Files
lerobot/tests/datasets/test_streaming_video_encoder.py
T
Caroline Pascal 3dd19d043e 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>
2026-06-27 14:21:21 +02:00

668 lines
24 KiB
Python

#!/usr/bin/env python
# Copyright 2026 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.
"""Tests for streaming video encoding."""
import queue
import threading
import numpy as np
import pytest
pytest.importorskip("av", reason="av is required (install lerobot[dataset])")
import av # noqa: E402
from lerobot.configs import RGBEncoderConfig
from lerobot.datasets.pyav_utils import get_codec
from lerobot.datasets.video_utils import (
StreamingVideoEncoder,
_CameraEncoderThread,
)
from lerobot.utils.constants import OBS_IMAGES
# Cross-codec validation tests only fire when the target codec is present
# in the local FFmpeg build; on other platforms validate() is a no-op.
_has_videotoolbox = get_codec("h264_videotoolbox") is not None
_videotoolbox_only = pytest.mark.skipif(
not _has_videotoolbox, reason="h264_videotoolbox not in local FFmpeg build"
)
# ─── _CameraEncoderThread tests ───
class TestCameraEncoderThread:
def test_encodes_valid_mp4(self, tmp_path):
"""Test that the encoder thread creates a valid MP4 file with correct frame count."""
num_frames = 30
height, width = 64, 96
fps = 30
video_path = tmp_path / "test_output" / "test.mp4"
frame_queue: queue.Queue = queue.Queue(maxsize=60)
result_queue: queue.Queue = queue.Queue(maxsize=1)
stop_event = threading.Event()
enc_cfg = RGBEncoderConfig(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30, preset=13)
encoder_thread = _CameraEncoderThread(
video_path=video_path,
fps=fps,
video_encoder=enc_cfg,
frame_queue=frame_queue,
result_queue=result_queue,
stop_event=stop_event,
)
encoder_thread.start()
# Feed frames (HWC uint8)
for _ in range(num_frames):
frame = np.random.randint(0, 255, (height, width, 3), dtype=np.uint8)
frame_queue.put(frame)
# Send sentinel
frame_queue.put(None)
encoder_thread.join(timeout=60)
assert not encoder_thread.is_alive()
# Check result
status, data = result_queue.get(timeout=5)
assert status == "ok"
assert data is not None # Stats should be returned
assert "mean" in data
assert "std" in data
assert "min" in data
assert "max" in data
assert "count" in data
# Verify the MP4 file is valid
assert video_path.exists()
with av.open(str(video_path)) as container:
stream = container.streams.video[0]
# The frame count should match
total_frames = sum(1 for _ in container.decode(stream))
assert total_frames == num_frames
def test_handles_chw_input(self, tmp_path):
"""Test that CHW format input is handled correctly."""
num_frames = 5
fps = 30
video_path = tmp_path / "test_chw" / "test.mp4"
frame_queue: queue.Queue = queue.Queue(maxsize=60)
result_queue: queue.Queue = queue.Queue(maxsize=1)
stop_event = threading.Event()
enc_cfg = RGBEncoderConfig(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30, preset=13)
encoder_thread = _CameraEncoderThread(
video_path=video_path,
fps=fps,
video_encoder=enc_cfg,
frame_queue=frame_queue,
result_queue=result_queue,
stop_event=stop_event,
)
encoder_thread.start()
# Feed CHW frames
for _ in range(num_frames):
frame = np.random.randint(0, 255, (3, 64, 96), dtype=np.uint8)
frame_queue.put(frame)
frame_queue.put(None)
encoder_thread.join(timeout=60)
status, _ = result_queue.get(timeout=5)
assert status == "ok"
assert video_path.exists()
def test_stop_event_cancellation(self, tmp_path):
"""Test that setting the stop event causes the thread to exit."""
fps = 30
video_path = tmp_path / "test_cancel" / "test.mp4"
frame_queue: queue.Queue = queue.Queue(maxsize=60)
result_queue: queue.Queue = queue.Queue(maxsize=1)
stop_event = threading.Event()
enc_cfg = RGBEncoderConfig(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30, preset=13)
encoder_thread = _CameraEncoderThread(
video_path=video_path,
fps=fps,
video_encoder=enc_cfg,
frame_queue=frame_queue,
result_queue=result_queue,
stop_event=stop_event,
)
encoder_thread.start()
# Feed a few frames
for _ in range(3):
frame = np.random.randint(0, 255, (64, 96, 3), dtype=np.uint8)
frame_queue.put(frame)
# Signal stop instead of sending sentinel
stop_event.set()
encoder_thread.join(timeout=10)
assert not encoder_thread.is_alive()
# ─── StreamingVideoEncoder tests ───
class TestStreamingVideoEncoder:
def _make_encoder_config(self, **kwargs):
"""Helper to build an RGBEncoderConfig."""
return RGBEncoderConfig(**kwargs)
def test_single_camera_episode(self, tmp_path):
"""Test encoding a single camera episode."""
video_keys = [f"{OBS_IMAGES}.laptop"]
encoder = StreamingVideoEncoder(
fps=30,
rgb_encoder=self._make_encoder_config(
vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30, preset=13
),
)
encoder.start_episode(video_keys, tmp_path)
num_frames = 20
for _ in range(num_frames):
frame = np.random.randint(0, 255, (64, 96, 3), dtype=np.uint8)
encoder.feed_frame(f"{OBS_IMAGES}.laptop", frame)
results = encoder.finish_episode()
assert f"{OBS_IMAGES}.laptop" in results
mp4_path, stats = results[f"{OBS_IMAGES}.laptop"]
assert mp4_path.exists()
assert stats is not None
# Verify frame count
with av.open(str(mp4_path)) as container:
stream = container.streams.video[0]
total_frames = sum(1 for _ in container.decode(stream))
assert total_frames == num_frames
encoder.close()
def test_multi_camera_episode(self, tmp_path):
"""Test encoding multiple cameras simultaneously."""
video_keys = [f"{OBS_IMAGES}.laptop", f"{OBS_IMAGES}.phone"]
encoder = StreamingVideoEncoder(
fps=30,
rgb_encoder=self._make_encoder_config(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30),
)
encoder.start_episode(video_keys, tmp_path)
num_frames = 15
for _ in range(num_frames):
frame0 = np.random.randint(0, 255, (64, 96, 3), dtype=np.uint8)
frame1 = np.random.randint(0, 255, (64, 96, 3), dtype=np.uint8)
encoder.feed_frame(video_keys[0], frame0)
encoder.feed_frame(video_keys[1], frame1)
results = encoder.finish_episode()
for key in video_keys:
assert key in results
mp4_path, stats = results[key]
assert mp4_path.exists()
assert stats is not None
encoder.close()
def test_sequential_episodes(self, tmp_path):
"""Test that multiple sequential episodes work correctly."""
video_keys = [f"{OBS_IMAGES}.cam"]
encoder = StreamingVideoEncoder(
fps=30,
rgb_encoder=self._make_encoder_config(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30),
)
for ep in range(3):
encoder.start_episode(video_keys, tmp_path)
num_frames = 10 + ep * 5
for _ in range(num_frames):
frame = np.random.randint(0, 255, (64, 96, 3), dtype=np.uint8)
encoder.feed_frame(f"{OBS_IMAGES}.cam", frame)
results = encoder.finish_episode()
mp4_path, stats = results[f"{OBS_IMAGES}.cam"]
assert mp4_path.exists()
with av.open(str(mp4_path)) as container:
stream = container.streams.video[0]
total_frames = sum(1 for _ in container.decode(stream))
assert total_frames == num_frames
encoder.close()
def test_cancel_episode(self, tmp_path):
"""Test that canceling an episode cleans up properly."""
video_keys = [f"{OBS_IMAGES}.cam"]
encoder = StreamingVideoEncoder(
fps=30,
rgb_encoder=self._make_encoder_config(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30),
)
encoder.start_episode(video_keys, tmp_path)
for _ in range(5):
frame = np.random.randint(0, 255, (64, 96, 3), dtype=np.uint8)
encoder.feed_frame(f"{OBS_IMAGES}.cam", frame)
encoder.cancel_episode()
# Should be able to start a new episode after cancel
encoder.start_episode(video_keys, tmp_path)
for _ in range(5):
frame = np.random.randint(0, 255, (64, 96, 3), dtype=np.uint8)
encoder.feed_frame(f"{OBS_IMAGES}.cam", frame)
results = encoder.finish_episode()
assert f"{OBS_IMAGES}.cam" in results
encoder.close()
def test_feed_without_start_raises(self, tmp_path):
"""Test that feeding frames without starting an episode raises."""
encoder = StreamingVideoEncoder(fps=30)
with pytest.raises(RuntimeError, match="No active episode"):
encoder.feed_frame("cam", np.zeros((64, 96, 3), dtype=np.uint8))
encoder.close()
def test_finish_without_start_raises(self, tmp_path):
"""Test that finishing without starting raises."""
encoder = StreamingVideoEncoder(fps=30)
with pytest.raises(RuntimeError, match="No active episode"):
encoder.finish_episode()
encoder.close()
def test_close_is_idempotent(self, tmp_path):
"""Test that close() can be called multiple times safely."""
encoder = StreamingVideoEncoder(fps=30)
encoder.close()
encoder.close() # Should not raise
def test_video_duration_matches_frame_count(self, tmp_path):
"""Test that encoded video duration matches num_frames / fps."""
video_keys = [f"{OBS_IMAGES}.cam"]
encoder = StreamingVideoEncoder(
fps=30,
rgb_encoder=self._make_encoder_config(
vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30, preset=13
),
)
encoder.start_episode(video_keys, tmp_path)
num_frames = 90 # 3 seconds at 30fps
for _ in range(num_frames):
frame = np.random.randint(0, 255, (64, 96, 3), dtype=np.uint8)
encoder.feed_frame(f"{OBS_IMAGES}.cam", frame)
results = encoder.finish_episode()
mp4_path, _ = results[f"{OBS_IMAGES}.cam"]
expected_duration = num_frames / 30.0 # 3.0 seconds
with av.open(str(mp4_path)) as container:
stream = container.streams.video[0]
total_frames = sum(1 for _ in container.decode(stream))
if stream.duration is not None:
actual_duration = float(stream.duration * stream.time_base)
else:
actual_duration = float(container.duration / av.time_base)
assert total_frames == num_frames
# Allow small tolerance for duration due to codec framing
assert abs(actual_duration - expected_duration) < 0.5, (
f"Video duration {actual_duration:.2f}s != expected {expected_duration:.2f}s"
)
encoder.close()
def test_multi_camera_start_episode_called_once(self, tmp_path):
"""Test that with multiple cameras, no frames are lost due to double start_episode."""
video_keys = [f"{OBS_IMAGES}.cam1", f"{OBS_IMAGES}.cam2"]
encoder = StreamingVideoEncoder(
fps=30,
rgb_encoder=self._make_encoder_config(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30),
)
encoder.start_episode(video_keys, tmp_path)
num_frames = 30
for _ in range(num_frames):
frame0 = np.random.randint(0, 255, (64, 96, 3), dtype=np.uint8)
frame1 = np.random.randint(0, 255, (64, 96, 3), dtype=np.uint8)
encoder.feed_frame(video_keys[0], frame0)
encoder.feed_frame(video_keys[1], frame1)
results = encoder.finish_episode()
# Both cameras should have all frames
for key in video_keys:
mp4_path, stats = results[key]
assert mp4_path.exists()
with av.open(str(mp4_path)) as container:
stream = container.streams.video[0]
total_frames = sum(1 for _ in container.decode(stream))
assert total_frames == num_frames, (
f"Camera {key}: expected {num_frames} frames, got {total_frames}"
)
encoder.close()
def test_encoder_threads_passed_to_thread(self, tmp_path):
"""Test that encoder_threads is stored and passed through to encoder threads."""
video_keys = [f"{OBS_IMAGES}.cam"]
cfg = RGBEncoderConfig(
vcodec="libsvtav1",
pix_fmt="yuv420p",
g=2,
crf=30,
)
encoder = StreamingVideoEncoder(
fps=30,
rgb_encoder=cfg,
encoder_threads=2,
)
assert encoder._encoder_threads == 2
encoder.start_episode(video_keys, tmp_path)
# Verify codec options include thread tuning for libsvtav1 (lp=…)
thread = encoder._threads[f"{OBS_IMAGES}.cam"]
codec_opts = thread.video_encoder.get_codec_options(encoder_threads=thread.encoder_threads)
assert "svtav1-params" in codec_opts or "threads" in codec_opts
# Feed some frames and finish to ensure it works end-to-end
num_frames = 10
for _ in range(num_frames):
frame = np.random.randint(0, 255, (64, 96, 3), dtype=np.uint8)
encoder.feed_frame(f"{OBS_IMAGES}.cam", frame)
results = encoder.finish_episode()
mp4_path, stats = results[f"{OBS_IMAGES}.cam"]
assert mp4_path.exists()
assert stats is not None
with av.open(str(mp4_path)) as container:
stream = container.streams.video[0]
total_frames = sum(1 for _ in container.decode(stream))
assert total_frames == num_frames
encoder.close()
def test_encoder_threads_none_by_default(self, tmp_path):
"""Test that encoder_threads defaults to None (codec auto-detect)."""
encoder = StreamingVideoEncoder(fps=30)
assert encoder._encoder_threads is None
encoder.close()
def test_graceful_frame_dropping(self, tmp_path):
"""Test that full queue drops frames instead of crashing."""
video_keys = [f"{OBS_IMAGES}.cam"]
encoder = StreamingVideoEncoder(
fps=30,
rgb_encoder=self._make_encoder_config(
vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30, preset=13
),
queue_maxsize=1,
)
encoder.start_episode(video_keys, tmp_path)
# Feed many frames quickly - with queue_maxsize=1, some will be dropped
num_frames = 50
for _ in range(num_frames):
frame = np.random.randint(0, 255, (64, 96, 3), dtype=np.uint8)
encoder.feed_frame(f"{OBS_IMAGES}.cam", frame)
# Should not raise - frames are dropped gracefully
results = encoder.finish_episode()
assert f"{OBS_IMAGES}.cam" in results
mp4_path, _ = results[f"{OBS_IMAGES}.cam"]
assert mp4_path.exists()
# Some frames should have been dropped (queue was tiny)
dropped = encoder._dropped_frames.get(f"{OBS_IMAGES}.cam", 0)
# We can't guarantee drops but can verify no crash occurred
assert dropped >= 0
encoder.close()
# ─── Integration tests with LeRobotDataset ───
class TestStreamingEncoderIntegration:
def test_add_frame_save_episode_streaming(self, tmp_path):
"""Full integration test: add_frame -> save_episode with streaming encoding."""
from lerobot.datasets.lerobot_dataset import LeRobotDataset
features = {
"observation.images.cam": {
"dtype": "video",
"shape": (64, 96, 3),
"names": ["height", "width", "channels"],
},
"action": {"dtype": "float32", "shape": (6,), "names": ["j1", "j2", "j3", "j4", "j5", "j6"]},
}
dataset = LeRobotDataset.create(
repo_id="test/streaming",
fps=30,
features=features,
root=tmp_path / "streaming_test",
use_videos=True,
streaming_encoding=True,
)
assert dataset.writer._streaming_encoder is not None
num_frames = 20
for _ in range(num_frames):
frame = {
"observation.images.cam": np.random.randint(0, 255, (64, 96, 3), dtype=np.uint8),
"action": np.random.randn(6).astype(np.float32),
"task": "test task",
}
dataset.add_frame(frame)
dataset.save_episode()
# Verify dataset metadata
assert dataset.meta.total_episodes == 1
assert dataset.meta.total_frames == num_frames
# Verify stats exist for the video key
assert dataset.meta.stats is not None
assert "observation.images.cam" in dataset.meta.stats
assert "action" in dataset.meta.stats
dataset.finalize()
def test_streaming_disabled_creates_pngs(self, tmp_path):
"""Test that disabling streaming encoding falls back to PNG path."""
from lerobot.datasets.lerobot_dataset import LeRobotDataset
features = {
"observation.images.cam": {
"dtype": "video",
"shape": (64, 96, 3),
"names": ["height", "width", "channels"],
},
"action": {"dtype": "float32", "shape": (6,), "names": ["j1", "j2", "j3", "j4", "j5", "j6"]},
}
dataset = LeRobotDataset.create(
repo_id="test/no_streaming",
fps=30,
features=features,
root=tmp_path / "no_streaming_test",
use_videos=True,
streaming_encoding=False,
)
assert dataset.writer._streaming_encoder is None
num_frames = 5
for _ in range(num_frames):
frame = {
"observation.images.cam": np.random.randint(0, 255, (64, 96, 3), dtype=np.uint8),
"action": np.random.randn(6).astype(np.float32),
"task": "test task",
}
dataset.add_frame(frame)
# With streaming disabled, PNG files should be written
images_dir = dataset.root / "images"
assert images_dir.exists()
dataset.save_episode()
dataset.finalize()
def test_multi_episode_streaming(self, tmp_path):
"""Test recording multiple episodes with streaming encoding."""
from lerobot.datasets.lerobot_dataset import LeRobotDataset
features = {
"observation.images.cam": {
"dtype": "video",
"shape": (64, 96, 3),
"names": ["height", "width", "channels"],
},
"action": {"dtype": "float32", "shape": (2,), "names": ["j1", "j2"]},
}
dataset = LeRobotDataset.create(
repo_id="test/multi_ep",
fps=30,
features=features,
root=tmp_path / "multi_ep_test",
use_videos=True,
streaming_encoding=True,
)
for ep in range(3):
num_frames = 10 + ep * 5
for _ in range(num_frames):
frame = {
"observation.images.cam": np.random.randint(0, 255, (64, 96, 3), dtype=np.uint8),
"action": np.random.randn(2).astype(np.float32),
"task": f"task_{ep}",
}
dataset.add_frame(frame)
dataset.save_episode()
assert dataset.meta.total_episodes == 3
assert dataset.meta.total_frames == 10 + 15 + 20
dataset.finalize()
def test_clear_episode_buffer_cancels_streaming(self, tmp_path):
"""Test that clearing episode buffer cancels streaming encoding."""
from lerobot.datasets.lerobot_dataset import LeRobotDataset
features = {
"observation.images.cam": {
"dtype": "video",
"shape": (64, 96, 3),
"names": ["height", "width", "channels"],
},
"action": {"dtype": "float32", "shape": (2,), "names": ["j1", "j2"]},
}
dataset = LeRobotDataset.create(
repo_id="test/cancel",
fps=30,
features=features,
root=tmp_path / "cancel_test",
use_videos=True,
streaming_encoding=True,
)
# Add some frames
for _ in range(5):
frame = {
"observation.images.cam": np.random.randint(0, 255, (64, 96, 3), dtype=np.uint8),
"action": np.random.randn(2).astype(np.float32),
"task": "task",
}
dataset.add_frame(frame)
# Cancel and re-record
dataset.clear_episode_buffer()
# Record a new episode
for _ in range(10):
frame = {
"observation.images.cam": np.random.randint(0, 255, (64, 96, 3), dtype=np.uint8),
"action": np.random.randn(2).astype(np.float32),
"task": "task",
}
dataset.add_frame(frame)
dataset.save_episode()
assert dataset.meta.total_episodes == 1
assert dataset.meta.total_frames == 10
dataset.finalize()
def test_multi_camera_add_frame_streaming(self, tmp_path):
"""Test that start_episode is called once with multiple video keys."""
from lerobot.datasets.lerobot_dataset import LeRobotDataset
features = {
"observation.images.cam1": {
"dtype": "video",
"shape": (64, 96, 3),
"names": ["height", "width", "channels"],
},
"observation.images.cam2": {
"dtype": "video",
"shape": (64, 96, 3),
"names": ["height", "width", "channels"],
},
"action": {"dtype": "float32", "shape": (2,), "names": ["j1", "j2"]},
}
dataset = LeRobotDataset.create(
repo_id="test/multi_cam",
fps=30,
features=features,
root=tmp_path / "multi_cam_test",
use_videos=True,
streaming_encoding=True,
)
num_frames = 15
for _ in range(num_frames):
frame = {
"observation.images.cam1": np.random.randint(0, 255, (64, 96, 3), dtype=np.uint8),
"observation.images.cam2": np.random.randint(0, 255, (64, 96, 3), dtype=np.uint8),
"action": np.random.randn(2).astype(np.float32),
"task": "test task",
}
dataset.add_frame(frame)
dataset.save_episode()
assert dataset.meta.total_episodes == 1
assert dataset.meta.total_frames == num_frames
dataset.finalize()