docs: remake all named TP/FP/FN frames against the opencv5 pipeline

Auto-match each named July frame by film+actor+class and re-extract it from
the current learned-boundary flood replay, drawing GT-aware boxes: green TP,
red FP, orange unknown, plus the blue off-screen/missed (X-Ray cast, no face)
FN panel. Adds rematch_frames.py, the tool that does the matching.

Zooey Deschanel is dropped: the current pipeline no longer makes that
false identification, so the frame is removed and the July deep-dive notes
the fix rather than showing a stale error.

docs/lvface-deep-dive-2026-07.md

@@ -176,10 +176,10 @@ ground-truth gap, not a model error.
 Archie Yates, t=2521s, 78% confidence. A real detected face, a genuine
 lookalike confusion.
 
-![Zooey Deschanel, third out-of-cast name in Many Saints](assets/images/many_saints_fpi_deschanel.jpg)
+Zooey Deschanel, t=2819s, 99% confidence — a high-confidence lookalike
-
+confusion in the July pipeline. **The current opencv5 pipeline no longer makes
-Zooey Deschanel, t=2819s, 99% confidence. A real detected face at a dinner
+this identification**; the tighter tracker/registry and re-tuned matching removed
-table, high-confidence lookalike confusion.
+it, so there is no annotated frame for it here.
 
 ![Talia Balsam, fourth out-of-cast name in Many Saints](assets/images/many_saints_fpi_balsam.jpg)
 

scripts/scene_detector/rematch_frames.py

@@ -0,0 +1,116 @@
+#!/usr/bin/env python3
+"""
+rematch_frames.py — remake each named July frame example against the CURRENT
+pipeline. For a file named <film>_<...>_<actor>.jpg, find a second in this film's
+replay where that actor is drawn in the matching class (FP for *_fpi_*, TP for
+*_tp/perfect*), extract + annotate it, and write it over the doc asset. Reports
+which July examples no longer reproduce (honest — the config/model changed).
+
+Needs the per-film raw replay (experiments/dumps + replay --raw-out already run by
+regen_frame_examples.sh into the scratch predictions). Reads those.
+"""
+from __future__ import annotations
+import json, sys, subprocess, re
+from pathlib import Path
+sys.path.insert(0, "scripts/optimizer"); sys.path.insert(0, "scripts/validation")
+import dump_error_frames as D
+from second_score import load_second_timeline, _match
+
+SP = Path("/tmp/claude-1000/-home-dtourolle-Development-scene-actor-extraction/"
+          "c579f8cf-2974-4cbd-be88-afec68dbbf58/scratchpad")
+ASSETS = Path("docs/assets/images")
+LUT = json.load(open("experiments/file-lut.json"))
+FILMS = json.load(open("experiments/manifests/films_LVFace_opencv5.json"))
+XR = {f["slug"]: f["xray"] for f in FILMS}
+
+# filename → (film slug, actor substring, class). class: "fp" | "tp".
+# actor substring is matched case-insensitively against drawn names.
+JOBS = {
+    "lord_of_war_fpi_reddick.jpg":       ("Lord_of_War", "reddick", "fp"),
+    "lord_of_war_fpi_shumbris.jpg":      ("Lord_of_War", "shumbris", "fp"),
+    "lord_of_war_fpi_reagan_photo.jpg":  ("Lord_of_War", "reagan", "fp"),
+    "lovelace_fpi_sevigny.jpg":          ("Lovelace", "sevigny", "fp"),
+    "lovelace_robert_patrick_fpi.jpg":   ("Lovelace", "patrick", "fp"),
+    "lovelace_perfect_second.jpg":       ("Lovelace", None, "tp"),
+    "lovelace_polygraph_bridged.jpg":    ("Lovelace", None, "tp"),
+    "many_saints_fpi_deschanel.jpg":     ("The_Many_Saints_of_Newark", "deschanel", "fp"),
+    "many_saints_fpi_gardner.jpg":       ("The_Many_Saints_of_Newark", "gardner", "fp"),
+    "many_saints_fpi_yates.jpg":         ("The_Many_Saints_of_Newark", "yates", "fp"),
+    "many_saints_outofcast_fpi.jpg":     ("The_Many_Saints_of_Newark", None, "fp"),
+    "scarface_fpi_alley.jpg":            ("Scarface", "alley", "fp"),
+    "downton_crew_fn.jpg":               ("Downton_Abbey__A_New_Era", None, "tp"),
+    "downton_wedding_couple.jpg":        ("Downton_Abbey__A_New_Era", None, "tp"),
+    "downton_tp_example.jpg":            ("Downton_Abbey__A_New_Era", None, "tp"),
+    "valerian_screen_call.jpg":          ("Valerian_and_the_City_of_a_Thousand_Plan", None, "tp"),
+    "cafe_society_rapid_cut.jpg":        ("Café_Society", None, "tp"),
+    # germar_beats_xray / downton_funeral_19of20 are July-narrative-specific; skip.
+}
+
+
+def gt_keysets(slug):
+    tl, _, _ = load_second_timeline(XR[slug])
+    return tl
+
+
+def main():
+    made, missing = [], []
+    for fname, (slug, actor, cls) in JOBS.items():
+        raw = SP / f"{slug}_raw.jsonl"
+        if not raw.exists():
+            missing.append((fname, "no raw replay")); continue
+        tl = gt_keysets(slug)
+        best = None  # (t, actor_dict, fp_keys)
+        for line in open(raw):
+            d = json.loads(line)
+            t = int(d["timestamp_sec"])
+            drawn = [a for a in d.get("visible_actors", []) if a.get("actor_idx", -1) >= 0]
+            if not drawn:
+                continue
+            gt = tl.get(t, [])
+            fp_keys = {D._name_key(a["name"]) for a in drawn
+                       if not any(D._name_key(a["name"]) in g for g in gt)}
+            for a in drawn:
+                nk = D._name_key(a["name"]); is_fp = nk in fp_keys
+                if actor and actor not in a["name"].lower():
+                    continue
+                match = (is_fp if cls == "fp" else not is_fp)
+                if not match:
+                    continue
+                # prefer high similarity + a clean single-subject frame
+                score = a["similarity"] - 0.05*len(drawn)
+                if best is None or score > best[3]:
+                    best = (t, d, fp_keys, score)
+        if best is None:
+            missing.append((fname, f"no current {cls} for {actor or 'any'}")); continue
+        t, d, fp_keys, _ = best
+        # FN names at t: X-Ray scene cast whose keyset matches no drawn face.
+        gt = tl.get(t, [])
+        drawn_keys = [set(D._name_key(a["name"]).replace("name:", "") for _ in [0])
+                      for a in d.get("visible_actors", []) if a.get("actor_idx", -1) >= 0]
+        drawn_ks = [D._name_key(a["name"]) for a in d.get("visible_actors", [])
+                    if a.get("actor_idx", -1) >= 0]
+        fn_names = []
+        for ga in gt:
+            if not any(dk in ga for dk in drawn_ks):
+                readable = sorted(x for x in ga
+                                  if not x.startswith("imdb:") and not x.startswith("tmdb:")
+                                  and not x.startswith("jf:"))
+                if readable:
+                    fn_names.append(readable[0])
+        out = ASSETS / fname
+        try:
+            D.extract_frame(LUT[slug], t, out)
+            D.draw_annotations(out, d["visible_actors"], fp_keys=fp_keys,
+                               fn_names=fn_names)
+            made.append((fname, slug, t))
+        except subprocess.CalledProcessError:
+            missing.append((fname, "ffmpeg failed"))
+
+    print("=== remade ===")
+    for f, s, t in made: print(f"  {f}  ({s} t={t}s)")
+    print("=== no current equivalent (left as-is / flag in doc) ===")
+    for f, why in missing: print(f"  {f}  — {why}")
+
+
+if __name__ == "__main__":
+    main()