Improve vision matching for dissolve-heavy beats

README.md

@@ -154,6 +154,11 @@ Modell beschrieben. Die Beschreibungen liegen in
 `.cache/vision_descriptions.json` und werden wiederverwendet. Vision erzeugt
 nur zusätzliche Suchanker; der eigentliche Match muss weiterhin durch CV,
 Content-Reranking, Timing und Duration-Coverage bestätigt werden.
+Gecachte Szenenbeschreibungen zählen nur, wenn sie vom aktuell konfigurierten
+Vision-Modell stammen. Bei langen semantisch passenden Source-Szenen beschreibt
+der Vision-Layer zusätzlich wenige lokale Zeitfenster und cached auch diese
+Fenster, damit eine grob ähnliche Szene nicht automatisch mit dem falschen
+Bewegungs- oder Dialogmoment gleichgesetzt wird.
 Der gewichtete Vision-Seed-Pfad ersetzt standardmäßig keinen normalen
 FFmpeg-Vollscan. Vision-Beschreibungen sind semantische Hinweise, aber keine
 Beweise; der volle CV-Scan bleibt deshalb aktiv, damit falsch bewertete
@@ -235,6 +240,9 @@ Bild-/Phasenvalidierung wie der normale Matcher. Nur nicht gelöste Beats fallen
 danach auf den vollständigen Scan zurück. Die Qualitätsparameter für lokale
 Vision-Szenenscans und Refine-Kandidaten bleiben dabei erhalten; der Prepass ist
 eine Reihenfolge-Optimierung, kein Qualitätsdeckel.
+Provisional Treffer aus diesem schnellen Prepass sind nicht endgültig: wenn sie
+unterhalb der Confirmed-Schwelle bleiben, läuft zusätzlich der vollständige
+CV-Scan und darf den besseren oder bestätigten Treffer übernehmen.
 OpenRouter-/Vision-Rate-Limits werden mit progressiv längeren Pausen erneut
 versucht. Billing-, Credit- oder Token-Guthaben-Fehler werden dagegen sofort als
 echter Blocker gemeldet, weil Warten dort nicht hilft.
@@ -278,6 +286,10 @@ auf-/abgeblendete Referenzframes aus Score, Inhalts-Reranking,
 Phasen-Alignment und Motion-Templates herausgenommen. Blenden sollen bestimmen,
 wie der Clip später exportiert wird, aber nicht, ob der Bildinhalt als Match
 gilt.
+Sichtbare Fade-Rampen werden nur in eine matchbare Insel hinein erweitert, wenn
+sie strukturell stark zum ersten bzw. letzten scorebaren Frame derselben
+Einstellung passen. Doppelbelichtungen aus Cross-Dissolves bleiben dadurch
+Übergangsmaterial und werden nicht als einzelner Quellclip erzwungen.
 Treffer unter `provisional_content_threshold` werden gar nicht mehr gespeichert
 oder aus alten Cache-Ergebnissen übernommen. Das verhindert, dass offensichtlich
 falsche Szenen im Report als Match-Kandidat weiterleben.
@@ -399,15 +411,15 @@ scoreable_contrast_min  = 24.0 # Kontrastarme Blenden/Titelinseln ignorieren
 [vision]
 enabled = false # Kostenkontrolle: per CLI mit --vision aktivierbar
 model = "google/gemma-4-31b-it" # Muss ein visionfähiges OpenAI-kompatibles Modell sein
-scene_candidate_top_k = 8 # Nur wenige Top-Szenen pro Beat beschreiben
+scene_candidate_top_k = 48 # Breiter Vision-Kandidatenpool für schwierige Beats
-max_new_descriptions_per_run = 12 # API-Kosten pro Lauf begrenzen
+max_new_descriptions_per_run = 24 # Gecachte Beschreibungen pro Lauf; Rate-Limits bekommen Backoff
-max_seed_scenes = 3 # Nur beste Vision-Szenen als Suchanker weitergeben
+max_seed_scenes = 8 # Mehr Vision-Szenen als Suchanker, kein manueller Override
 seed_points_per_scene = 12 # Inpoint-Samples pro Vision-Szene
 seed_score = 0.88 # Vision-Seeds bekommen mehr Priorität als normale Scene-Seeds
-max_refine_candidates = 6 # Vision-Pfad prüft mehrere Bewegungsphasen derselben Szene
+max_refine_candidates = 12 # Vision-Pfad prüft mehrere Bewegungsphasen derselben Szene
 local_scan_step_s = 0.12 # Dichte lokale Bildsuche in Vision-Szenen
 local_scan_max_points_per_scene = 180 # Laufzeitgrenze pro Source-Szene
-local_scan_top_candidates = 18 # Beste lokale Kandidaten gehen ins Refinement
+local_scan_top_candidates = 36 # Beste lokale Kandidaten gehen ins Refinement
 local_scan_tie_break_score_delta = 0.08 # Ähnliche Vision-Treffer: frühere Phase bevorzugen
 multi_shot_cut_corr_threshold = 0.20 # Interne Trailer-Umschnitte erkennen
 multi_shot_boundary_tolerance_s = 0.20 # Source-Grenze muss zum Trailer-Cut passen

cli.py

@@ -458,7 +458,23 @@ def _find_scene_for_in_point(cfg, in_point_s: float):
 def _reference_scoreable_segments(beat, cfg) -> list[tuple[float, float]]:
     """Find visible source-matchable islands inside a trailer beat."""
     from src.cv.frame_extractor import grab_frame_at_path
-    from src.cv.global_scan import _is_scoreable_reference_frame
+    from src.cv.global_scan import (
+        _corr_same_size,
+        _is_scoreable_reference_frame,
+        _prepare_haystack,
+        _reference_visibility_stats,
+    )
+
+    def is_visible(frame) -> bool:
+        if frame is None:
+            return False
+        mean_luma, p90_luma, contrast = _reference_visibility_stats(frame, cfg)
+        visible_luma = (
+            mean_luma >= cfg.cv.deep_scan.scoreable_luma_mean_min * 0.45
+            or p90_luma >= cfg.cv.deep_scan.scoreable_luma_p90_min * 0.50
+        )
+        visible_contrast = contrast >= max(8.0, cfg.cv.deep_scan.scoreable_contrast_min * 0.30)
+        return visible_luma and visible_contrast
 
     step_s = max(0.08, cfg.cv.deep_scan.span_sample_step_s)
     min_segment_s = max(0.32, step_s * 3.0)
@@ -487,7 +503,46 @@ def _reference_scoreable_segments(beat, cfg) -> list[tuple[float, float]]:
         if end - start >= min_segment_s:
             raw.append((start, end))
 
-    return raw
+    expanded: list[tuple[float, float]] = []
+    same_shot_corr_min = 0.72
+    for start_s, end_s in raw:
+        start_anchor = grab_frame_at_path(beat.trailer_path, beat.start_s + start_s)
+        end_anchor = grab_frame_at_path(beat.trailer_path, beat.start_s + max(start_s, end_s - step_s))
+        start_feature = _prepare_haystack(start_anchor, cfg) if start_anchor is not None else None
+        end_feature = _prepare_haystack(end_anchor, cfg) if end_anchor is not None else None
+
+        soft_start = start_s
+        t = round(start_s - step_s, 6)
+        while t >= 0.0:
+            frame = grab_frame_at_path(beat.trailer_path, beat.start_s + t)
+            if not is_visible(frame):
+                break
+            if start_feature is not None and _corr_same_size(_prepare_haystack(frame, cfg), start_feature) < same_shot_corr_min:
+                break
+            soft_start = max(0.0, t)
+            t = round(t - step_s, 6)
+
+        soft_end = end_s
+        t = round(end_s, 6)
+        while t <= beat.duration_s + 1e-6:
+            frame = grab_frame_at_path(beat.trailer_path, beat.start_s + t)
+            if not is_visible(frame):
+                break
+            if end_feature is not None and _corr_same_size(_prepare_haystack(frame, cfg), end_feature) < same_shot_corr_min:
+                break
+            soft_end = min(beat.duration_s, t + step_s)
+            t = round(t + step_s, 6)
+
+        if soft_end - soft_start >= min_segment_s:
+            expanded.append((soft_start, soft_end))
+
+    merged: list[tuple[float, float]] = []
+    for start_s, end_s in expanded:
+        if merged and start_s - merged[-1][1] <= bridge_gap_s:
+            merged[-1] = (merged[-1][0], max(merged[-1][1], end_s))
+        else:
+            merged.append((start_s, end_s))
+    return merged
 
 
 def _trim_beats_to_single_visual_island(beats: list, cfg) -> tuple[list, dict[int, tuple[float, float]]]:
@@ -555,6 +610,28 @@ def _apply_single_island_segments(results: list, trims: dict[int, tuple[float, f
     return expanded
 
 
+def _merge_best_results(existing: list, candidates: list, cfg) -> list:
+    """Merge matches by beat, preferring confirmed or higher-scoring results."""
+    by_id = {r.beat_id: r for r in existing}
+    for candidate in candidates:
+        old = by_id.get(candidate.beat_id)
+        if old is None:
+            by_id[candidate.beat_id] = candidate
+            continue
+        candidate_confirmed = candidate.match_score >= cfg.cv.deep_scan.match_threshold or candidate.is_confirmed
+        old_confirmed = old.match_score >= cfg.cv.deep_scan.match_threshold or old.is_confirmed
+        if (
+            candidate_confirmed and not old_confirmed
+            or candidate.match_score > old.match_score + cfg.cv.deep_scan.duration_tie_break_score_delta
+            or (
+                candidate.match_score >= old.match_score - cfg.cv.deep_scan.duration_tie_break_score_delta
+                and candidate.duration_s > old.duration_s
+            )
+        ):
+            by_id[candidate.beat_id] = candidate
+    return sorted(by_id.values(), key=lambda r: r.beat_id)
+
+
 def _attach_visual_segments(results: list, beats: list, cfg) -> list:
     """Attach automatic sub-shot matches for multi-island trailer beats."""
     from dataclasses import replace
@@ -657,16 +734,21 @@ def _run_segment_match(segment_beat, continuity, cfg, allow_fullscan: bool = Tru
             seed_in_points=continuity,
         )
         if fast_matches:
-            return fast_matches
+            if not allow_fullscan or all(
+                m.is_confirmed or m.match_score >= cfg.cv.deep_scan.match_threshold
+                for m in fast_matches
+            ):
+                return fast_matches
 
     if not allow_fullscan:
-        return []
+        return fast_matches if cfg.vision.enabled else []
 
-    return run_matching(
+    full_matches = run_matching(
         cfg,
         [segment_beat],
         seed_in_points=continuity,
     )
+    return _merge_best_results(fast_matches if cfg.vision.enabled else [], full_matches, cfg)
 
 
 def _match_unmatched_visual_segments(
@@ -862,9 +944,21 @@ def cmd_match(args: argparse.Namespace, cfg) -> list:
             seed_in_points=seed_in_points,
         )
 
-    if len(results) < len(scan_beats):
+    if len(results) < len(scan_beats) or any(
-        matched_ids = {r.beat_id for r in results}
+        not r.is_confirmed and r.match_score < cfg.cv.deep_scan.match_threshold
-        remaining_beats = [b for b in scan_beats if b.beat_id not in matched_ids]
+        for r in results
+    ):
+        results_by_id = {r.beat_id: r for r in results}
+        remaining_beats = [
+            b for b in scan_beats
+            if (
+                b.beat_id not in results_by_id
+                or (
+                    not results_by_id[b.beat_id].is_confirmed
+                    and results_by_id[b.beat_id].match_score < cfg.cv.deep_scan.match_threshold
+                )
+            )
+        ]
         if remaining_beats:
             full_results = run_matching(
                 cfg,
@@ -872,7 +966,7 @@ def cmd_match(args: argparse.Namespace, cfg) -> list:
                 force_reindex=args.force_reindex,
                 seed_in_points=seed_in_points,
             )
-            results = sorted([*results, *full_results], key=lambda r: r.beat_id)
+            results = _merge_best_results(results, full_results, cfg)
     results = _apply_single_island_segments(results, single_island_trims)
     results = _match_unmatched_visual_segments(
         results,

config.toml

@@ -173,15 +173,15 @@ max_tokens         = 350
 
 # Cost controls: per beat, only the top scene-level candidates are described,
 # and cached descriptions in .cache/vision_descriptions.json are reused.
-scene_candidate_top_k       = 8
+scene_candidate_top_k       = 48
-max_new_descriptions_per_run = 12
+max_new_descriptions_per_run = 24
-max_seed_scenes             = 3
+max_seed_scenes             = 8
 seed_points_per_scene       = 12
 seed_score                  = 0.88
-max_refine_candidates       = 6
+max_refine_candidates       = 12
 local_scan_step_s           = 0.12
 local_scan_max_points_per_scene = 180
-local_scan_top_candidates   = 18
+local_scan_top_candidates   = 36
 local_scan_tie_break_score_delta = 0.08
 multi_shot_cut_corr_threshold = 0.20
 multi_shot_boundary_tolerance_s = 0.20

src/llm/vision_cache.py

@@ -261,6 +261,107 @@ def _scene_seed_points(scene: Scene, max_points: int) -> list[float]:
     return [scene.start_s + step * idx for idx in range(max_points)]
 
 
+def _scene_window_ranges(scene: Scene, beat: TrailerBeat, max_windows: int) -> list[tuple[float, float]]:
+    if max_windows <= 0 or scene.duration_s <= 0:
+        return []
+    window_s = min(scene.duration_s, max(1.0, beat.duration_s))
+    if scene.duration_s <= window_s + 0.2:
+        return [(scene.start_s, min(scene.end_s, scene.start_s + window_s))]
+
+    usable_start = scene.start_s
+    usable_end = max(scene.start_s, scene.end_s - window_s)
+    if max_windows == 1:
+        starts = [usable_start + (usable_end - usable_start) * 0.5]
+    else:
+        step = (usable_end - usable_start) / max(1, max_windows - 1)
+        starts = [usable_start + step * idx for idx in range(max_windows)]
+    return [(start_s, min(scene.end_s, start_s + window_s)) for start_s in starts]
+
+
+def _cached_scene_descriptions(
+    cache: dict,
+    scenes_by_id: dict[int, Scene],
+    cfg: AppConfig,
+) -> list[tuple[Scene, str]]:
+    descriptions: list[tuple[Scene, str]] = []
+    items = cache.get("items", {})
+    if not isinstance(items, dict):
+        return descriptions
+    current_model_marker = f":{cfg.vision.provider}:{cfg.vision.model}:"
+    for key, item in items.items():
+        if current_model_marker not in str(key):
+            continue
+        if not isinstance(item, dict) or item.get("kind") != "scene":
+            continue
+        scene_id = item.get("item_id")
+        if not isinstance(scene_id, int):
+            continue
+        scene = scenes_by_id.get(scene_id)
+        description = item.get("description", "")
+        if scene is not None and isinstance(description, str) and description.strip():
+            descriptions.append((scene, description))
+    return descriptions
+
+
+def _add_window_seed_descriptions(
+    *,
+    beat: TrailerBeat,
+    beat_desc: str,
+    ranked: list[tuple[float, Scene, str]],
+    cfg: AppConfig,
+    cache: dict,
+    budget: list[int],
+    ranked_by_scene: dict[int, tuple[float, Scene, str]],
+) -> list[tuple[float, float]]:
+    points: list[tuple[float, float]] = []
+    if budget[0] <= 0:
+        return points
+
+    scenes_to_probe = ranked[: max(1, cfg.vision.max_seed_scenes)]
+    windows_per_scene = max(1, min(6, cfg.vision.seed_points_per_scene // 2))
+    for _, scene, _ in scenes_to_probe:
+        if budget[0] <= 0:
+            break
+        if scene.duration_s <= max(beat.duration_s * 1.6, 6.0):
+            continue
+        for start_s, end_s in _scene_window_ranges(scene, beat, windows_per_scene):
+            if budget[0] <= 0:
+                break
+            desc = _describe_sample(
+                kind="scene_window",
+                item_id=scene.scene_id,
+                label=f"source scene {scene.scene_id} window {start_s:.2f}-{end_s:.2f}",
+                video_path=scene.source_path,
+                start_s=start_s,
+                end_s=end_s,
+                cfg=cfg,
+                cache=cache,
+                budget=budget,
+            )
+            if not desc:
+                continue
+            score = _text_similarity(beat_desc, desc)
+            if score < cfg.vision.similarity_threshold:
+                continue
+            semantic_score = min(0.99, score + 0.30)
+            existing = ranked_by_scene.get(scene.scene_id)
+            if existing is None or semantic_score > existing[0]:
+                ranked_by_scene[scene.scene_id] = (semantic_score, scene, "window")
+            logger.info(
+                "Beat %d: vision window seed scene=%d start=%.3fs score=%.3f",
+                beat.beat_id,
+                scene.scene_id,
+                start_s,
+                semantic_score,
+            )
+            weighted_score = max(
+                cfg.cv.deep_scan.coarse_candidate_threshold,
+                min(0.99, cfg.vision.seed_score * (0.78 + min(1.0, semantic_score) * 0.22)),
+            )
+            points.append((start_s, weighted_score))
+    return points
+
+
 def build_vision_seed_in_points(
     beats: Sequence[TrailerBeat],
     scenes: Sequence[Scene],
@@ -308,7 +409,7 @@ def build_vision_seed_in_points(
             phash_max_distance=64,
         )
 
-        ranked: list[tuple[float, Scene]] = []
+        ranked_by_scene: dict[int, tuple[float, Scene, str]] = {}
         for hit in hits:
             scene = scenes_by_id.get(hit.scene_id)
             if scene is None:
@@ -328,16 +429,45 @@ def build_vision_seed_in_points(
                 continue
             score = _text_similarity(beat_desc, scene_desc)
             if score >= cfg.vision.similarity_threshold:
-                ranked.append((score, scene))
+                ranked_by_scene[scene.scene_id] = (min(0.99, score + 0.25), scene, "vision")
 
-        ranked.sort(key=lambda item: item[0], reverse=True)
+            # Keep the strongest low-level visual candidates as seeds as well.
-        points: list[tuple[float, float]] = []
+            # Text descriptions can miss timing-specific repeats inside one
-        for score, scene in ranked[:cfg.vision.max_seed_scenes]:
+            # scene; the deep scan still has to validate every seed frame.
+            vibe_score = max(0.0, min(1.0, float(hit.combined_score)))
+            existing = ranked_by_scene.get(scene.scene_id)
+            if existing is None or vibe_score > existing[0]:
+                ranked_by_scene[scene.scene_id] = (vibe_score, scene, "vibe")
+
+        for scene, scene_desc in _cached_scene_descriptions(cache, scenes_by_id, cfg):
+            score = _text_similarity(beat_desc, scene_desc)
+            if score < cfg.vision.similarity_threshold:
+                continue
+            semantic_score = min(0.99, score + 0.25)
+            existing = ranked_by_scene.get(scene.scene_id)
+            if existing is None or semantic_score > existing[0]:
+                ranked_by_scene[scene.scene_id] = (semantic_score, scene, "cache")
+
+        ranked = sorted(ranked_by_scene.values(), key=lambda item: item[0], reverse=True)
+        window_points = _add_window_seed_descriptions(
+            beat=beat,
+            beat_desc=beat_desc,
+            ranked=ranked,
+            cfg=cfg,
+            cache=cache,
+            budget=budget,
+            ranked_by_scene=ranked_by_scene,
+        )
+        ranked = sorted(ranked_by_scene.values(), key=lambda item: item[0], reverse=True)
+        seed_limit = min(len(ranked), max(cfg.vision.max_seed_scenes, cfg.vision.max_seed_scenes * 2))
+        points: list[tuple[float, float]] = [*window_points]
+        for score, scene, source in ranked[:seed_limit]:
             logger.info(
-                "Beat %d: vision seed scene=%d score=%.3f",
+                "Beat %d: vision seed scene=%d score=%.3f source=%s",
                 beat.beat_id,
                 scene.scene_id,
                 score,
+                source,
             )
             weighted_score = max(
                 cfg.cv.deep_scan.coarse_candidate_threshold,