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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
@@ -17,7 +17,7 @@ This makes `save_episode()` near-instant (the video is already encoded by the ti
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| Parameter | CLI Flag | Type | Default | Description |
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| ----------------------- | --------------------------------- | ------------- | ------------- | ----------------------------------------------------------------- |
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| `streaming_encoding` | `--dataset.streaming_encoding` | `bool` | `True` | Enable real-time encoding during capture |
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| `vcodec` | `--dataset.camera_encoder.vcodec` | `str` | `"libsvtav1"` | Video codec. `"auto"` detects best HW encoder |
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| `vcodec` | `--dataset.rgb_encoder.vcodec` | `str` | `"libsvtav1"` | Video codec. `"auto"` detects best HW encoder |
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| `encoder_threads` | `--dataset.encoder_threads` | `int \| None` | `None` (auto) | Threads per encoder instance. `None` will leave the vcoded decide |
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| `encoder_queue_maxsize` | `--dataset.encoder_queue_maxsize` | `int` | `30` | Max buffered frames per camera (~1s at 30fps). Consumes RAM |
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@@ -82,15 +82,15 @@ Use HW encoding when:
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### Available HW Encoders
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| Encoder | Platform | Hardware | CLI Value |
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| ------------------- | ------------- | ------------------------------------------------------------------------------------------------ | --------------------------------------------------- |
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| `h264_videotoolbox` | macOS | Apple Silicon / Intel | `--dataset.camera_encoder.vcodec=h264_videotoolbox` |
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| `hevc_videotoolbox` | macOS | Apple Silicon / Intel | `--dataset.camera_encoder.vcodec=hevc_videotoolbox` |
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| `h264_nvenc` | Linux/Windows | NVIDIA GPU | `--dataset.camera_encoder.vcodec=h264_nvenc` |
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| `hevc_nvenc` | Linux/Windows | NVIDIA GPU | `--dataset.camera_encoder.vcodec=hevc_nvenc` |
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| `h264_vaapi` | Linux | Intel/AMD GPU | `--dataset.camera_encoder.vcodec=h264_vaapi` |
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| `h264_qsv` | Linux/Windows | Intel Quick Sync | `--dataset.camera_encoder.vcodec=h264_qsv` |
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| `auto` | Any | Probes the system for available HW encoders. Falls back to `libsvtav1` if no HW encoder is found | `--dataset.camera_encoder.vcodec=auto` |
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| Encoder | Platform | Hardware | CLI Value |
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| ------------------- | ------------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------ |
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| `h264_videotoolbox` | macOS | Apple Silicon / Intel | `--dataset.rgb_encoder.vcodec=h264_videotoolbox` |
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| `hevc_videotoolbox` | macOS | Apple Silicon / Intel | `--dataset.rgb_encoder.vcodec=hevc_videotoolbox` |
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| `h264_nvenc` | Linux/Windows | NVIDIA GPU | `--dataset.rgb_encoder.vcodec=h264_nvenc` |
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| `hevc_nvenc` | Linux/Windows | NVIDIA GPU | `--dataset.rgb_encoder.vcodec=hevc_nvenc` |
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| `h264_vaapi` | Linux | Intel/AMD GPU | `--dataset.rgb_encoder.vcodec=h264_vaapi` |
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| `h264_qsv` | Linux/Windows | Intel Quick Sync | `--dataset.rgb_encoder.vcodec=h264_qsv` |
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| `auto` | Any | Probes the system for available HW encoders. Falls back to `libsvtav1` if no HW encoder is found | `--dataset.rgb_encoder.vcodec=auto` |
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> [!NOTE]
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> In order to use the HW accelerated encoders you might need to upgrade your GPU drivers.
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@@ -100,15 +100,15 @@ Use HW encoding when:
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## 5. Troubleshooting
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| Symptom | Likely Cause | Fix |
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| ------------------------------------------------------------------ | -------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| System freezes or choppy robot movement or Rerun visualization lag | CPU starved (100% load usage) | Close other apps, reduce encoding throughput, lower `encoder_threads`, use `h264`, use `display_data=False`. If the CPU continues to be at 100% then it might be insufficient for your setup, consider `--dataset.streaming_encoding=false` or HW encoding (`--dataset.camera_encoder.vcodec=auto`) |
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| "Encoder queue full" warnings or dropped frames in dataset | Encoder can't keep up (Queue overflow) | If CPU is not at 100%: Increase `encoder_threads`, increase `encoder_queue_maxsize` or use HW encoding (`--dataset.camera_encoder.vcodec=auto`). |
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| High RAM usage | Queue filling faster than encoding | `encoder_threads` too low or CPU insufficient. Reduce `encoder_queue_maxsize` or use HW encoding |
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| Large video files | Using HW encoder or H.264 | Expected trade-off. Switch to `libsvtav1` if CPU allows |
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| `save_episode()` still slow | `streaming_encoding` is `False` | Set `--dataset.streaming_encoding=true` |
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| Encoder thread crash | Codec not available or invalid settings | Check `vcodec` is installed, try `--dataset.camera_encoder.vcodec=auto` |
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| Recorded dataset is missing frames | CPU/GPU starvation or occasional load spikes | If ~5% of frames are missing, your system is likely overloaded — follow the recommendations above. If fewer frames are missing (~2%), they are probably due to occasional transient load spikes (often at startup) and can be considered expected. |
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| Symptom | Likely Cause | Fix |
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| ------------------------------------------------------------------ | -------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| System freezes or choppy robot movement or Rerun visualization lag | CPU starved (100% load usage) | Close other apps, reduce encoding throughput, lower `encoder_threads`, use `h264`, use `display_data=False`. If the CPU continues to be at 100% then it might be insufficient for your setup, consider `--dataset.streaming_encoding=false` or HW encoding (`--dataset.rgb_encoder.vcodec=auto`) |
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| "Encoder queue full" warnings or dropped frames in dataset | Encoder can't keep up (Queue overflow) | If CPU is not at 100%: Increase `encoder_threads`, increase `encoder_queue_maxsize` or use HW encoding (`--dataset.rgb_encoder.vcodec=auto`). |
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| High RAM usage | Queue filling faster than encoding | `encoder_threads` too low or CPU insufficient. Reduce `encoder_queue_maxsize` or use HW encoding |
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| Large video files | Using HW encoder or H.264 | Expected trade-off. Switch to `libsvtav1` if CPU allows |
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| `save_episode()` still slow | `streaming_encoding` is `False` | Set `--dataset.streaming_encoding=true` |
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| Encoder thread crash | Codec not available or invalid settings | Check `vcodec` is installed, try `--dataset.rgb_encoder.vcodec=auto` |
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| Recorded dataset is missing frames | CPU/GPU starvation or occasional load spikes | If ~5% of frames are missing, your system is likely overloaded — follow the recommendations above. If fewer frames are missing (~2%), they are probably due to occasional transient load spikes (often at startup) and can be considered expected. |
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## 6. Recommended Configurations
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@@ -146,7 +146,7 @@ On very constrained systems, streaming encoding may compete too heavily with the
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# 2camsx 640x480x3 @30fps: Requires some tuning.
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# Use H.264, disable streaming, consider batching encoding
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lerobot-record --dataset.camera_encoder.vcodec=h264 --dataset.streaming_encoding=false ...
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lerobot-record --dataset.rgb_encoder.vcodec=h264 --dataset.streaming_encoding=false ...
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```
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## 7. Closing note
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