1120 lines
48 KiB
Python
1120 lines
48 KiB
Python
from __future__ import annotations
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from typing import Any, Mapping
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CAMERA_DIRECTIONS = (
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"front-right quarter view",
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"right side view",
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"back-right quarter view",
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"back view",
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"back-left quarter view",
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"left side view",
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"front-left quarter view",
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"front view",
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)
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CAMERA_ELEVATIONS = ("low-angle shot", "eye-level shot", "elevated shot", "high-angle shot")
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CAMERA_DISTANCES = (
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"wide shot",
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"full-body shot",
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"three-quarter body shot",
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"medium shot",
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"close-up",
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"extreme close-up",
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)
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SCENE_CAMERA_PROFILES: tuple[dict[str, Any], ...] = (
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{
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"key": "business_cafe",
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"family": "coworking",
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"terms": ("business cafe", "work cafe", "coworking counter", "cafe counter with laptops", "coffee-counter work spots"),
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"layout_label": "Business cafe camera layout",
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"place": "business cafe coworking counter",
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"foreground": "counter edge, laptop corner, and small plant",
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"midground": "bar stools, warm desk lamps, and coffee-counter work spots",
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"background": "plants, mirror strip, menu wall, and repeated cafe work tables",
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"detail_label": "cafe details",
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"composition": {
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"woman": "business-cafe selfie frame with the woman near a counter edge and warm work-table depth behind her",
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"man": "business-cafe portrait frame with the man near a counter edge and warm work-table depth behind him",
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"default": "business-cafe frame with the subjects near a counter edge and warm work-table depth behind them",
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},
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},
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{
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"key": "office_after_hours",
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"family": "coworking",
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"terms": ("corporate office", "office after hours", "copier", "office lounge"),
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"layout_label": "Office camera layout",
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"place": "empty after-hours office",
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"foreground": "copier alcove edge, chair backs, and nearest desk corner",
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"midground": "repeating desks, glass partition seams, and muted monitor glow",
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"background": "rows of empty workstations, city-light windows, and quiet office depth",
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"detail_label": "office details",
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"composition": {
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"woman": "after-hours office frame with the woman near a desk edge and glass-partition depth behind her",
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"man": "after-hours office frame with the man near a desk edge and glass-partition depth behind him",
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"default": "after-hours office frame with the subjects near a desk edge and glass-partition depth behind them",
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},
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},
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{
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"key": "coworking_lounge",
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"family": "coworking",
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"terms": (
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"coworking",
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"cowork",
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"shared office",
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"laptops",
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"warm desks",
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"repeating desks",
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"glass partitions",
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),
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"layout_label": "Coworking camera layout",
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"place": "coworking lounge",
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"foreground": "near desk edge, laptop corner, and chair back",
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"midground": "warm work desks, laptop tables, and glass partition seams",
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"background": "tall windows, repeated desk rows, plants, and soft shared-office depth",
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"detail_label": "coworking details",
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"composition": {
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"woman": "coworking lounge selfie frame with the woman near a desk edge and tall-window depth behind her",
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"man": "coworking lounge portrait frame with the man near a desk edge and tall-window depth behind him",
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"default": "coworking lounge frame with the subjects near a desk edge and tall-window depth behind them",
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},
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},
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{
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"key": "classical_library",
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"family": "library",
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"terms": (
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"classical library",
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"library stacks",
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"large library",
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"grand library",
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"reading room",
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"book stacks",
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"rare-books",
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"rare books",
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"rolling ladders",
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),
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"layout_label": "Library camera layout",
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"place": "classical library",
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"foreground": "near bookshelf edge, reading-table corner, and brass lamp",
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"midground": "towering bookshelves, rolling ladders, carved columns, and marble floor lines",
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"background": "arched windows, repeated book aisles, warm brass lamps, and deep quiet library depth",
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"detail_label": "library details",
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"composition": {
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"woman": "classical library frame with the woman near a bookshelf edge and long shelf depth behind her",
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"man": "classical library frame with the man near a bookshelf edge and long shelf depth behind him",
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"default": "classical library frame with the subjects near a bookshelf edge and long shelf depth behind them",
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},
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},
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{
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"key": "creator_bedroom",
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"family": "private_creator",
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"terms": (
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"creator bedroom",
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"content setup",
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"phone tripod",
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"ring light",
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"phone on a mini tripod",
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"creator studio",
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"creator-shot framing",
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"vertical creator-video",
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),
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"layout_label": "Creator room camera layout",
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"place": "private creator room",
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"foreground": "bed edge, phone tripod, and rumpled sheets",
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"midground": "ring light stand, warm lamps, pillows, and creator props",
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"background": "soft bedding, curtains, mirror edge, and warm private-room depth",
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"detail_label": "creator-room details",
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"composition": {
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"woman": "creator-room frame with the woman near the bed edge and phone-tripod setup behind her",
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"man": "creator-room frame with the man near the bed edge and phone-tripod setup behind him",
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"default": "creator-room frame with the subjects near the bed edge and phone-tripod setup behind them",
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},
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},
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{
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"key": "mirror_room",
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"family": "private_creator",
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"terms": (
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"mirror selfie setup",
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"mirror wall",
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"mirror-facing",
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"floor mirror",
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"vanity mirror",
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"phone reflection",
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"reflected bodies",
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"black lacquer mirror",
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"neon mirror wall",
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),
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"layout_label": "Mirror-room camera layout",
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"place": "private mirror room",
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"foreground": "mirror edge, reflected phone angle, and floor reflection line",
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"midground": "bedside surface, vanity bulbs, glossy furniture, and reflected body plane",
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"background": "mirror depth, warm lamps, curtains, and repeated reflected sightlines",
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"detail_label": "mirror-room details",
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"composition": {
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"woman": "mirror-room frame with the woman aligned to the reflected phone angle and room depth behind her",
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"man": "mirror-room frame with the man aligned to the reflected phone angle and room depth behind him",
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"default": "mirror-room frame with the subjects aligned to the reflected phone angle and room depth behind them",
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},
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},
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{
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"key": "boudoir_bedroom",
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"family": "private_creator",
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"terms": (
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"boudoir bedroom",
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"silk-sheet bed",
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"silk sheets",
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"velvet headboard",
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"canopy bed",
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"four-poster bed",
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"satin bedding",
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"bedside phone",
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"hotel bedroom",
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),
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"layout_label": "Boudoir bedroom camera layout",
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"place": "boudoir bedroom",
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"foreground": "sheet fold, bedside edge, and pillow line",
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"midground": "rumpled bedding, warm lamps, canopy curtains, and soft floor shadows",
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"background": "headboard, drapes, mirror edge, and intimate bedroom depth",
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"detail_label": "bedroom details",
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"composition": {
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"woman": "boudoir bedroom frame with the woman on or beside the bed and warm bedroom depth behind her",
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"man": "boudoir bedroom frame with the man on or beside the bed and warm bedroom depth behind him",
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"default": "boudoir bedroom frame with the subjects on or beside the bed and warm bedroom depth behind them",
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},
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},
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{
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"key": "bathroom_shower",
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"family": "private_creator",
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"terms": (
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"bathroom counter",
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"private bathroom",
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"shower room",
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"wet tile",
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"steam",
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"steamy",
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"glass reflections",
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"vanity counter",
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"wet towels",
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),
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"layout_label": "Bathroom camera layout",
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"place": "private bathroom",
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"foreground": "counter edge, glass partition line, and towel edge",
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"midground": "mirror haze, vanity bulbs, wet tile, and reflected glass seams",
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"background": "shower wall, warm reflected light, steam, and tight private-room depth",
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"detail_label": "bathroom details",
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"composition": {
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"woman": "bathroom frame with the woman near the mirror or glass partition and tile depth behind her",
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"man": "bathroom frame with the man near the mirror or glass partition and tile depth behind him",
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"default": "bathroom frame with the subjects near the mirror or glass partition and tile depth behind them",
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},
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},
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{
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"key": "private_studio",
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"family": "private_creator",
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"terms": (
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"fetish studio",
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"private studio",
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"glossy black floor",
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"harness-wall",
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"chrome studio",
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"industrial loft",
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"neon-lit lacquer",
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"reflective panels",
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"controlled rim light",
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),
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"layout_label": "Private studio camera layout",
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"place": "private studio set",
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"foreground": "floor reflection edge, prop stand, and lighting-stand line",
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"midground": "controlled lights, reflective panels, backdrop seams, and studio props",
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"background": "dark curtains, glossy walls, rim light, and staged private-set depth",
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"detail_label": "studio details",
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"composition": {
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"woman": "private studio frame with the woman on the glossy floor plane and controlled lights behind her",
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"man": "private studio frame with the man on the glossy floor plane and controlled lights behind him",
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"default": "private studio frame with the subjects on the glossy floor plane and controlled lights behind them",
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},
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},
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{
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"key": "car_interior",
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"family": "private_creator",
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"terms": (
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"parked car interior",
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"private car backseat",
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"car backseat",
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"dashboard glow",
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"tinted windows",
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"seat reflections",
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),
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"layout_label": "Car interior camera layout",
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"place": "parked car interior",
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"foreground": "seat edge, door frame, and dashboard glow",
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"midground": "upholstery seams, window reflections, center console, and tight cabin geometry",
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"background": "tinted windows, rear seat depth, and enclosed car interior shadows",
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"detail_label": "car-interior details",
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"composition": {
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"woman": "car interior frame with the woman inside the tight cabin geometry and window reflections behind her",
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"man": "car interior frame with the man inside the tight cabin geometry and window reflections behind him",
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"default": "car interior frame with the subjects inside the tight cabin geometry and window reflections behind them",
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},
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},
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{
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"key": "hotel_corridor",
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"family": "semi_public",
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"terms": (
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"hotel corridor",
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"hotel service corridor",
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"hotel service alcove",
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"service alcove",
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"service hallway",
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"service hall",
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"repeating numbered doors",
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"numbered doors",
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"luggage carts",
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"stair landing",
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"hotel stair landing",
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),
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"layout_label": "Hotel corridor camera layout",
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"place": "hotel corridor",
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"foreground": "nearest doorframe edge, patterned carpet line, and wall sconce",
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"midground": "repeating numbered doors, brass wall lamps, service-alcove turns, and luggage carts",
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"background": "long corridor perspective, closed doors, warm late-night depth, and quiet hotel sightlines",
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"detail_label": "hotel corridor details",
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"composition": {
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"woman": "hotel corridor frame with the woman near a doorframe edge and repeated doors behind her",
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"man": "hotel corridor frame with the man near a doorframe edge and repeated doors behind him",
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"default": "hotel corridor frame with the subjects near a doorframe edge and repeated doors behind them",
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},
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},
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{
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"key": "parking_garage",
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"family": "semi_public",
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"terms": (
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"parking garage",
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"parking deck",
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"underground garage",
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"multi-level parking",
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"concrete pillars",
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"numbered pillars",
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"painted floor lines",
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"painted bay lines",
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"parked cars",
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),
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"layout_label": "Parking garage camera layout",
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"place": "parking garage",
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"foreground": "nearest concrete pillar, painted floor line, and car bumper edge",
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"midground": "repeating concrete pillars, parked cars, painted bay lines, and glossy concrete lanes",
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"background": "shadowed corners, fluorescent depth, numbered pillars, and long garage perspective",
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"detail_label": "parking garage details",
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"composition": {
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"woman": "parking garage frame with the woman beside a concrete pillar and repeated bay lines behind her",
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"man": "parking garage frame with the man beside a concrete pillar and repeated bay lines behind him",
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"default": "parking garage frame with the subjects beside a concrete pillar and repeated bay lines behind them",
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},
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},
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{
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"key": "theater_backstage",
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"family": "semi_public",
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"terms": (
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"theater backstage",
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"backstage wings",
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"cabaret backstage",
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"prop storage",
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"prop racks",
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"costume racks",
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"costume rails",
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"stage ropes",
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"scenery flats",
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),
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"layout_label": "Backstage camera layout",
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"place": "theater backstage",
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"foreground": "curtain edge, prop trunk corner, and costume-rack line",
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"midground": "layered velvet curtains, costume racks, prop shelves, and vanity bulb mirrors",
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"background": "dark stage wings, repeated scenery flats, narrow backstage passages, and warm light spill",
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"detail_label": "backstage details",
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"composition": {
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"woman": "backstage frame with the woman partly framed by curtains and costume racks behind her",
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"man": "backstage frame with the man partly framed by curtains and costume racks behind him",
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"default": "backstage frame with the subjects partly framed by curtains and costume racks behind them",
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},
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},
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{
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"key": "wine_cellar",
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"family": "semi_public",
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"terms": (
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"wine cellar",
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"wine storage",
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"bottle racks",
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"bottle shelves",
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"arched cellar",
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"brick niches",
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"cellar corridor",
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"stacked bottle",
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),
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"layout_label": "Wine cellar camera layout",
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"place": "wine cellar",
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"foreground": "near bottle-rack edge, crate corner, and stone floor line",
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"midground": "repeating bottle racks, arched brick niches, narrow aisles, and low amber lamps",
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"background": "cool shadowed depth, stacked shelves, cellar arches, and secluded rack rows",
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"detail_label": "wine cellar details",
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"composition": {
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"woman": "wine cellar frame with the woman between bottle racks and arched cellar depth behind her",
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"man": "wine cellar frame with the man between bottle racks and arched cellar depth behind him",
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"default": "wine cellar frame with the subjects between bottle racks and arched cellar depth behind them",
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},
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},
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{
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"key": "museum_archive",
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"family": "semi_public",
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"terms": (
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"museum archive",
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"gallery storage",
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"rare-books archive",
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"archive room",
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"storage shelves",
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"labeled boxes",
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"rolling shelves",
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"catalog drawers",
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"compact shelving",
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),
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"layout_label": "Archive camera layout",
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"place": "museum archive",
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"foreground": "storage-shelf edge, archive box corner, and work-table line",
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"midground": "labeled boxes, rolling shelves, frame racks, catalog drawers, and long work tables",
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"background": "compact shelving rows, soft overhead lights, archival aisles, and hidden storage depth",
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"detail_label": "archive details",
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"composition": {
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"woman": "archive frame with the woman beside labeled storage shelves and compact rows behind her",
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"man": "archive frame with the man beside labeled storage shelves and compact rows behind him",
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"default": "archive frame with the subjects beside labeled storage shelves and compact rows behind them",
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},
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},
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{
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"key": "laundromat_late_night",
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"family": "semi_public",
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"terms": (
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"laundromat",
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"coin laundry",
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"washing machines",
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"stacked dryers",
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"washer-door",
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"washer door",
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"folding tables",
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"detergent shelves",
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"machine row",
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),
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"layout_label": "Laundromat camera layout",
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"place": "late-night laundromat",
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"foreground": "folding-table edge, chrome washer door, and tiled floor line",
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"midground": "repeating washing machines, stacked dryers, detergent shelves, and empty machine rows",
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"background": "cool fluorescent depth, mirrored machine doors, front glass, and quiet back-corner sightlines",
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"detail_label": "laundromat details",
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"composition": {
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"woman": "laundromat frame with the woman near a folding table and repeated washer doors behind her",
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"man": "laundromat frame with the man near a folding table and repeated washer doors behind him",
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"default": "laundromat frame with the subjects near a folding table and repeated washer doors behind them",
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},
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},
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{
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"key": "train_station_lockers",
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"family": "semi_public",
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"terms": (
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"train-station locker",
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"train station locker",
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"locker corridor",
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"station underpass",
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"station service passage",
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"metal lockers",
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"vending machines",
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"utility doors",
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"warning stripes",
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),
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"layout_label": "Station locker camera layout",
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"place": "train-station locker corridor",
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"foreground": "locker edge, vending-machine corner, and tiled floor line",
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"midground": "repeating metal lockers, tiled wall seams, poster frames, and utility doors",
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"background": "fluorescent underpass depth, stair railings, warning stripes, and hidden side alcoves",
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"detail_label": "station locker details",
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"composition": {
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"woman": "station locker frame with the woman beside metal lockers and tiled depth behind her",
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"man": "station locker frame with the man beside metal lockers and tiled depth behind him",
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"default": "station locker frame with the subjects beside metal lockers and tiled depth behind them",
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},
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},
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{
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"key": "nightclub_back_hall",
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"family": "semi_public",
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"terms": (
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"nightclub back hallway",
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"club vip corridor",
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"vip club corridor",
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"music venue greenroom",
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"greenroom corridor",
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"coat-check racks",
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"neon strips",
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"velvet ropes",
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"mirrored wall panels",
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"stickered doors",
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),
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"layout_label": "Nightclub back-hall camera layout",
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"place": "nightclub back hallway",
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"foreground": "black door edge, velvet-rope post, and mirrored wall strip",
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"midground": "repeated dark doors, neon strips, coat-check racks, mirrored panels, and booth edges",
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"background": "distant colored dance-floor light, dim practical lamps, cable cases, and narrow hallway depth",
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"detail_label": "nightclub back-hall details",
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"composition": {
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"woman": "nightclub back-hall frame with the woman near a dark door edge and neon hallway depth behind her",
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"man": "nightclub back-hall frame with the man near a dark door edge and neon hallway depth behind him",
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|
"default": "nightclub back-hall frame with the subjects near a dark door edge and neon hallway depth behind them",
|
|
},
|
|
},
|
|
{
|
|
"key": "restaurant_private_booth",
|
|
"family": "semi_public",
|
|
"terms": (
|
|
"restaurant private booth",
|
|
"private booth",
|
|
"bistro back corner",
|
|
"after-hours dining",
|
|
"afterhours dining",
|
|
"high banquettes",
|
|
"dark wood partitions",
|
|
"folded napkins",
|
|
"stacked chairs",
|
|
"small round tables",
|
|
),
|
|
"layout_label": "Restaurant booth camera layout",
|
|
"place": "restaurant private booth",
|
|
"foreground": "table edge, high banquette back, and dark wood partition",
|
|
"midground": "repeating table lamps, folded napkins, mirrored wall panels, and empty tables",
|
|
"background": "after-hours dining-room depth, stacked chairs, service doorway, and secluded sightlines",
|
|
"detail_label": "restaurant booth details",
|
|
"composition": {
|
|
"woman": "restaurant booth frame with the woman beside a high banquette and table lamps behind her",
|
|
"man": "restaurant booth frame with the man beside a high banquette and table lamps behind him",
|
|
"default": "restaurant booth frame with the subjects beside a high banquette and table lamps behind them",
|
|
},
|
|
},
|
|
)
|
|
|
|
SCENE_CAMERA_PROFILE_KEYS = {str(profile["key"]): dict(profile) for profile in SCENE_CAMERA_PROFILES}
|
|
|
|
THEME_PROFILE_KEYS = {
|
|
"classical_library": "classical_library",
|
|
"creator_bedroom": "creator_bedroom",
|
|
"mirror_room": "mirror_room",
|
|
"boudoir_bedroom": "boudoir_bedroom",
|
|
"bathroom_shower": "bathroom_shower",
|
|
"private_studio": "private_studio",
|
|
"car_interior": "car_interior",
|
|
"fetish_studio": "private_studio",
|
|
"hotel_corridor": "hotel_corridor",
|
|
"parking_garage": "parking_garage",
|
|
"theater_backstage": "theater_backstage",
|
|
"wine_cellar": "wine_cellar",
|
|
"museum_archive": "museum_archive",
|
|
"laundromat_late_night": "laundromat_late_night",
|
|
"train_station_lockers": "train_station_lockers",
|
|
"nightclub_back_hall": "nightclub_back_hall",
|
|
"restaurant_private_booth": "restaurant_private_booth",
|
|
}
|
|
|
|
SCENE_SLUG_PROFILE_KEYS = {
|
|
"coworking_lounge_window": "coworking_lounge",
|
|
"business_cafe_counter": "business_cafe",
|
|
"office_afterhours_affair": "office_after_hours",
|
|
"classical_large_library": "classical_library",
|
|
"old_world_reading_room": "classical_library",
|
|
"hidden_library_stacks": "classical_library",
|
|
"library_stacks_secret": "classical_library",
|
|
"creator_bedroom_ring_light": "creator_bedroom",
|
|
"onlyfans_mirror_bedroom": "mirror_room",
|
|
"walk_in_closet_tryon": "mirror_room",
|
|
"hotel_bed_phone_tripod": "creator_bedroom",
|
|
"bathroom_counter_selfie": "bathroom_shower",
|
|
"vanity_ring_light_close": "mirror_room",
|
|
"apartment_floor_content": "creator_bedroom",
|
|
"balcony_phone_selfie": "creator_bedroom",
|
|
"car_interior_creator_selfie": "car_interior",
|
|
"shower_steam_phone_reflection": "bathroom_shower",
|
|
"studio_bedroom_backdrop": "creator_bedroom",
|
|
"couch_lamp_creator_clip": "creator_bedroom",
|
|
"large_bedroom_mirror_selfie": "mirror_room",
|
|
"antique_mirror_boudoir": "mirror_room",
|
|
"bathroom_mirror_haze": "bathroom_shower",
|
|
"closet_full_length_mirror": "mirror_room",
|
|
"hotel_mirror_city_view": "mirror_room",
|
|
"neon_mirror_wall": "mirror_room",
|
|
"gold_vanity_mirror": "mirror_room",
|
|
"black_lacquer_mirror_room": "mirror_room",
|
|
"hardcore_bedroom_mirror_pair": "mirror_room",
|
|
"hardcore_hotel_mirror_pair": "mirror_room",
|
|
"hardcore_shower_mirror_pair": "bathroom_shower",
|
|
"hardcore_threesome_mirror_suite": "mirror_room",
|
|
"warm_boudoir_canopy_bed": "boudoir_bedroom",
|
|
"silk_bed_close_creator": "boudoir_bedroom",
|
|
"velvet_headboard_bedroom": "boudoir_bedroom",
|
|
"four_poster_lingerie_room": "boudoir_bedroom",
|
|
"hotel_satin_bedroom": "boudoir_bedroom",
|
|
"rose_lamp_bedroom": "boudoir_bedroom",
|
|
"black_latex_studio_floor": "private_studio",
|
|
"red_velvet_lacquer_room": "private_studio",
|
|
"industrial_loft_private_set": "private_studio",
|
|
"neon_lacquer_private_room": "private_studio",
|
|
"harness_wall_studio": "private_studio",
|
|
"chrome_fetish_set": "private_studio",
|
|
"costume_dressing_room_phone": "theater_backstage",
|
|
"burlesque_stage_close": "theater_backstage",
|
|
"cabaret_backstage_vanity": "theater_backstage",
|
|
"after_dark_private_office": "office_after_hours",
|
|
"fantasy_parlor_content_set": "private_studio",
|
|
"cosplay_hotel_mirror": "mirror_room",
|
|
"hardcore_bedroom_phone_tripod": "creator_bedroom",
|
|
"hardcore_hotel_bed_city": "boudoir_bedroom",
|
|
"hardcore_mirror_bedroom": "mirror_room",
|
|
"hardcore_low_mattress_studio": "private_studio",
|
|
"hardcore_velvet_room": "private_studio",
|
|
"hardcore_shower_room": "bathroom_shower",
|
|
"hardcore_lounge_couch": "private_studio",
|
|
"hardcore_floor_cushion_room": "boudoir_bedroom",
|
|
"hardcore_ring_light_bed": "creator_bedroom",
|
|
"hardcore_bathroom_counter": "bathroom_shower",
|
|
"hardcore_walk_in_closet_floor": "mirror_room",
|
|
"hardcore_car_backseat": "car_interior",
|
|
"bed_edge_close_contact": "boudoir_bedroom",
|
|
"low_bed_mirror_angle": "mirror_room",
|
|
"hotel_bed_overhead": "boudoir_bedroom",
|
|
"floor_mattress_creator_set": "creator_bedroom",
|
|
"canopy_bed_explicit_set": "boudoir_bedroom",
|
|
"velvet_bedroom_wide": "boudoir_bedroom",
|
|
"penetration_mirror_bedroom": "mirror_room",
|
|
"penetration_edge_of_bed": "boudoir_bedroom",
|
|
"penetration_low_mattress": "private_studio",
|
|
"penetration_couch_lounge": "private_studio",
|
|
"penetration_shower_bench": "bathroom_shower",
|
|
"penetration_floor_cushions": "boudoir_bedroom",
|
|
"oral_bed_kneeling_close": "boudoir_bedroom",
|
|
"oral_mirror_floor": "mirror_room",
|
|
"oral_couch_front_view": "private_studio",
|
|
"oral_shower_steam": "bathroom_shower",
|
|
"oral_vanity_floor": "mirror_room",
|
|
"oral_hotel_bed_close": "boudoir_bedroom",
|
|
"anal_rear_mirror_bed": "mirror_room",
|
|
"anal_bent_over_couch": "private_studio",
|
|
"anal_edge_bed_low_angle": "boudoir_bedroom",
|
|
"anal_shower_wall": "bathroom_shower",
|
|
"anal_velvet_bench": "private_studio",
|
|
"anal_floor_mattress_mirror": "mirror_room",
|
|
"threesome_wide_bedroom": "boudoir_bedroom",
|
|
"threesome_hotel_suite": "boudoir_bedroom",
|
|
"threesome_floor_cushions": "boudoir_bedroom",
|
|
"threesome_studio_mattress": "private_studio",
|
|
"threesome_shower_room": "bathroom_shower",
|
|
"threesome_velvet_lounge": "private_studio",
|
|
"group_suite_wide_bed": "boudoir_bedroom",
|
|
"group_studio_mattress_room": "private_studio",
|
|
"group_velvet_orgy_room": "private_studio",
|
|
"group_lounge_couches": "private_studio",
|
|
"group_floor_pillow_room": "boudoir_bedroom",
|
|
"group_shower_spa_room": "bathroom_shower",
|
|
"group_rooftop_private_party": "creator_bedroom",
|
|
"group_hotel_party_bedroom": "boudoir_bedroom",
|
|
"group_backstage_private_room": "theater_backstage",
|
|
"group_neon_loft_room": "private_studio",
|
|
"group_mirror_wall_suite": "mirror_room",
|
|
"group_lacquer_mirror_lounge": "mirror_room",
|
|
"climax_bed_close_flash": "boudoir_bedroom",
|
|
"climax_mirror_counter": "mirror_room",
|
|
"climax_floor_sheets": "boudoir_bedroom",
|
|
"climax_hotel_bed_flash": "boudoir_bedroom",
|
|
"climax_shower_tile": "bathroom_shower",
|
|
"climax_velvet_couch": "private_studio",
|
|
}
|
|
|
|
PROFILE_TEXT_FIELDS = (
|
|
"key",
|
|
"family",
|
|
"layout_label",
|
|
"place",
|
|
"foreground",
|
|
"midground",
|
|
"background",
|
|
"detail_label",
|
|
)
|
|
|
|
MISMATCHED_COMPOSITION_TERMS = (
|
|
"outfit-check",
|
|
"outfit check",
|
|
"mirror view",
|
|
"mirror pose",
|
|
"bag",
|
|
"shoes",
|
|
"footwear",
|
|
)
|
|
|
|
|
|
def _clean_text(value: Any) -> str:
|
|
return " ".join(str(value or "").strip().split())
|
|
|
|
|
|
def _profile_by_key(value: Any) -> dict[str, Any]:
|
|
key = str(value or "").strip()
|
|
if not key:
|
|
return {}
|
|
if key in SCENE_CAMERA_PROFILE_KEYS:
|
|
return dict(SCENE_CAMERA_PROFILE_KEYS[key])
|
|
mapped_key = THEME_PROFILE_KEYS.get(key)
|
|
if mapped_key and mapped_key in SCENE_CAMERA_PROFILE_KEYS:
|
|
return dict(SCENE_CAMERA_PROFILE_KEYS[mapped_key])
|
|
return {}
|
|
|
|
|
|
def _profile_title(value: str) -> str:
|
|
text = _clean_text(value).replace("_", " ").replace("-", " ")
|
|
if not text:
|
|
return "Scene"
|
|
return " ".join(part[:1].upper() + part[1:] for part in text.split())
|
|
|
|
|
|
def _default_composition(profile: dict[str, Any]) -> dict[str, str]:
|
|
place = _clean_text(profile.get("place")) or "scene"
|
|
foreground = _clean_text(profile.get("foreground")) or "foreground anchor"
|
|
background = _clean_text(profile.get("background")) or "environment depth"
|
|
return {
|
|
"woman": f"{place} frame with the woman near {foreground} and {background} behind her",
|
|
"man": f"{place} frame with the man near {foreground} and {background} behind him",
|
|
"default": f"{place} frame with the subjects near {foreground} and {background} behind them",
|
|
}
|
|
|
|
|
|
def normalize_scene_camera_profile(value: Any) -> dict[str, Any]:
|
|
if not isinstance(value, dict):
|
|
return {}
|
|
base = _profile_by_key(value.get("base_profile_key") or value.get("extends"))
|
|
merged = dict(base)
|
|
for key, raw_value in value.items():
|
|
if key in ("base_profile_key", "extends"):
|
|
continue
|
|
merged[key] = raw_value
|
|
has_profile_fields = any(_clean_text(merged.get(key)) for key in ("layout_label", "place", "foreground", "midground", "background"))
|
|
if not has_profile_fields:
|
|
return {}
|
|
key = _clean_text(merged.get("key") or merged.get("slug") or merged.get("name") or base.get("key") or "custom_scene")
|
|
place = _clean_text(merged.get("place") or merged.get("name") or key.replace("_", " "))
|
|
profile = {field: _clean_text(merged.get(field)) for field in PROFILE_TEXT_FIELDS}
|
|
profile["key"] = key
|
|
profile["family"] = profile["family"] or "custom"
|
|
profile["place"] = place
|
|
profile["layout_label"] = profile["layout_label"] or f"{_profile_title(place)} camera layout"
|
|
profile["foreground"] = profile["foreground"] or base.get("foreground", "foreground anchor")
|
|
profile["midground"] = profile["midground"] or base.get("midground", "midground environment anchors")
|
|
profile["background"] = profile["background"] or base.get("background", "background depth")
|
|
profile["detail_label"] = profile["detail_label"] or f"{place} details"
|
|
composition = merged.get("composition")
|
|
if isinstance(composition, dict):
|
|
profile["composition"] = {
|
|
str(key): _clean_text(text)
|
|
for key, text in composition.items()
|
|
if _clean_text(text)
|
|
}
|
|
else:
|
|
base_composition = base.get("composition") if isinstance(base.get("composition"), dict) else {}
|
|
profile["composition"] = dict(base_composition) if base_composition else _default_composition(profile)
|
|
if not profile["composition"]:
|
|
profile["composition"] = _default_composition(profile)
|
|
return profile
|
|
|
|
|
|
def _scene_entry_text(scene_entry: Any) -> str:
|
|
if not isinstance(scene_entry, dict):
|
|
return ""
|
|
return str(
|
|
scene_entry.get("prompt")
|
|
or scene_entry.get("description")
|
|
or scene_entry.get("text")
|
|
or scene_entry.get("name")
|
|
or ""
|
|
).strip()
|
|
|
|
|
|
def _scene_entry_profile_key(scene_entry: Any) -> str:
|
|
if not isinstance(scene_entry, dict):
|
|
return ""
|
|
explicit = str(
|
|
scene_entry.get("scene_camera_profile_key")
|
|
or scene_entry.get("camera_profile_key")
|
|
or scene_entry.get("camera_profile")
|
|
or scene_entry.get("profile")
|
|
or ""
|
|
).strip()
|
|
if explicit:
|
|
return explicit
|
|
slug = str(scene_entry.get("slug") or "").strip()
|
|
return SCENE_SLUG_PROFILE_KEYS.get(slug, "")
|
|
|
|
|
|
def _scene_entry_profile(scene_entry: Any) -> dict[str, Any]:
|
|
if not isinstance(scene_entry, dict):
|
|
return {}
|
|
for key in ("scene_camera_profile", "camera_profile"):
|
|
profile = normalize_scene_camera_profile(scene_entry.get(key))
|
|
if profile:
|
|
return profile
|
|
profile = normalize_scene_camera_profile(scene_entry.get("profile"))
|
|
if profile:
|
|
return profile
|
|
return normalize_scene_camera_profile(scene_entry)
|
|
|
|
|
|
def scene_camera_profile(
|
|
scene_text: Any = "",
|
|
*,
|
|
scene_entry: Any = None,
|
|
theme: Any = "",
|
|
profile_key: Any = "",
|
|
) -> dict[str, Any]:
|
|
inline_explicit_profile = normalize_scene_camera_profile(profile_key)
|
|
if inline_explicit_profile:
|
|
return inline_explicit_profile
|
|
explicit_profile = _profile_by_key(profile_key)
|
|
if explicit_profile:
|
|
return explicit_profile
|
|
inline_entry_profile = _scene_entry_profile(scene_entry)
|
|
if inline_entry_profile:
|
|
return inline_entry_profile
|
|
entry_profile = _profile_by_key(_scene_entry_profile_key(scene_entry))
|
|
if entry_profile:
|
|
return entry_profile
|
|
theme_profile = _profile_by_key(theme)
|
|
if theme_profile:
|
|
return theme_profile
|
|
if isinstance(scene_entry, dict):
|
|
entry_theme_profile = _profile_by_key(scene_entry.get("theme"))
|
|
if entry_theme_profile:
|
|
return entry_theme_profile
|
|
text = " ".join(part for part in (str(scene_text or ""), _scene_entry_text(scene_entry)) if part).lower()
|
|
if not text:
|
|
return {}
|
|
for profile in SCENE_CAMERA_PROFILES:
|
|
if any(term in text for term in profile["terms"]):
|
|
return dict(profile)
|
|
return {}
|
|
|
|
|
|
def is_coworking_scene(scene_text: Any) -> bool:
|
|
return scene_camera_profile(scene_text).get("family") == "coworking"
|
|
|
|
|
|
def is_scene_camera_aware(scene_text: Any) -> bool:
|
|
return bool(scene_camera_profile(scene_text))
|
|
|
|
|
|
def _compact_label(value: Any, compact_labels: Mapping[str, str] | None = None) -> str:
|
|
text = str(value or "")
|
|
if compact_labels and text in compact_labels:
|
|
return compact_labels[text]
|
|
return text.replace("_", " ")
|
|
|
|
|
|
def camera_geometry_phrase(parsed: dict[str, Any], compact_labels: Mapping[str, str] | None = None) -> str:
|
|
direction = str(parsed.get("orbit_direction") or "").strip()
|
|
elevation = str(parsed.get("orbit_elevation_label") or "").strip()
|
|
distance = str(parsed.get("orbit_distance_label") or "").strip()
|
|
custom = str(parsed.get("custom_camera_prompt") or "").strip()
|
|
if not any((direction, elevation, distance)) and custom:
|
|
return custom
|
|
parts = [part for part in (direction, elevation, distance) if part and part != "auto"]
|
|
if parts:
|
|
return ", ".join(parts)
|
|
compact_parts = [
|
|
_compact_label(parsed.get(key), compact_labels)
|
|
for key in ("shot_size", "angle", "distance")
|
|
]
|
|
compact_parts = [part for part in compact_parts if part and part != "auto"]
|
|
return ", ".join(compact_parts)
|
|
|
|
|
|
def camera_direction_from_text(text: Any) -> str:
|
|
source = str(text or "").lower()
|
|
for label in CAMERA_DIRECTIONS:
|
|
if label in source:
|
|
return label
|
|
return ""
|
|
|
|
|
|
def camera_elevation_from_text(text: Any) -> str:
|
|
source = str(text or "").lower()
|
|
for label in CAMERA_ELEVATIONS:
|
|
if label in source:
|
|
return label
|
|
return ""
|
|
|
|
|
|
def camera_distance_from_text(text: Any) -> str:
|
|
source = str(text or "").lower()
|
|
for label in CAMERA_DISTANCES:
|
|
if label in source:
|
|
return label
|
|
return ""
|
|
|
|
|
|
def coworking_location_profile(scene_text: Any) -> dict[str, str]:
|
|
profile = scene_camera_profile(scene_text)
|
|
if profile.get("family") == "coworking":
|
|
return profile
|
|
return scene_camera_profile("coworking lounge")
|
|
|
|
|
|
def scene_subject_terms(subject_kind: str, pov_labels: list[str] | None = None) -> tuple[str, str]:
|
|
if pov_labels:
|
|
return "the visible partner", "them"
|
|
if subject_kind == "woman":
|
|
return "the woman", "her"
|
|
if subject_kind == "man":
|
|
return "the man", "him"
|
|
if subject_kind == "couple":
|
|
return "the couple", "them"
|
|
return "the subjects", "them"
|
|
|
|
|
|
def coworking_subject_terms(subject_kind: str, pov_labels: list[str] | None = None) -> tuple[str, str]:
|
|
return scene_subject_terms(subject_kind, pov_labels)
|
|
|
|
|
|
def scene_direction_detail(
|
|
direction: str,
|
|
profile: dict[str, str],
|
|
pov_labels: list[str] | None = None,
|
|
subject_kind: str = "subjects",
|
|
) -> str:
|
|
direction = str(direction or "").strip().lower()
|
|
foreground = profile["foreground"]
|
|
midground = profile["midground"]
|
|
background = profile["background"]
|
|
detail_label = profile.get("detail_label") or "location details"
|
|
subject, pronoun = scene_subject_terms(subject_kind, pov_labels)
|
|
if pov_labels:
|
|
if "right side" in direction:
|
|
return f"{subject} is in right-side profile; {midground} run behind {pronoun} toward {background}, with {detail_label} kept at the frame edges"
|
|
if "left side" in direction:
|
|
return f"{subject} is in left-side profile; {midground} run behind {pronoun} toward {background}, with {detail_label} kept at the frame edges"
|
|
if "back-right" in direction or "back-left" in direction:
|
|
return f"{subject} stays close in one continuous diagonal first-person body angle; {midground} lead toward {background} behind {pronoun} at the edges, not in the lower foreground"
|
|
if direction == "back view":
|
|
return f"the viewer looks past {subject}'s back toward {midground}, then into {background}; only POV body cues sit low in frame"
|
|
if "front-right" in direction or "front-left" in direction:
|
|
return f"{subject} fills the first-person front-quarter view; {midground} recede diagonally behind {pronoun} toward {background}"
|
|
return f"{subject} faces the viewer in first-person view; {midground} and {background} stay behind {pronoun}, not between viewer and body"
|
|
if "right side" in direction or "left side" in direction:
|
|
return f"{subject} is held in side profile along the {foreground}; {midground} run laterally behind {pronoun}, with {background} still readable"
|
|
if "back-right" in direction or "back-left" in direction:
|
|
return f"{subject} is viewed from a rear-quarter angle, partly turning back toward camera; the {foreground} stays low in frame while {midground} lead into {background}"
|
|
if direction == "back view":
|
|
return f"{subject} is seen from behind with the {foreground} at camera side, facing into {midground} and {background}"
|
|
if "front-right" in direction or "front-left" in direction:
|
|
return f"{subject} is placed beside the {foreground}; {midground} recede diagonally behind {pronoun} toward {background}"
|
|
return f"{subject} faces camera beside the {foreground}; {midground} sit between {pronoun} and {background}"
|
|
|
|
|
|
def coworking_direction_detail(
|
|
direction: str,
|
|
profile: dict[str, str],
|
|
pov_labels: list[str] | None = None,
|
|
subject_kind: str = "subjects",
|
|
) -> str:
|
|
return scene_direction_detail(direction, profile, pov_labels, subject_kind)
|
|
|
|
|
|
def scene_distance_detail(
|
|
distance: str,
|
|
profile: dict[str, str],
|
|
subject_kind: str,
|
|
pov_labels: list[str] | None = None,
|
|
) -> str:
|
|
distance = str(distance or "").strip().lower()
|
|
subject, _pronoun = scene_subject_terms(subject_kind, pov_labels)
|
|
if pov_labels:
|
|
if "wide" in distance or "full-body" in distance or "full body" in distance:
|
|
return f"wide POV keeps {subject} readable with {profile['place']} context behind them and environmental anchors only beside or beyond the action"
|
|
if "close" in distance:
|
|
return f"close POV keeps {subject} dominant with {profile['place']} context only at the sides or background"
|
|
return f"medium POV keeps {subject} dominant with room context beside or behind them"
|
|
if "wide" in distance or "full-body" in distance or "full body" in distance:
|
|
return f"wide crop keeps the {profile['foreground']}, {profile['midground']}, and {profile['background']} readable"
|
|
if "close" in distance:
|
|
return f"close crop keeps one anchor from the {profile['foreground']} visible"
|
|
return f"medium crop keeps {subject} dominant"
|
|
|
|
|
|
def coworking_distance_detail(
|
|
distance: str,
|
|
profile: dict[str, str],
|
|
subject_kind: str,
|
|
pov_labels: list[str] | None = None,
|
|
) -> str:
|
|
return scene_distance_detail(distance, profile, subject_kind, pov_labels)
|
|
|
|
|
|
def scene_elevation_detail(
|
|
elevation: str,
|
|
profile: dict[str, str],
|
|
subject_kind: str,
|
|
pov_labels: list[str] | None = None,
|
|
) -> str:
|
|
elevation = str(elevation or "").strip().lower()
|
|
subject, pronoun = scene_subject_terms(subject_kind, pov_labels)
|
|
if pov_labels:
|
|
if "low-angle" in elevation:
|
|
return f"low angle keeps POV body cues low while the {profile['background']} rises behind {pronoun}"
|
|
if "elevated" in elevation:
|
|
return f"elevated POV keeps the viewer's eye line slightly higher than {subject}, with location anchors only beside or behind {pronoun}"
|
|
if "high-angle" in elevation:
|
|
return f"high angle looks down from the viewer's position with {profile['midground']} only in the background"
|
|
return f"eye-level angle keeps {profile['midground']} behind {pronoun}"
|
|
if "low-angle" in elevation:
|
|
return f"low angle keeps the {profile['foreground']} low while {profile['background']} rises behind {pronoun}"
|
|
if "elevated" in elevation:
|
|
return f"elevated angle shows the {profile['foreground']} and {profile['midground']} around {pronoun}"
|
|
if "high-angle" in elevation:
|
|
return f"high angle shows the {profile['place']} layout and placement of {pronoun}"
|
|
return f"eye-level angle keeps {profile['midground']} visually stable"
|
|
|
|
|
|
def coworking_elevation_detail(
|
|
elevation: str,
|
|
profile: dict[str, str],
|
|
subject_kind: str,
|
|
pov_labels: list[str] | None = None,
|
|
) -> str:
|
|
return scene_elevation_detail(elevation, profile, subject_kind, pov_labels)
|
|
|
|
|
|
def scene_camera_directive(
|
|
scene_text: Any,
|
|
parsed: dict[str, Any],
|
|
pov_labels: list[str] | None = None,
|
|
subject_kind: str = "subjects",
|
|
compact_labels: Mapping[str, str] | None = None,
|
|
*,
|
|
scene_entry: Any = None,
|
|
theme: Any = "",
|
|
profile_key: Any = "",
|
|
) -> str:
|
|
profile = scene_camera_profile(scene_text, scene_entry=scene_entry, theme=theme, profile_key=profile_key)
|
|
if not profile:
|
|
return ""
|
|
direction = str(parsed.get("orbit_direction") or "").strip()
|
|
elevation = str(parsed.get("orbit_elevation_label") or "").strip()
|
|
distance = str(parsed.get("orbit_distance_label") or "").strip()
|
|
custom_prompt = str(parsed.get("custom_camera_prompt") or "").strip()
|
|
direction = direction or camera_direction_from_text(custom_prompt)
|
|
elevation = elevation or camera_elevation_from_text(custom_prompt)
|
|
distance = distance or camera_distance_from_text(custom_prompt)
|
|
if not any((direction, elevation, distance, custom_prompt)):
|
|
return ""
|
|
direction_detail = scene_direction_detail(direction, profile, pov_labels, subject_kind)
|
|
distance_detail = scene_distance_detail(distance, profile, subject_kind, pov_labels)
|
|
elevation_detail = scene_elevation_detail(elevation, profile, subject_kind, pov_labels)
|
|
geometry = camera_geometry_phrase(parsed, compact_labels)
|
|
geometry_clause = f" ({geometry})" if geometry else ""
|
|
if pov_labels:
|
|
return (
|
|
f"{profile['layout_label']} from POV{geometry_clause}: {direction_detail}. "
|
|
f"{distance_detail}; {elevation_detail}; lower foreground is reserved for POV body or hand cues; "
|
|
f"use the multiangle camera only as first-person spatial geometry."
|
|
)
|
|
return (
|
|
f"{profile['layout_label']}{geometry_clause}: {direction_detail}; "
|
|
f"{distance_detail}; {elevation_detail}."
|
|
)
|
|
|
|
|
|
def coworking_camera_scene_directive(
|
|
scene_text: Any,
|
|
parsed: dict[str, Any],
|
|
pov_labels: list[str] | None = None,
|
|
subject_kind: str = "subjects",
|
|
compact_labels: Mapping[str, str] | None = None,
|
|
) -> str:
|
|
if not is_coworking_scene(scene_text):
|
|
return ""
|
|
return scene_camera_directive(scene_text, parsed, pov_labels, subject_kind, compact_labels)
|
|
|
|
|
|
def profile_composition_text(profile: dict[str, Any], subject_kind: str) -> str:
|
|
composition = profile.get("composition") if isinstance(profile.get("composition"), dict) else {}
|
|
if subject_kind == "woman" and composition.get("woman"):
|
|
return str(composition["woman"])
|
|
if subject_kind == "man" and composition.get("man"):
|
|
return str(composition["man"])
|
|
text = str(composition.get("default") or f"{profile['place']} frame with the subjects clearly placed in the room")
|
|
if subject_kind == "couple":
|
|
text = text.replace("the subjects", "the couple")
|
|
if "composition" not in text.lower():
|
|
text = f"{text} composition"
|
|
return text
|
|
|
|
|
|
def contextual_composition_prompt(
|
|
scene_text: Any,
|
|
composition: Any,
|
|
subject_kind: str = "subjects",
|
|
*,
|
|
scene_entry: Any = None,
|
|
theme: Any = "",
|
|
profile_key: Any = "",
|
|
) -> str:
|
|
text = str(composition or "").strip()
|
|
if not text:
|
|
return text
|
|
profile = scene_camera_profile(scene_text, scene_entry=scene_entry, theme=theme, profile_key=profile_key)
|
|
if not profile:
|
|
return text
|
|
lower = text.lower()
|
|
profile_lower = " ".join(
|
|
str(profile.get(key, "")).lower()
|
|
for key in ("place", "foreground", "midground", "background")
|
|
)
|
|
already_matches = any(term and term in lower for term in profile_lower.replace(",", " ").split())
|
|
mismatched = any(term in lower for term in MISMATCHED_COMPOSITION_TERMS)
|
|
office_generic = any(term in lower for term in ("office-lobby", "office lobby", "walking composition", "outfit-check"))
|
|
if not mismatched and not office_generic and already_matches:
|
|
return text
|
|
if not mismatched and not office_generic and profile.get("family") != "coworking":
|
|
return text
|
|
return profile_composition_text(profile, subject_kind)
|
|
|
|
|
|
def coworking_composition_prompt(scene_text: Any, composition: Any, subject_kind: str = "subjects") -> str:
|
|
return contextual_composition_prompt(scene_text, composition, subject_kind)
|
|
|
|
|
|
def camera_scene_directive_for_context(
|
|
scene_text: Any,
|
|
parsed_camera_config: dict[str, Any],
|
|
pov_labels: list[str] | None = None,
|
|
subject_kind: str = "subjects",
|
|
compact_labels: Mapping[str, str] | None = None,
|
|
*,
|
|
scene_entry: Any = None,
|
|
theme: Any = "",
|
|
profile_key: Any = "",
|
|
) -> str:
|
|
if (
|
|
parsed_camera_config.get("camera_detail") == "off"
|
|
or parsed_camera_config.get("camera_mode") == "disabled"
|
|
):
|
|
return ""
|
|
return scene_camera_directive(
|
|
scene_text,
|
|
parsed_camera_config,
|
|
pov_labels,
|
|
subject_kind,
|
|
compact_labels,
|
|
scene_entry=scene_entry,
|
|
theme=theme,
|
|
profile_key=profile_key,
|
|
)
|