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https://github.com/huggingface/lerobot.git
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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
Vendored
+45
-1
@@ -39,12 +39,56 @@ DUMMY_VIDEO_INFO = {
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"video.crf": 30,
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"video.preset": 12,
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"video.fast_decode": 0,
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"video.is_depth_map": False,
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"is_depth_map": False,
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"has_audio": False,
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}
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DUMMY_CAMERA_FEATURES = {
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"laptop": {"shape": (64, 96, 3), "names": ["height", "width", "channels"], "info": DUMMY_VIDEO_INFO},
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"phone": {"shape": (64, 96, 3), "names": ["height", "width", "channels"], "info": DUMMY_VIDEO_INFO},
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}
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DUMMY_DEPTH_VIDEO_INFO = {
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**DUMMY_VIDEO_INFO,
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"is_depth_map": True,
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}
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DUMMY_DEPTH_VIDEO_INFO_FULL = {
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**{k: v for k, v in DUMMY_VIDEO_INFO.items() if k != "video.preset"},
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"video.codec": "hevc",
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"video.pix_fmt": "gray12le",
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"is_depth_map": True,
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"video.depth_min": 0.05,
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"video.depth_max": 8.0,
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"video.shift": 2.5,
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"video.use_log": True,
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}
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DUMMY_DEPTH_CAMERA_FEATURES = {
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"laptop_depth": {
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"shape": (64, 96, 1),
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"names": ["height", "width", "channels"],
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"info": DUMMY_DEPTH_VIDEO_INFO,
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},
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}
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DUMMY_CAMERA_FEATURES_WITH_DEPTH = {**DUMMY_CAMERA_FEATURES, **DUMMY_DEPTH_CAMERA_FEATURES}
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DUMMY_CHW = (3, 96, 128)
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DUMMY_HWC = (96, 128, 3)
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# Default video feature set used by video-encoding persistence tests.
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DUMMY_VIDEO_FEATURES = {
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"observation.images.cam": {
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"dtype": "video",
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"shape": (64, 96, 3),
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"names": ["height", "width", "channels"],
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},
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"action": {"dtype": "float32", "shape": (2,), "names": ["a", "b"]},
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}
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DUMMY_VIDEO_KEY = "observation.images.cam"
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DUMMY_DEPTH_FEATURES = {
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"observation.images.depth": {
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"dtype": "video",
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"shape": (64, 96, 1),
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"names": ["height", "width", "channels"],
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"info": {"is_depth_map": True},
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},
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"action": {"dtype": "float32", "shape": (2,), "names": ["a", "b"]},
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}
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DUMMY_DEPTH_KEY = "observation.images.depth"
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Vendored
+38
@@ -49,6 +49,39 @@ from tests.fixtures.constants import (
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)
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def add_frames(dataset: LeRobotDataset, num_frames: int) -> None:
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"""Append ``num_frames`` synthetic frames to ``dataset``.
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Generates per-feature payloads from ``dataset.meta``: uint16 depth ramps for
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keys in ``dataset.meta.depth_keys``, uint8 random noise for video/image keys,
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and float32 zeros for everything else. ``DEFAULT_FEATURES`` (timestamp,
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frame_index, ...) are auto-populated by ``add_frame`` and skipped here.
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"""
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video_keys = dataset.meta.video_keys
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depth_keys = dataset.meta.depth_keys
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# Smooth gradient base reused per (H, W) to keep depth frames cheap to
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# encode (HEVC Main 12 hates white noise).
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_depth_base_cache: dict[tuple[int, int], np.ndarray] = {}
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for i in range(num_frames):
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frame: dict = {"task": "test"}
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for key, ft in dataset.meta.features.items():
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if key in DEFAULT_FEATURES:
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continue
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shape = ft["shape"]
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if key in depth_keys:
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h, w, _ = shape
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base = _depth_base_cache.setdefault(
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(h, w),
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np.linspace(100.0, 10_000.0, h * w, dtype=np.float32).reshape(h, w, 1),
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)
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frame[key] = (base + 50.0 * i).clip(0, 65535).astype(np.uint16)
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elif key in video_keys:
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frame[key] = np.random.randint(0, 256, shape, dtype=np.uint8)
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else:
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frame[key] = np.zeros(shape, dtype=np.float32)
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dataset.add_frame(frame)
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class LeRobotDatasetFactory(Protocol):
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def __call__(self, *args, **kwargs) -> LeRobotDataset: ...
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@@ -485,10 +518,14 @@ def lerobot_dataset_factory(
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hf_dataset: datasets.Dataset | None = None,
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data_files_size_in_mb: float = DEFAULT_DATA_FILE_SIZE_IN_MB,
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chunks_size: int = DEFAULT_CHUNK_SIZE,
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camera_features: dict | None = None,
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**kwargs,
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) -> LeRobotDataset:
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# Instantiate objects
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if info is None:
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info_kwargs = {}
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if camera_features is not None:
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info_kwargs["camera_features"] = camera_features
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info = info_factory(
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total_episodes=total_episodes,
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total_frames=total_frames,
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@@ -496,6 +533,7 @@ def lerobot_dataset_factory(
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use_videos=use_videos,
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data_files_size_in_mb=data_files_size_in_mb,
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chunks_size=chunks_size,
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**info_kwargs,
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)
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if stats is None:
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stats = stats_factory(features=info.features)
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