"""s19 — 공간지각 중심 재설계 (사용자 재정의, 2026-07-05 저녁): - **Stage 1 (LLM, gpt-5.5)**: 씬 원문 전체+장소 멤버+제작자 정정 → 사람이 공간을 지각하듯 **관계 중심 공간 서술** 생성 — 포함(무엇이 무엇 위/안에), 상대 위치(어느 부분), 상대 크기(부모 대비), 층수/높이(추정 표시), 이격, 동선. 좌표/치수/요소 설명/재질/무드 전부 금지. EN(렌더 소비)+KO(열람). - **Stage 2**: 같은 SPATIAL LAYOUT 줄들을 공유하고 스타일 계약만 분기 — bd(블록 다이어그램, s18 STYLE_FLAT)와 fp(도면, s15 STYLE_2D+스타일 ref)를 **각각 직행 T2I** 생성. fp 는 bd 이미지를 참조하지 않는다(체인 아님). - 산출: out/blockset/spatial_{bd,fp}_{gpt,nb2}.png, plans/spatial_layout_v1, spatial.html (부분 게시 + 30s 새로고침). 사용: .venv/bin/python s19_spatial.py [--only all|llm|images|html] """ import argparse import datetime import html as html_mod import sys from pathlib import Path sys.path.insert(0, str(Path(__file__).parent)) import forest_lib as F # noqa: E402 import s13_blockset_v2 as S13 # noqa: E402 import s14_blockgen as S14 # noqa: E402 import s15_fp_redo as S15 # noqa: E402 import s18_blocks_lean as S18 # noqa: E402 OUTB = F.OUT / "blockset" PAGE = Path(__file__).parent / "spatial.html" SPATIAL_SYSTEM = "\n".join([ "You are analysing ONE real filming location from the FULL ORIGINAL", "SCENE TEXTS and place-member data given below. Write its SPATIAL", "STRUCTURE the way a person perceives a space — relationships, not an", "inventory of parts:", "", "- CONTAINMENT: what stands on / sits inside what (ground, building,", " open decks, units, fixtures).", "- RELATIVE POSITION: which part of its parent each thing occupies", " (front/back, left/right, corner/edge/centre). Pick ONE consistent", " viewpoint for the whole property and keep it throughout.", "- RELATIVE SIZE: rough share of the parent (about a third of the deck,", " a one-room hut, a stride wide). NO coordinates, NO metric dimensions.", "- LEVELS & HEIGHT: how many storeys the building has (estimate when the", " text is silent — put it in the inferred notes), which level each open", " area or unit sits on, what is higher than what.", "- SPACING: roughly how far apart neighbouring things are (steps, arm", " spans), and what directly touches or abuts what.", "- CIRCULATION: the path a person walks through the property", " (street -> entrance -> ... -> top), naming which side each link is on.", "", "Rules:", "- One spatial fact per line, plain present tense, roughly 8-18 words.", "- Keep it LEAN: 12-22 lines covering every permanent structure the", " scenes rely on — nothing else.", "- Never define or describe what things are: no materials, colours,", " mood, story props, people. Names + spatial relations only.", "- Never reveal the interior room layout of any enclosed unit.", "- spatial_layout_ko: the same lines in Korean, same order.", "- evidence: for facts grounded in the texts, cite source (scene index /", " member id) + the exact quote. Assumptions go to inferred_notes.", "- A creator-correction block, when present, overrides the scene texts.", ]) SPATIAL_SCHEMA = { "type": "object", "additionalProperties": False, "properties": { "spatial_layout_en": {"type": "array", "items": {"type": "string"}}, "spatial_layout_ko": {"type": "array", "items": {"type": "string"}}, "inferred_notes_en": {"type": "array", "items": {"type": "string"}}, "inferred_notes_ko": {"type": "array", "items": {"type": "string"}}, "evidence": { "type": "array", "items": { "type": "object", "additionalProperties": False, "properties": { "about_en": {"type": "string"}, "source": {"type": "string"}, "quote": {"type": "string"}, }, "required": ["about_en", "source", "quote"], }, }, }, "required": ["spatial_layout_en", "spatial_layout_ko", "inferred_notes_en", "inferred_notes_ko", "evidence"], } def corrections_block(): ov = S14.load_overrides() lines = list(ov.get("facts_extra") or []) if not lines: return "" return ("CREATOR CORRECTIONS (override the scene texts where they" " conflict):\n" + "\n".join(lines) + "\n\n") def run_llm(recon): idxs, scenes = S13.scene_texts(recon) user = (corrections_block() + "PLACE MEMBERS (production data):\n" + S14.loc_lines(recon) + "\n\nFULL ORIGINAL SCENE TEXTS (every scene at this place):\n\n" + scenes) sp = F.llm("s19_spatial_extract", SPATIAL_SYSTEM, user, SPATIAL_SCHEMA) F.save_plan("spatial_layout_v1", sp) print(f"spatial: scenes {idxs} | layout {len(sp['spatial_layout_en'])}줄" f" | evidence {len(sp['evidence'])}건") return sp def bullet(lines): return "\n".join(f"- {l}" for l in lines) def bd_prompt(sp): return (S18.STYLE_FLAT + "\n\nSPATIAL LAYOUT — what is where (containment, relative" " position & size, levels, spacing). Draw exactly these" " relations as blocks:\n" + bullet(sp["spatial_layout_en"])) def fp_prompt(sp): return "\n\n".join([ "SPATIAL LAYOUT — what is where (containment, relative position &" " size, levels, spacing). Honour every relation below; compose the" " exact plan geometry yourself:\n" + bullet(sp["spatial_layout_en"]), S15.STYLE_REF_NOTE, S15.STYLE_2D, ]) def run_images(sp, recon): prompts = {} try: prompts = F.load_plan("spatial_prompts") except Exception: pass pb = bd_prompt(sp) pf = fp_prompt(sp) prompts.update({ "spatial_bd_gpt.png": pb, "spatial_bd_nb2.png": pb, "spatial_fp_gpt.png": pf, "spatial_fp_nb2.png": pf, }) F.save_plan("spatial_prompts", prompts) build_page(sp, prompts) F.img_nb2("s19_spatial_bd_nb2", pb, [], out_path=OUTB / "spatial_bd_nb2.png") build_page(sp, prompts) F.img_gpt("s19_spatial_bd_gpt", pb, refs=None, out_path=OUTB / "spatial_bd_gpt.png") build_page(sp, prompts) fp_style = S14.indoor_fp(recon) refs_nb2 = [(S14.NB2_STYLE_LABEL, fp_style)] if fp_style else [] F.img_nb2("s19_spatial_fp_nb2", pf, refs_nb2, out_path=OUTB / "spatial_fp_nb2.png") build_page(sp, prompts) F.img_gpt("s19_spatial_fp_gpt", pf, refs=[fp_style] if fp_style else None, out_path=OUTB / "spatial_fp_gpt.png") build_page(sp, prompts) def _esc(t): return html_mod.escape(str(t)) def _img_cell(fname, title): p = OUTB / fname if p.exists(): ts = datetime.datetime.fromtimestamp(p.stat().st_mtime) return (f"
" f"
{_esc(title)} — {fname}" f" ({ts:%H:%M:%S})
") return (f"
생성 중…
" f"
{_esc(title)} — {fname}
") def build_page(sp, prompts=None): rows_layout = "".join( f"{_esc(ko)}{_esc(en)}" for ko, en in zip(sp["spatial_layout_ko"], sp["spatial_layout_en"])) rows_inf = "".join( f"{_esc(ko)}{_esc(en)}" for ko, en in zip(sp["inferred_notes_ko"], sp["inferred_notes_en"])) rows_ev = "".join( f"{_esc(e['about_en'])}{_esc(e['source'])}" f"{_esc(e['quote'])}" for e in sp["evidence"]) prom_html = "" if prompts: prom_html = "".join( f"
{_esc(k)}
{_esc(v)}
" for k, v in sorted(prompts.items())) all_done = all((OUTB / f).exists() for f in ( "spatial_bd_gpt.png", "spatial_bd_nb2.png", "spatial_fp_gpt.png", "spatial_fp_nb2.png")) refresh = "" if all_done else "" page = f""" {refresh} s19 공간지각 — bd/fp 직행

s19 — 공간지각 중심: LLM 공간 서술 → bd / fp 각각 직행

재설계(사용자 재정의): LLM 이 씬 원문에서 사람의 공간지각처럼 관계 중심 서술(포함·상대 위치·상대 크기·층수/높이·이격·동선)을 생성하고, 같은 배치 정보를 공유한 채 스타일 계약만 분기해 bd 와 fp 를 각각 직행 생성(fp 는 bd 를 참조하지 않음). 좌표/나열/요소 설명 없음.

① LLM 공간 서술 (한국어 = 영어 원문 번역, 같은 순서)

{rows_layout}
한국어EN (렌더 입력 원문)

추정 노트 (원문 무근거 — LLM 추정 표시)

{rows_inf or ""}
한국어EN
없음

근거 인용 (evidence)

{rows_ev}
대상출처인용

② 블록 다이어그램 (bd) — 같은 SPATIAL LAYOUT + STYLE_FLAT

{_img_cell('spatial_bd_gpt.png', 'gpt-image-2')} {_img_cell('spatial_bd_nb2.png', 'nb2')}

③ 도면 (fp) — 같은 SPATIAL LAYOUT + STYLE_2D + 실내 fp 스타일 ref (bd 미경유)

{_img_cell('spatial_fp_gpt.png', 'gpt-image-2')} {_img_cell('spatial_fp_nb2.png', 'nb2')}

④ 실전송 프롬프트

{prom_html}

30초 자동 새로고침 · 생성 순서: bd_nb2 → bd_gpt → fp_nb2 → fp_gpt

""" PAGE.write_text(page, encoding="utf-8") print(f"page -> {PAGE}") def main(): ap = argparse.ArgumentParser() ap.add_argument("--only", default="all", choices=["all", "llm", "images", "html"]) args = ap.parse_args() recon = F.load_recon() if args.only in ("all", "llm"): sp = run_llm(recon) else: sp = F.load_plan("spatial_layout_v1") try: prompts = F.load_plan("spatial_prompts") except Exception: prompts = None build_page(sp, prompts) if args.only in ("all", "images"): run_images(sp, recon) F.runlog({"kind": "stage", "stage": "s19_spatial", "done": args.only}) if __name__ == "__main__": main()