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93065ec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 | """End-to-end usage example for `ReactSegmentDataset` (the mode2_v1 schema).
Run against a local checkout of the React HF dataset:
python examples/demo_react_segment.py \\
--segments_root processed/mode2_v1/motherboard \\
--tasks_json tasks.json \\
--n_samples 4 \\
--out_dir /tmp/react_segment_demo_out
What this does
--------------
1. Builds `ReactSegmentDataset` — same window-enumeration / filtering API
as `ReactWindowDataset` but operates over pre-sliced clean segments
(no `bad_frames.json` lookup; data is clean by construction).
2. Prints how many contact-rich windows were sampled + how many were
rejected by the contact / motion filters.
3. Renders a static PNG grid of `--n_samples` random windows.
4. For each picked window also writes an H.264 MP4 clip
(`sample_window_NN.mp4`) so you can visually inspect motion.
Self-contained: numpy + torch + Pillow + cv2 (ffmpeg if available, falls
back to cv2.VideoWriter otherwise).
"""
import argparse
import shutil
import subprocess
import sys
from pathlib import Path
import cv2
import numpy as np
import torch
from PIL import Image
sys.path.insert(0, str(Path(__file__).parent))
from react_segment_dataset import ReactSegmentDataset
def _to_hwc(t):
return t.permute(1, 2, 0).numpy() if t.ndim == 3 else t.numpy()
def _view_to_rgb(view_chw_uint8):
return view_chw_uint8.permute(1, 2, 0).numpy()[..., ::-1].copy()
def make_static_grid(ds, sample_indices, out_path: Path, *,
n_cols: int = 6, cell_scale: int = 3) -> None:
rows = []
for idx in sample_indices:
s = ds[idx]
T = s["view"].shape[0]
pick = np.linspace(0, T - 1, n_cols).astype(int)
cells = []
for t in pick:
view = _view_to_rgb(s["view"][t]).astype(np.uint8)
tac_L = _to_hwc(s["tactile_left"][t]).astype(np.uint8)
tac_R = _to_hwc(s["tactile_right"][t]).astype(np.uint8)
triplet = np.concatenate([view, tac_L, tac_R], axis=1)
triplet = cv2.resize(
triplet,
(triplet.shape[1] * cell_scale, triplet.shape[0] * cell_scale),
interpolation=cv2.INTER_NEAREST,
)
cells.append(triplet)
rows.append(np.concatenate(cells, axis=1))
H_row = rows[0].shape[0]
W_row = rows[0].shape[1]
label_h = 88
pad_y = 16
canvas_h = 60 + len(rows) * (H_row + label_h + pad_y)
canvas = np.full((canvas_h, W_row + 20, 3), 245, np.uint8)
cv2.putText(canvas, f"ReactSegmentDataset — {n_cols} evenly-spaced frames per sample (time runs left → right)",
(10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (50, 50, 50), 2, cv2.LINE_AA)
cell_w = W_row // n_cols
for r, idx in enumerate(sample_indices):
s = ds[idx]
y0 = 60 + r * (H_row + label_h + pad_y)
dur_s = float(s["timestamps"][-1] - s["timestamps"][0])
mL = float(s["tactile_left_mixed"].max())
mR = float(s["tactile_right_mixed"].max())
h5a = s.get("h5_frame_start", "—")
cv2.putText(
canvas,
(f"sample #{idx} · {s['source_episode']}/seg{int(s['source_segment_idx']):02d} · "
f"H5 frame {h5a} · ({dur_s:.2f}s) · active: {','.join(s['active_sensors'])} · "
f"peak mixed L={mL:.2f} R={mR:.2f}"),
(10, y0 + 24), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (40, 40, 40), 1, cv2.LINE_AA,
)
T = s["view"].shape[0]
pick = np.linspace(0, T - 1, n_cols).astype(int)
for c, t in enumerate(pick):
cv2.putText(canvas, f"t = {int(t)}",
(10 + c * cell_w + 8, y0 + 56),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, (90, 90, 90), 1, cv2.LINE_AA)
cv2.putText(canvas, "view | tactile_left | tactile_right",
(10, y0 + 76), cv2.FONT_HERSHEY_SIMPLEX, 0.42, (130, 130, 130), 1, cv2.LINE_AA)
canvas[y0 + label_h:y0 + label_h + H_row, 10:10 + W_row] = rows[r]
out_path.parent.mkdir(parents=True, exist_ok=True)
Image.fromarray(canvas).save(out_path)
print(f" grid -> {out_path} ({out_path.stat().st_size / 1024:.1f} KB)")
def _write_mp4_h264(frames_rgb, out_path: Path, fps: float = 15.0) -> None:
H, W = frames_rgb[0].shape[:2]
out_path.parent.mkdir(parents=True, exist_ok=True)
if shutil.which("ffmpeg") is not None:
cmd = ["ffmpeg", "-y", "-hide_banner", "-loglevel", "error",
"-f", "rawvideo", "-pix_fmt", "rgb24",
"-s", f"{W}x{H}", "-r", f"{fps:.3f}",
"-i", "-",
"-c:v", "libx264", "-pix_fmt", "yuv420p",
"-preset", "medium", "-crf", "20",
"-movflags", "+faststart",
"-an", str(out_path)]
proc = subprocess.Popen(cmd, stdin=subprocess.PIPE)
for f in frames_rgb:
assert f.shape == (H, W, 3) and f.dtype == np.uint8
proc.stdin.write(f.tobytes())
proc.stdin.close()
if proc.wait() != 0:
raise RuntimeError("ffmpeg failed")
return
vw = cv2.VideoWriter(str(out_path),
cv2.VideoWriter_fourcc(*"mp4v"), fps, (W, H))
for f in frames_rgb:
vw.write(cv2.cvtColor(f, cv2.COLOR_RGB2BGR))
vw.release()
def make_window_mp4(ds, sample_idx: int, out_path: Path,
*, scale: int = 3, fps: float = 15.0) -> None:
s = ds[sample_idx]
T = s["view"].shape[0]
h5a = s.get("h5_frame_start", "—")
frames = []
for t in range(T):
view = _view_to_rgb(s["view"][t]).astype(np.uint8)
tac_L = _to_hwc(s["tactile_left"][t]).astype(np.uint8)
tac_R = _to_hwc(s["tactile_right"][t]).astype(np.uint8)
triplet = np.concatenate([view, tac_L, tac_R], axis=1)
triplet = cv2.resize(triplet,
(triplet.shape[1] * scale, triplet.shape[0] * scale),
interpolation=cv2.INTER_NEAREST)
H, W, _ = triplet.shape
header = np.full((28, W, 3), 235, np.uint8)
cv2.putText(
header,
f"#{sample_idx} {s['source_episode']}/seg{int(s['source_segment_idx']):02d} "
f"H5 frame {h5a}+{t} ({t+1}/{T}) view | tactile_L | tactile_R",
(8, 19), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (40, 40, 40), 1, cv2.LINE_AA,
)
frames.append(np.concatenate([header, triplet], axis=0))
_write_mp4_h264(frames, out_path, fps=fps)
print(f" mp4 -> {out_path.name} ({out_path.stat().st_size / 1024:.1f} KB)")
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--segments_root", required=True,
help="processed/mode2_v1/motherboard")
ap.add_argument("--tasks_json", default="tasks.json")
ap.add_argument("--n_samples", type=int, default=4)
ap.add_argument("--out_dir", default="/tmp/react_segment_demo_out")
ap.add_argument("--window_length", type=int, default=16)
ap.add_argument("--seed", type=int, default=42)
ap.add_argument("--mp4_fps", type=float, default=15.0)
ap.add_argument("--no_mp4", action="store_true")
ap.add_argument("--no_motion_filter", action="store_true")
args = ap.parse_args()
print("=== Building dataset (segments are clean by construction) ===")
ds = ReactSegmentDataset(
segments_root = args.segments_root,
tasks_json_path= args.tasks_json,
window_length = args.window_length,
stride = 1,
window_step = max(1, args.window_length // 2),
contact_metric = "mixed",
tactile_threshold = 0.4,
min_contact_fraction = 0.5,
which_sensors = "both",
respect_active_sensors= True,
require_motion = not args.no_motion_filter,
min_motion_mps = 0.01,
min_motion_fraction = 0.25,
which_sensors_must_move = "all_active",
)
if len(ds) == 0:
print("No windows passed the filters. Lower thresholds or disable a filter.")
return
rng = np.random.default_rng(args.seed)
pick = rng.choice(len(ds), min(args.n_samples, len(ds)), replace=False)
print(f"\n=== One sample's structure (#{int(pick[0])}) ===")
s0 = ds[int(pick[0])]
for k, v in s0.items():
if isinstance(v, torch.Tensor):
print(f" {k:30s} {tuple(v.shape)} {v.dtype}")
else:
print(f" {k:30s} {v!r}")
out_dir = Path(args.out_dir)
print(f"\n=== Static grid of {len(pick)} random windows ===")
make_static_grid(ds, pick, out_dir / "sample_grid.png")
if not args.no_mp4:
print(f"\n=== Per-window MP4 clips ({len(pick)} files, H.264) ===")
for i, idx in enumerate(pick):
make_window_mp4(ds, int(idx),
out_dir / f"sample_window_{i:02d}.mp4",
fps=args.mp4_fps)
if __name__ == "__main__":
main()
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