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dd253d4 | 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 | #!/usr/bin/env python3
"""Track cheez-it box + red-cross mark on all 100 success push-box demos (384x384),
saving combined point tracks WITH per-point object labels (0=box, 1=mark).
Source: rise_data_384_pushbox_dualmask videos_small (384 RGB, WM-domain-matched).
Success set = episodes whose meta _src starts with 'tableHF' (the 100 teleop demos).
Loads SAM3 + CoTracker ONCE and loops (no per-video model reload).
Per episode -> <OUT>/<src>/tracks_labeled.npz with:
tracks (T,N,2) float32, vis (T,N) bool, labels (N,) int [0=box,1=mark],
n_box, n_mark, episode_index, src, H, W
Also writes a combined overlay mp4 for the first --n_overlay episodes (spot-check).
"""
import sys, os, json
from pathlib import Path
import numpy as np
import cv2
import torch
import imageio
sys.path.insert(0, "/home/hongyi/PWM/external")
import sam3.model_builder as _mb
from sam3.model_builder import build_sam3_video_predictor
from cotracker.predictor import CoTrackerPredictor
from track_objects_in_video import load_video, sam3_propagate, run_cotracker, COTRACKER_CKPT
EPISODES_JSONL = "/data0/hongyi/rise_data_384_pushbox_dualmask/Push-Box/meta/episodes.jsonl"
VS_ROOT = "/data0/hongyi/rise_data_384_pushbox_dualmask/Push-Box/videos_small"
CAM = "observation.images.top_head"
CHUNK_SIZE = 100
OUT_ROOT = "/data0/hongyi/reward_model_data/3pointr_data_push_box_2d_boxmark"
PROMPTS = [("box", "cheez-it box"), ("mark", "red cross mark")]
STRIDE = 3
N_OVERLAY = 5
def find_video(ep_idx):
chunk = ep_idx // CHUNK_SIZE
return f"{VS_ROOT}/chunk-{chunk:03d}/{CAM}/episode_{ep_idx:06d}.mp4"
def track_one(predictor, cot, tmp_mp4, frames, prompt):
"""Returns (tracks (T,N,2), vis (T,N)) or None if SAM3 finds nothing."""
masks, n_obj = sam3_propagate(predictor, tmp_mp4, prompt)
if masks is None:
return None
masks_arr = np.stack([masks[i] for i in range(len(frames))], axis=0)
if masks_arr[0].any():
init_idx = 0
else:
nz = [i for i in range(len(frames)) if masks_arr[i].any()]
if not nz:
return None
init_idx = nz[0]
tr, vis = run_cotracker(cot, frames, masks_arr[init_idx], STRIDE, init_frame_idx=init_idx)
if tr is None:
return None
return tr, vis
def main():
os.makedirs(OUT_ROOT, exist_ok=True)
# success episodes (tableHF teleop demos)
succ = []
for line in open(EPISODES_JSONL):
d = json.loads(line)
if str(d.get("_src", "")).startswith("tableHF"):
succ.append((int(d["episode_index"]), d["_src"], int(d.get("length", 0))))
succ.sort()
print(f"[build] {len(succ)} success episodes (tableHF)", flush=True)
bpe = str(Path(_mb.__file__).parent / "assets" / "bpe_simple_vocab_16e6.txt.gz")
print("[build] loading SAM3 ...", flush=True)
predictor = build_sam3_video_predictor(bpe_path=bpe, gpus_to_use=[0],
apply_temporal_disambiguation=False)
print("[build] loading CoTracker3 ...", flush=True)
cot = CoTrackerPredictor(checkpoint=COTRACKER_CKPT).cuda()
manifest = []
for k, (ep_idx, src, length) in enumerate(succ):
vid = find_video(ep_idx)
if not os.path.exists(vid):
print(f"[skip] {src}: video missing {vid}", flush=True)
continue
outdir = os.path.join(OUT_ROOT, src)
os.makedirs(outdir, exist_ok=True)
frames = load_video(vid, None) # full episode at 384
H, W = frames[0].shape[:2]
tmp_mp4 = os.path.join(outdir, "input.mp4")
imageio.mimwrite(tmp_mp4, frames, fps=15, macro_block_size=1)
res = {tag: track_one(predictor, cot, tmp_mp4, frames, prompt) for tag, prompt in PROMPTS}
if res["box"] is None:
print(f"[warn] {src}: BOX not detected — skipping", flush=True)
manifest.append({"src": src, "episode_index": ep_idx, "status": "no_box"})
continue
tb, vb = res["box"]
T, nb = tb.shape[0], tb.shape[1]
if res["mark"] is not None:
tm, vm = res["mark"]
nm = tm.shape[1]
tracks = np.concatenate([tb, tm], axis=1).astype(np.float32) # (T, nb+nm, 2)
vis = np.concatenate([vb, vm], axis=1)
labels = np.concatenate([np.zeros(nb, np.int64), np.ones(nm, np.int64)])
mark_ok = True
else:
tracks, vis, labels, nm, mark_ok = tb.astype(np.float32), vb, np.zeros(nb, np.int64), 0, False
print(f"[warn] {src}: MARK not detected — saving box-only", flush=True)
np.savez(os.path.join(outdir, "tracks_labeled.npz"),
tracks=tracks, vis=vis, labels=labels,
n_box=nb, n_mark=nm, episode_index=ep_idx, src=src, H=H, W=W)
# box->mark gap (centroids) for the manifest sanity signal
bc0, bcl = tb[0].mean(0), tb[-1].mean(0)
if mark_ok:
mc = tm[0].mean(0)
d0, dl = float(np.linalg.norm(bc0 - mc)), float(np.linalg.norm(bcl - mc))
else:
d0 = dl = -1.0
manifest.append({"src": src, "episode_index": ep_idx, "status": "ok",
"n_box": nb, "n_mark": nm, "T": T, "mark_ok": mark_ok,
"box_mark_d0": round(d0, 1), "box_mark_dlast": round(dl, 1)})
print(f"[{k+1}/{len(succ)}] {src}: box={nb} mark={nm} T={T} gap {d0:.0f}->{dl:.0f}px", flush=True)
if k < N_OVERLAY:
ov = os.path.join(outdir, "combined_overlay.mp4")
vw = imageio.get_writer(ov, fps=15, macro_block_size=1)
for t in range(T):
im = frames[t].copy()
for pi in range(nb):
x, y = tb[t, pi]; cv2.circle(im, (int(x), int(y)), 1, (0, 255, 0), -1)
if mark_ok:
for pi in range(nm):
x, y = tm[t, pi]; cv2.circle(im, (int(x), int(y)), 1, (255, 0, 0), -1)
vw.append_data(im)
vw.close()
with open(os.path.join(OUT_ROOT, "manifest.json"), "w") as f:
json.dump(manifest, f, indent=2)
ok = sum(1 for m in manifest if m.get("status") == "ok")
mk = sum(1 for m in manifest if m.get("mark_ok"))
print(f"[build] DONE: {ok} ok, {mk} with mark, {len(manifest)-ok} failed. -> {OUT_ROOT}", flush=True)
if __name__ == "__main__":
main()
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