))
# print(yaml.safe_dump(d, default_flow_style=False))
#
G = get_meausurements_graph(root)
fn = os.path.join(outdir, "out1.pdf")
plot_measurement_graph(root, G, fn)
def lane_pose_test1():
outdir = get_comptests_output_dir()
# load one of the maps (see the list using dt-world-draw-maps)
dw = load_map("udem1")
v = 5
# define a SampledSequence with timestamp, command
commands_sequence = SampledSequence.from_iterator(
[
# we set these velocities at 1.0
(1.0, WheelVelocityCommands(0.1 * v, 0.1 * v)),
# at 2.0 we switch and so on
(2.0, WheelVelocityCommands(0.1 * v, 0.4 * v)),
(4.0, WheelVelocityCommands(0.1 * v, 0.4 * v)),
(5.0, WheelVelocityCommands(0.1 * v, 0.2 * v)),
(6.0, WheelVelocityCommands(0.1 * v, 0.1 * v)),
]
)
# we upsample the sequence by 5
commands_sequence = commands_sequence.upsample(5)
## Simulate the dynamics of the vehicle
# start from q0
q0 = geo.SE2_from_translation_angle([1.8, 0.7], 0)
# instantiate the class that represents the dynamics
dynamics = reasonable_duckiebot()
# this function integrates the dynamics
poses_sequence = get_robot_trajectory(dynamics, q0, commands_sequence)
#################
# Visualization and rule evaluation
# Creates an object 'duckiebot'
ego_name = "duckiebot"
db = DB18() # class that gives the appearance
# convert from SE2 to SE2Transform representation
transforms_sequence = poses_sequence.transform_values(SE2Transform.from_SE2)
# puts the object in the world with a certain "ground_truth" constraint
dw.set_object(ego_name, db, ground_truth=transforms_sequence)
# Rule evaluation (do not touch)
interval = SampledSequence.from_iterator(enumerate(commands_sequence.timestamps))
evaluated = evaluate_rules(
poses_sequence=transforms_sequence,
interval=interval,
world=dw,
ego_name=ego_name,
)
timeseries = make_timeseries(evaluated)
# Drawing
area = RectangularArea((0, 0), (3, 3))
draw_static(dw, outdir, area=area, timeseries=timeseries)
def maps():
outdir = get_comptests_output_dir()
map_names = list_maps()
print(map_names)
for map_name in map_names:
duckietown_map = load_map(map_name)
out = os.path.join(outdir, map_name)
draw_map(out, duckietown_map)
def wb2():
root = PlacedObject()
for map_name in list_maps():
tm = load_map(map_name)
root.set_object(map_name, tm)
d = root.as_json_dict()
# print(json.dumps(d, indent=4))
# print(yaml.safe_dump(d, default_flow_style=False))
# print('------')
r1 = Serializable.from_json_dict(d)
d1 = r1.as_json_dict()
def lane_pose_test1():
outdir = get_comptests_output_dir()
dm = load_map('udem1')
print(get_object_tree(dm, attributes=True))
res = get_skeleton_graph(dm)
# area = RectangularArea((0, 0), (3, 3))
draw_static(res.root2, outdir + '/root2')
# draw_static(dm, outdir + '/orig')
print(get_object_tree(res.root2, attributes=True))
def lane_pose_segment1():
outdir = get_comptests_output_dir()
dm = load_map("udem1")
_ = get_object_tree(dm, attributes=True)
res = get_skeleton_graph(dm)
# area = RectangularArea((0, 0), (3, 3))
draw_static(res.root2, outdir + "/root2")
# draw_static(dm, outdir + '/orig')
_ = get_object_tree(res.root2, attributes=True)
def __init__(self,
env_config,
eval_lenght_sec=15,
eval_map=DEFAULT_EVALUATION_MAP):
_env_config = copy.deepcopy(env_config)
# An official evaluation episode is 15 seconds long
_env_config['episode_max_steps'] = eval_lenght_sec * _env_config[
'simulation_framerate']
# Agets should be evaluated on the official eval map
_env_config['training_map'] = eval_map
self.map_name = _env_config['training_map']
# Make testing env
self.env = launch_and_wrap_env(_env_config)
# Set up evaluator
# Creates an object 'duckiebot'
self.ego_name = 'duckiebot'
self.db = DB18() # class that gives the appearance
# load one of the maps
self.dw = load_map(self.map_name)
def check_car_dynamics_correct(klass, CarCommands, CarParams):
"""
:param klass: the implementation
:return:
"""
# load one of the maps (see the list using dt-world-draw-maps)
outdir = get_comptests_output_dir()
dw = load_map('udem1')
v = 5
# define a SampledSequence with timestamp, command
commands_sequence = SampledSequence.from_iterator([
# we set these velocities at 1.0
(1.0, CarCommands(0.1 * v, 0.1 * v)),
# at 2.0 we switch and so on
(2.0, CarCommands(0.1 * v, 0.4 * v)),
(4.0, CarCommands(0.1 * v, 0.4 * v)),
(5.0, CarCommands(0.1 * v, 0.2 * v)),
(6.0, CarCommands(0.1 * v, 0.1 * v)),
])
# we upsample the sequence by 5
commands_sequence = commands_sequence.upsample(5)
## Simulate the dynamics of the vehicle
# start from q0 and v0
q0 = geo.SE2_from_translation_angle([1.8, 0.7], 0)
v0 = geo.se2.zero()
c0 = q0, v0
# instantiate the class that represents the dynamics
dynamics = CarParams(10.0)
# this function integrates the dynamics
poses_sequence = get_robot_trajectory(dynamics, q0, commands_sequence)
#################
# Visualization and rule evaluation
# Creates an object 'duckiebot'
ego_name = 'duckiebot'
db = DB18() # class that gives the appearance
# convert from SE2 to SE2Transform representation
transforms_sequence = poses_sequence.transform_values(
SE2Transform.from_SE2)
# puts the object in the world with a certain "ground_truth" constraint
dw.set_object(ego_name, db, ground_truth=transforms_sequence)
# Rule evaluation (do not touch)
interval = SampledSequence.from_iterator(
enumerate(commands_sequence.timestamps))
evaluated = evaluate_rules(poses_sequence=transforms_sequence,
interval=interval,
world=dw,
ego_name=ego_name)
timeseries = make_timeseries(evaluated)
# Drawing
area = RectangularArea((0, 0), (3, 3))
draw_static(dw, outdir, area=area, timeseries=timeseries)
expected = {
1.0:
geo.SE2_from_translation_angle([1.8, 0.7], 0.0),
1.6:
geo.SE2_from_translation_angle([2.09998657, 0.70245831],
0.016389074695313716),
2.0:
geo.SE2_from_translation_angle([2.29993783, 0.70682836],
0.027315124492189525),
2.4:
geo.SE2_from_translation_angle([2.49991893, 0.70792121],
-0.016385672773040847),
2.8:
geo.SE2_from_translation_angle([2.69975684, 0.70027647],
-0.060086470038271216),
3.2:
geo.SE2_from_translation_angle([2.89906999, 0.68390873],
-0.10378726730350164),
3.6:
geo.SE2_from_translation_angle([3.09747779, 0.65884924],
-0.14748806456873198),
4.0:
geo.SE2_from_translation_angle([3.2946014, 0.62514586],
-0.19118886183396236),
4.6:
geo.SE2_from_translation_angle([3.58705642, 0.55852875],
-0.25674005773180786),
5.0:
geo.SE2_from_translation_angle([3.77932992, 0.50353298],
-0.3004408549970382),
6.0:
geo.SE2_from_translation_angle([4.2622088, 0.37429798],
-0.22257046876429318),
}
for t, expected_pose in expected.items():
assert np.allclose(poses_sequence.at(t=t), expected_pose), t
print('All tests passed successfully!')