diff --git a/CUTTER_REPORT.html b/CUTTER_REPORT.html index c823559..54ce10c 100644 --- a/CUTTER_REPORT.html +++ b/CUTTER_REPORT.html @@ -132,5 +132,4 @@ table.ov tr:hover { background: rgba(255, 255, 255, 0.05); }
-

Cutter & Match Report

Generiert: 2026-05-09 05:09:38Trailer: BehindTheRedDoor_Trailer_REFERENCE.mp4 @ 25.000 fpsSource: BehindTheRedDoor_FTR_1080P_2398_Fixed.mp4 @ 23.976 fps
25 Beats — 23 automatisch (4 bestätigt) — 2 manuell.
Recent Changes:
Retune weak multi-shot segment phases instead of hiding them

Legende

OKBestätigt — direkt in Schnitt-Timeline übernehmen
?Vorläufig — Phase und Aktion im NLE visuell prüfen
MAN.Kein Treffer — manuell suchen oder Schwarzbild einfügen

Übersicht

BeatTrailer TC In–OutDauerSource TC InSceneScoreStatus
0000:00:00:00–00:00:03:003.00s00:00:03:1910.597?
0100:00:03:00–00:00:08:105.40s00:00:04:0910.380?
0200:00:08:10–00:00:16:248.56s00:00:35:0530.761OK
0300:00:16:24–00:00:19:032.16s01:02:19:034360.572?
0400:00:19:03–00:00:20:161.52s01:02:21:094370.728OK
0500:00:20:16–00:00:26:095.72s00:01:33:03100.499?
0600:00:26:09–00:00:29:062.88s00:01:03:0750.396?
0700:00:29:06–00:00:31:172.44s01:20:10:105530.497?
0800:00:31:17–00:00:33:161.96s00:00:51:0750.620?
0900:00:33:16–00:00:36:193.12s01:20:29:035570.674OK
1000:00:36:19–00:00:40:023.32s01:20:35:16558+559+5560.660?
1100:00:40:02–00:00:42:032.04s01:20:40:185590.636?
1200:00:42:03–00:00:50:068.12s01:14:26:06519+130.701OK
1300:00:50:06–00:00:53:213.60s00:43:19:133080.636?
1400:00:53:21–00:00:57:023.24s00:43:24:103090.626?
1500:00:57:02–00:01:01:124.40s00:02:10:0817+3090.621?
1600:01:01:12–00:01:04:123.00s01:05:12:124510.626?
1700:01:04:12–00:01:09:034.64s01:31:18:04623+720.399?
1800:01:09:03–00:01:10:191.64s———MAN.
1900:01:10:19–00:01:12:131.76s00:16:48:131260.403?
2000:01:12:13–00:01:15:143.04s01:27:05:036130.417?
2100:01:15:14–00:01:17:131.96s00:23:55:001750.526?
2200:01:17:13–00:01:19:232.40s01:03:05:114420.544?
2300:01:19:23–00:01:25:145.64s01:04:35:214460.534?
2400:01:25:14–00:01:32:076.72s———MAN.

Beat-Details

Beat 00

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:00:00–00:00:03:00  (3.00s)
Phase logo animation assembling from distorted shapes with motion blur
Bild centered, symmetrical, abstract black void
Source
00:00:03:19–00:00:05:09
Scene 1 · Score 0.597
⚠ Score 0.597 unter 0.65 — visuell prüfen
python cli.py rematch --beat 0

Beat 01

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:03:00–00:00:08:10  (5.40s)
Phase Dynamic motion blur and shifting optical distortions across the text
Bild Centered, symmetrical layout with overlapping circular glass-like distortions, Abstract black void
Source
00:00:04:09–00:00:06:03
Scene 1 · Score 0.380
⚠ Score 0.380 unter 0.65 — visuell prüfen
python cli.py rematch --beat 1

Beat 02

OKBestätigt
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:08:10–00:00:16:24  (8.56s)
Phase drawing a heart shape on a foggy surface
Bild extreme close-up, shifting from profile of a face to a hand interacting with a surface, indoor, obscured glass or foggy surface
Source
00:00:35:05–00:00:43:06
Scene 3 · Score 0.761
python cli.py rematch --beat 2

Beat 03

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:16:24–00:00:19:03  (2.16s)
Phase speaking and smiling slightly
Bild close-up shot, centered face, outdoor rocky environment
Source
01:02:19:03–01:02:20:20
Scene 436 · Score 0.572
⚠ Score 0.572 unter 0.65 — visuell prüfen
python cli.py rematch --beat 3

Beat 04

OKBestätigt
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:19:03–00:00:20:16  (1.52s)
Phase lifting a camera to eye level to take a photo
Bild medium shot, subject centered, shallow depth of field with blurred treeline background, outdoor forest landscape, overcast sky
Source
01:02:21:09–01:02:22:08
Scene 437 · Score 0.728
python cli.py rematch --beat 4

Beat 05

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:20:16–00:00:26:09  (5.72s)
Phase a metallic cylindrical object is brought toward and touches the skin of the forearm
Bild extreme close-up, shallow depth of field, indistinct dark interior
Source
00:01:33:03–00:01:37:09
Scene 10 · Score 0.499
⚠ Score 0.499 unter 0.65 — visuell prüfen
python cli.py rematch --beat 5

Beat 06

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:26:09–00:00:29:06  (2.88s)
Phase hand reaching toward and touching an ornate decorative box
Bild close-up, shallow depth of field, hand entering from left frame, dark interior
Source
00:01:03:07–00:01:05:03
Scene 5 · Score 0.396
⚠ Score 0.396 unter 0.65 — visuell prüfen
python cli.py rematch --beat 6

Beat 07

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:29:06–00:00:31:17  (2.44s)
Phase man appears to be engaged in conversation
Bild man in a light gray sweater and scarf, seated on a white couch, with a window in the background, indoor with a view of the ocean
Source
01:20:10:10–01:20:12:14
Scene 553 · Score 0.497
⚠ Score 0.497 unter 0.65 — visuell prüfen
python cli.py rematch --beat 7

Beat 08

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:31:17–00:00:33:16  (1.96s)
Phase static or slow drifting
Bild close-up, diagonal curve from top-left to bottom-center, dark, indistinct void
Source
00:00:51:07–00:00:53:01
Scene 5 · Score 0.620
⚠ Score 0.620 unter 0.65 — visuell prüfen
python cli.py rematch --beat 8

Beat 09

OKBestätigt
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:33:16–00:00:36:19  (3.12s)
Phase speaking, transitioning from closed eyes to open mouth and focused gaze
Bild medium close-up, subject positioned right of center, profile/three-quarter view, indoor room next to a large window overlooking a blue horizon/sea
Source
01:20:29:03–01:20:32:06
Scene 557 · Score 0.674
python cli.py rematch --beat 9

Beat 10

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:36:19–00:00:40:02  (3.32s)
Phase conversation
Bild alternating close-ups and a medium two-shot, indoor living room with large windows showing a blue exterior landscape
Source
01:20:35:16  (multi-shot)
Scene Scenes 558, 559, 556 · 3 Segmente
  • Seg 1: 01:20:35:16  dur 0.88s  @ off 0.00s  sc 558  score 0.674
  • Seg 2: 01:20:36:14  dur 1.76s  @ off 0.88s  sc 559  score 0.649
  • Seg 3: 01:20:22:14  dur 0.68s  @ off 2.64s  sc 556  score 0.672
python cli.py rematch --beat 10

Beat 11

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:40:02–00:00:42:03  (2.04s)
Phase static talking head with slight facial expression changes
Bild medium close-up, subject positioned right of center, profile/three-quarter view facing left, indoor room with a large window showing a blue sea/horizon background
Source
01:20:40:18–01:20:42:18
Scene 559 · Score 0.636
⚠ Score 0.636 unter 0.65 — visuell prüfen
python cli.py rematch --beat 11

Beat 12

OKBestätigt
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:42:03–00:00:50:06  (8.12s)
Phase static profile shot transitioning to black/darkness
Bild medium close-up, profile view, subject positioned on the right side of the frame, dark outdoor environment, blurred trees in background
Source
01:14:26:06  (multi-shot)
Scene Scenes 519, 13 · 2 Segmente
  • Seg 1: 01:14:26:06  dur 3.52s  @ off 0.16s  sc 519  score 0.721
  • Seg 2: 00:01:47:14  dur 2.88s  @ off 4.88s  sc 13  score 0.676
python cli.py rematch --beat 12

Beat 13

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:50:06–00:00:53:21  (3.60s)
Phase static conversation; woman on right is standing and holding a cup
Bild wide shot, two figures positioned on opposite sides of a round dining table, modern glass-walled sunroom or conservatory overlooking a snowy landscape
Source
00:43:19:13–00:43:23:04
Scene 308 · Score 0.636
⚠ Score 0.636 unter 0.65 — visuell prüfen
python cli.py rematch --beat 13

Beat 14

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:53:21–00:00:57:02  (3.24s)
Phase static conversation, subject holding a white cup
Bild medium shot, subject positioned on the left, vertical window frame dividing the right third of the frame, interior room with a large window overlooking a snowy pine forest
Source
00:43:24:10–00:43:27:16
Scene 309 · Score 0.626
⚠ Score 0.626 unter 0.65 — visuell prüfen
python cli.py rematch --beat 14

Beat 15

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:57:02–00:01:01:12  (4.40s)
Phase static conversation
Bild medium shot, profile view of two characters facing each other, indoor room with a large window overlooking a snowy forest
Source
00:02:10:08  (multi-shot)
Scene Scenes 17, 309 · 2 Segmente
  • Seg 1: 00:02:10:08  dur 2.80s  @ off 0.24s  sc 17  score 0.720
  • Seg 2: 00:45:27:10  dur 1.28s  @ off 3.04s  sc 309  score 0.380
python cli.py rematch --beat 15

Beat 16

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:01:01:12–00:01:04:12  (3.00s)
Phase man reaches out and touches the red door with a small object
Bild medium side profile shot, subject on left, door on right, indoor dim environment, adjacent to a red wooden door
Source
01:05:12:12–01:05:15:07
Scene 451 · Score 0.626
⚠ Score 0.626 unter 0.65 — visuell prüfen
python cli.py rematch --beat 16

Beat 17

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:01:04:12–00:01:09:03  (4.64s)
Phase Static intimacy transitioning to a spatial arrangement of figures
Bild Medium shot, eye-level. First two frames: static shot of couple in bed. Third frame: wide shot of women among white blocks, Bedroom with bedside table and lamp; transition to a white minimalist interior with pedestals
Source
01:31:18:04  (multi-shot)
Scene Scenes 623, 72 · 3 Segmente
  • Seg 1: 01:31:18:04  dur 1.92s  @ off 0.24s  sc 623  score 0.384
  • Seg 2: 00:09:06:13  dur 1.04s  @ off 2.80s  sc 72  score 0.434
  • Seg 3: 00:09:07:18  dur 0.50s  @ off 4.00s  sc 72  score 0.384
⚠ Score 0.399 unter 0.65 — visuell prüfen
python cli.py rematch --beat 17

Beat 18

MAN.Kein Treffer
Trailer 18
— manuell setzen —
Trailer
00:01:09:03–00:01:10:19  (1.64s)
Phase Woman in foreground turns her head from profile to face the camera while speaking
Bild Medium shot, three-quarter profile of woman in foreground left, two women positioned behind her to the right, Indoors, minimalist dark background
— kein automatischer Treffer —
python cli.py rematch --beat 18

Beat 19

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:01:10:19–00:01:12:13  (1.76s)
Phase static conversation, subtle facial expression change
Bild medium close-up, over-the-shoulder shot with a blurred figure in the foreground right, outdoor dark forest or wooded area
Source
00:16:48:13–00:16:49:10
Scene 126 · Score 0.403
⚠ Score 0.403 unter 0.65 — visuell prüfen
python cli.py rematch --beat 19

Beat 20

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:01:12:13–00:01:15:14  (3.04s)
Phase man kisses woman's forehead, then they pull back slightly to face each other
Bild extreme close-up, profile view, faces facing each other, indoor, blurred background
Source
01:27:05:03–01:27:06:00
Scene 613 · Score 0.417
⚠ Score 0.417 unter 0.65 — visuell prüfen
python cli.py rematch --beat 20

Beat 21

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:01:15:14–00:01:17:13  (1.96s)
Phase hand raised to mouth, slight facial movement
Bild extreme close-up, face partially obscured by shadow, dark interior
Source
00:23:55:00–00:23:56:23
Scene 175 · Score 0.526
⚠ Score 0.526 unter 0.65 — visuell prüfen
python cli.py rematch --beat 21

Beat 22

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:01:17:13–00:01:19:23  (2.40s)
Phase man looks up and speaks, transitioning from downward gaze to forward gaze
Bild close-up, profile to three-quarter view, outdoor rocky environment, blurred background
Source
01:03:05:11–01:03:07:07
Scene 442 · Score 0.544
⚠ Score 0.544 unter 0.65 — visuell prüfen
python cli.py rematch --beat 22

Beat 23

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:01:19:23–00:01:25:14  (5.64s)
Phase static posture, head tilted upwards looking at the sky
Bild medium shot, subject positioned on the left third, facing away from camera towards the water, rocky shoreline next to a body of water under an overcast sky
Source
01:04:35:21–01:04:41:02
Scene 446 · Score 0.534
⚠ Score 0.534 unter 0.65 — visuell prüfen
python cli.py rematch --beat 23

Beat 24

MAN.Kein Treffer
Trailer 24
— manuell setzen —
Trailer
00:01:25:14–00:01:32:07  (6.72s)
— kein automatischer Treffer —
python cli.py rematch --beat 24
- +

Cutter & Match Report

Generiert: 2026-05-09 05:42:40Trailer: BehindTheRedDoor_Trailer_REFERENCE.mp4 @ 25.000 fpsSource: BehindTheRedDoor_FTR_1080P_2398_Fixed.mp4 @ 23.976 fps
25 Beats — 23 automatisch (4 bestätigt) — 2 manuell.
Recent Changes:
Retune weak multi-shot segment phases

Legende

OKBestätigt — direkt in Schnitt-Timeline übernehmen
?Vorläufig — Phase und Aktion im NLE visuell prüfen
MAN.Kein Treffer — manuell suchen oder Schwarzbild einfügen

Übersicht

BeatTrailer TC In–OutDauerSource TC InSceneScoreStatus
0000:00:00:00–00:00:03:003.00s00:00:03:1910.597?
0100:00:03:00–00:00:08:105.40s00:00:04:0910.380?
0200:00:08:10–00:00:16:248.56s00:00:35:0530.761OK
0300:00:16:24–00:00:19:032.16s01:02:19:034360.572?
0400:00:19:03–00:00:20:161.52s01:02:21:094370.728OK
0500:00:20:16–00:00:26:095.72s00:01:33:03100.499?
0600:00:26:09–00:00:29:062.88s00:01:03:0750.396?
0700:00:29:06–00:00:31:172.44s01:20:10:105530.497?
0800:00:31:17–00:00:33:161.96s00:00:51:0750.620?
0900:00:33:16–00:00:36:193.12s01:20:29:035570.674OK
1000:00:36:19–00:00:40:023.32s01:20:35:16558+559+5560.660?
1100:00:40:02–00:00:42:032.04s01:20:40:185590.636?
1200:00:42:03–00:00:50:068.12s01:14:26:06519+130.701OK
1300:00:50:06–00:00:53:213.60s00:43:19:133080.636?
1400:00:53:21–00:00:57:023.24s00:43:24:163090.626?
1500:00:57:02–00:01:01:124.40s00:02:10:0817+3090.706?
1600:01:01:12–00:01:04:123.00s01:05:12:124510.626?
1700:01:04:12–00:01:09:034.64s01:31:18:04623+720.399?
1800:01:09:03–00:01:10:191.64s———MAN.
1900:01:10:19–00:01:12:131.76s00:16:48:131260.403?
2000:01:12:13–00:01:15:143.04s01:27:05:036130.417?
2100:01:15:14–00:01:17:131.96s00:23:55:001750.526?
2200:01:17:13–00:01:19:232.40s01:03:05:114420.544?
2300:01:19:23–00:01:25:145.64s01:04:35:214460.534?
2400:01:25:14–00:01:32:076.72s———MAN.

Beat-Details

Beat 00

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:00:00–00:00:03:00  (3.00s)
Phase logo animation assembling from distorted shapes with motion blur
Bild centered, symmetrical, abstract black void
Source
00:00:03:19–00:00:05:09
Scene 1 · Score 0.597
⚠ Score 0.597 unter 0.65 — visuell prüfen
python cli.py rematch --beat 0

Beat 01

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:03:00–00:00:08:10  (5.40s)
Phase Dynamic motion blur and shifting optical distortions across the text
Bild Centered, symmetrical layout with overlapping circular glass-like distortions, Abstract black void
Source
00:00:04:09–00:00:06:03
Scene 1 · Score 0.380
⚠ Score 0.380 unter 0.65 — visuell prüfen
python cli.py rematch --beat 1

Beat 02

OKBestätigt
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:08:10–00:00:16:24  (8.56s)
Phase drawing a heart shape on a foggy surface
Bild extreme close-up, shifting from profile of a face to a hand interacting with a surface, indoor, obscured glass or foggy surface
Source
00:00:35:05–00:00:43:06
Scene 3 · Score 0.761
python cli.py rematch --beat 2

Beat 03

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:16:24–00:00:19:03  (2.16s)
Phase speaking and smiling slightly
Bild close-up shot, centered face, outdoor rocky environment
Source
01:02:19:03–01:02:20:20
Scene 436 · Score 0.572
⚠ Score 0.572 unter 0.65 — visuell prüfen
python cli.py rematch --beat 3

Beat 04

OKBestätigt
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:19:03–00:00:20:16  (1.52s)
Phase lifting a camera to eye level to take a photo
Bild medium shot, subject centered, shallow depth of field with blurred treeline background, outdoor forest landscape, overcast sky
Source
01:02:21:09–01:02:22:08
Scene 437 · Score 0.728
python cli.py rematch --beat 4

Beat 05

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:20:16–00:00:26:09  (5.72s)
Phase a metallic cylindrical object is brought toward and touches the skin of the forearm
Bild extreme close-up, shallow depth of field, indistinct dark interior
Source
00:01:33:03–00:01:37:09
Scene 10 · Score 0.499
⚠ Score 0.499 unter 0.65 — visuell prüfen
python cli.py rematch --beat 5

Beat 06

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:26:09–00:00:29:06  (2.88s)
Phase hand reaching toward and touching an ornate decorative box
Bild close-up, shallow depth of field, hand entering from left frame, dark interior
Source
00:01:03:07–00:01:05:03
Scene 5 · Score 0.396
⚠ Score 0.396 unter 0.65 — visuell prüfen
python cli.py rematch --beat 6

Beat 07

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:29:06–00:00:31:17  (2.44s)
Phase man appears to be engaged in conversation
Bild man in a light gray sweater and scarf, seated on a white couch, with a window in the background, indoor with a view of the ocean
Source
01:20:10:10–01:20:12:14
Scene 553 · Score 0.497
⚠ Score 0.497 unter 0.65 — visuell prüfen
python cli.py rematch --beat 7

Beat 08

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:31:17–00:00:33:16  (1.96s)
Phase static or slow drifting
Bild close-up, diagonal curve from top-left to bottom-center, dark, indistinct void
Source
00:00:51:07–00:00:53:01
Scene 5 · Score 0.620
⚠ Score 0.620 unter 0.65 — visuell prüfen
python cli.py rematch --beat 8

Beat 09

OKBestätigt
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:33:16–00:00:36:19  (3.12s)
Phase speaking, transitioning from closed eyes to open mouth and focused gaze
Bild medium close-up, subject positioned right of center, profile/three-quarter view, indoor room next to a large window overlooking a blue horizon/sea
Source
01:20:29:03–01:20:32:06
Scene 557 · Score 0.674
python cli.py rematch --beat 9

Beat 10

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:36:19–00:00:40:02  (3.32s)
Phase conversation
Bild alternating close-ups and a medium two-shot, indoor living room with large windows showing a blue exterior landscape
Source
01:20:35:16  (multi-shot)
Scene Scenes 558, 559, 556 · 3 Segmente
  • Seg 1: 01:20:35:16  dur 0.88s  @ off 0.00s  sc 558  score 0.674
  • Seg 2: 01:20:36:14  dur 1.76s  @ off 0.88s  sc 559  score 0.649
  • Seg 3: 01:20:22:14  dur 0.68s  @ off 2.64s  sc 556  score 0.672
python cli.py rematch --beat 10

Beat 11

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:40:02–00:00:42:03  (2.04s)
Phase static talking head with slight facial expression changes
Bild medium close-up, subject positioned right of center, profile/three-quarter view facing left, indoor room with a large window showing a blue sea/horizon background
Source
01:20:40:18–01:20:42:18
Scene 559 · Score 0.636
⚠ Score 0.636 unter 0.65 — visuell prüfen
python cli.py rematch --beat 11

Beat 12

OKBestätigt
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:42:03–00:00:50:06  (8.12s)
Phase static profile shot transitioning to black/darkness
Bild medium close-up, profile view, subject positioned on the right side of the frame, dark outdoor environment, blurred trees in background
Source
01:14:26:06  (multi-shot)
Scene Scenes 519, 13 · 2 Segmente
  • Seg 1: 01:14:26:06  dur 3.52s  @ off 0.16s  sc 519  score 0.721
  • Seg 2: 00:01:47:14  dur 2.88s  @ off 4.88s  sc 13  score 0.676
python cli.py rematch --beat 12

Beat 13

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:50:06–00:00:53:21  (3.60s)
Phase static conversation; woman on right is standing and holding a cup
Bild wide shot, two figures positioned on opposite sides of a round dining table, modern glass-walled sunroom or conservatory overlooking a snowy landscape
Source
00:43:19:13–00:43:23:04
Scene 308 · Score 0.636
⚠ Score 0.636 unter 0.65 — visuell prüfen
python cli.py rematch --beat 13

Beat 14

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:53:21–00:00:57:02  (3.24s)
Phase static conversation, subject holding a white cup
Bild medium shot, subject positioned on the left, vertical window frame dividing the right third of the frame, interior room with a large window overlooking a snowy pine forest
Source
00:43:24:16–00:43:27:19
Scene 309 · Score 0.626
⚠ Score 0.626 unter 0.65 — visuell prüfen
python cli.py rematch --beat 14

Beat 15

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:00:57:02–00:01:01:12  (4.40s)
Phase static conversation
Bild medium shot, profile view of two characters facing each other, indoor room with a large window overlooking a snowy forest
Source
00:02:10:08  (multi-shot)
Scene Scenes 17, 309 · 2 Segmente
  • Seg 1: 00:02:10:08  dur 2.80s  @ off 0.24s  sc 17  score 0.720
  • Seg 2: 00:43:26:20  dur 1.28s  @ off 3.04s  sc 309  score 0.674
python cli.py rematch --beat 15

Beat 16

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:01:01:12–00:01:04:12  (3.00s)
Phase man reaches out and touches the red door with a small object
Bild medium side profile shot, subject on left, door on right, indoor dim environment, adjacent to a red wooden door
Source
01:05:12:12–01:05:15:07
Scene 451 · Score 0.626
⚠ Score 0.626 unter 0.65 — visuell prüfen
python cli.py rematch --beat 16

Beat 17

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:01:04:12–00:01:09:03  (4.64s)
Phase Static intimacy transitioning to a spatial arrangement of figures
Bild Medium shot, eye-level. First two frames: static shot of couple in bed. Third frame: wide shot of women among white blocks, Bedroom with bedside table and lamp; transition to a white minimalist interior with pedestals
Source
01:31:18:04  (multi-shot)
Scene Scenes 623, 72 · 3 Segmente
  • Seg 1: 01:31:18:04  dur 1.92s  @ off 0.24s  sc 623  score 0.384
  • Seg 2: 00:09:06:13  dur 1.04s  @ off 2.80s  sc 72  score 0.434
  • Seg 3: 00:09:07:18  dur 0.50s  @ off 4.00s  sc 72  score 0.384
⚠ Score 0.399 unter 0.65 — visuell prüfen
python cli.py rematch --beat 17

Beat 18

MAN.Kein Treffer
Trailer 18
— manuell setzen —
Trailer
00:01:09:03–00:01:10:19  (1.64s)
Phase Woman in foreground turns her head from profile to face the camera while speaking
Bild Medium shot, three-quarter profile of woman in foreground left, two women positioned behind her to the right, Indoors, minimalist dark background
— kein automatischer Treffer —
python cli.py rematch --beat 18

Beat 19

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:01:10:19–00:01:12:13  (1.76s)
Phase static conversation, subtle facial expression change
Bild medium close-up, over-the-shoulder shot with a blurred figure in the foreground right, outdoor dark forest or wooded area
Source
00:16:48:13–00:16:49:10
Scene 126 · Score 0.403
⚠ Score 0.403 unter 0.65 — visuell prüfen
python cli.py rematch --beat 19

Beat 20

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:01:12:13–00:01:15:14  (3.04s)
Phase man kisses woman's forehead, then they pull back slightly to face each other
Bild extreme close-up, profile view, faces facing each other, indoor, blurred background
Source
01:27:05:03–01:27:06:00
Scene 613 · Score 0.417
⚠ Score 0.417 unter 0.65 — visuell prüfen
python cli.py rematch --beat 20

Beat 21

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:01:15:14–00:01:17:13  (1.96s)
Phase hand raised to mouth, slight facial movement
Bild extreme close-up, face partially obscured by shadow, dark interior
Source
00:23:55:00–00:23:56:23
Scene 175 · Score 0.526
⚠ Score 0.526 unter 0.65 — visuell prüfen
python cli.py rematch --beat 21

Beat 22

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:01:17:13–00:01:19:23  (2.40s)
Phase man looks up and speaks, transitioning from downward gaze to forward gaze
Bild close-up, profile to three-quarter view, outdoor rocky environment, blurred background
Source
01:03:05:11–01:03:07:07
Scene 442 · Score 0.544
⚠ Score 0.544 unter 0.65 — visuell prüfen
python cli.py rematch --beat 22

Beat 23

?Vorläufig
▶ Trailer  /  ▶ Source (Frame-Locked Compare)
Trailer
00:01:19:23–00:01:25:14  (5.64s)
Phase static posture, head tilted upwards looking at the sky
Bild medium shot, subject positioned on the left third, facing away from camera towards the water, rocky shoreline next to a body of water under an overcast sky
Source
01:04:35:21–01:04:41:02
Scene 446 · Score 0.534
⚠ Score 0.534 unter 0.65 — visuell prüfen
python cli.py rematch --beat 23

Beat 24

MAN.Kein Treffer
Trailer 24
— manuell setzen —
Trailer
00:01:25:14–00:01:32:07  (6.72s)
— kein automatischer Treffer —
python cli.py rematch --beat 24
diff --git a/CUTTER_REPORT.md b/CUTTER_REPORT.md index 902e111..01eade5 100644 --- a/CUTTER_REPORT.md +++ b/CUTTER_REPORT.md @@ -1,6 +1,6 @@ # Cutter-Report — manuelles Nachschneiden -Generiert: **2026-05-08 12:46:56** +Generiert: **2026-05-09 05:42:40** - **Trailer**: `BehindTheRedDoor_Trailer_REFERENCE.mp4` @ 25.000 fps - **Source** : `BehindTheRedDoor_FTR_1080P_2398_Fixed.mp4` @ 23.976 fps @@ -35,8 +35,8 @@ Trailer-TC in Trailer-Framerate, Source-TC in Source-Framerate. | 11 | 00:00:40:02 – 00:00:42:03 | 01:20:40:18 – 01:20:42:18 | 559 | 0.636 | ? | | 12 | 00:00:42:03 – 00:00:50:06 | 01:14:26:06 – 01:14:29:18 | 519 (+1) | 0.701 | OK | | 13 | 00:00:50:06 – 00:00:53:21 | 00:43:19:13 – 00:43:23:04 | 308 | 0.636 | ? | -| 14 | 00:00:53:21 – 00:00:57:02 | 00:43:30:23 – 00:43:34:02 | 309 | 0.444 | ? | -| 15 | 00:00:57:02 – 00:01:01:12 | 00:02:10:08 – 00:02:13:03 | 17 (+1) | 0.650 | ? | +| 14 | 00:00:53:21 – 00:00:57:02 | 00:43:24:16 – 00:43:27:19 | 309 | 0.626 | ? | +| 15 | 00:00:57:02 – 00:01:01:12 | 00:02:10:08 – 00:02:13:03 | 17 (+1) | 0.706 | ? | | 16 | 00:01:01:12 – 00:01:04:12 | 01:05:12:12 – 01:05:15:07 | 451 | 0.626 | ? | | 17 | 00:01:04:12 – 00:01:09:03 | 01:31:18:04 – 01:31:20:02 | 623 (+2) | 0.399 | ? | | 18 | 00:01:09:03 – 00:01:10:19 | — – — | — | — | MAN. | @@ -201,7 +201,7 @@ Trailer-TC in Trailer-Framerate, Source-TC in Source-Framerate. - **Trailer**: 00:00:36:19 – 00:00:40:02 (3.32 s) - **Source** : 01:20:35:16 – 01:20:36:13 (scenes 558, 559, 556 (3 Segmente), score 0.660) - Seg 1: TC 01:20:35:16 dur 0.88s @ Trailer-Offset 0.00s (scene 558) - - Seg 2: TC 01:20:36:13 dur 1.76s @ Trailer-Offset 0.88s (scene 559) + - Seg 2: TC 01:20:36:14 dur 1.76s @ Trailer-Offset 0.88s (scene 559) - Seg 3: TC 01:20:22:14 dur 0.68s @ Trailer-Offset 2.64s (scene 556) - **Rematch**: `python cli.py rematch --beat 10` - **Phase**: conversation @@ -262,8 +262,8 @@ Trailer-TC in Trailer-Framerate, Source-TC in Source-Framerate. ### Beat 14 — ? / Vorläufig - **Trailer**: 00:00:53:21 – 00:00:57:02 (3.24 s) -- **Source** : 00:43:30:23 – 00:43:34:02 (scene 309, score 0.444) -- ⚠ Score 0.444 unter 0.65 — visuell prüfen +- **Source** : 00:43:24:16 – 00:43:27:19 (scene 309, score 0.626) +- ⚠ Score 0.626 unter 0.65 — visuell prüfen - **Rematch**: `python cli.py rematch --beat 14` - **Phase**: static conversation, subject holding a white cup - **Bild**: medium shot, subject positioned on the left, vertical window frame dividing the right third of the frame, interior room with a large window overlooking a snowy pine forest @@ -272,14 +272,14 @@ Trailer-TC in Trailer-Framerate, Source-TC in Source-Framerate. | Trailer | Source | |:---:|:---:| -| ![Trailer 14](data:image/jpeg;base64,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) 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) | +| ![Trailer 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) 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K9bj20h9Um8mmUwwtRsiMr9O5TzKSXAKh0KVy7p3F+RhzXPc3a+U8xEMQjERYg506eN68brZHKu0T0b6dJpoDeJiXJBY1paYsoKg0IrcxKBfRRy9Q/H4hJgCK1HDhdT5jzKUxGE5JWlPvWp47FrBDrIqB0uvnmcc0b0hUmlKtwuJ1VXRcI/QQ2kG18qowbUxXLLKtTbJTc19lm4FkWakm1Mul88dnQIPNy+jloz0jHDjxKdP18LpB3b6zfaN+gelTKX5X3Qv+1sRTiNKdDQU8qmt0BvWb6z/aNxy/I1hlC36MNp9IOUN0xsB+Zv6MxWLZ17rU6383ej5pzrlh6YyH337u7E5Xr+v5MP2MuPsa7VBzrY+pgTS5532hqVIvrMAOufG3Y5GjzRiDxt0gAUtqlbQ0K4xDNEKmtON4mKYVFiowCUNy7NQKk0tIohNKwk1bZWI0pkBzJp02t3E5ioqbTw1BlaUkPrQ5HazYmRVIFa/K0sml92xXY72SqsNkNTbZlK5VsdpMhbDMbQ0GtLXe2tXGxNdb3V42hHg2dbbdqm46r4d7O0Bwelug1scjO3tLaa02sgJKxVrbkPreR91x1XCTY+R91gSk1YDLoLIz0A260WqhFBkLg6Mg3BF8NG9jWvOo3ut+kja8Rhzq1HsWr567X1aj53XPSJg0+Hp/qm+3cT0XHYgi7ZB/5fX9G/8VrqYu1Yf/QC/Uf+K1ji2/Yqfj3RYeV8ynweHgWNmWsMdace6LLbmGMk1UJev3gLRMOCcPhyP9TH9kXbOU+j/MeYoHhKon3mvoj2ZjLqssRKyM2qUbcBYWNxJBIyy2HC1rmUBwUsmHkcNIh75XYHp7Lp+JcEs7bDYdT4+F8/LLNG0EmJ2Kl3P9TacZMvncsRIW342KDU3gahCHO3negHU+62EGfwtxkJck8LAQmHJfrH3Wq4eSgtNjIAFbNili62hFYODYWKi/FjYdafG30YDOuV7JLE6AipZTWgHS68CW3kOKg5jA0E3dCll+Gx9o3tGxmG+i4lo9QZa1VuBFz5FAYnm3Iaug8DUV+YvsUO+VNDTY1vp43cM+DnmvyHYcThRpGmnU03Nxh7L6VJVmVSO7p42AGZa1UEbeVyjlMTq4O3uvXtogsMJ1kB9RAPdFK/G1yJInppjUGlK5Vusx4tKa2JoD6uxNqD8zWSAiAFHrRVAqfbeqkjOSYXjsLg44/3TFvvLvXxtJGJonZ0pStKivxtwYfmWIXM6NVK6jvbo5ZInrFnPgQBbl6VCsbZXpxuWUVPECliVpaviuX4palh3RaWoFaeN4SRotEVDdPbahA0qmkZYGorTpaphIVkh092ouWDikTGlO6MgQT0ulC6A5UWrAlmiGpmY0G+9m0tqNBGu9vlgFqTtfRo5m8g+KRZIWRqZjjdTgjSN2Rk1itKg2VzLmUcxAR3TSduBtDUnUSJON8/JPOjWKY/iCseSioqRW4RRlmORIAzFwJBbvPT9turIsY06jU8eNLzVbZoJHP9ITl2n/5q/Z43SH9d/rv9o3dPSBY0HLygejYtTqO3dXMdKmuV0pvXf67/AGjccmzWGhW9Hxq53ywdcZAP86/d5SkLaWdQRwvwXkev/K/LtHrfS4NPnqv2SVoi5+keuOPD43r+u6UjLmVtezFCTFxwLqJHsuQmZ0D50IrdckxEb6Y0XUA2RPHwtdXEK8YSoBA26C+lO2YONGSS3gPG2agm3Sw4XQR1Xrlb5ch11C04HjcxKWbOwQ0EYiVQMhvaXXvGynoV8bAJo1oUSdqW0WvmNb1FJ4VslIlqyvNV6wotLbpS0JMXrbXCtRfDO0Tgc7lXTbbX1a2iOB87nrahz9lji9LUtAODK+bNW8j7rzVUXA+ofI+62xKvr1AeQucboufGyJ5cN2cAVRVQK042K8oYbAC+V4NtmSMspzAF1P0iCifD0/1Tfbu2RRg5vUC6r6Sqq4jD6TX8J/t3lP4lx2V0XacN/oFfqP8AxWuq3acN/oNfqv8AxTcce37FT8e4rYZW+i4dh/qkp/dF2flPOeYcuXRHJRczmK3XMBWTDYcCuUSGnstTZuxiq3rH1V4n+jxva6Rn1UvqJ+Nndu0kc6pJWZix4k5k3VcU+piK1A+Z62q4yQlizHJR7LRJ2ovnVj7b5JPJ0RVAZUMc+ls6RbsebHwvXsFD0KWWF1BhxA94tmuig8qfC3YT+Ifh8LAaBdUmyjhxuIE1akUtZjVSeFymVQtbISOG7RsNLp9cU36cbB7PEqT32oa/C1HBSaZR0ORHUWbioggNOHuNnwEW+WTaYUVjQFVz4KwPHoDcsfhmjfX+Xr4Wn8umLLppWnuNr88RODzyzBUV4f09L14neDDkQi4iI0EiBqHrYIBCNXcGt2yHlryx17RkBpwqLH5jy/ssM0moORxpQ0vpcHVmHdaIctmwYhpLEXbgab+VmrLEh1RDRX8pGdgRTSSYVB3UoNI0gEm1HA4LSNUh1Vzod/bei0gM0YuZxVTXPZsrOD5Lq059Gt11AQgKPKloUsM0VXdaj7o4XeUZqmFczY9nRHJHEDO6hTU9LPabEwGqg0P3haeZG7QtTM3hN2apUhSi1qQNVOudnRynDyJIa5Zey0OGbM1NkduGNC+V5W0NNl0g5x2506PbW1mORSp1EUofK/PI5RqAVq2b28qtpUtpPjekeZ+cmcorwiWNjwrysUckknIDKtuLy/QofswO7XvHfxtooiMGUgMdq9bckxEgiCM3q18zXh5XPb6FKyE2JXSUaJTtmBtZkcuAlUHs8h62WfkLRNerLibn2kgiCtpGgGlBQmvWzHk9SqAvSSVGjwSomlfpsRXPOmlvZW6I/rv9d/tG7TzWR2jwynVQY2FuFK0Izrn8Lqz+u/13+0bx5Hbs1hoN5WzJzLAstajFQ0pvXVwv17RjsTEcPHAQpOoySDSa35PyJxFznlrtsuNw5P8AfF+4YrnuHLlQR5/0XrwVTsx5m7VKxNi5ScMKvIC3hcuzCtUb3k2LDUYNUeFwV9QqNr6VXgy/LyEFWUBqXpeptpSa53jetdACgtVypbFSLcXM0BplbLtTa0RzUBmbFNCa1ubNqGZthVztGh4AA2bDJHCD3Q5PysCudvSsgZWy2zFqEhM2piaU8LZFykcMa22DxtCZneaTetleqDlaJGhuBslx3NVR0salbAmje5NcL4GtkJLVnc2eqnyPuto+F63qnyPusgK2FQgGlMheqi7GlkYlYEP4WpkyIrvtYbVqGF8rNgl5BIoU0FLp3pGirPh6NqrE1fDv3aQBJwpdW9JI+znw/jE5/wA+8+T4lR2V27VhP9DAbd1/4puqXacIf/wy/Vf+Kbz49v2Lnpe5ZuVuI+W4ZSo7QxjUbZxMoSpOZ9wFywXdwcNK6uyXIjLa0rHTJHXPUdqdT4mzyPCGK2JU7tiXHBK1Plafim7RieBag8h/TZkzsY1p3m1GlNhXoPDrYhAV0XfRVj0r/RfOag57kzeYB+F7JUeNuQp2jMa5+sfHe2gxVgDtUCh8TvaEUdGpo26Ka+YW2Q2izoxSUg7ftFjTppcDw91gpE4pLedtYpWw0G9tlpa5UslBcUUzyjv0FqmNcxxFa1zRR5WlQ/Siw06QOJJsifVK4QtUxjveZ2+Q+dl6FiryqQdox4AAH2ml3bFMj4XSFq60Hx2pdHwkQWMxKwEjsN9jT8o8q1u+4dHWDJirgVHHh0zvXhbRz8uQiOXRh1RiYm0VGrf+7v8AG67iRJOGUT6hmSDle4kznvuxJNgp2xJoKk9L6nJ6MFGhrDzNDUf1F2TA4uM6VLd49drrKn8TTTvE09tlOjwnvCliMnEZFzI41uDR6h43X4uasoo1CAMrfw3NdZKv4943suSLMurQ1i+XzHvvISPAWlrhV15Vb2WoTY5zJRXNK8LVoUhbNWzI71z+Mngu6RXZsFCYWk1hWGycTaQiq1amh6X6HNh8H2WligyOeV07DYjD4OeTVGsq17vxsTik0MZWC4dH1AgMR4A2q6mBO6+FLtPKpMNim7ZRFGo2jy1H2XDm2H7adnRQoFBRRubzlx0rTsPdN00VlnkY5kC4RmN5fxa040tSkwRWEuwapyApQ+dgLhCCDn1YnheeVstOJOeGH1on7o4H1rFnZ09YfGyWVQMipHjcpWOJoCBUDe1VJ5wSIHNzXCYZzxxsAy/7jndPf95J9d/tm7tz+KCHB4XM9ocZDpr0AOqn67pL+u/13+0bmapmsMoewZIxmFI3GIhp/fF+gRwlmYlgd8roOACNjsGHOlDioAx6KZBU36hG0GHn7gqmoipGZFd6XXFG7J5HQ5BANGZy4i1JJI6UqBS0/EYpWqFFBSvS03tHbaud7d3DCVmOWL7YmNc63k+LUoCqWjBJ3NAhqLkmFxU9aHNeFbXPkfgeqDlxzkBdOdflagxyFh4LBOlTJTVws+SKmmhqeN6QUqyRKiJK0zWxxTjlZVMu9lbUpRiFUEXoAhqWuV8y69zveE04XAGoBsBNpTI8LnG0ZyKnfe+Z9QFwaOVVrSgJysiZLpDGhqLa11uZQEE8bbO2VgTTIxFDeVvAabi91KbAkK53wN81xrnZQSdaXuvunyPuvDcDTSfI+66CJXaAqMhsLxgu9LGQHLPpbqd5iSfZfGnZs0O6UyKjzN1L0pkLzYWtMopBl9e7QxNaKMrqHpJUSYav3JPtCxyfEMdlcu24AqOVRlhVe9qH9ntjX5XVYlVjRm0hu7WhNPE04DjdywcOjAIDoZRqOpTWMjWT637aeV5ce37Fz0vcVp5eywfdpWmRHS6nPMHemw/rn7bVuYzt2KIOKg06L49K3X2HfIPEEi8uR2yoqjXlNQq5AfEi21UvXrt8b7Lc3IMGrwyyNwWcrIj931QNNeo2r/XhbEkekU4Z066eFuI2kttsfLK25a6qnOu/nZEWIH1oGPraaHxsbFvQRnzHwyvFZolOXAEDqKWBiJdSah6rMCg4qQCGB8svOwIQkgrZSKj2hiYi3lxZXZfnZorsWnDQJUEnbO0aCeuJmY+q8jEew5HypbIxkrRkkhR6tF3OXE3CNaEDrU2i3ZadAejqNVNLU/tA/rF3bBznstSNkRVc9q+d0rAuIowC3eADN0UcBXrdh5XIDA43BkJ08dDHIgdK3pxvJlPQdII9RMupvcbGkxISmmLSvhubfxAxTgBUoNh5WMcJNuzZjhfZnwc+EJck1JRKBs4NLfxGM+kuW2rwtjFxNGDr3sI0NKXm20UElqeN8pP3qXkUWpNRrQGy1hjrUcOtgWP4RQVcl1TLIstST0W9fHfRAArBj5XFdcgC6TlWlpMp77VGxu7a0RVh+K5wcTGq9mqlfzDj7LRi2qpNl0hYfdNxyC0oKdbhzb2Wo0sEYTIpBQkHqDdkwUXNHj+kLKWGxFan4WiRR1Iyy4jrdigxU2HhZYtI99PCzFrzf8Ey/gybEzjJy1R1Np0mKfau/C4ETYhznU7mz4OWRmplmjBpXfO5UXJ/7EFiws0wqFLAVJ8KWRDhZsQtY0oNq+Vqwx2EwcYWOQOWqtKfMmxcJzFoY9FFIGrLzN6/b409/wCCLb8CJz/lmIh5bDiJdOn6bhwvEgktXyvz6T95J9d/tG/TfSbHRHk2HiLM8kmNwzdVXTqOfS/Mpf3kn13+0bx5Uk8G3HdZJ4f/ABGHpv20VPPWL9DV5HkJ3O1+d4f/ABEH6aH+It+ncuXXLKozO4rsLriHkCVwylQ8pYmmSja7Bh+XsQrIopTjaEnbamO6rn/yuy4XGK2GWrkPUAgbX0wo55tjYhSja+7Tj421BHGhogzO562dKsUx0ii8Sa72Lqiw0qjcna9MEWHRNDFqDKS3QCtiSRSM2ptKA8LKWWManV0r03sabErNQAAEbkcbdk5GplNANQNju9XBIAoKZW4QTS2XjWtA9pSMVNb1c92259Cd1K+25NGQ6gNv8rZxDEMR03tYTC2Qt4yyBV723A2NnkbgWrchHS5Na8baB63E1uJfwtDQ6BWpN9oqwAFtdoaUtzWR4WjRune2mA3t1WuDkMdrROqDfN3VPHI+64gEXpbJvI+6yhENSFoKcB7raEhD7XozG+dLxF3JviRv4ClmWvAi6f6TyCSXDUFAEkp/eF2V+zD5ZjwuuekygNhD1SXLpRlsz+Ix2VcXcsDLHBy1DTJe0J2J0iQ555VPC6ZdpwURl5WFBzKyr/nkj5i8Y3k1Y9jlJTXFUqwU1O5452hyttuPA7g9PLobsUjKmDhapX8NVbKtTT9u3hdXxU+tjp299xKOSlLA6wdYw4YaWqpHEEdR42xq3z8rEJJN9Xwt6hsKeQAGvH5D+m2Y5tLHVmGOYtlmrcbaBYvMdUUbIdWgaW8RXuk+yo9lpOIykIHqj1fI/wBNwileFtSGh2IOYI6EW7M6PshBp97IHpSlfnaINfX19dAC42FKcN/bZWHY1Yk+qpceFONpyNnZmHf8WvCjKfJhncspCrFL+G68TTxJzqT7bsuDfspYhqqJgVSuWl6V012KMBUeOV1KDunLwH9eotZinWJkVj3Vq6HfSaU/XYW0L0eiAFVFe7kLZnEzJVWr4WFHIjwK0TBxQca3y40r3WBFL9C0sM42siXjNdDryNpyii1tYxuiRdQOZ4WnAfh3lLZotHLIadmG0qdzZhVU0rWp62PCF+7qs3tCfu18bUyZEe3eB14CnytmKFnZpSBp1V87hKdUiqW1ZbgbWqx9mAEEZA+8TkbtV5YNCRjHhcUVFUjpeQmN1ChSaZtao8OHdj3aZW0gghB05HrxvObXgbBhOqVGj2W+2K1ZBKAC5aYGBY8D8bbdg2w3uLYcMDSaRW7pIrZaFdVZQakHOuXwtrQGIHdHjeyoOpPlanQaI0j1GtALlDK5/DVcutKm2WXs6VFl4bEyYfbSFPEi9ItN+gsSOeYaWPCwySR0H0qIIx65k0FegupTfvZPrt77vPpFJJLgYmeVGH0mDSBWtake66PN++k+u3vuOSrLho7D/wCIgrkO2i/iC/R8MyCWQBjmaAjKovznD0+kQV27aKvl2i36XEkKTOCCF1ZGzxOrJ5A6dohpMTnahvEn0KO952BOQGYocuHlbXaLQcet32d2Z0Lxx8bn8Pu0GZY7mwJcbM75EEkUr0raXqUODnS59sFaqLXPKtntKW2PUuGFgijRe6zGlWt2WWGOuhAD43Wk5liajMjwGVqRLlatSpzvaM09Gbg0P/S2StB7bZaRWOrjxtk6gp2se9LGglixNRc21KlTubcWJliEhGR2sd5C5zNbWxIhq5m8JBNRleG26mwEcrS+LCtxIam1tncW0EdyrbpIJ4WKWzuVcrAR4XG4BhXO5VqbQEybbYd0+RuV4RkfI2gK7BIK7/ltt56EgC2mhZAOhFqXLcOsjAuAcjlfJL8cm6E/MZ1uvekLFmwxJ/LL71u6cxhSGVdA05V8LpXpAKNhvqy+9bDdwsK+RXrs+D/0UP8A/T+JdXtdSbs+VIBuzOv+cbzht+xcvHuCYrFySBI69yMUp1PEm0+t6xuFrElcaXwN7YCQvb45XtgSQUDffpcTfcPO42hNvr2+siRtxGo2WVxpcdrARYw5AB1GlwmxWvurtlaep2+F9nW1LIGxe5ZzV8FKDuh9deFOo8RdzZjJ3we62Y8RfmFaG/T/AEdljnwCahVlJX4bXvD8sGUsKxg5g2KFqpz43YcVAoQsB8LQlSqtnmGsuLQppogpZSaZC+Ua3AOrPobmxIytyJAveO9hZAxVSIRRk0OW1aG4GVgAabdbHOJPdYBqVuTa5D0qK2uLvBJDWzGpG5seQjUam3o3klPZgCxMYDHKKgAi3qHApDDpHGGeVCDQ6QczbMmjLQag7WM0+tRqXWaUXLa5a2IrpoRsLMqFEiCOljsTQ0NyLMzV4XxPepSgpedFbMDs9MreCEi2EYKxJNelnQntQeBHzsxFoQucoww0TcBiIQfndZm/eyfXb33buepTBJU7YmHKnGp36ZXUZv30n1299zIMdEsMQMRATsJoq/8AqLfpcxEpagyzpfmcBpNCf/Fi+2Lv+Ilqx0gqK7cLYjIiTpytsmtw1E3tK3ZJqksaDM2VHCZDn3bFVKGu1va5KUBPnZEI7EqcmrS3xiH/ADMbTwXoc7diSaXJUJsrt4HHkUJJQBmTSlsrikIyPheHByn1iB7bl9FVaCoPldJT2D8RR+nB4ljrtbQNbY+joBUGyYwON6rt5M3Q7HQ79LbYZ5Xq0qRedb0ASRpM1HG25FpvchvcTvmbRByK0t+JVqAxvOyY7C4smdonPQNlehrgRS+W0R2t63qk+B91tG5gjQa9D7rNCQxOAhfCmWKZG0qCRx2uHJ4YpEZgT2igmnhdf1ugFGIDb2uckr2k36O+bkacXjwaRTT35B+ZB3nAGdFGV030ljMb4YEEHTJv5pd6lxCYfmCu4qAu10/0yxSYvFwyIKKVeg/uXkkvs/UrP3Cn2on/AAOH8Gl+1adal/0EX1pPtXnH+jRicbhcmuNhhOudt3MGtonG+F9cRlaJI8L65HYXlLRMvb6+tE6+IuV9aJBeP9drcbgbbGTW8BVSOhsoWQN3z0QkrFiE+6yke0XRDtdy9DO9PiV/sIfmbvj+aIn8S8TU7Jh1F1pmCsyb1INB+27Liu7Gct7rcaH6QMtVb25NkQ0ORgM2aE+21KOBJK/kH3TYkg7Nsu7xpxttJdTVFbCfUXkUJcPHF2QyNWBt/F4VRQ6xnlQWnibXKlQDTgbPjhrQu2dc+lL1TvwS8HR8tw8YGqQq5273vtAx6FZaE1obs0+FiBaQyDJc63WsTSVqptXK55KoYZJRZ5g+d48jeZvBWJKnfalxrq2vBmlDkcopnkbbLGViP+QvChrtfDWvttVDRpWgGYqPhS9V2iOV6qCm4tlZdJNVqLqwEecus2AqN1mw7H+/T2b3T5/38v6R/fdj5gdWDk/SQ/xVuuT/AL+b9K/vvOey4mRfvYv0kf2xd8OpWKniboUZpJGRwkjP+eLujTSGQlhxPCzDyCQc0YUbi4kAcbBWSRs97kGZ8gc7rZIdEise81BxJsgR6j3Mx1sBcK5OZHxshcU+HGj4ZXSVbQGK8eFhjXU/eJ4cL1sVpGmJaUsWHtZvWbSPnarHh1ZQIxsczxvZZWDN/UBVJG70h34XJqDKynQV00b22wyDf4DrdJUCyKBt7ekndwAQAB4XoYodqV4W49GHD9dmxGlGYrfMVDGmYvCelt2QDsdGa3HhaoyreRlUz38LWOUiOfEUc50yukLdCbBGxamx8bg8LITUWv4jDwdo+qQKwOVLT4Y4XZ6yEU8d7SewnRQdrIAa08LnNhGjqKEdK3ZMNgwirICrAnetg80Z2fSduFLIO2StlSpuMtGU5UyPutQeJWoaUtiSOqnyPuuSyoSQyRhWbjS7ByP/AKk/2BaVicJNGy1rSgz3Gdr/ACmIRLMK1JUAm+SSwzeOxF5ghbF6QK90Z9PO6j6Q6AcMqjYS1bqe5dx5s5SfSvHc9bq/pTGsf0OlASslQOHqXFVxlL5FTtR/6ONfGQ/51ptqDZRgdF995xLYnm8rem8sMTrktwuS2hNNxuRuNojgzF9eLvcuNoDr6l9e2idfX19ZEbayI8/aLZIucRoPI++1CyRGRF+kfysSGTnOLSVdY+hM4Hisi/tvz4rXPqLWfRfFTYTHyGJihkw8kZI3pqU3ccSRElcWj1LG8yUyTw9ggGo0rmVutYWYYbFiQxh6hgFOwrxs+LCS41zFEdTkaiRnX22kzBo5AvFW008b2lezOCSTQS7dpIWc0rw8LPgwHbxF4yq06ne07DxdvOIzRCdy2yi1ucQRQthoXYtUVdT3KWIq9gbrQE2EhgeHtJdNSQxGdMsrdDwq2jV2iniP12nM1XCet/aOfws/sJAhIiFBmWHC6v0H3B8a8jowjUKCKUrWtgRwNGFBoa2oa+Az62ydbPkKL4jK8pSbNI0gaXDlzkaU3uOjsVC1BJs2VSFoPj1sEFeNfC4Ddm1bbe+MRpXa5E0HC2m1PxtoK+poyttyBlcyjBd7YKk9RdAYFj2BwUv14P4q3WsT/iJv0r++7Pj0H+T5mHCSAHx/FXO6xif8TP8ApX99zMKMhNJocq0liy/7xd8xkvbPTQFoW2HjdCh/fRfpY/ti/RuYzQkIsaEUY1J38rMNMEtoCEKiINqHiK32FlSIMSgYnbwvIMO0hLN3U603vAgDMOA43dtEj6kH1hZplw8emqk0G/GxNI7PIlj5UtmSum3s0CrFBsYCTpNB872LGkUXUQK7i0YMTbqBWNHJp4W95WPVC+Mempsi4IyzzF527sQRUCwIWRDQJWvE2ohyRmPZe0ZNktI1pWbO49selbmi6rbKgG9KIHUNc7xiSbilbcO4s0JygmycNO2Hk1Lve/SAqgKoy42PGpdxwBtENOIMslWpvnZcUkQV10a2PytMRO+c8uFmwuEyCVPWzfqS0KOFll0mJVI/Nabi5nlem1MrWIsSscetqV+6N7RsQyTuAo01NkmNWxkMyjPPxthmNG8jap9GWFCWraXIlQTwzuaNFQrycgxTop1q2QNOJFLFXDfQ+1DAKTSlqz81KSqqVMWmlacQLT5V7WOSZgaPsb55yTwkXx3eRKk5TLNMMQF7RRQgDw4X596Uwzo2HeZGjLvONJFKUCX7LyvELhgvamgrUHyHG6L/ADJJaDlpYgsZcW22ylYqCxyJLj/wVFv7lHlA3syU+4e6w7Lk/doeNP13zxNmBm8uRvLDEy+F7cbQkjeXvC8tE0W6eFs25wtASvr69sgZ19fX1oDLiuTef6rnceloQ1MxZ/Iu7zNFyzWUZ+QNpkDZ0s/lx0c0gJ6n5obteCfU9U5di/8AJ0pajUYHvLwytBY9piVb1tUtc+NW42oLIug5k+60ljqUNtU/rvaTwkRBbZZsVyDGwYzQUXU0XbAKcinTztzDcqlxeGkkiZUKDNGyNncoxzTY9BNrxGnDmNczqFffbOP0wKez7RWLEMDkB4U3unVXsybehCGBkjkTUQCaknhZaxEa0OJWMEauoboPOzcBgZubYhMLBkaamkfKg43b5OUcu5Gg+k9nMABQH12fj7OlylbFyrG2eaRmZKsqk03NKClt/SXbumtK1uzc45nHigBCgiQZFQBtda0s9XCm85JJ0jWOVlUMS65Dv7LZMIU572aqamrwFhyONXh87FFoaKgHetlO6MBQUoLHWMyt3VLU6ZW+daihXIWcgwyAZeNxZ1pSnytxSD+W2+zcnUGpYETeYEfQJgBTvwV8fxlur4vLFYj9K/vu1cxDfQZgRSjwH/8Act1jmApjcT+lexIpA6evH9eP7Yu7zHSSDUmpujJ66fXT7Yu/TyLIaU9tiAsehxTGJYjkudD+22QRUqCPE2OiZ5nLhetFQnvAD43pskk0zjIcLh2hbdvZbtUIHeBpcXC70FgBKMd7MVsgFE/L8bC1ZAXKuojhZToRUTFRpHp0qT16W7HOJN8rRfWagpazhMMr77KNT9AB+3YXUZNvY0qDE63zCt62QBAyIrcA9L2szodjU2/2fUWP2jb0spZdSbZ3pZLGWUkXOMU9bbhb7V7MAi2NXd03F/kA4uBxsvCsKk1G1ipH2hoAOprfLGpk0jjZEVe0w4jYMFDjYixIqGVWIqoI8rFkgIyrU9LcgDFgg42QUWTEdnOuTUoKDKt1+ZDEG1EUofdaoQ0WGkrWqmlB+2wJo4xErjU9QdXhldAWCESNO6IJNxnXr0trE9vhz+IG07U/LlxvMFj4YmVuz1eqTX533M8emKlVVqtfh5XyJpreTaLfYPSQJhgSmovmDSorXIXTfT/W2C5ezqQe3npXoYo/2XZh2i9hho5gyk6mFPVAzyN1n09mjkwHL1Ql9E81X61jGVPC65f/ABlR+X+Ty2zTnhY2465F9gpT32FZv/RJ+ll9y3yRNmBG8veN9axMvLleGloTuF5ei8tE65rtcLmu9oDblcNje1siTvrjW+rZAbeG+vrApDsXrWoQHTi8K/DtEB/vU/XbOAwbYp61KotKsBU1PAcPOzsThvos0aK2saopFNKH94AQR1B6XUQtM9FaNI4my3BpaGe6grapLI8uHeuy1pYMiFYlr0rezdmcPIrcrxDQ4+CWudB4DLyu+YKfAY7G4mTExKahaKTkTxIF+cYZoI54G7xFMx0tcWZTiGaunanW9Y6MZxstIxOA5fzpVDmOL6ORRcqGtaVvOb4zlHNU1IWTQWGo1qxpwqdrpXMp9WLBrXub0pW2IXViK10ncjhYk3eAKCw82T7JWGR7oOdd7FfEyodEex2sr6OZKmN61J9lgljDOhNO44rxvJx1ZsmmmG4bDu+ZZfEbWNisPGJkomitd+N3XkWAweNLySyxgO1VqQCD7rB9IEwRx2HgjYuEqJKZ06UvXqqwZd/yrJV4X0FgKedvVSh1cdzbsmBRHYUYA5rkRYbQUrqYnpcNtFpWaVieun8oqaHe0qSRmAOwB4WfpVQaGliyhFQqOOdxdsvq0BcymB5dLxOqCnsmW6zzP/H4r9M9rXMYVk5e51FSkkTeDAuEofLVX2Wkc2TsuY4tK10zMK9driYUJ6+sn10+2Lv8iBRwGd0BfWT66fbF38rIWK6SxzsQ8hfgbHZ0zOdlYPSH0FAQ9QSczQjaljRLqeh+FreHWCOTNQO6TVmppIHAjj0vWP8Aglic2FwigDVID1trsIq91ifE2qpicMYjWJ3bgDsPGtpTtqY5CPw3sypegEiS4cEEkGnXYW00MVd6W47kpTtKjptY5YbC5bQhEGHE0ipHSp4nIADMsT0A3tSaZSghjaqKfWpRpKdeOkflBtOwZPbEadQKPrpwTTmx6UytUw+G7QIuGdXdl16ZAImI46dTUNOOe11EI9GwZNNc6H5H9lkQxpIwp8+NgYZyzqcidemgzrXIkeFqAVhQjYH3G9Ymch7FIaAUoBcYFAQ0zrdm7PCT4UFqAgZ9drSGwka0EWbNexl2sWMLyqPHQ6lajLlpHG2cXykYSMmRlDLmF4mxMI+IwUxZX00GY/Ll77exePbHSK0lAx7oHzrck073gakw0cmHEimj022PlafFpjzYfG5urF5CNQGy+FtTa5FCeFdXhZKobLAv3M61ytT5bGiyHtMt9PWvW0WOGRz3c9O+dPhWy5JHWjMTWmgA7+JsJ0w+B/EYhkDxiStWzFiHEytH2daJQ5U8LEPeNbIii7QMOgPus3Y0Mph5sOitLCVEnejB45bVtrFo47NmGe21LVpgT2QxLt2Kr+GqtqZXA42i4uWXtEEgOnOgbj43y1RrHZLU4xCLGKnTT/laR6d4lWwHL8OIVjMUshZh6zFo/wA1rGEWfE4uMRBY2GasxoB4m0L00gkGAw80jAs2MkQ+NItx4WZ/Blr5I82tQA/2BT0nk+yLT7VEFeWV6YhvmovliaMSzvfXI5XGtonUvaXl9aEy8vr60Tr0XG9tEcbrcblW4VtAWjAejcuKgSeWdIkkXUoUdo5B2JzVV8qk2of/AF3Ap6z4h/8AuRB/mpX53XMHjcRh1AjldR0BNPhtazDzPGSmnaA9aoh//wCb0TjXxIal6j7cm5cv5Zf/AFj+y2z6OJiFcwGWMqjvWUqYu4pahJVGzpQU1Z8LcfmOIjGctPqhV9wtjD404p2/FcttRicxYbXoGKfqKHL8IcNDGgrsK1yqTubRJ8SuIxbMvqo3ZqPBDv7Wqbs8Es0elwNZjZHA6gMK08aZjxtzEdnPjMT2kCyRSMW1CNVK6xXUjqAQwOYpbGkbyV4DJXD4cEDTqUN51FsSOzxrq4C7bJyCXG8sgnwza+6AVpQmmRpwuvYzAz4KOkqFDTKu2XS+lo5IyVteToI6xI5ANPiLeJYd7rbeCmaHQRpanXbPraq8cmI1OCmW9P1WEiHsShqxGJOvgtB/TbywvHVk0Mg3FbEWn0iTU1cqXJF7GpJ34VtsWayVNa6QczSwJgSG48a2TLK5XhSu1jSseNOGX9elhlwHFlMMYBY9aW4smhklWWrbnqLaWhUUzJ4XJ+zCrpXv6jqrtSyNC23OsRiAI2ZCaesQNrk+CR4GmeXcVooG915sSdChOzFD03t44/ENRSoWopltZv1yCvTAxKukbmxM3So4GzpsSZIirJnSmQ49bHgi1RtVgtBXzuKRSbEvmKsOXTHhqhp4/jLaLzn/AEnjP0ze4Wt8zkLcskXgrxfxVtF50a80xv6dvcLy5P8ARURMX1k+un2xfozTOhYI2ZrU8b844r9ZPtC7+TqkqoPG3j8hkQUNoL5gj9dm4V+7ISNZVNj0JoTbKisclPu1+BF7hkYl1Ud4xtlXemd2Akwkzq3G80LTM1t2chXK007e62SFpWtRawDYTUDbbIbL7QBdIoK/G3I4O1WoZcs88rQHR4jsMIsainaamc7aqGgDUzI8K0sIl5HqTXxyHsFlYsAJDSgAqppxO+XsNzwlFcP2YfoDt52rLEW8Fho1CuSAQCaew3kczxAAbEEGudbaerKX1Aa+4qjapz+F8BobTXVSg8K8fnfQjN6LRgcMmJStTqpSnA24MPBhJBHM2jckjh4W5gJUh7ONTpY8etqWO5fgiZJJp+/pDDPqANvO9LMbzRXMSxavYnXGW7xpmAOPnaZreJzuWXNT0Byr8LXcLg5ll0Iy6XP5s+7Stbamw4WZYyKrGD3gOFdibS7QyspAUMtdeZFc1HmONbZkIiT1ScqAnLc9BZiw6wz6NJHTZ/Cm1pGIeYgakpuKeI8LRFHDx602FG3tnFYQjvjannc8LzCLsTG6nUNr5MclaMMhWy6JzYnCPWOlPnalGkaRa6VyIanlZMmJwa4VgkNWI9b22nQY0RRvHQHtAd+FiqHLAR2kenTHSQqMvWZjTf2WPiFlndAIWZgNqE1pvvdm5dyybEyRqQYzoD6q1NKeNqUkU+HxkBkKaE1BTQAHLjxvPqn9DXv1dbKmrSytPNh1WFQixsHGeobkHYG656b4dY+V4BjMZpDOwanqKOyOX1r9Jw0uGOGZJ4iq4rEMQQO6y13HEC6n/MiPBR8hwCYcJ3OYMpZeIMDkVuObEGGErkjxK1nDLq5VN/ZnJ+S2j3YeVR9ry3FL/wCI/wBhb5Yb/g3lorzb3lzYUtuwE29uN7aElQG+03lb2toCOi+0G51va2KGxvO+Km3K3mqlo2PQEbGy0mEJyztPJ4i+18bIAzEYntchkLFVmU1UlSNiNxcSeN7aEuuCxxlgWQZN6r06j9u9v/TWU1qf1G6xyqYq7x8HGr2r/Ra2FrvkLk2iz2z0Ixq47l8kdM4JKeyQah863Z8fyvC4+PRMoOXtHlfnP8ucVF9L5hhA4U9lhpkU71BZZB80J6Vv1SQBiG4rfZBtxR5X7C68sqx5PL+aeiM+DGvDHtYxuuzAWgxwup062H3l2I879qZmVSQurwtDxHJsPzUOSnYvsGXI+0cb06gXL/8Ao8pwrQhpS4BBanwtVQ4XEwMiw0ZvVauYtzHchxHKyyPG0q1LK4BI9tNrr5xUkZAWgpxW5eDbEtMnPhnwzMknd/MP1WnuAxranMzyGsjFjTjnaa3rf1r5XDNY6CAqLpIyuFBryLV34U+dy9bamVxesZy0nKygMNj5U7KWcrHU+w18Kml5icDLgSp9bjUd5bbOJMjKKAMg2rkaeHG1LDc1TBxkTRCR2NQfujoRd0vYjIlyOJIiCu9TUDO0uQgcaG1rEYtZoWZFAGvhwraM6hyPPO85L+S0A8wH/wCMmzA70X8VbR+fCnN8fT/Xn7K3YOdxonLpezzUNDvvXtF4b+V1/nufN8caAfjnb6q3jyf6NIiTxH1l+0LvdNL5E3ROI+sv2hfouWog03sQGRCLVUgcVYfK9jkCFTua/EHK5RnRID0a8dezcr929GBjmJp2rA7VND1FtiFhHrHqltPtpW38QqNGjgmjsx8UJ3B9u14DTDqq1JqXPuy8LQGHDqsYbUCTw4ix+2YrTYWbNoZ4YkUoVBaQmm5Go+wDaw8PEHYKzFS1NBArVq5A8bICeMAHYL/4YPmTx+GVlYYRFVDsaA1HgenkbzmURBjffuha1GbDf2DrtbeHDOQwGwJHnTKzpgZYcN9GYgaaZ1qdvD525GkHbBX24062AQI4071dL0z3I05keFbaQkAzvUBgTGufeZdz10jrxN39wnrYficSFlPZlhTrkbdMnaJG4kZyd61Gk18/naSjtPGeBA8q04+JpvaqsOikYpmFrn1Fc/K1Ntj1SFeDmJwpRye10EVFPDa3MV6RCeqJCqB98qknb58bRW0VCr6o+JyzJ8bZXCNK7Mp9TM9adb1RFKwkYtxFpPqg9xQTttUk+OXlY8s0ocM5qSPlcpxRtOkgd39Z/Xnb0XbR99FUhBqLsNVANloajeyhYCwAb7pyr7bI7FCtBmSRVq+qP6bDftJCZGJZmJJ61uZoFUCoahLfssiECIISFbUpBB8CRlYx7kSkmrMrZfdG2fieFvMywhU3PrP5nYewXkhjYVJ7oU0H5j0H9NgKPTIOVOIk7OSp0irnjl0sDmfJMe8OqOZXZcwtPdduTDExIASO6u3lckwzJxJ8zW8u/wBTl7NMo3LsJiiV7akTRxFUR+pOZ6Z35x/MjDzx4bCsyBUOJIYr6hk7JvZWl/QRw5cUZVbzF+S/zXwJw3o/hAvqDmStToWikG9xyzTg0bcMm+Q+ezdq5CK4LEfpG/hi6tdt9Hx/seK/SH+EL5+L5fwztnorWKTs3I9o8jnYl2HHcvlbleF5iorEZXwkhGYWRFDrU/2lJA8rr1h7Cjr68vrATb68vrRJVvq3G+tElW+uN7aJwNyuFyFonW4NrbFzLUytEVOVJXEFvuofnaviMWmGXWQWOYRfvNTr0G5N13D4t8PXSAQd68fbccRipMSyl6AKCFUbAE5+JJ4k2Ck6RcvQTFth/SDBEt+8MkTljQNrQnMnjqUUv6fEWQpmL+POUYn6Lj8JLsI8RCx8tYr8jf2FBAxjH4pqRWvC+jjlg4f2V8WS7HPI0vtDruMuot8YY5EtUi5di1d707fU5aYmT4VpkcK9dQIIPCt+J8x5bPgsecNoqxakZp6wJyoeov3jstBNTWwsXhIMSUMqKzRsGRqZqR0O91suHI4fU8Snw0uGrHOjRtXiDt58bSWVg+XvGd+/YjDYTFroniV/Na/O6xivQvCSOJcJJ2TA10nvJ5dbXFM1j+wvKo83XAzFAQtWI2AzNj4iDswNRodip4ed3XHYLm2COowig/8A7IhXLqQBW6XimkaZtRLFt/OuW+edmSSNIy7egEc6GtD16fsNxlWZjqIJ4Vrb2igDdRtWu+9f65W4wZlGXDSN/j5i4LB4FI1LX1ht4i9gzk/rw4XEq0bAuaUIy61uf7uRq5Z5eVjwIzztF+gSkhgCYjlTftVp7K3XvSNQOdcwA/15+wtrnN+9y6Y70aLfb96tonpHX/LfMK79vw+ot58hcBEAqyj+2n2hfqcmCo5YqdNTmM6UvytjSnmv2hfqUkzgspcrmbHF5DLwAstJGAzFzdWkjrXOPu/9vD4bXDUNRzrbsToH0/fGnPbw+d6UScq/gjPukOu/5txlcjh5WQaRXTEmxHEkk0uKnSrg07pDeVDQ/K4yTtHGEp6rUr4esMx52iTf/GMrHu101OX5acLU+V4MSyI2sgxM4yXVQUNDQ8a7WmyhXZJVyB0V8zx+VrGGlfCxzRpk7tpaQbqKVYKPq5VtiAE5myOsXZ6WAZ0DZ7DbUxyrYuGLxzBGUqW7pHHvDI+6yFi7RCoJbTiBWo2DZVp091lQrEXEjDUVY7Vz+6LXsTVcu761NFmKg0y0gbeylswYoYp2Vi1TSh4qBkNPsyNqE6LAskdCzMC+R4kUCgfeG7b2lwwGEMikdpIpqwzCLT1K13NasR5WaEMjjM85ES00Oqtn6vdz8yfffTY10keNQVXV3jkzPTq1NvAZXBcVHgA6x96VisZkNaAhAGdfcOPG4xxS4oHswCy1qLc+P5AHoQ71zA0V+X7bfhZoGLb9pHtwCnKref5RbcSSmuqMroQV/tUGQvTK4MZkULrI7m2QObZ+VB8r2jozZY5IIcR9GTTUGF9vGnGwMRy+TCRMEkOkrUrxOZAF5h8U80oXD0BVFpq2BJ737Bathp21sZl7QhaNq2GZ2/VdIh4ZUIJWiAYKtVzIPEXOSaJ5WfS2oindppGQ9uVkYuJjI1ECgn2CuRtPXUXnZRlHU+Xe02lIb9Z8zmd+t6+YkBBLAZeAHDzvVSQ6SBUsQF2JJJI4+2tvNh2HaNXuKDqYH3eJ4CxsOj3qIfhp9VfcLcpcY/3afVX3C3L4RUUZS/Kf5wD/ANuQHpj4fsPfq9L8r/m+K+jUX/HwfZe5ZrBfkfMZu4ejgrgsX+k/3V1C7r6MCuAxn6T/AHV1xfP+GXP4nqv8teWYPnPopj8HjIhLDNjHDLsR+HGQysM1ZTmrDY3456W8iw/IOd4rAQTvOkIjIaRQHHaLq0NpyJUU7wArXa/bv5PtTkmKXh9Nan/pR34z6bTNP6Tc3dv/AJToPARqqD5CwLf5MprLS4W49t3LLR19eX1gJ19e31onX19ei0TLlW8vbIDbw73t6bROF8b5b472QDq+qfI+6/tL0fnTG8n5fiVz7bCwOT4lBX51v4tj3F/XnoDL23opylumH0f3HZf1W6M+RJpX6lv03mm5X1mzLpH0GHh18aW0+F1WZfWVOSJfDBgP0Yn8otpsKwzXLytTvrr7kifsR9WJgDAUdD8K1tNxXJOXY4fi4dCTx00N2a8oLP3X6E/Y9JHmcvoJgC9YppIwd1rX4HcWh809EcThi0kA1oF4HvZZ5gml+z6F6D4XFokcFSoIORHW6+8vKLUORf8AK/c+ZsXI0riuiPsxp7td/jmbyeMPGkgPRWHGtMj5Hhf0AfRnkhbV9BgB6haWJL6Hckl1f7OU1UrodhsajKtLP3Is0z6Hz5zJNPLMWGy0rCR4n6TGMvYbQvSn/T/Mv0/+7S/ZfT30T5dy30dxmKgMwdDhwAz6locRGNqeN+OelYp6Qcy/Tj+El5ckk9GsCtsMvaPeL9GkdpCTS/PNOoqOrIPiwF+0809H8TywnXJC437pevwKfrs8W2M3VFbjg1ZhlrxU5Gg41OR8q1ucUOpmA05kEV36/PpZ8MCOjO2RDKnd4k51Nf1XPAxhMYpIBEYLHLfOg8Mq3tRNgkiFZ9hRqg8NxncTDrgy4GvwFP2WZDGnbPUsSkrkk8a1HW4xRUiLHMaZaAVHeY6QT4AcLFDZiYTt4o1UglRGaVzIViD7acONmNEpDsAwMmtxXKi5AHodjl0tvDYRkVxqqyxtnnkSUII8qm3sfLSu9co/DbNqeIyt0hAIphHLK5IUhToXPvsabU6b52dhxDBq0gktVlNPVFKaq8dOdPGwY4e2xI00BVqiu2w3tRnlEcJ0qO6nZdKnVQtx+FlMRnXIwc1YKWGmm7I65ivWla3kCqi94k90/WI/oF7Jq0y6TpVT2aCuwp+2xYnOiBSTmC3sL0p8rUxDcRBF2YVQAMiTuWY5k1OY8rfgTS9FJDGhJ8elhzTGSOLgxlYseoOwAG1BZuHPE56SzeJ3NPIUurRNC6HHaO4FP3aEnj3M/iTX2WhY+GQS6mFasMxt/UVt3C41p3mcjLUjUHSlCOmeVx5kXVV75IYhqdKuKCu+10naJ8jCNoVmFVNQBQ7ClfndrwGETGIzdsxTRGZOBBoTkfhdWVBROpJ8sh0sjl2MfDzaATpaoIHwu0yZq0LU7DDJIqr2qkjS1c6b148DvYwjwM2HNFOGdhU1NAwX5Urnae76GKjMEnRUnuiu/n4bXspMsWe0VAepJrSnQDpxstgS0cuHxKosgh7RI9Sgg0rq4lvbtbE0E7dxYjRdRNAQq5cB+s5m7ryukMaYZu8ux8SKmp9psWTDmCF21V7+kDwJ2PhYQO2Wf//Z) | ### Beat 15 — ? / Vorläufig - **Trailer**: 00:00:57:02 – 00:01:01:12 (4.40 s) -- **Source** : 00:02:10:08 – 00:02:13:03 (scenes 17, 309 (2 Segmente), score 0.621) +- **Source** : 00:02:10:08 – 00:02:13:03 (scenes 17, 309 (2 Segmente), score 0.706) - Seg 1: TC 00:02:10:08 dur 2.80s @ Trailer-Offset 0.24s (scene 17) - - Seg 2: TC 00:45:27:10 dur 1.28s @ Trailer-Offset 3.04s (scene 309, retuned phase) + - Seg 2: TC 00:43:26:20 dur 1.28s @ Trailer-Offset 3.04s (scene 309) - **Rematch**: `python cli.py rematch --beat 15` - **Phase**: static conversation - **Bild**: medium shot, profile view of two characters facing each other, indoor room with a large window overlooking a snowy forest diff --git a/README.md b/README.md index a54b100..7e117a3 100644 --- a/README.md +++ b/README.md @@ -36,6 +36,10 @@ Was du bekommst sind zwei Dateien, mit denen du arbeitest: 5. Bei `MAN.`-Beats selbst die passende Stelle im Spielfilm suchen — die Beschreibung im Report sagt dir was du suchst. +Für die visuelle Kontrolle ist zusätzlich **`CUTTER_REPORT.html`** relevant: +er enthält die frame-locked Compare-Clips. Der alte `match_report.html` ist +nicht mehr Teil des Workflows. + Alles andere unten ist Hintergrund für den Tool-Verantwortlichen. --- @@ -48,7 +52,7 @@ Alles andere unten ist Hintergrund für den Tool-Verantwortlichen. | **1** | Schneller Vibe-Check: für jeden Beat die Top-K ähnlichsten Szenen aus dem Spielfilm vorauswählen (Histogramm + pHash). | | **2** | Optional: Vision-LLM beschreibt unsichere Szenen mit 3-Frame-Samples; die Beschreibungen liegen gecached vor. | | **3** | Frame-genaue Verfeinerung pro Beat (OpenCV-Templatematching, Bewegungsphasen-Vergleich). | -| **4** | Phasen-Reparatur: bei segmentierten Beats wird die Bewegungsphase im Source mit der sichtbaren Trailerphase abgeglichen. | +| **4** | Phasen-Reparatur: bei segmentierten Beats wird die Bewegungsphase im Source saliency-gewichtet mit der sichtbaren Trailerphase abgeglichen. | | **5** | Recovery: Beats ohne Treffer werden via Vision-Phasensuche in den Top-K Szenen nochmal probiert. | | **6** | Export als FCPXML 1.10 oder CMX-3600-EDL plus `CUTTER_REPORT.md`. | @@ -56,6 +60,10 @@ Alles andere unten ist Hintergrund für den Tool-Verantwortlichen. Vergleich ausgeblendet, damit Title-Cards, Logos und Letterbox die Treffer nicht verfälschen. +**Cutter-Report-Caching:** Vorhandene Compare-Clips werden wiederverwendet. +Bei gezielten Rematches wird nur der betroffene Beat neu gerendert, damit der +Report schnell aktuell bleibt und keine unnötigen Videoartefakte neu entstehen. + **Wichtig:** Auch wenn Vision aktiviert ist — der finale Match bleibt CV-verifiziert. Das LLM liefert nur zusätzliche Suchanker. diff --git a/cli.py b/cli.py index 0e2459d..8a62348 100644 --- a/cli.py +++ b/cli.py @@ -131,7 +131,7 @@ def _auto_commit_push_reports(project_root: "Path") -> None: # type: ignore[nam log.warning("Auto-commit/push failed (non-fatal): %s", exc) -def _regenerate_cutter_report(cfg: "AppConfig") -> None: # type: ignore[name-defined] +def _regenerate_cutter_report(cfg: "AppConfig", force_beats: set[int] | None = None) -> None: # type: ignore[name-defined] """Re-render CUTTER_REPORT.{md,html} with Frame-Locked Compare clips. Called from every match-style command after the cache is written so all @@ -141,8 +141,19 @@ def _regenerate_cutter_report(cfg: "AppConfig") -> None: # type: ignore[name-de """ project_root = cfg.paths.cache_dir.parent try: + import os from scripts.generate_cutter_report import render_report - md, html = render_report(project_root, with_stills=True, with_clips=True) + old_force = os.environ.get("CUTTER_REPORT_FORCE_BEATS") + try: + if force_beats: + os.environ["CUTTER_REPORT_FORCE_BEATS"] = ",".join(str(b) for b in sorted(force_beats)) + md, html = render_report(project_root, with_stills=True, with_clips=True) + finally: + if force_beats: + if old_force is None: + os.environ.pop("CUTTER_REPORT_FORCE_BEATS", None) + else: + os.environ["CUTTER_REPORT_FORCE_BEATS"] = old_force (project_root / "CUTTER_REPORT.md").write_text(md, encoding="utf-8") (project_root / "CUTTER_REPORT.html").write_text(html, encoding="utf-8") @@ -293,6 +304,8 @@ def _normalize_cached_results(beats: list, results: list, cfg) -> list: segment, in_point_s=in_point_s, out_point_s=in_point_s + float(segment.duration_s), + match_score=max(float(segment.match_score), float(_phase_score)), + is_confirmed=float(_phase_score) >= cfg.cv.deep_scan.match_threshold, ) repaired_segments.append(segment) @@ -1430,6 +1443,8 @@ def _attach_visual_segments(results: list, beats: list, cfg) -> list: seg, in_point_s=in_point_s, out_point_s=in_point_s + seg.duration_s, + match_score=max(seg.match_score, _phase_score), + is_confirmed=_phase_score >= cfg.cv.deep_scan.match_threshold, ) else: seg = repaired @@ -1693,27 +1708,34 @@ def _local_same_scene_segment_match(segment_beat, beat, segment_offset_s: float, def _phase_probe_segment_in_scene(segment_beat, scene: dict, original_in_s: float, cfg): - """Retune a weak multi-shot segment inside its own scene using cheap frame features.""" + """Retune a weak multi-shot segment inside its own scene using saliency-weighted frames.""" import cv2 import numpy as np - offsets = [0.0, 0.28, 0.56, 0.84, 1.12] + offsets = [0.0, 0.16, 0.32, 0.48, 0.64, 0.80, 0.96, 1.12] size = (160, 90) - def feature(frame): + def prepared_gray(frame): if frame is None: return None h, w = frame.shape[:2] frame = frame.copy() - frame[: int(h * 0.16), : int(w * 0.28)] = 0 + # Timecode overlays and letterbox edges are trailer/source-specific and + # should not pull the phase toward the wrong moment. + frame[: int(h * 0.16), : int(w * 0.32)] = 0 gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) gray = cv2.resize(gray, size) - edges = cv2.Canny(gray, 40, 120) - vec = np.concatenate([ - gray.reshape(-1).astype("float32") / 255.0, - edges.reshape(-1).astype("float32") / 255.0, - ]) - return (vec - vec.mean()) / (vec.std() + 1e-6) + return cv2.equalizeHist(gray).astype("float32") / 255.0 + + def edge(gray): + return cv2.Canny((gray * 255).astype("uint8"), 45, 130).astype("float32") / 255.0 + + def pair_score(ref_gray, src_gray, mask): + if ref_gray is None or src_gray is None: + return None + pixel = 1.0 - float((np.abs(ref_gray - src_gray) * mask).sum()) + edge_score = 1.0 - float((np.abs(edge(ref_gray) - edge(src_gray)) * mask).sum()) + return 0.65 * pixel + 0.35 * edge_score def frame_at(cap, t_s): cap.set(cv2.CAP_PROP_POS_MSEC, t_s * 1000.0) @@ -1722,39 +1744,69 @@ def _phase_probe_segment_in_scene(segment_beat, scene: dict, original_in_s: floa trailer_cap = cv2.VideoCapture(str(cfg.paths.reference_trailer)) refs = [ - feature(frame_at(trailer_cap, segment_beat.start_s + offset)) + prepared_gray(frame_at(trailer_cap, segment_beat.start_s + offset)) for offset in offsets if offset <= segment_beat.duration_s + 0.04 ] refs = [ref for ref in refs if ref is not None] - if len(refs) < 3: + if len(refs) < 4: return None + ref_stack = np.stack(refs, axis=0) + edge_stack = np.stack([edge(ref) for ref in refs], axis=0) + saliency = ref_stack.std(axis=0) * 1.25 + edge_stack.mean(axis=0) * 0.75 + saliency[:, : int(size[0] * 0.12)] *= 0.15 + saliency[: int(size[1] * 0.16), : int(size[0] * 0.32)] = 0.0 + threshold = np.quantile(saliency, 0.72) + mask = (saliency >= threshold).astype("float32") + mask /= mask.sum() + 1e-6 + scene_start = float(scene["start_s"]) scene_end = float(scene["end_s"]) scan_end = max(scene_start, scene_end - max(0.04, segment_beat.duration_s)) - max_points = 96 + max_points = 400 step_s = max(0.08, (scan_end - scene_start) / max_points) source_cap = cv2.VideoCapture(str(cfg.paths.source_movie)) + source_fps = source_cap.get(cv2.CAP_PROP_FPS) or _scene_fps_light(scene, cfg) + stride = max(1, int(round(step_s * source_fps))) + start_frame = max(0, int(round(scene_start * source_fps))) + end_frame = max(start_frame, int(round(scene_end * source_fps))) + times: list[float] = [] + source_frames: list = [] + frame_idx = start_frame + while frame_idx <= end_frame: + source_cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx) + ok, frame = source_cap.read() + if not ok: + break + times.append(frame_idx / source_fps) + source_frames.append(prepared_gray(frame)) + frame_idx += stride + candidates: list[tuple[float, float, float]] = [] - t = scene_start - while t <= scan_end: + for i, t in enumerate(times): + if t > scan_end: + break vals = [] for offset, ref in zip(offsets, refs): - src = feature(frame_at(source_cap, t + offset)) - if src is not None: - vals.append(float(np.dot(ref, src) / len(ref))) - if len(vals) >= 3: - candidates.append((sum(vals) / len(vals), min(vals), t)) - t = round(t + step_s, 6) + j = int(round((t + offset - scene_start) / step_s)) + if 0 <= j < len(source_frames): + score = pair_score(ref, source_frames[j], mask) + else: + score = None + if score is not None: + vals.append(score) + if len(vals) >= 4: + avg_score = sum(vals) / len(vals) + candidates.append((0.55 * avg_score + 0.45 * min(vals), min(vals), t)) if not candidates: return None candidates.sort(reverse=True) best_score = candidates[0][0] - near_tie = [c for c in candidates if c[0] >= best_score - 0.01] + near_tie = [c for c in candidates if c[0] >= best_score - 0.002] chosen = min(near_tie, key=lambda c: abs(c[2] - original_in_s)) return chosen[2], chosen[0] @@ -1858,7 +1910,8 @@ def cmd_match(args: argparse.Namespace, cfg) -> list: results_to_save = results _save_results(results_to_save, cfg) - _regenerate_cutter_report(cfg) + force_report_beats = {int(args.beat)} if getattr(args, "beat", None) is not None else None + _regenerate_cutter_report(cfg, force_beats=force_report_beats) print(f"\n✅ {len(results)} / {len(beats)} beats matched.") for r in results: diff --git a/docs/ALGORITHM.md b/docs/ALGORITHM.md index 17be4e3..8e5505b 100644 --- a/docs/ALGORITHM.md +++ b/docs/ALGORITHM.md @@ -148,8 +148,17 @@ Phasenfehler nicht durch Schwarz verdeckt werden. Für Multi-Shot-Beats gilt zusätzlich eine Segment-Schwelle pro sichtbarer Einstellung. Ein gutes erstes Segment darf kein zweites Segment mit schwachem Score mitziehen. Segmente unter `multi_shot_segment_threshold` werden nicht als -Source-Material ausgegeben; der entsprechende Beat-Bereich bleibt im -Cutter-Report offen, bis ein eigenständig belastbarer Treffer gefunden wird. +stabile Wahrheit behandelt, sondern innerhalb derselben plausiblen Source-Scene +nachjustiert. Die Nachjustierung nutzt eine saliency-gewichtete Mehrframe-Prüfung: +Timecodes und statische Randbereiche werden entwertet, kontrastreiche und über +mehrere Trailerframes unterscheidbare Bildbereiche zählen stärker. Dadurch kann +eine schwache zweite Einstellung phasengenauer repariert werden, ohne den Fehler +durch Schwarzbild zu verdecken oder einen Beat manuell zu kuratieren. + +Der Cutter-Report verwendet Clip-Caching. Bereits vorhandene Compare-Clips werden +wiederverwendet; bei gezielten Rematches wird nur der betroffene Beat neu gerendert +(`CUTTER_REPORT_FORCE_BEATS`). So bleibt der Report aktuell, ohne alle Beats jedes +Mal neu zu kodieren. ## Vision-Seeds vs. Vollscan diff --git a/output/cutter_clips/beat_15_compare.mp4 b/output/cutter_clips/beat_15_compare.mp4 index 9a59905..35bdfa4 100644 Binary files a/output/cutter_clips/beat_15_compare.mp4 and b/output/cutter_clips/beat_15_compare.mp4 differ diff --git a/output/cutter_clips/beat_15_source.mp4 b/output/cutter_clips/beat_15_source.mp4 index 0a86755..40169fe 100644 Binary files a/output/cutter_clips/beat_15_source.mp4 and b/output/cutter_clips/beat_15_source.mp4 differ diff --git a/scripts/generate_cutter_report.py b/scripts/generate_cutter_report.py index 2f700ad..177908f 100644 --- a/scripts/generate_cutter_report.py +++ b/scripts/generate_cutter_report.py @@ -22,6 +22,7 @@ from __future__ import annotations import argparse import base64 import json +import os import re import subprocess import sys @@ -134,6 +135,19 @@ def _run(cmd: list[str], timeout: int = 120) -> bool: return False +def _forced_beats() -> set[int]: + raw = os.environ.get("CUTTER_REPORT_FORCE_BEATS", "") + forced: set[int] = set() + for part in re.split(r"[,;\s]+", raw): + if not part: + continue + try: + forced.add(int(part)) + except ValueError: + continue + return forced + + def extract_still(video_path: Path, t_s: float, out: Path) -> bool: """Always render fresh.""" if not video_path.exists(): @@ -410,6 +424,7 @@ def collect_rows( stills_dir.mkdir(parents=True, exist_ok=True) if with_clips: clips_dir.mkdir(parents=True, exist_ok=True) + force_beats = _forced_beats() rows: list[BeatRow] = [] for beat in beats: @@ -454,13 +469,17 @@ def collect_rows( if with_stills: t_still = beat_still_time(beat["start_s"], beat["end_s"]) tjpg = stills_dir / f"beat_{bid:02d}_trailer.jpg" - if extract_still(trailer_path, t_still, tjpg): + if tjpg.exists() and bid not in force_beats: + trailer_still = tjpg + elif extract_still(trailer_path, t_still, tjpg): trailer_still = tjpg if rec is not None: src_dur = max(0.04, rec["out_point_s"] - rec["in_point_s"]) s_still = rec["in_point_s"] + min(0.4, src_dur * 0.3) sjpg = stills_dir / f"beat_{bid:02d}_source.jpg" - if extract_still(source_path, s_still, sjpg): + if sjpg.exists() and bid not in force_beats: + source_still = sjpg + elif extract_still(source_path, s_still, sjpg): source_still = sjpg if with_clips: @@ -468,12 +487,16 @@ def collect_rows( # Trailer clip (cutter-side, simple) tmp4 = clips_dir / f"beat_{bid:02d}_trailer.mp4" - if extract_clip(trailer_path, beat["start_s"], beat_dur, tmp4): + if tmp4.exists() and bid not in force_beats: + trailer_clip = tmp4 + elif extract_clip(trailer_path, beat["start_s"], beat_dur, tmp4): trailer_clip = tmp4 if rec is not None: smp4 = clips_dir / f"beat_{bid:02d}_source.mp4" - if num_segs >= 2: + if smp4.exists() and bid not in force_beats: + source_clip = smp4 + elif num_segs >= 2: seg_specs = [ (float(s["in_point_s"]), max(0.04, float(s["out_point_s"]) - float(s["in_point_s"]))) @@ -502,7 +525,9 @@ def collect_rows( "match_score": rec.get("match_score", 0.0), "is_confirmed": rec.get("is_confirmed", False), }] - if build_compare_clip( + if cmp4.exists() and bid not in force_beats: + compare_clip = cmp4 + elif build_compare_clip( trailer_path, beat["start_s"], beat_dur, source_path, compare_segs, cmp4,