Melbar
199 lines
7.9 KiB
TOML
199 lines
7.9 KiB
TOML
# =============================================================================
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# AI Trailer Generator v2 — Central Configuration
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# =============================================================================
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# All tunable parameters, thresholds, and file paths are defined here.
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# NO hardcoded values are allowed in the Python source code.
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# =============================================================================
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[project]
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name = "AI Trailer Generator v2"
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version = "2.0.0"
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log_level = "INFO" # DEBUG | INFO | WARNING | ERROR
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# -----------------------------------------------------------------------------
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# [paths] — External video sources (read-only access)
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# -----------------------------------------------------------------------------
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[paths]
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source_movie = "B:/Proxy/BehindTheRedDoor_FTR_1080P_2398_Fixed.mp4"
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reference_trailer = "F:/Encodings/BehindTheRedDoor_Trailer_REFERENCE.mp4"
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# Output destinations (inside project sandbox)
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output_dir = "output"
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cache_dir = ".cache"
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proxy_dir = "proxy"
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# -----------------------------------------------------------------------------
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# [video] — Decode / proxy settings
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# -----------------------------------------------------------------------------
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[video]
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# Target FPS for internal frame extraction (0 = use source FPS)
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extract_fps = 1.0
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# Proxy resolution for template matching (width x height)
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proxy_width = 640
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proxy_height = 360
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# -----------------------------------------------------------------------------
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# [cv] — Computer Vision engine parameters
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# Phase 1 — "Vibe Check" (histogram / perceptual hash scene-level filter)
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# Phase 2 — "Deep Scan" (template matching frame-level precision)
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# -----------------------------------------------------------------------------
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[cv]
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[cv.vibe_check]
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# Number of top candidate scenes to forward to Deep Scan
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top_k_candidates = 100
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# Histogram comparison method:
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# CORREL=0 | CHISQR=1 | INTERSECT=2 | BHATTACHARYYA=3
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hist_compare_method = 0
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# Histogram bins per channel (hue, saturation)
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hist_bins_hue = 50
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hist_bins_saturation = 60
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# pHash similarity threshold (lower = stricter; 0–64 range)
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# NOTE: 12 is for near-duplicate detection. Cross-video matching
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# (trailer vs source movie with different grading/compression)
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# needs 25–35. Start at 32 and tighten if you get false positives.
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phash_max_distance = 32
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# ---- Text-Safe Crop -------------------------------------------------------
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# Fraction of frame height to EXCLUDE from the top (e.g. logos, title cards)
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crop_top_fraction = 0.15
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# Fraction of frame height to EXCLUDE from the bottom (e.g. letterbox, subs)
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crop_bottom_fraction = 0.30
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[cv.deep_scan]
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# Step size in SECONDS between sampled frames during the coarse scan pass
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coarse_step_seconds = 0.5
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# Minimum template match score (0.0–1.0) to accept a candidate as a hit
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match_threshold = 0.65
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# Store/report lower-confidence automatic candidates for visual review instead
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# of dropping them as "NO MATCH". Confirmed exports can still use match_threshold.
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provisional_match_threshold = 0.45
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# Lower gate for entering temporal multi-frame refinement. The final decision
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# still uses sequence/span scoring; this only avoids rejecting real matches
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# because one midpoint frame is weak.
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coarse_candidate_threshold = 0.50
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# Candidate ranking weights. Duration coverage matters when the same visual
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# shot appears multiple times: prefer the occurrence that can cover the beat.
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sequence_score_weight = 0.55
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span_score_weight = 0.15
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coarse_score_weight = 0.10
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duration_score_weight = 0.20
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duration_tie_break_score_delta = 0.03
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min_duration_coverage = 0.65
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continuity_seed_offsets_s = [-1.0, 0.0, 0.5, 1.0, 1.5, 2.0, 3.0]
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scene_seed_top_k = 30
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scene_seed_points_per_scene = 6
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content_rerank_candidate_count = 100
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skip_coarse_scan_with_weighted_seeds = false
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# cv2.matchTemplate method:
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# TM_CCOEFF_NORMED=5 (recommended), TM_CCORR_NORMED=3
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match_method = 5
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# If a coarse hit is found, refine by scanning ± this many seconds
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refine_window_seconds = 0.6
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refine_step_seconds = 0.04 # ≈ 1 frame at 25 fps
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content_align_window_seconds = 0.48
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content_align_sample_step_s = 0.28
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content_validation_weight = 0.35
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provisional_content_threshold = 0.42
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# When several adjacent frame offsets score almost the same, prefer the earlier
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# one. This avoids matches that are visually correct but start a few frames late.
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start_tie_break_score_delta = 0.015
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start_preroll_frames = 0
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# Automatic temporal verification after a coarse image hit.
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# More candidates reduces false positives from visually similar shots.
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sequence_candidate_count = 240
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sequence_min_distance_s = 1.0
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max_refine_candidates = 6
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# Match-span detection: trim when the source starts drifting into a different shot.
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span_sample_step_s = 0.08
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trim_tail_frames = 4
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# If a refined in-point lands this close to a detected scene end, treat it as
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# the next scene. Scene detectors often place cuts a frame or two around the
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# visible boundary.
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scene_boundary_epsilon_s = 0.12
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scoreable_luma_mean_min = 24.0
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scoreable_luma_p90_min = 58.0
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scoreable_contrast_min = 24.0
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# -----------------------------------------------------------------------------
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# [scene_detection] — PySceneDetect parameters (used to segment source movie)
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# -----------------------------------------------------------------------------
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[scene_detection]
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# Threshold for ContentDetector (lower = more sensitive)
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content_threshold = 27.0
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# Minimum scene duration in seconds
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min_scene_duration_s = 1.5
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# -----------------------------------------------------------------------------
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# [whisper] — Dialogue / audio analysis
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# -----------------------------------------------------------------------------
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[whisper]
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model = "large-v3"
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language = "ar"
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device = "cuda" # cuda | cpu
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compute_type = "float16" # float16 | int8 | float32
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# -----------------------------------------------------------------------------
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# [llm] — Used ONLY for thematic segmentation / dramaturgy
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# -----------------------------------------------------------------------------
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[llm]
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provider = "openrouter"
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base_url = "https://openrouter.ai/api/v1"
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model = "google/gemma-4-31b-it"
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timeout_seconds = 120
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temperature = 0.3
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max_tokens = 4096
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# -----------------------------------------------------------------------------
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# [vision] — Optional cached visual descriptions for ambiguous matching
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# -----------------------------------------------------------------------------
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[vision]
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# Disabled by default to avoid surprise API cost. Enable when you want the
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# matcher to ask a vision-capable model for cached 3-frame scene descriptions.
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enabled = false
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provider = "openrouter"
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base_url = "https://openrouter.ai/api/v1"
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model = "google/gemma-4-31b-it"
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timeout_seconds = 90
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temperature = 0.0
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max_tokens = 350
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# Cost controls: per beat, only the top scene-level candidates are described,
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# and cached descriptions in .cache/vision_descriptions.json are reused.
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scene_candidate_top_k = 8
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max_new_descriptions_per_run = 12
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max_seed_scenes = 3
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seed_points_per_scene = 12
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seed_score = 0.88
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max_refine_candidates = 6
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local_scan_step_s = 0.12
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local_scan_max_points_per_scene = 180
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local_scan_top_candidates = 18
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local_scan_tie_break_score_delta = 0.08
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multi_shot_cut_corr_threshold = 0.20
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multi_shot_boundary_tolerance_s = 0.20
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fullscan_fallback = false
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content_threshold = 0.22
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similarity_threshold = 0.18
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# -----------------------------------------------------------------------------
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# [export] — FCPXML / EDL export settings
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# -----------------------------------------------------------------------------
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[export]
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fcpxml_version = "1.10"
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edl_frame_rate = 23.976 # fps used in EDL timecode generation
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output_format = "fcpxml" # fcpxml | edl | both
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