This shows examples of:
MultibodyPlant
and SceneGraph
to a diagramMultibodyPlant
meshcat
visualizationSceneGraphInspector
to query SceneGraph
geometriesSceneGraph
geometries with MultibodyPlant
bodiesRenderLabel
s from given geometriesModelInstanceIndex
.import os
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
from pydrake.common import FindResourceOrThrow
from pydrake.geometry import ConnectDrakeVisualizer
from pydrake.geometry.render import (
DepthCameraProperties,
RenderLabel,
MakeRenderEngineVtk,
RenderEngineVtkParams,
)
from pydrake.math import RigidTransform, RollPitchYaw
from pydrake.multibody.parsing import Parser
from pydrake.multibody.plant import AddMultibodyPlantSceneGraph
from pydrake.multibody.tree import BodyIndex
from pydrake.systems.analysis import Simulator
from pydrake.systems.framework import DiagramBuilder
from pydrake.systems.meshcat_visualizer import MeshcatVisualizer
from pydrake.systems.sensors import RgbdSensor
def xyz_rpy_deg(xyz, rpy_deg):
"""Shorthand for defining a pose."""
rpy_deg = np.asarray(rpy_deg)
return RigidTransform(RollPitchYaw(rpy_deg * np.pi / 180), xyz)
reserved_labels = [
RenderLabel.kDoNotRender,
RenderLabel.kDontCare,
RenderLabel.kEmpty,
RenderLabel.kUnspecified,
]
def colorize_labels(image):
"""Colorizes labels."""
# TODO(eric.cousineau): Revive and use Kuni's palette.
cc = mpl.colors.ColorConverter()
color_cycle = plt.rcParams["axes.prop_cycle"]
colors = np.array([cc.to_rgb(c["color"]) for c in color_cycle])
bg_color = [0, 0, 0]
image = np.squeeze(image)
background = np.zeros(image.shape[:2], dtype=bool)
for label in reserved_labels:
background |= image == int(label)
color_image = colors[image % len(colors)]
color_image[background] = bg_color
return color_image
builder = DiagramBuilder()
plant, scene_graph = AddMultibodyPlantSceneGraph(builder, 0.0)
parser = Parser(plant)
iiwa_file = FindResourceOrThrow(
"drake/manipulation/models/iiwa_description/sdf/"
"iiwa14_no_collision.sdf")
iiwa_1 = parser.AddModelFromFile(iiwa_file, model_name="iiwa_1")
plant.WeldFrames(
plant.world_frame(), plant.GetFrameByName("iiwa_link_0", iiwa_1),
X_AB=xyz_rpy_deg([0, 0, 0], [0, 0, 0]))
iiwa_2 = parser.AddModelFromFile(iiwa_file, model_name="iiwa_2")
plant.WeldFrames(
plant.world_frame(), plant.GetFrameByName("iiwa_link_0", iiwa_2),
X_AB=xyz_rpy_deg([0, 1, 0], [0, 0, 0]))
renderer_name = "renderer"
scene_graph.AddRenderer(
renderer_name, MakeRenderEngineVtk(RenderEngineVtkParams()))
# N.B. These properties are chosen arbitrarily.
depth_prop = DepthCameraProperties(
width=640, height=480, fov_y=np.pi/4,
renderer_name=renderer_name,
z_near=0.01, z_far=10.)
world_id = plant.GetBodyFrameIdOrThrow(plant.world_body().index())
X_WB = xyz_rpy_deg([4, 0, 0], [-90, 0, 90])
sensor = RgbdSensor(
world_id, X_PB=X_WB,
color_properties=depth_prop, depth_properties=depth_prop)
builder.AddSystem(sensor)
builder.Connect(
scene_graph.get_query_output_port(),
sensor.query_object_input_port())
You will need to have launched the drake_visualizer
binary
before calling simulator.Initialize(...)
.
ConnectDrakeVisualizer(builder, scene_graph)
meshcat
Visualizer.¶This will show a meshcat
widget inside of this notebook.
Note: This currently does not work in a remote workflow (e.g.
Docker without appropriate port forwarding, Binder, Google Colab, etc.).
Please see #12645
for more details. If this does appear to work, then it is most likely due
to you running a local instance of the meshcat
server.
meshcat_vis = builder.AddSystem(
MeshcatVisualizer(scene_graph, zmq_url="new", open_browser=False))
builder.Connect(
scene_graph.get_pose_bundle_output_port(),
meshcat_vis.get_input_port(0))
meshcat_vis.vis.jupyter_cell()
plant.Finalize()
diagram = builder.Build()
diagram_context = diagram.CreateDefaultContext()
sensor_context = sensor.GetMyMutableContextFromRoot(diagram_context)
sg_context = scene_graph.GetMyMutableContextFromRoot(diagram_context)
simulator = Simulator(diagram)
simulator.Initialize()
Note that this uses the default labeling scheme, using body.index()
.
color = sensor.color_image_output_port().Eval(sensor_context).data
label = sensor.label_image_output_port().Eval(sensor_context).data
fig, ax = plt.subplots(1, 2, figsize=(10, 5))
ax[0].imshow(color)
ax[1].imshow(colorize_labels(label))
We will loop through each geometry item, get it's corresponding body, and then remap the original label to a label that is only distinct for model instances.
NOTE: If the labels in the given plant merge the two model instances together, then this will not fix that. The correct behavior would be to update the RenderLabel values in the geometry instances themselves.
TODO(eric.cousineau): Add an example of updating only the RenderLabel.
query_object = scene_graph.get_query_output_port().Eval(sg_context)
inspector = query_object.inspector()
label_by_model = label.copy()
for geometry_id in inspector.GetAllGeometryIds():
body = plant.GetBodyFromFrameId(inspector.GetFrameId(geometry_id))
geometry_label = inspector.GetPerceptionProperties(
geometry_id).GetProperty("label", "id")
# WARNING: If you do not cast the `geometry_label` to `int`, this
# comparison will take a long time since NumPy will do
# element-by-element comparison using `RenderLabel.__eq__`.
mask = (label == int(geometry_label))
label_by_model[mask] = int(body.model_instance())
plt.imshow(colorize_labels(label_by_model))
Although we do not yet support interactive meshcat visualization, you can produce a window that gives an interactive meshcat interface (as in, you can still access all the controls, move the camera, and replay or record the animation), but any future calls from the MeshcatVisualizer will not affect what's displayed there. This should work on Binder. Please see #12645 for more details.
meshcat_vis.vis.render_static()
This is particularly useful if you would like to play back recorded simulation. Open the controls in the meshcat window to play the animation.
# Set the context to make the simulation valid and slightly interesting.
simulator.get_mutable_context().SetTime(.0)
plant_context = plant.GetMyContextFromRoot(simulator.get_mutable_context())
plant.get_actuation_input_port(iiwa_1).FixValue(plant_context, np.zeros((7,1)))
plant.get_actuation_input_port(iiwa_2).FixValue(plant_context, np.zeros((7,1)))
plant.SetPositions(plant_context, iiwa_1, [0.2, 0.4, 0, 0, 0, 0, 0])
# Reset the recording (in case you are running this cell more than once).
meshcat_vis.reset_recording()
# Start recording and simulate.
meshcat_vis.start_recording()
simulator.AdvanceTo(1.0)
# Publish the recording to meshcat.
meshcat_vis.publish_recording()
# Render meshcat's current state.
meshcat_vis.vis.render_static()