feat(depth maps): adding support for depth in LeRobot (#3644)

* feat(depth): add depth quantization helpers and tests

* feat(video): add ffv1 to supported codecs

* feat(depth): persist depth metadata

* feat(depth): extend quantization tools to better fit the encoding/decoding pipeline

* feat(depth): plumb DepthEncoderConfig through LeRobotDataset and DatasetWriter

* feat(depth): wire StreamingVideoEncoder + writer to depth encoder

* feat(depth): wire DatasetReader to decode_depth_frames

* feat(cameras/realsense): expose async depth in metric meters

* feat(features): route 2D camera shapes to observation.depth.<key>

* feat(robots/so_follower): emit + populate depth keys when use_depth

* feat(record): plumb DepthEncoderConfig through lerobot-record

* feat(viz): render depth observations as rr.DepthImage in Viridis

* feat(depth maps writer): adding support for raw depth maps recording with image writer

* chore(format): format code

* feat(depth shape): ensuring depth maps shape is always including the channel

* feat(is_depth): simplifying is_depth nested name + legacy support

* fix(stop_event): fixing stop_event race condition in camera classes

* fix(plumbing): fixing missing parts in the depth maps pipeline

* chore(typos): fixing typos

* test(fix): fixing exisiting tests to still work with latest features

* tests(depth): adding new tests for depth integration validation

* feat(pix_fmt channels): use PyAv to check get pixel formats number of channels

* feat(refactor): refactor DepthEncoderConfig quantization pipeline, so that the methods do not live in the config class. Add pixel format - channels validation.Move the default pixel format for depth in the config file.

* fix(pre-commit): fixing mutable defautl value

* fix(info): fixing info metadata update when is_depth_map was set

* tests(typos): fixing typos in tests

* fix(realsense): fixing typo in realsense serial number

* fix(normalization): restricting 255 normalization to non depth/uint8 images only

* fix(typo): fixing typo

* fix(TIFF): add missing quantization and cleanup for TIFF files

* feat(batched dequantization): optimizing dequantize_depth for torch based batched dequantization

* feat(tools): adding depth support in LeRobotDataset edition tools

* test(aggregate): extending aggregation tests to depth frames

* test(cleaning): cleaning up tests

* fix(from_video_info): fixing early validation issue in from_video_info

* fix(typo): fixing typo

* fix(is_depth): adding missing doctrings and is_depth arguments in video decoding functions

Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>

* fix(depth units): fixing depth units output for the realsense cameras

* feat(output unit): adding support for output unit specification at dataset reading/training time

Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>

* test(depth): cleaning up depth tests

* test(depth encoding): updating and cleaning video/depth encoding tests

* chore(format): formatting code

* docs(depth): improving depth maps docs

* test(fix): fixing depth tests

* test(dataset tools): adding missing tests for new dataset edition tools features

* chore(format): formatting code

* fix(pyav check): fixing PyAV option validation for integer codec options by normalizing
numeric values before calling `is_integer()`

Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>

* docs(mermaid): fixing mermaid diagram

* fix(rebase): rebase follow up corrections

* feat(dataset tools): adding missing docstrings and features for depth fill support in dataset edition tools

* docs(docstring): updating docstrings

* docs(dataset tools): updating docs

* fix(save images): fixing image saving in dataset tools

* fix(update video info): fixing update video info logic to match the recording and editing use cases

* test(reencode): fixing reencoding monkeypatch

* fix(review): add Claude review

* chore(format): format code

* fix(update video info): ditching the differentiated approahces for video info update - video info are always updated unless for preserved keys.

* chore(rebase): fixing rebase merge conflicts

* test(visualization): fixing visualization tests

* feat(docstrings): adding explicit docstring for encoding parameters. Docstrigns will now show up as description in the CLI --help.

* feat(mm as default): adding a global DEFAULT_DEPTH_UNIT variable setting mm as default depth unit

* fix(RGB <-> camera): renaming camera_encoder to rgb_encoder for clarity

* chore(TODO): removing deprecated TODO

* doc(write_u16_plane): improving docstrings for write_u16_plane

* feat(units): adding constants for depth frames units (m and mm)

* fix(spam): replacing spamming warning but a debug log

* feat(leagcy metadata): adding automatic metadata update for legacy 'video.is_depth_map' feature

* fix(copy&reindex): fixing metadat reshaping for single channel frames

* fix(ImageNet): excluding dpeth frames from ImageNet stats

* fix(PyAV container seek): fixing initial  PyAV container seek to be robust againsy codec choice

* feat(lerobot-dataset-viz): adding support for depth in lerobot-dataset-viz

* fix(compress): removing rerun compression for DepthImages

* fix(signle channel squeeze): fixing single channel squeezing

* chore(format): format code

* fix(streaming): adding support for dequantization in streaming_dataset.py

* refactor(read depth): factorizing depth reading methods for realsense camera and adding support for depth-only usage

* chore(renaming): fixing missed RGBEncoderConfig renamings

* docs(renaming): reflecting renamings in a clearer way in the docs

* chore(annotation): excluding depth from the annotation pipeline

* feat(robots): adding depth support in compatible follower robots

* feat(LeSadKiwi): excluding LeKiwi from depth support (for now)

* chore(fail): removing misplaced file

* chore(fail): removing misplaced file

* fix(remove ffv1): removing ffv1 as it does not support MP4

* docs(cheat sheet): adding depth and video encoding to the cheat sheet

* fix(lossless): tuning depth encoding parameters for lossless depth storage

* test(fix): fixing failing tests

* depth(ZMQ): excluding ZMQ from depth support

* Revert "depth(ZMQ): excluding ZMQ from depth support"

This reverts commit b95cf4e4c2.

* fix(image transforms): excluding depth frames from images transforms

* fix(typo): typo

* fix(stats): fixing stats computation for depth frames

* fix(TIFF vs. pytorch): adding an extra uint16 to float32 conversion for depth maps stored as raw TIFF images

* fix(typos): fixing typos

* test(dtype): fixing stats computation typing tests

---------

Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Wensi Ai <wsai@stanford.edu>
This commit is contained in:
Caroline Pascal
2026-06-27 14:21:21 +02:00
committed by GitHub
parent 6a788fbdb0
commit 3dd19d043e
69 changed files with 2740 additions and 679 deletions
+74 -8
View File
@@ -29,7 +29,10 @@ from lerobot.configs import VIDEO_ENCODER_INFO_KEYS
from lerobot.datasets.aggregate import aggregate_datasets
from lerobot.datasets.feature_utils import features_equal_for_merge
from lerobot.datasets.lerobot_dataset import LeRobotDataset
from tests.fixtures.constants import DUMMY_REPO_ID
from tests.fixtures.constants import (
DUMMY_CAMERA_FEATURES_WITH_DEPTH,
DUMMY_REPO_ID,
)
def assert_data_shards_one_row_group_per_episode(root):
@@ -211,6 +214,26 @@ def assert_dataset_iteration_works(aggr_ds):
pass
def assert_depth_keys_preserved(aggr_ds, ds_0, ds_1):
"""Test that depth keys are correctly preserved after aggregation.
Ensures that the ``is_depth_map`` marker on visual features survives
aggregation, so that downstream consumers (e.g. the dataset reader's
depth decoding path) keep working on the merged dataset.
"""
expected_depth_keys = set(ds_0.meta.depth_keys)
assert expected_depth_keys == set(ds_1.meta.depth_keys), (
"Source datasets disagree on depth_keys; test setup is inconsistent"
)
actual_depth_keys = set(aggr_ds.meta.depth_keys)
assert actual_depth_keys == expected_depth_keys, (
f"Expected depth_keys {expected_depth_keys}, got {actual_depth_keys}"
)
for key in expected_depth_keys:
info = aggr_ds.meta.info.features[key].get("info") or {}
assert info.get("is_depth_map") is True, f"Depth marker lost on feature {key!r} after aggregation"
def assert_video_timestamps_within_bounds(aggr_ds):
"""Test that all video timestamps are within valid bounds for their respective video files.
@@ -260,7 +283,11 @@ def assert_video_timestamps_within_bounds(aggr_ds):
def test_aggregate_datasets(tmp_path, lerobot_dataset_factory):
"""Test basic aggregation functionality with standard parameters."""
"""Test basic aggregation functionality with standard parameters.
Source datasets include both RGB and depth video features so the same
aggregation flow is exercised on the ``is_depth_map`` branch.
"""
ds_0_num_frames = 400
ds_1_num_frames = 800
ds_0_num_episodes = 10
@@ -272,14 +299,21 @@ def test_aggregate_datasets(tmp_path, lerobot_dataset_factory):
repo_id=f"{DUMMY_REPO_ID}_0",
total_episodes=ds_0_num_episodes,
total_frames=ds_0_num_frames,
camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
)
ds_1 = lerobot_dataset_factory(
root=tmp_path / "test_1",
repo_id=f"{DUMMY_REPO_ID}_1",
total_episodes=ds_1_num_episodes,
total_frames=ds_1_num_frames,
camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
)
# Confirm depth was actually wired into the source datasets so the
# rest of the assertions exercise the depth aggregation path.
assert len(ds_0.meta.depth_keys) > 0, "ds_0 should expose at least one depth key"
assert len(ds_1.meta.depth_keys) > 0, "ds_1 should expose at least one depth key"
aggregate_datasets(
repo_ids=[ds_0.repo_id, ds_1.repo_id],
roots=[ds_0.root, ds_1.root],
@@ -306,6 +340,7 @@ def test_aggregate_datasets(tmp_path, lerobot_dataset_factory):
assert_episode_indices_updated_correctly(aggr_ds, ds_0, ds_1)
assert_video_frames_integrity(aggr_ds, ds_0, ds_1)
assert_video_timestamps_within_bounds(aggr_ds)
assert_depth_keys_preserved(aggr_ds, ds_0, ds_1)
assert_dataset_iteration_works(aggr_ds)
@@ -423,7 +458,11 @@ def test_aggregate_incomplete_video_encoder_info_warns_and_nuls_encoders(
def test_aggregate_with_low_threshold(tmp_path, lerobot_dataset_factory):
"""Test aggregation with small file size limits to force file rotation/sharding."""
"""Test aggregation with small file size limits to force file rotation/sharding.
Depth video features are included to verify that file rotation/concat
correctly handles depth-marked features alongside regular RGB ones.
"""
ds_0_num_episodes = ds_1_num_episodes = 10
ds_0_num_frames = ds_1_num_frames = 400
@@ -432,14 +471,19 @@ def test_aggregate_with_low_threshold(tmp_path, lerobot_dataset_factory):
repo_id=f"{DUMMY_REPO_ID}_small_0",
total_episodes=ds_0_num_episodes,
total_frames=ds_0_num_frames,
camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
)
ds_1 = lerobot_dataset_factory(
root=tmp_path / "small_1",
repo_id=f"{DUMMY_REPO_ID}_small_1",
total_episodes=ds_1_num_episodes,
total_frames=ds_1_num_frames,
camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
)
assert len(ds_0.meta.depth_keys) > 0, "ds_0 should expose at least one depth key"
assert len(ds_1.meta.depth_keys) > 0, "ds_1 should expose at least one depth key"
# Use the new configurable parameters to force file rotation
aggregate_datasets(
repo_ids=[ds_0.repo_id, ds_1.repo_id],
@@ -470,6 +514,7 @@ def test_aggregate_with_low_threshold(tmp_path, lerobot_dataset_factory):
assert_episode_indices_updated_correctly(aggr_ds, ds_0, ds_1)
assert_video_frames_integrity(aggr_ds, ds_0, ds_1)
assert_video_timestamps_within_bounds(aggr_ds)
assert_depth_keys_preserved(aggr_ds, ds_0, ds_1)
assert_dataset_iteration_works(aggr_ds)
# Check that multiple files were actually created due to small size limits
@@ -489,7 +534,8 @@ def test_video_timestamps_regression(tmp_path, lerobot_dataset_factory):
"""Regression test for video timestamp bug when merging datasets.
This test specifically checks that video timestamps are correctly calculated
and accumulated when merging multiple datasets.
and accumulated when merging multiple datasets. Depth video features are
included so depth timestamps are also covered by the regression.
"""
datasets = []
for i in range(3):
@@ -498,9 +544,13 @@ def test_video_timestamps_regression(tmp_path, lerobot_dataset_factory):
repo_id=f"{DUMMY_REPO_ID}_regression_{i}",
total_episodes=2,
total_frames=100,
camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
)
datasets.append(ds)
for i, ds in enumerate(datasets):
assert len(ds.meta.depth_keys) > 0, f"Dataset {i} should expose at least one depth key"
aggregate_datasets(
repo_ids=[ds.repo_id for ds in datasets],
roots=[ds.root for ds in datasets],
@@ -517,12 +567,21 @@ def test_video_timestamps_regression(tmp_path, lerobot_dataset_factory):
aggr_ds = LeRobotDataset(f"{DUMMY_REPO_ID}_regression_aggr", root=tmp_path / "regression_aggr")
assert_video_timestamps_within_bounds(aggr_ds)
# Depth keys must survive the merge for the regression to cover the
# ``is_depth_map`` decoding branch.
assert set(aggr_ds.meta.depth_keys) == set(datasets[0].meta.depth_keys)
depth_keys = set(aggr_ds.meta.depth_keys)
for i in range(len(aggr_ds)):
item = aggr_ds[i]
for key in aggr_ds.meta.video_keys:
assert key in item, f"Video key {key} missing from item {i}"
assert item[key].shape[0] == 3, f"Expected 3 channels for video key {key}"
# Depth frames are single-channel (1, H, W) after dequantization;
# standard RGB frames keep the 3-channel layout.
expected_channels = 1 if key in depth_keys else 3
assert item[key].shape[0] == expected_channels, (
f"Expected {expected_channels} channels for video key {key}, got {item[key].shape}"
)
def assert_image_schema_preserved(aggr_ds):
@@ -639,25 +698,31 @@ def test_aggregate_image_datasets(tmp_path, lerobot_dataset_factory):
ds_0_num_episodes = 2
ds_1_num_episodes = 3
# Create two image-based datasets (use_videos=False)
# Create two image-based datasets (use_videos=False) with a mix of RGB
# and depth-marked cameras so the depth path is exercised in image mode.
ds_0 = lerobot_dataset_factory(
root=tmp_path / "image_0",
repo_id=f"{DUMMY_REPO_ID}_image_0",
total_episodes=ds_0_num_episodes,
total_frames=ds_0_num_frames,
use_videos=False, # Image-based dataset
use_videos=False,
camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
)
ds_1 = lerobot_dataset_factory(
root=tmp_path / "image_1",
repo_id=f"{DUMMY_REPO_ID}_image_1",
total_episodes=ds_1_num_episodes,
total_frames=ds_1_num_frames,
use_videos=False, # Image-based dataset
use_videos=False,
camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
)
# Verify source datasets have image keys
assert len(ds_0.meta.image_keys) > 0, "ds_0 should have image keys"
assert len(ds_1.meta.image_keys) > 0, "ds_1 should have image keys"
# And that the depth marker actually made it onto an image feature.
assert len(ds_0.meta.depth_keys) > 0, "ds_0 should expose at least one depth key"
assert len(ds_1.meta.depth_keys) > 0, "ds_1 should expose at least one depth key"
# Aggregate the datasets
aggregate_datasets(
@@ -692,6 +757,7 @@ def test_aggregate_image_datasets(tmp_path, lerobot_dataset_factory):
# Image-specific assertions
assert_image_schema_preserved(aggr_ds)
assert_image_frames_integrity(aggr_ds, ds_0, ds_1)
assert_depth_keys_preserved(aggr_ds, ds_0, ds_1)
# Verify images can be accessed and have correct shape
sample_item = aggr_ds[0]