| """Per-frame parquet: poses, contact metrics, and tactile validity flags. |
| |
| Row ``i`` corresponds to frame ``i`` of every MP4 in the same episode. |
| |
| New in this version: ``tactile_{left,right}_is_new``. A tactile row is only a |
| fresh sensor reading when that flag is True — the GelSight Mini tops out at |
| 18.75 fps while rows are written at 30 Hz, so some duplication is unavoidable, |
| and legacy recordings duplicated far more (see ``contact.NewFrameTracker``). |
| Train tactile dynamics on the flagged rows, not on all of them. |
| """ |
| from __future__ import annotations |
|
|
| from pathlib import Path |
|
|
| import numpy as np |
| import pyarrow as pa |
| import pyarrow.parquet as pq |
|
|
| |
| TACTILE_FLAG_COLUMNS = ("tactile_left_is_new", "tactile_right_is_new") |
|
|
|
|
| def build_table(source, tactile: dict, object_pose: np.ndarray | None = None) -> pa.Table: |
| """Assemble the per-frame table for one episode. |
| |
| source: EpisodeSource; tactile: {side: TactileResult} |
| """ |
| T = source.T |
| left, right = tactile["left"], tactile["right"] |
| cols = { |
| "frame_idx": np.arange(T, dtype=np.int32), |
| "timestamp": source.trimmed_cam_ts.astype(np.float64), |
| "sensor_left_pose": list(source.pose_left), |
| "sensor_right_pose": list(source.pose_right), |
| "tactile_left_intensity": left.intensity, |
| "tactile_left_area": left.area, |
| "tactile_left_mixed": left.mixed, |
| "tactile_right_intensity": right.intensity, |
| "tactile_right_area": right.area, |
| "tactile_right_mixed": right.mixed, |
| "tactile_left_is_new": left.is_new, |
| "tactile_right_is_new": right.is_new, |
| "source_h5_frame": (np.arange(T) + source.trim).astype(np.int32), |
| } |
| if object_pose is not None: |
| cols["object_pose"] = list(object_pose) |
| return pa.table(cols) |
|
|
|
|
| def write_table(table: pa.Table, path: Path) -> None: |
| path.parent.mkdir(parents=True, exist_ok=True) |
| pq.write_table(table, str(path)) |
|
|
|
|
| def add_index_columns(table: pa.Table, task: str, task_index: int, |
| episode: str, episode_index: int) -> pa.Table: |
| """Attach the LeRobot-style task/episode/frame index columns.""" |
| n = table.num_rows |
| additions = { |
| "task": pa.array([task] * n, pa.string()), |
| "task_index": pa.array(np.full(n, task_index, np.int64)), |
| "episode": pa.array([episode] * n, pa.string()), |
| "episode_index": pa.array(np.full(n, episode_index, np.int64)), |
| "frame_index": pa.array(np.arange(n, dtype=np.int64)), |
| } |
| for name, arr in additions.items(): |
| if name in table.column_names: |
| table = table.set_column(table.schema.get_field_index(name), name, arr) |
| else: |
| table = table.append_column(name, arr) |
| return table |
|
|
|
|
| def backfill_is_new(table: pa.Table, left_is_new: np.ndarray, |
| right_is_new: np.ndarray) -> pa.Table: |
| """Add/replace the tactile validity flags on an existing parquet.""" |
| for name, values in zip(TACTILE_FLAG_COLUMNS, (left_is_new, right_is_new)): |
| arr = pa.array(np.asarray(values, dtype=bool)) |
| if name in table.column_names: |
| table = table.set_column(table.schema.get_field_index(name), name, arr) |
| else: |
| table = table.append_column(name, arr) |
| return table |
|
|
