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curl -L -o tactile.py https://huggingface.co/datasets/yxma/React/resolve/fd3fe6bfe5e7783d1685886905ef0f63d74e43c4/preprocess/tactile.py
3.18 kB
| """Two-pass GelSight processing: reference selection, then scalars + encode. | |
| Pass 1 finds the no-contact (p01) reference frame; pass 2 computes the contact | |
| metrics against it, flags genuinely-new frames, and streams the video out. | |
| Frames are read from HDF5 in blocks so memory stays bounded regardless of | |
| episode length. | |
| Both passes read through the ``TactileAlignment`` index map, so a timestamped | |
| recording is resampled onto the camera clock here — not patched up later. | |
| """ | |
| from __future__ import annotations | |
| from dataclasses import dataclass | |
| from pathlib import Path | |
| import numpy as np | |
| from .config import CHUNK, P01_SMOOTH_WIN | |
| from .contact import (NewFrameTracker, contact_metrics, duplication_stats, | |
| l2_diff, pick_p01_reference) | |
| from .encode import rgb_writer | |
| from .h5io import TactileAlignment | |
| class TactileResult: | |
| side: str | |
| intensity: np.ndarray | |
| area: np.ndarray | |
| mixed: np.ndarray | |
| is_new: np.ndarray | |
| ref_index: int # index into the source H5 dataset | |
| stats: dict | |
| def _gather(ds, indices: np.ndarray) -> np.ndarray: | |
| """Read `indices` (non-decreasing) from an H5 dataset in one contiguous slice.""" | |
| lo, hi = int(indices[0]), int(indices[-1]) | |
| span = ds[lo:hi + 1] | |
| return span[indices - lo] | |
| def _blocks(total: int, size: int = CHUNK): | |
| for s in range(0, total, size): | |
| yield s, min(s + size, total) | |
| def process_side(h5file, side: str, align: TactileAlignment, out_path: Path, | |
| encode: bool = True) -> TactileResult: | |
| """Run both passes for one GelSight and write its MP4.""" | |
| ds = h5file[f"gelsight/{side}/frames"] # (N, H, W, 3) uint8 RGB | |
| idx_map = align.index_map | |
| T = len(idx_map) | |
| # ── pass 1: intensity vs the first frame -> smoothed argmin = p01 ──────── | |
| ref0 = ds[0].astype(np.float32) | |
| rough = np.empty(T, np.float32) | |
| for s, e in _blocks(T): | |
| rough[s:e] = l2_diff(_gather(ds, idx_map[s:e]), ref0).mean(axis=(1, 2)) | |
| p01_local = pick_p01_reference(rough, P01_SMOOTH_WIN) | |
| ref_index = int(idx_map[p01_local]) | |
| reference = ds[ref_index] | |
| # ── pass 2: metrics against p01 + new-frame flags + encode ────────────── | |
| intensity = np.empty(T, np.float32) | |
| area = np.empty(T, np.float32) | |
| mixed = np.empty(T, np.float32) | |
| is_new = np.empty(T, bool) | |
| tracker = NewFrameTracker() | |
| writer = rgb_writer(out_path) if encode else None | |
| ctx = writer if writer is not None else _NullCtx() | |
| with ctx: | |
| for s, e in _blocks(T): | |
| block = _gather(ds, idx_map[s:e]) # uint8 RGB | |
| i, a, m = contact_metrics(block, reference) | |
| intensity[s:e], area[s:e], mixed[s:e] = i, a, m | |
| is_new[s:e] = tracker.update(block) | |
| if writer is not None: | |
| writer.write(block[..., ::-1]) # RGB -> BGR | |
| return TactileResult(side, intensity, area, mixed, is_new, ref_index, | |
| duplication_stats(is_new)) | |
| class _NullCtx: | |
| def __enter__(self): | |
| return self | |
| def __exit__(self, *a): | |
| return False | |