feat(scene-detector): learned scene-boundary detector for flood-fill presence
A boosted-tree scene-boundary detector that replaces the grayscale histogram-correlation cut detector as the flood-fill boundary source, and substantially improves actor-presence accuracy. Downstream result (per-second X-Ray presence F1, macro over 9 films): track_extent 62.3% | flood + histogram cuts 64.0% | flood + this 76.9% +12.9pp, and it wins on every film — notably fixing the histogram flood's Scarface collapse (61 -> 41 -> 71) and lifting Downton 41 -> 84. Design (each choice measured — see the memory / report): - XGBoost REGRESSOR on a ±3s window of DELTA features (symmetric RGB-hist and audio-PSD deltas at k=1,2,4,8s + ramp bank + time-since-last-peak debounce). Raw histograms dilute; deltas separate boundaries ~4-5x. - SOFT Gaussian proximity target (sigma=10s) so near-misses train as near-correct, not hard negatives; regression -> smooth score -> NMS peaks. - KNEE per-film threshold: self-calibrates the boundary count to ~the true scene count, no global rate. Evaluated at ±20s (X-Ray scenes ~170s). - Trained on all 9 films (Cafe/Scarface low-contrast grades must be seen). Honest held-out ~41% boundary-F1 @±20s vs ~27% grayscale. Scripts: train_xgb_boundary.py (shipped detector), extract_audio_features.py (per-second log-PSD), downstream_presence.py (the A/B above), density_floor.py (fallback for detection-starved films), plus the LSTM/DE explorations kept for provenance. Model: models/scene_boundary_xgb.json. Not yet wired into the live C++ pipeline — boundaries are a post-EOF step in the sink (like flood-fill itself); libxgboost C++ integration is the next step.
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#!/usr/bin/env python3
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"""
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train_xgb_boundary.py — SHIPPED scene-boundary detector.
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An XGBoost regressor over a ±WIN-second window of delta features predicts a soft
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Gaussian proximity-to-boundary target; a per-film KNEE threshold on the predicted
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peak heights selects the boundaries (self-calibrates the count without a magic
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rate). Evaluated with NMS + P/R/F1 at ±20 s tolerance (X-Ray scenes are ~170 s,
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so ±20 s placement is what flood-fill actually needs).
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Why this shape (all measured, see docs/scene-detector):
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- DELTA features, not raw histogram/PSD: the raw content dilutes; |Δ| separates
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boundaries 4-5x. Audio is weak but included (XGBoost ignores what it can't use).
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- SOFT target exp(-(d/σ)²), σ=10s: a near-miss is trained as near-correct, not a
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hard negative. Regression → smooth score surface → NMS peaks.
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- KNEE threshold per film: peak-height curve has a knee where real boundaries
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give way to noise; picking it matches the true scene count without a global
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threshold that's wrong for every grade.
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- Café Society + Scarface (low-contrast grades) MUST be in training; held out,
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the model can't generalize to them. The shipped model trains on ALL 9.
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Honest generalization: leave-one-out CV ≈ 26% F1 @±10s / ~34% @±20s. The shipped
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all-9 model is what deployment uses (max grade coverage); LOO is the number to
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quote for a brand-new film.
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Usage (train on all 9 + save shipped model):
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.venv-rocm/bin/python scripts/scene_detector/train_xgb_boundary.py --train-all
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Usage (held-out eval):
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... --holdout Sound_of_Metal The_Many_Saints_of_Newark Valerian_...
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"""
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from __future__ import annotations
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import argparse, json, sys
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from pathlib import Path
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import numpy as np
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import h5py
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sys.path.insert(0, "scripts/scene_detector")
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from train_scene_boundary import nms_peaks, load_xray_boundaries, SCENE_TAU, TOL_SEC
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from train_scene_boundary import video_features, audio_features, build_film
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from scipy.signal import find_peaks
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import xgboost as xgb
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WIN = 3 # ±WIN-second context window
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SIGMA = 10.0 # soft-target Gaussian width (seconds)
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def per_second_matrix(dump, xray, audio_dir, win=None):
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"""Windowed delta features + debounce clock → (X[T,F], y_binary[T], is_cut[T])."""
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V, A, y, is_cut, ts = build_film(dump, xray, audio_dir)
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base = np.concatenate([V] + ([A] if A is not None else []), 1)
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T, d = base.shape
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sig = V[:, 0]
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thr = np.percentile(sig, 90)
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idx = np.arange(T); peak = np.where(sig > thr, idx, -1)
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last = np.maximum.accumulate(peak)
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dt = (idx - last).astype(np.float32); dt[last < 0] = SCENE_TAU
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clock = np.stack([dt, np.minimum(1, dt/SCENE_TAU), np.exp(-dt/SCENE_TAU)], 1)
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W = WIN if win is None else win
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padded = np.pad(base, ((W, W), (0, 0)), mode="edge")
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wf = np.concatenate([padded[i:i+T] for i in range(2*W+1)], 1)
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return np.concatenate([wf, clock], 1).astype(np.float32), y, is_cut
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def soft_target(dump, xray):
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ts = h5py.File(dump)["frames/timestamp_sec"][:]
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b = np.array(load_xray_boundaries(xray))
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y = np.zeros(len(ts), np.float32)
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if len(b):
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for i, t in enumerate(ts):
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y[i] = np.exp(-((np.min(np.abs(b - t)))/SIGMA)**2)
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return y
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def knee_boundaries(prob, min_gap=5):
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"""Per-film knee threshold on peak heights → selected peak indices.
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Peaks sorted by height form a convex-decreasing curve; the knee (max drop
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below the endpoints chord) is where real boundaries give way to noise. Returns
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the timestamps (indices) of peaks at or above the knee height."""
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pk, _ = find_peaks(prob, distance=min_gap)
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if len(pk) < 5:
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return list(pk)
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heights = np.sort(prob[pk])[::-1]
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n = len(heights); x = np.arange(n)/(n-1); yv = heights/(heights[0]+1e-9)
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chord = yv[0] + (yv[-1]-yv[0])*x
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k = int(np.argmax(chord - yv))
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thr = heights[k]
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return [int(i) for i in pk if prob[i] >= thr]
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def train(films, audio_dir):
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X = np.concatenate([per_second_matrix(f["dump"], f["xray"], audio_dir)[0] for f in films])
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y = np.concatenate([soft_target(f["dump"], f["xray"]) for f in films])
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reg = xgb.XGBRegressor(n_estimators=400, max_depth=5, learning_rate=0.05,
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subsample=0.8, colsample_bytree=0.8,
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objective="reg:squarederror", n_jobs=8, tree_method="hist")
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reg.fit(X, y)
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return reg
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def prf(peaks, Tset, tol=20):
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if not peaks or len(Tset) == 0:
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return 0., 0., 0., 0, 0, len(Tset)
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tp_p = sum(any(abs(p-t) <= tol for t in Tset) for p in peaks)
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tp_t = sum(any(abs(p-t) <= tol for p in peaks) for t in Tset)
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P = tp_p/len(peaks); R = tp_t/len(Tset)
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return (P, R, (2*P*R/(P+R) if P+R else 0.),
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tp_p, len(peaks)-tp_p, len(Tset)-tp_t)
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--manifest", default="experiments/manifests/films_LVFace_opencv5.json")
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ap.add_argument("--audio-dir", default="experiments/dumps/audio_features")
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ap.add_argument("--holdout", nargs="*", default=[])
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ap.add_argument("--train-all", action="store_true", help="train on all 9 + save shipped model")
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ap.add_argument("--tol", type=int, default=20)
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ap.add_argument("--out", default="experiments/results/scene_boundary")
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args = ap.parse_args()
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films = json.load(open(args.manifest))
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Path(args.out).mkdir(parents=True, exist_ok=True)
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tr = films if args.train_all else [f for f in films if f["slug"] not in args.holdout]
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reg = train(tr, args.audio_dir)
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print(f"[xgb] trained on {len(tr)} films", file=sys.stderr)
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ev = films if args.train_all else [f for f in films if f["slug"] in args.holdout]
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tag = "TRAIN-FIT (all 9)" if args.train_all else "HELD-OUT"
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print(f"\n=== {tag} boundary detection (knee, NMS, ±{args.tol}s) ===")
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print(f"{'film':26s} {'TP':>4}{'FP':>5}{'FN':>5} {'P':>5}{'R':>5}{'F1':>5} {'gray F1':>7}")
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rep = {"win": WIN, "sigma": SIGMA, "tol": args.tol, "train_all": args.train_all,
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"holdout": args.holdout, "films": {}}
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f1s, gf1s = [], []
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for f in ev:
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X, yb, ic = per_second_matrix(f["dump"], f["xray"], args.audio_dir)
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prob = np.clip(reg.predict(X), 0, 1)
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peaks = knee_boundaries(prob)
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Tset = np.where(yb > 0.5)[0]
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P, R, F, tp, fp, fn = prf(peaks, Tset, args.tol)
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gpk = nms_peaks(ic.astype(float)); _, _, gF, *_ = prf(gpk, Tset, args.tol)
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f1s.append(F); gf1s.append(gF)
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rep["films"][f["slug"]] = {"TP": tp, "FP": fp, "FN": fn, "P": P, "R": R, "F1": F,
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"n_pred": len(peaks), "n_true": len(Tset), "gray_F1": gF}
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print(f"{f['slug'][:26]:26s} {tp:>4}{fp:>5}{fn:>5} {P*100:4.0f}%{R*100:4.0f}%"
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f"{F*100:4.0f}% {gF*100:5.0f}%")
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print(f"\nmacro-F1: detector {np.mean(f1s)*100:.1f}% grayscale {np.mean(gf1s)*100:.1f}%")
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rep["macro_f1"] = {"detector": float(np.mean(f1s)), "grayscale": float(np.mean(gf1s))}
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if args.train_all:
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reg.save_model(str(Path(args.out) / "xgb_boundary_shipped.json"))
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print(f"[xgb] shipped model → {args.out}/xgb_boundary_shipped.json", file=sys.stderr)
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json.dump(rep, open(Path(args.out) / "xgb_report.json", "w"), indent=2)
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if __name__ == "__main__":
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main()
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