Use motion phase for in-scene timing
This commit is contained in:
@@ -137,6 +137,11 @@ Die Inpoint-Feinjustage bestimmt den Versatz lokal aus dem Bildinhalt: Um einen
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groben Treffer herum werden mehrere Referenzframes gegen mehrere Source-Offsets
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groben Treffer herum werden mehrere Referenzframes gegen mehrere Source-Offsets
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verglichen, und der beste gemeinsame Offset wird übernommen. Das ist schneller
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verglichen, und der beste gemeinsame Offset wird übernommen. Das ist schneller
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als ein erneuter globaler Scan und vermeidet pauschale Frame-Prerolls.
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als ein erneuter globaler Scan und vermeidet pauschale Frame-Prerolls.
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Zusätzlich wird die Bewegungsphase über Frame-zu-Frame-Differenzen verglichen.
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Dadurch kann der Matcher innerhalb derselben Source-Szene unterscheiden, ob
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zwei Figuren noch sprechen, sich annähern, bereits im Kontakt sind oder sich
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wieder voneinander lösen. Ein optisch ähnlicher Standbild-Treffer reicht damit
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nicht mehr aus, wenn der Bewegungsverlauf nicht zur Referenz passt.
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Schwarze Referenzframes aus Blenden oder Titel-Tails werden für diese
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Schwarze Referenzframes aus Blenden oder Titel-Tails werden für diese
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Offset-Messung ausgelassen, damit echte Bildbewegung und nicht die Blende selbst
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Offset-Messung ausgelassen, damit echte Bildbewegung und nicht die Blende selbst
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den Inpoint bestimmt.
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den Inpoint bestimmt.
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@@ -167,8 +172,8 @@ nicht gespeichert, selbst wenn der Low-Level-CV-Score hoch ist.
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Wenn die Szene selbst plausibel ist, aber der konkrete Source-Zeitpunkt diese
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Wenn die Szene selbst plausibel ist, aber der konkrete Source-Zeitpunkt diese
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Aktionsphase verfehlt, sucht der Matcher automatisch dichter innerhalb derselben
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Aktionsphase verfehlt, sucht der Matcher automatisch dichter innerhalb derselben
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Source-Szene nach lokalen Vision-Fenstern mit der passenden Aktion und richtet
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Source-Szene nach lokalen Vision-Fenstern mit der passenden Aktion und richtet
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den Inpoint darauf neu aus. Erst wenn auch diese In-Scene-Reparatur scheitert,
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den Inpoint mit der Motion-Phase-Prüfung darauf neu aus. Erst wenn auch diese
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wird der Treffer verworfen.
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In-Scene-Reparatur scheitert, wird der Treffer verworfen.
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Der gewichtete Vision-Seed-Pfad ersetzt standardmäßig keinen normalen
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Der gewichtete Vision-Seed-Pfad ersetzt standardmäßig keinen normalen
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FFmpeg-Vollscan. Vision-Beschreibungen sind semantische Hinweise, aber keine
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FFmpeg-Vollscan. Vision-Beschreibungen sind semantische Hinweise, aber keine
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Beweise; der volle CV-Scan bleibt deshalb aktiv, damit falsch bewertete
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Beweise; der volle CV-Scan bleibt deshalb aktiv, damit falsch bewertete
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@@ -640,7 +640,7 @@ def _filter_semantically_invalid_vision_matches(results: list, beats: list, cfg)
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from dataclasses import replace
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from dataclasses import replace
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from src.llm.vision_cache import find_action_window_in_scene, validate_match_window_with_vision
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from src.llm.vision_cache import find_action_window_in_scene, validate_match_window_with_vision
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from src.cv.scene_indexer import build_scene_index
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from src.cv.scene_indexer import build_scene_index
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from src.cv.global_scan import align_in_point_by_content
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from src.cv.global_scan import align_in_point_by_content, align_in_point_by_motion
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logger = logging.getLogger(__name__)
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logger = logging.getLogger(__name__)
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beats_by_id = {beat.beat_id: beat for beat in beats}
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beats_by_id = {beat.beat_id: beat for beat in beats}
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@@ -655,12 +655,18 @@ def _filter_semantically_invalid_vision_matches(results: list, beats: list, cfg)
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return None
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return None
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start_s, end_s, semantic_score, reason = found
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start_s, end_s, semantic_score, reason = found
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window_s = max(1.0, min(4.0, (end_s - start_s) * 1.5))
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window_s = max(1.0, min(4.0, (end_s - start_s) * 1.5))
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aligned_in_s, content_score = align_in_point_by_content(
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motion_in_s, motion_score = align_in_point_by_motion(
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check_beat,
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check_beat,
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start_s,
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start_s,
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cfg,
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cfg,
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search_window_s=window_s,
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search_window_s=window_s,
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)
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)
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aligned_in_s, content_score = align_in_point_by_content(
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check_beat,
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motion_in_s,
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cfg,
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search_window_s=min(window_s, 0.8),
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)
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aligned_in_s = max(scene.start_s, min(aligned_in_s, max(scene.start_s, scene.end_s - check_beat.duration_s)))
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aligned_in_s = max(scene.start_s, min(aligned_in_s, max(scene.start_s, scene.end_s - check_beat.duration_s)))
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ok, verify_reason = validate_match_window_with_vision(
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ok, verify_reason = validate_match_window_with_vision(
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check_beat,
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check_beat,
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@@ -679,7 +685,7 @@ def _filter_semantically_invalid_vision_matches(results: list, beats: list, cfg)
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verify_reason,
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verify_reason,
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)
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)
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return None
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return None
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score = max(content_score, min(0.99, semantic_score))
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score = max(content_score, min(0.99, semantic_score * 0.75 + motion_score * 0.25))
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return scene, aligned_in_s, score, f"{reason}; {verify_reason}"
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return scene, aligned_in_s, score, f"{reason}; {verify_reason}"
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kept = []
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kept = []
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+64
-3
@@ -827,6 +827,50 @@ def _motion_phase_score(
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return float((sum(scores) / len(scores)) * 0.65 + min(scores) * 0.35)
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return float((sum(scores) / len(scores)) * 0.65 + min(scores) * 0.35)
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def align_in_point_by_motion(
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beat: TrailerBeat,
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estimated_in_point_s: float,
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cfg: AppConfig,
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search_window_s: float | None = None,
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) -> tuple[float, float]:
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"""
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Align a candidate by matching the frame-to-frame motion pattern.
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This catches the common failure mode where the right source scene is found,
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but the in-point is a few seconds too early or late inside a repeated
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conversation/action beat.
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"""
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motion_templates = _prepare_motion_templates(beat, cfg)
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if len(motion_templates) < 2:
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return estimated_in_point_s, 0.0
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with open_video(cfg.paths.source_movie) as cap:
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fps = float(cap.get(cv2.CAP_PROP_FPS)) or cfg.export.edl_frame_rate
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frame_step_s = 1.0 / fps
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window_s = (
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search_window_s
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if search_window_s is not None
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else cfg.cv.deep_scan.content_align_window_seconds
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)
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start_s = max(0.0, estimated_in_point_s - window_s)
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end_s = estimated_in_point_s + window_s
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tie_delta = cfg.cv.deep_scan.start_tie_break_score_delta
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best_in = estimated_in_point_s
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best_score = -1.0
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t = start_s
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while t <= end_s:
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score = _motion_phase_score(cap, t, motion_templates, cfg)
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if score > best_score + tie_delta:
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best_score = score
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best_in = t
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elif score >= best_score - tie_delta and abs(t - estimated_in_point_s) < abs(best_in - estimated_in_point_s):
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best_in = t
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t = round(t + frame_step_s, 6)
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return best_in, max(0.0, best_score)
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def estimate_usable_source_duration(
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def estimate_usable_source_duration(
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beat: TrailerBeat,
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beat: TrailerBeat,
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in_point_s: float,
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in_point_s: float,
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@@ -1190,6 +1234,7 @@ def run_global_scan(
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for _, coarse_score, in_point_s in reranked_candidates[:refine_limit]
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for _, coarse_score, in_point_s in reranked_candidates[:refine_limit]
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]
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]
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validation_templates = _prepare_validation_templates(b, cfg)
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validation_templates = _prepare_validation_templates(b, cfg)
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motion_templates = _prepare_motion_templates(b, cfg)
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logger.info(
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logger.info(
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'Beat %d: content-reranked top %d / %d candidates.',
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'Beat %d: content-reranked top %d / %d candidates.',
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b.beat_id,
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b.beat_id,
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@@ -1270,6 +1315,16 @@ def run_global_scan(
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if matchable_duration_s > 0 else 0.0
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if matchable_duration_s > 0 else 0.0
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)
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)
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motion_score = 0.0
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if len(motion_templates) >= 2:
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with open_video(cfg.paths.source_movie) as motion_cap:
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motion_score = _motion_phase_score(
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motion_cap,
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adjusted_in_s,
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motion_templates,
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cfg,
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)
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if is_weighted_seed_candidate and scene is not None and content_score >= content_gate:
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if is_weighted_seed_candidate and scene is not None and content_score >= content_gate:
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contiguous_usable_s = _contiguous_scene_coverage_duration(
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contiguous_usable_s = _contiguous_scene_coverage_duration(
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b,
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b,
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@@ -1299,11 +1354,15 @@ def run_global_scan(
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final_score * (1.0 - scan_cfg.content_validation_weight)
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final_score * (1.0 - scan_cfg.content_validation_weight)
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+ content_score * scan_cfg.content_validation_weight
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+ content_score * scan_cfg.content_validation_weight
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)
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)
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if len(motion_templates) >= 2:
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motion_score_clamped = max(0.0, min(1.0, motion_score))
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final_score = final_score * 0.82 + motion_score_clamped * 0.18
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if is_weighted_seed_candidate:
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if is_weighted_seed_candidate:
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vision_provisional_score = (
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vision_provisional_score = (
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content_score * 0.55
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content_score * 0.45
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+ duration_coverage * 0.33
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+ duration_coverage * 0.33
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+ coarse_score * 0.12
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+ coarse_score * 0.12
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+ max(0.0, min(1.0, motion_score)) * 0.10
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)
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)
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final_score = max(final_score, vision_provisional_score)
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final_score = max(final_score, vision_provisional_score)
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if content_score < scan_cfg.match_threshold and not is_weighted_seed_candidate:
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if content_score < scan_cfg.match_threshold and not is_weighted_seed_candidate:
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@@ -1332,7 +1391,7 @@ def run_global_scan(
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if duration_coverage < scan_cfg.min_duration_coverage:
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if duration_coverage < scan_cfg.min_duration_coverage:
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rejected_short_candidates += 1
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rejected_short_candidates += 1
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logger.debug(
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logger.debug(
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'Beat %d short candidate in=%.3fs scene=%s sequence=%.3f span=%.3f coarse=%.3f content=%.3f coverage=%.2f final=%.3f',
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'Beat %d short candidate in=%.3fs scene=%s sequence=%.3f span=%.3f coarse=%.3f content=%.3f motion=%.3f coverage=%.2f final=%.3f',
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b.beat_id,
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b.beat_id,
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adjusted_in_s,
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adjusted_in_s,
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scene.scene_id if scene is not None else 'none',
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scene.scene_id if scene is not None else 'none',
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@@ -1340,6 +1399,7 @@ def run_global_scan(
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span_score,
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span_score,
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coarse_score,
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coarse_score,
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content_score,
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content_score,
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motion_score,
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duration_coverage,
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duration_coverage,
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final_score,
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final_score,
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)
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)
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@@ -1364,7 +1424,7 @@ def run_global_scan(
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continue
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continue
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logger.debug(
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logger.debug(
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'Beat %d candidate in=%.3fs scene=%s sequence=%.3f span=%.3f coarse=%.3f content=%.3f coverage=%.2f final=%.3f',
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'Beat %d candidate in=%.3fs scene=%s sequence=%.3f span=%.3f coarse=%.3f content=%.3f motion=%.3f coverage=%.2f final=%.3f',
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b.beat_id,
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b.beat_id,
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adjusted_in_s,
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adjusted_in_s,
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scene.scene_id if scene is not None else 'none',
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scene.scene_id if scene is not None else 'none',
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@@ -1372,6 +1432,7 @@ def run_global_scan(
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span_score,
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span_score,
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coarse_score,
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coarse_score,
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content_score,
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content_score,
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motion_score,
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duration_coverage,
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duration_coverage,
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final_score,
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final_score,
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)
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)
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