build_widget_ui(obj_attr_mgr, prim_attr_mgr)
# %%
# @title Scripted vs. Dynamic Motion { display-mode: "form" }
# @markdown A quick script to generate video data for AI classification of dynamically dropping vs. kinematically moving objects.
remove_all_objects(sim)
# @markdown Set the scene as dynamic or kinematic:
scenario_is_kinematic = True # @param {type:"boolean"}
# add the selected object
obj_id_1 = sim.add_object_by_handle(sel_file_obj_handle)
# place the object
set_object_state_from_agent(
sim, obj_id_1, offset=np.array([0, 2.0, -1.0]), orientation=ut.random_quaternion()
)
if scenario_is_kinematic:
# use the velocity control struct to setup a constant rate kinematic motion
sim.set_object_motion_type(habitat_sim.physics.MotionType.KINEMATIC, obj_id_1)
vel_control = sim.get_object_velocity_control(obj_id_1)
vel_control.controlling_lin_vel = True
vel_control.linear_velocity = np.array([0, -1.0, 0])
# simulate and collect observations
example_type = "kinematic vs dynamic"
observations = simulate(sim, dt=2.0)
if make_video:
vut.make_video(
observations,
def sample_object_state(
sim, object_id, from_navmesh=True, maintain_object_up=True, max_tries=100, bb=None
):
# check that the object is not STATIC
if sim.get_object_motion_type(object_id) is habitat_sim.physics.MotionType.STATIC:
print("sample_object_state : Object is STATIC, aborting.")
if from_navmesh:
if not sim.pathfinder.is_loaded:
print("sample_object_state : No pathfinder, aborting.")
return False
elif not bb:
print(
"sample_object_state : from_navmesh not specified and no bounding box provided, aborting."
)
return False
tries = 0
valid_placement = False
# Note: following assumes sim was not reconfigured without close
scene_collision_margin = stage_attr_mgr.get_template_by_ID(0).margin
while not valid_placement and tries < max_tries:
tries += 1
# initialize sample location to random point in scene bounding box
sample_location = np.array([0, 0, 0])
if from_navmesh:
# query random navigable point
sample_location = sim.pathfinder.get_random_navigable_point()
else:
sample_location = np.random.uniform(bb.min, bb.max)
# set the test state
sim.set_translation(sample_location, object_id)
if maintain_object_up:
# random rotation only on the Y axis
y_rotation = mn.Quaternion.rotation(
mn.Rad(random.random() * 2 * math.pi), mn.Vector3(0, 1.0, 0)
)
sim.set_rotation(y_rotation * sim.get_rotation(object_id), object_id)
else:
# unconstrained random rotation
sim.set_rotation(ut.random_quaternion(), object_id)
# raise object such that lowest bounding box corner is above the navmesh sample point.
if from_navmesh:
obj_node = sim.get_object_scene_node(object_id)
xform_bb = habitat_sim.geo.get_transformed_bb(
obj_node.cumulative_bb, obj_node.transformation
)
# also account for collision margin of the scene
y_translation = mn.Vector3(
0, xform_bb.size_y() / 2.0 + scene_collision_margin, 0
)
sim.set_translation(
y_translation + sim.get_translation(object_id), object_id
)
# test for penetration with the environment
if not sim.contact_test(object_id):
valid_placement = True
if not valid_placement:
return False
return True
# %%
# @title Scripted vs. Dynamic Motion { display-mode: "form" }
# @markdown A quick script to generate video data for AI classification of dynamically dropping vs. kinematically moving objects.
remove_all_objects(sim)
# @markdown Set the scene as dynamic or kinematic:
scenario_is_kinematic = True # @param {type:"boolean"}
# add the selected object
obj_id_1 = sim.add_object_by_handle(sel_file_obj_handle)
# place the object
set_object_state_from_agent(sim,
obj_id_1,
offset=np.array([0, 2.0, -1.0]),
orientation=ut.random_quaternion())
if scenario_is_kinematic:
# use the velocity control struct to setup a constant rate kinematic motion
sim.set_object_motion_type(habitat_sim.MotionType.KINEMATIC, obj_id_1)
vel_control = sim.get_object_velocity_control(obj_id_1)
vel_control.controlling_lin_vel = True
vel_control.linear_velocity = np.array([0, -1.0, 0])
# simulate and collect observations
example_type = "kinematic vs dynamic"
observations = simulate(sim, dt=2.0)
if make_video:
vut.make_video(observations, "color_sensor_1st_person", "color",
output_path + example_type)