feat(WIP): adding support for multi-process camera video capture

src/lerobot/cameras/opencv/camera_opencv.py

@@ -23,6 +23,8 @@ import platform
 import time
 from pathlib import Path
 from threading import Event, Lock, Thread
+from multiprocessing import Process, Event as EventProcess, JoinableQueue as Queue
+from queue import Empty
 from typing import Any
 
 from numpy.typing import NDArray  # type: ignore  # TODO: add type stubs for numpy.typing
@@ -119,11 +121,10 @@ class OpenCVCamera(Camera):
 
         self.videocapture: cv2.VideoCapture | None = None
 
-        self.thread: Thread | None = None
-        self.stop_event: Event | None = None
-        self.frame_lock: Lock = Lock()
+        self.process: Process | None = None
+        self.stop_event: EventProcess | None = None
+        self.frame_queue: Queue = Queue()
         self.latest_frame: NDArray[Any] | None = None
-        self.new_frame_event: Event = Event()
 
         self.rotation: int | None = get_cv2_rotation(config.rotation)
         self.backend: int = get_cv2_backend()
@@ -442,37 +443,36 @@ class OpenCVCamera(Camera):
         while not self.stop_event.is_set():
             try:
                 color_image = self.read()
-
-                with self.frame_lock:
-                    self.latest_frame = color_image
-                self.new_frame_event.set()
+                self.frame_queue.put_nowait(color_image)
 
             except DeviceNotConnectedError:
                 break
             except Exception as e:
                 logger.warning(f"Error reading frame in background thread for {self}: {e}")
 
-    def _start_read_thread(self) -> None:
+    def _start_read_process(self) -> None:
         """Starts or restarts the background read thread if it's not running."""
-        if self.thread is not None and self.thread.is_alive():
-            self.thread.join(timeout=0.1)
+        if self.process is not None and self.process.is_alive():
+            self.frame_queue.join()
+            self.process.join()
         if self.stop_event is not None:
             self.stop_event.set()
 
         self.stop_event = Event()
-        self.thread = Thread(target=self._read_loop, args=(), name=f"{self}_read_loop")
-        self.thread.daemon = True
-        self.thread.start()
+        self.process = Process(target=self._read_loop, args=(), name=f"{self}_read_loop")
+        self.process.daemon = True
+        self.process.start()
 
     def _stop_read_thread(self) -> None:
         """Signals the background read thread to stop and waits for it to join."""
         if self.stop_event is not None:
             self.stop_event.set()
 
-        if self.thread is not None and self.thread.is_alive():
-            self.thread.join(timeout=2.0)
+        if self.process is not None and self.process.is_alive():
+            self.frame_queue.join()
+            self.process.join()
 
-        self.thread = None
+        self.process = None
         self.stop_event = None
 
     def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
@@ -499,24 +499,32 @@ class OpenCVCamera(Camera):
         if not self.is_connected:
             raise DeviceNotConnectedError(f"{self} is not connected.")
 
-        if self.thread is None or not self.thread.is_alive():
-            self._start_read_thread()
+        if self.process is None or not self.process.is_alive():
+            self._start_read_process()
 
-        if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0):
-            thread_alive = self.thread is not None and self.thread.is_alive()
-            raise TimeoutError(
-                f"Timed out waiting for frame from camera {self} after {timeout_ms} ms. "
-                f"Read thread alive: {thread_alive}."
-            )
+        if self.latest_frame is None:
+            self.latest_frame = self.frame_queue.get()
+            self.frame_queue.task_done()
+            return self.latest_frame
 
-        with self.frame_lock:
-            frame = self.latest_frame
-            self.new_frame_event.clear()
+        try:
+            frame = self.frame_queue.get(timeout=timeout_ms / 1000.0)
+            self.frame_queue.task_done()
+        except Empty:
+            process_alive = self.process is not None and self.process.is_alive()
+            if process_alive:
+                logger.warning(f"{self} async_read timed out after {timeout_ms} ms but camera is still running.")
+                return self.latest_frame
+            else:
+                raise TimeoutError(
+                    f"{self} async_read timed out after {timeout_ms} ms: camera is not responding !"
+                )
 
         if frame is None:
             raise RuntimeError(f"Internal error: Event set but no frame available for {self}.")
-
-        return frame
+        else:
+            self.latest_frame = frame
+            return self.latest_frame
 
     def disconnect(self) -> None:
         """

src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py

@@ -50,9 +50,9 @@ from typing import Any
 
 import jsonlines
 import pandas as pd
-import pyarrow.parquet as pq
+import pyarrow as pa
 import tqdm
-from datasets import Dataset, concatenate_datasets
+from datasets import Dataset, Features, Image
 from huggingface_hub import HfApi, snapshot_download
 from requests import HTTPError
 
@@ -68,7 +68,6 @@ from lerobot.datasets.utils import (
     LEGACY_EPISODES_STATS_PATH,
     LEGACY_TASKS_PATH,
     cast_stats_to_numpy,
-    embed_images,
     flatten_dict,
     get_file_size_in_mb,
     get_parquet_file_size_in_mb,
@@ -175,33 +174,25 @@ def convert_tasks(root, new_root):
     write_tasks(df_tasks, new_root)
 
 
-def concat_data_files(
-    paths_to_cat: list[Path], new_root: Path, chunk_idx: int, file_idx: int, image_keys: list[str]
-):
-    """Concatenate multiple parquet data files into a single file.
-
-    Args:
-        paths_to_cat: List of parquet file paths to concatenate
-        new_root: Root directory for the new dataset
-        chunk_idx: Chunk index for the output file
-        file_idx: File index within the chunk
-        image_keys: List of feature keys that contain images
-    """
-
-    datasets_list: list[Dataset] = [Dataset.from_parquet(str(file)) for file in paths_to_cat]
-    concatenated_ds: Dataset = concatenate_datasets(datasets_list)
-
-    if len(image_keys) > 0:
-        logging.debug(f"Embedding {len(image_keys)} image features for optimal training performance")
-        concatenated_ds = embed_images(concatenated_ds)
+def concat_data_files(paths_to_cat, new_root, chunk_idx, file_idx, image_keys):
+    # TODO(rcadene): to save RAM use Dataset.from_parquet(file) and concatenate_datasets
+    dataframes = [pd.read_parquet(file) for file in paths_to_cat]
+    # Concatenate all DataFrames along rows
+    concatenated_df = pd.concat(dataframes, ignore_index=True)
 
     path = new_root / DEFAULT_DATA_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
     path.parent.mkdir(parents=True, exist_ok=True)
 
-    table = concatenated_ds.with_format("arrow")[:]
-    writer = pq.ParquetWriter(path, schema=table.schema, compression="snappy", use_dictionary=True)
-    writer.write_table(table)
-    writer.close()
+    if len(image_keys) > 0:
+        schema = pa.Schema.from_pandas(concatenated_df)
+        features = Features.from_arrow_schema(schema)
+        for key in image_keys:
+            features[key] = Image()
+        schema = features.arrow_schema
+    else:
+        schema = None
+
+    concatenated_df.to_parquet(path, index=False, schema=schema)
 
 
 def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int):