Files
lerobot/tests/utils/test_rerun_visualization.py
T
Steven Palma 052d329470 feat(visualization): add foxglove support (#3902)
* Add Foxglove display mode for teleoperate

Add a --display_mode flag (rerun|foxglove) to lerobot-teleoperate. When set
to foxglove, stream observations/actions over a Foxglove WebSocket server:
images as RawImage/CompressedImage, scalars as typed JSON channels with
schemas generated from the feature names (sanitized so paths don't need
quoting). Adds a `foxglove` extra.

* Add Foxglove display mode to lerobot-record

Wire the --display_mode flag (rerun|foxglove) into lerobot-record, matching
lerobot-teleoperate: route init/log through the backend-agnostic dispatchers
and stop the visualization backend on exit.

* update foxglove-sdk to 0.25.1

* Use static lerobot.Scalars schema for Foxglove state topics

Replace the per-topic JSON schema derived from feature names with a single
static lerobot.Scalars schema: a scalars array of {label, value} objects. The
same schema fits any robot regardless of which observation/action features it
reports, and the label field lets Foxglove name each series automatically so
one filtered path plots every feature.

* add foxglove option to dataset viz

* Make Foxglove dataset playback loop the sole frame emitter

Address review: the listener no longer emits frames, it only mutates
playback state and queues a one-shot seek index that the playback loop
services. The loop is now the only caller of emit_frame, so concurrent
random access into the on-disk dataset / video decoder never overlaps.

Also remove the dead server_holder and tighten the _foxglove_safe_name
docstring to state what it does and why.

* Label Foxglove dataset scalars with feature dimension names

Use the dataset's per-dimension feature names (e.g. joint names) as the
Foxglove series labels for /observation/state and /action/state instead
of bare indices. LeRobot stores `names` inconsistently (flat list,
{category: [...]}, or {name: index}), so _feature_dim_names handles each
and falls back to indices on any unknown format or length mismatch.

* Make Foxglove server host bindable and refactor topic/channel handling

Pass display_ip through as the Foxglove WebSocket bind host (127.0.0.1
for local only, 0.0.0.0 for all interfaces) instead of always binding
locally. In lerobot-dataset-viz, fold the separate --port into --web-port
so one flag covers both the Rerun web viewer and the Foxglove server port.

Add a _foxglove_topic() helper and thread a per-topic channel cache
through the log helpers so dataset playback stays self-contained instead
of mutating the module-global cache. Promote SUCCESS to constants.py.

* feat(viz): add support for foxglove in rollout + add to viz tag

* fix(docs): remove misleading installation note

* fix(visualization): no duplicated prefix, consolidated norm + warnings log

* chore(viz): minor improvements

* refactor(viz): split files + autoplay + updated docs + added minimal tests

* fix(viz): right tags + warning

* feat(deprecated ws-port): removing rerun's depreacted ws-port parameter in dataset visualization

* chore(web ports): adding global variables for default foxglove/rerun web ports

* feat(depth): adding depth support to foxglove visualizer. Because of foxglove limitations (min and max values on RawImage cannot be set from the SDK), depth is normalized between [0,1] when a depth range is provided.

* fix(rerun depth range): making rerun depth range computation safe against missing stats

* chore(foxglove depth): make it simple, and make it work.

* fix(scaling): fixing depth frames scaling

---------

Co-authored-by: Roman Shtylman <roman@foxglove.dev>
Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com>
2026-07-01 18:39:32 +02:00

311 lines
11 KiB
Python

#!/usr/bin/env python
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import sys
from types import SimpleNamespace
import numpy as np
import pytest
pytest.importorskip("rerun", reason="rerun-sdk is required (install lerobot[viz])")
from lerobot.types import TransitionKey
from lerobot.utils.constants import OBS_STATE
@pytest.fixture
def mock_rerun(monkeypatch):
"""
Provide a mock `rerun` module (and `rerun.blueprint` submodule) so tests don't
depend on the real library. Also reload the module-under-test so it binds to
this mock `rr`.
"""
calls = []
blueprints = []
class DummyScalar:
def __init__(self, value):
# Scalars may be built from a single float or from a 1D array batch.
self.value = value
class DummyImage:
def __init__(self, arr):
self.arr = arr
def compress(self, *a, **k):
return self
class DummyDepthImage:
def __init__(self, arr, colormap=None):
self.arr = arr
self.colormap = colormap
def dummy_log(key, obj=None, **kwargs):
# Accept either positional `obj` or keyword `entity` and record remaining kwargs.
if obj is None and "entity" in kwargs:
obj = kwargs.pop("entity")
calls.append((key, obj, kwargs))
def dummy_send_blueprint(blueprint, *a, **k):
blueprints.append(blueprint)
# Mock the `rerun.blueprint` submodule used to build the layout.
dummy_rrb = SimpleNamespace(
Spatial2DView=lambda origin=None, name=None: SimpleNamespace(
kind="Spatial2DView", origin=origin, name=name
),
TimeSeriesView=lambda name=None, contents=None: SimpleNamespace(
kind="TimeSeriesView", name=name, contents=contents
),
Grid=lambda *views: SimpleNamespace(kind="Grid", views=list(views)),
Blueprint=lambda root: SimpleNamespace(kind="Blueprint", root=root),
)
dummy_rr = SimpleNamespace(
__name__="rerun",
__package__="rerun",
__spec__=SimpleNamespace(name="rerun", submodule_search_locations=None),
Scalars=DummyScalar,
Image=DummyImage,
DepthImage=DummyDepthImage,
components=SimpleNamespace(Colormap=SimpleNamespace(Viridis="viridis")),
log=dummy_log,
send_blueprint=dummy_send_blueprint,
init=lambda *a, **k: None,
spawn=lambda *a, **k: None,
blueprint=dummy_rrb,
)
# Inject fake modules into sys.modules (both `rerun` and `rerun.blueprint`).
monkeypatch.setitem(sys.modules, "rerun", dummy_rr)
monkeypatch.setitem(sys.modules, "rerun.blueprint", dummy_rrb)
# Now import and reload the module under test, to bind to our rerun mock
import lerobot.utils.rerun_visualization as rv
importlib.reload(rv)
# Expose the reloaded module, the call recorder and the captured blueprints
yield rv, calls, blueprints
def _keys(calls):
"""Helper to extract just the keys logged to rr.log"""
return [k for (k, _obj, _kw) in calls]
def _obj_for(calls, key):
"""Find the first object logged under a given key."""
for k, obj, _kw in calls:
if k == key:
return obj
raise KeyError(f"Key {key} not found in calls: {calls}")
def _kwargs_for(calls, key):
for k, _obj, kw in calls:
if k == key:
return kw
raise KeyError(f"Key {key} not found in calls: {calls}")
def _views_by_kind(blueprint, kind):
"""Return the views of a given kind from the (single) blueprint's grid."""
return [v for v in blueprint.root.views if v.kind == kind]
def test_log_rerun_data_envtransition_scalars_and_image(mock_rerun):
rv, calls, blueprints = mock_rerun
# Build EnvTransition dict
obs = {
f"{OBS_STATE}.temperature": np.float32(25.0),
# CHW image should be converted to HWC for rr.Image
"observation.camera": np.zeros((3, 10, 20), dtype=np.uint8),
}
act = {
"action.throttle": 0.7,
# 1D array should be logged as a single Scalars batch under one entity path
"action.vector": np.array([1.0, 2.0], dtype=np.float32),
}
transition = {
TransitionKey.OBSERVATION: obs,
TransitionKey.ACTION: act,
}
# Extract observation and action data from transition like in the real call sites
obs_data = transition.get(TransitionKey.OBSERVATION, {})
action_data = transition.get(TransitionKey.ACTION, {})
rv.log_rerun_data(observation=obs_data, action=action_data)
# We expect:
# - observation.state.temperature -> Scalars
# - observation.camera -> Image (HWC) with static=True
# - action.throttle -> Scalars
# - action.vector -> single Scalars batch (no per-element suffix)
expected_keys = {
f"{OBS_STATE}.temperature",
"observation.camera",
"action.throttle",
"action.vector",
}
assert set(_keys(calls)) == expected_keys
# Check scalar types and values
temp_obj = _obj_for(calls, f"{OBS_STATE}.temperature")
assert type(temp_obj).__name__ == "DummyScalar"
assert float(temp_obj.value) == pytest.approx(25.0)
throttle_obj = _obj_for(calls, "action.throttle")
assert type(throttle_obj).__name__ == "DummyScalar"
assert float(throttle_obj.value) == pytest.approx(0.7)
# 1D vector logged as a single batched Scalars under one entity path
vec = _obj_for(calls, "action.vector")
assert type(vec).__name__ == "DummyScalar"
np.testing.assert_allclose(np.asarray(vec.value), [1.0, 2.0])
# Check image handling: CHW -> HWC
img_obj = _obj_for(calls, "observation.camera")
assert type(img_obj).__name__ == "DummyImage"
assert img_obj.arr.shape == (10, 20, 3) # transposed
assert _kwargs_for(calls, "observation.camera").get("static", False) is True # static=True for images
# A blueprint should have been built and sent exactly once, and cached on the function.
assert len(blueprints) == 1
assert rv.log_rerun_data.blueprint is blueprints[0]
bp = blueprints[0]
# One spatial view per image path
spatial_views = _views_by_kind(bp, "Spatial2DView")
assert {v.origin for v in spatial_views} == {"observation.camera"}
# One time-series view each for observation and action scalars
ts_views = {v.name: v for v in _views_by_kind(bp, "TimeSeriesView")}
assert set(ts_views) == {"observation", "action"}
assert ts_views["observation"].contents == [f"{OBS_STATE}.temperature"]
assert ts_views["action"].contents == ["action.throttle", "action.vector"]
def test_log_rerun_data_plain_list_ordering_and_prefixes(mock_rerun):
rv, calls, blueprints = mock_rerun
# First dict without prefixes treated as observation
# Second dict without prefixes treated as action
obs_plain = {
"temp": 1.5,
# Already HWC image => should stay as-is
"img": np.zeros((5, 6, 3), dtype=np.uint8),
"none": None, # should be skipped
}
act_plain = {
"throttle": 0.3,
"vec": np.array([9, 8, 7], dtype=np.float32),
}
# Extract observation and action data from list like the old function logic did
# First dict was treated as observation, second as action
rv.log_rerun_data(observation=obs_plain, action=act_plain)
# Expected keys with auto-prefixes. The 1D vector is a single batched Scalars.
expected = {
"observation.temp",
"observation.img",
"action.throttle",
"action.vec",
}
logged = set(_keys(calls))
assert logged == expected
# Scalars
t = _obj_for(calls, "observation.temp")
assert type(t).__name__ == "DummyScalar"
assert float(t.value) == pytest.approx(1.5)
throttle = _obj_for(calls, "action.throttle")
assert type(throttle).__name__ == "DummyScalar"
assert float(throttle.value) == pytest.approx(0.3)
# Image stays HWC
img = _obj_for(calls, "observation.img")
assert type(img).__name__ == "DummyImage"
assert img.arr.shape == (5, 6, 3)
assert _kwargs_for(calls, "observation.img").get("static", False) is True
# Vector logged as a single batched Scalars under one entity path
vec = _obj_for(calls, "action.vec")
assert type(vec).__name__ == "DummyScalar"
np.testing.assert_allclose(np.asarray(vec.value), [9, 8, 7])
# Blueprint sent once with the expected view layout
assert len(blueprints) == 1
bp = blueprints[0]
spatial_views = _views_by_kind(bp, "Spatial2DView")
assert {v.origin for v in spatial_views} == {"observation.img"}
ts_views = {v.name: v for v in _views_by_kind(bp, "TimeSeriesView")}
assert ts_views["observation"].contents == ["observation.temp"]
assert ts_views["action"].contents == ["action.throttle", "action.vec"]
def test_log_rerun_data_kwargs_only(mock_rerun):
rv, calls, blueprints = mock_rerun
rv.log_rerun_data(
observation={"observation.temp": 10.0, "observation.gray": np.zeros((8, 8, 1), dtype=np.uint8)},
action={"action.a": 1.0},
)
keys = set(_keys(calls))
assert "observation.temp" in keys
assert "observation.gray" in keys
assert "action.a" in keys
temp = _obj_for(calls, "observation.temp")
assert type(temp).__name__ == "DummyScalar"
assert float(temp.value) == pytest.approx(10.0)
img = _obj_for(calls, "observation.gray")
assert type(img).__name__ == "DummyDepthImage" # single-channel -> DepthImage
assert img.arr.shape == (8, 8, 1) # remains HWC
assert _kwargs_for(calls, "observation.gray").get("static", False) is True
a = _obj_for(calls, "action.a")
assert type(a).__name__ == "DummyScalar"
assert float(a.value) == pytest.approx(1.0)
# Blueprint sent once, with a spatial view for the image and time-series views for scalars
assert len(blueprints) == 1
bp = blueprints[0]
assert {v.origin for v in _views_by_kind(bp, "Spatial2DView")} == {"observation.gray"}
ts_views = {v.name: v for v in _views_by_kind(bp, "TimeSeriesView")}
assert ts_views["observation"].contents == ["observation.temp"]
assert ts_views["action"].contents == ["action.a"]
def test_log_rerun_data_blueprint_sent_only_once(mock_rerun):
"""The blueprint is built from the first call and not resent on subsequent calls."""
rv, calls, blueprints = mock_rerun
rv.log_rerun_data(observation={"temp": 1.0}, action={"a": 2.0})
assert len(blueprints) == 1
first_blueprint = rv.log_rerun_data.blueprint
rv.log_rerun_data(observation={"temp": 3.0}, action={"a": 4.0})
# Still only one blueprint, and the cached one is unchanged.
assert len(blueprints) == 1
assert rv.log_rerun_data.blueprint is first_blueprint