대시보드 폴드아웃/드릴다운 정리 + 페르소나 역린·misconduct 반응 + 게이트웨이 격리·RAG 비차단 수정
SSOT 대시보드:
- 한신대 기술분석 PDF(19쪽) 정합성 분석 + 이번 세션 발견 섹션 추가
- 섹션 폴드아웃(접기)·상단 목차(드릴다운)·모두 펼치기/접기 — 내용 보존, 레이아웃만 정리
페르소나 반응 강화('저항·반응 조절' 핵심 차별):
- PersonaCard.triggers(역린) 필드 + CCD 핵심상처 파생 역린 블록
- L0에 무례·모욕·조롱 시 현실적 동맹 균열 반응 지침
버그·성능 수정(라이브/E2E로 포착):
- 게이트웨이 페르소나 격리: --append-system-prompt를 --system-prompt(교체)로 + --exclude-dynamic-system-prompt-sections (내담자 캐릭터 붕괴·개발맥락 누출 차단)
- RAG: 임베더 동기 로드(약 7-13초)를 _warm_rag_caches 백그라운드 warm으로(세션 생성 블로킹 회귀 수정)
- voice TTS RMS 데드힌트 제거, init_state OpennessParams 파라미터객체화
- 한국어 PII(날짜·금액·주소) 마스킹 보강
- 레이아웃 시각 게이트: 폼 컨트롤 값 스크롤 오탐 제외(7/7)
검증: 백엔드 84/84, E2E 42(데스크톱 27·모바일 11·아바타 4), 시각 게이트 7/7
This commit is contained in:
Yun Chan
parent
cb2aebd76c
commit
085460b5e0
327 changed files with 31226 additions and 1829 deletions
410
docs/avatar-art/seoyeon/extract-character-sheet.py
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docs/avatar-art/seoyeon/extract-character-sheet.py
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#!/usr/bin/env python3
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"""Build a stable raster-parts rig for Seoyeon.
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The generated character sheet contains useful loose parts, but recomposing those
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parts directly is fragile because each item is drawn at a different scale. This
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script uses the approved assembled bust as the coordinate authority, creates a
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faceless base from it, then adds small aligned raster parts for the eyes, brows,
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nose, mouth, blink, and hair-sway overlays.
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Output files are full-canvas 900x1125 PNG layers so the React renderer can stack
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them without per-image layout math.
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"""
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from __future__ import annotations
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from pathlib import Path
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from typing import Iterable, Literal
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import numpy as np
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from PIL import Image, ImageDraw, ImageFilter
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ROOT = Path(__file__).resolve().parents[3]
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SHEET = ROOT / "docs/avatar-art/seoyeon/character-sheet-v1.png"
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PUB = ROOT / "apps/web/public/avatar/seoyeon"
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NEUTRAL = PUB / "neutral.png"
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OUT = PUB / "parts"
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PREVIEW = ROOT / "docs/avatar-art/seoyeon/character-sheet-rig-preview-v2.png"
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LEGACY_PREVIEW = ROOT / "docs/avatar-art/seoyeon/character-sheet-recompose-preview.png"
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CANVAS = (900, 1125)
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Box = tuple[int, int, int, int]
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ShapeKind = Literal["ellipse", "round", "rect"]
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def alpha_bbox(im: Image.Image, threshold: int = 10) -> Box | None:
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alpha = np.array(im.getchannel("A"))
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ys, xs = np.where(alpha > threshold)
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if len(xs) == 0:
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return None
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return int(xs.min()), int(ys.min()), int(xs.max() + 1), int(ys.max() + 1)
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def median_skin(im: Image.Image, box: Box) -> tuple[int, int, int, int]:
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arr = np.array(im.crop(box).convert("RGBA"))
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rgb = arr[:, :, :3].astype(np.float32)
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alpha = arr[:, :, 3] > 180
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luma = rgb[:, :, 0] * 0.2126 + rgb[:, :, 1] * 0.7152 + rgb[:, :, 2] * 0.0722
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chroma = rgb.max(axis=2) - rgb.min(axis=2)
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# Keep face skin, reject hair/linework/shirt.
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mask = alpha & (luma > 145) & (chroma > 8)
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if int(mask.sum()) < 20:
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mask = alpha
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color = np.median(rgb[mask], axis=0)
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return int(color[0]), int(color[1]), int(color[2]), 255
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def draw_shape(mask: Image.Image, box: Box, kind: ShapeKind, radius: int = 14) -> None:
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draw = ImageDraw.Draw(mask)
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if kind == "ellipse":
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draw.ellipse(box, fill=255)
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elif kind == "round":
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draw.rounded_rectangle(box, radius=radius, fill=255)
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else:
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draw.rectangle(box, fill=255)
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def shape_mask(
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size: tuple[int, int],
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shapes: Iterable[tuple[Box, ShapeKind, int]],
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feather: float = 0,
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) -> Image.Image:
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mask = Image.new("L", size, 0)
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for box, kind, radius in shapes:
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draw_shape(mask, box, kind, radius)
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if feather > 0:
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mask = mask.filter(ImageFilter.GaussianBlur(feather))
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return mask
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def erase_features(neutral: Image.Image) -> Image.Image:
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base = neutral.convert("RGBA")
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patches = [
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((286, 284, 424, 388), (418, 360, 476, 392), "round", 11),
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((476, 284, 614, 388), (418, 360, 476, 392), "round", 11),
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((404, 374, 498, 434), (392, 360, 512, 410), "ellipse", 9),
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((370, 404, 530, 476), (388, 382, 512, 428), "ellipse", 10),
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]
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for erase_box, sample_box, kind, feather in patches:
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color = median_skin(base, sample_box)
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fill = Image.new("RGBA", CANVAS, color)
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mask = Image.new("L", CANVAS, 0)
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draw_shape(mask, erase_box, kind, 30)
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mask = mask.filter(ImageFilter.GaussianBlur(feather))
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base = Image.composite(fill, base, mask)
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return base
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def save_image(name: str, im: Image.Image) -> Image.Image:
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out = OUT / f"{name}.png"
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im.save(out, optimize=True)
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print(f"{name:18} bbox={alpha_bbox(im)}")
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return im
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def save_masked_neutral(
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neutral: Image.Image,
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name: str,
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shapes: Iterable[tuple[Box, ShapeKind, int]],
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*,
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subtract_shapes: Iterable[tuple[Box, ShapeKind, int]] = (),
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feather: float = 0,
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mode: Literal["patch", "dark"] = "patch",
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dark_hi: float = 175,
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dark_lo: float = 80,
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) -> Image.Image:
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src = neutral.convert("RGBA")
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mask_im = shape_mask(CANVAS, shapes, feather=0)
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if subtract_shapes:
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erase_im = shape_mask(CANVAS, subtract_shapes, feather=0)
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mask_arr = np.array(mask_im)
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mask_arr[np.array(erase_im) > 0] = 0
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mask_im = Image.fromarray(mask_arr, "L")
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if feather > 0:
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mask_im = mask_im.filter(ImageFilter.GaussianBlur(feather))
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mask = np.array(mask_im).astype(np.float32) / 255.0
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arr = np.array(src).astype(np.float32)
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alpha = arr[:, :, 3] * mask
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if mode == "dark":
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rgb = arr[:, :, :3]
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luma = rgb[:, :, 0] * 0.2126 + rgb[:, :, 1] * 0.7152 + rgb[:, :, 2] * 0.0722
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dark = np.clip((dark_hi - luma) / max(1.0, dark_hi - dark_lo), 0, 1)
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alpha *= dark
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arr[:, :, 3] = alpha
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arr[alpha < 1, :3] = 0
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return save_image(name, Image.fromarray(np.clip(arr, 0, 255).astype(np.uint8), "RGBA"))
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def remove_sheet_bg(im: Image.Image, *, lo: float = 18.0, hi: float = 62.0) -> Image.Image:
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arr = np.array(im.convert("RGBA")).astype(np.float32)
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rgb = arr[:, :, :3]
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border = np.concatenate(
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[
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rgb[:5, :, :].reshape(-1, 3),
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rgb[-5:, :, :].reshape(-1, 3),
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rgb[:, :5, :].reshape(-1, 3),
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rgb[:, -5:, :].reshape(-1, 3),
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],
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axis=0,
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)
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bg = np.median(border, axis=0)
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dist_bg = np.sqrt(((rgb - bg.reshape(1, 1, 3)) ** 2).sum(axis=2))
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dist_white = np.sqrt(((255.0 - rgb) ** 2).sum(axis=2))
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alpha_bg = np.clip((dist_bg - lo) * 255.0 / max(1.0, hi - lo), 0, 255)
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alpha_white = np.clip((dist_white - 15.0) * 255.0 / 50.0, 0, 255)
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alpha = np.minimum(alpha_bg, alpha_white)
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luma = rgb[:, :, 0] * 0.2126 + rgb[:, :, 1] * 0.7152 + rgb[:, :, 2] * 0.0722
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chroma = rgb.max(axis=2) - rgb.min(axis=2)
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keep = (luma < 205) | (chroma > 16)
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alpha = np.where(keep & (alpha > 35), np.maximum(alpha, 230), alpha)
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arr[:, :, 3] = np.minimum(alpha, arr[:, :, 3])
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arr[arr[:, :, 3] < 1, :3] = 0
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return Image.fromarray(np.clip(arr, 0, 255).astype(np.uint8), "RGBA")
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def save_sheet_part(
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sheet: Image.Image,
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name: str,
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src_box: Box,
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dst_box: Box,
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*,
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matte_lo: float = 14.0,
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matte_hi: float = 54.0,
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) -> Image.Image:
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part = remove_sheet_bg(sheet.crop(src_box), lo=matte_lo, hi=matte_hi)
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dst_w = dst_box[2] - dst_box[0]
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dst_h = dst_box[3] - dst_box[1]
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part = part.resize((dst_w, dst_h), Image.Resampling.LANCZOS)
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canvas = Image.new("RGBA", CANVAS, (0, 0, 0, 0))
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canvas.alpha_composite(part, (dst_box[0], dst_box[1]))
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return save_image(name, canvas)
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def duplicate(src: str, dst: str) -> None:
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im = Image.open(OUT / f"{src}.png").convert("RGBA")
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save_image(dst, im)
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def make_preview(parts: dict[str, Image.Image]) -> Image.Image:
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bg = Image.new("RGBA", CANVAS, (30, 39, 36, 255))
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order = [
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"base-faceless",
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"hair-left",
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"hair-right",
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"hair-bangs",
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"brow-neutral",
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"eyes-neutral",
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"nose-neutral",
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"mouth-neutral",
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]
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for name in order:
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layer = parts.get(name)
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if layer is None:
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layer = Image.open(OUT / f"{name}.png").convert("RGBA")
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bg.alpha_composite(layer)
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# Add small comparison swatches for blink and mouth variants without touching
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# the app-visible neutral render.
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swatch = Image.new("RGBA", (360, 210), (30, 39, 36, 255))
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for i, name in enumerate(["eyelid-closed", "mouth-sad", "mouth-warm", "mouth-open"]):
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layer = Image.open(OUT / f"{name}.png").convert("RGBA")
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crop_box = alpha_bbox(layer) or (0, 0, 1, 1)
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crop = layer.crop(crop_box)
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crop.thumbnail((155, 85), Image.Resampling.LANCZOS)
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x = 18 + (i % 2) * 174
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y = 18 + (i // 2) * 98
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swatch.alpha_composite(crop, (x, y))
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bg.alpha_composite(swatch, (20, 895))
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return bg
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def main() -> int:
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OUT.mkdir(parents=True, exist_ok=True)
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neutral = Image.open(NEUTRAL).convert("RGBA")
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sheet = Image.open(SHEET).convert("RGBA")
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if neutral.size != CANVAS:
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raise ValueError(f"Expected {CANVAS}, got {neutral.size} for {NEUTRAL}")
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parts: dict[str, Image.Image] = {}
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parts["base-faceless"] = save_image("base-faceless", erase_features(neutral))
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# Compatibility/static layers used by older manifests or future experiments.
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parts["face-faceless"] = save_masked_neutral(
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parts["base-faceless"],
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"face-faceless",
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[((250, 205, 650, 535), "ellipse", 40)],
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feather=3,
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)
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parts["forehead"] = save_masked_neutral(
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parts["base-faceless"],
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"forehead",
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[((340, 228, 560, 315), "round", 22)],
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feather=4,
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)
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parts["neck"] = save_masked_neutral(
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neutral,
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"neck",
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[((352, 470, 548, 618), "round", 28)],
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feather=2,
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)
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parts["shoulders"] = save_masked_neutral(
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neutral,
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"shoulders",
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[((160, 545, 740, 820), "round", 28)],
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feather=2,
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)
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parts["hair-back"] = save_masked_neutral(
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neutral,
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"hair-back",
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[((170, 80, 730, 520), "round", 80)],
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subtract_shapes=[
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((262, 210, 638, 540), "ellipse", 72),
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((292, 280, 608, 388), "round", 34),
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],
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feather=2,
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mode="dark",
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dark_hi=172,
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dark_lo=70,
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)
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parts["hair-bangs"] = save_masked_neutral(
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neutral,
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"hair-bangs",
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[((326, 120, 574, 310), "round", 44)],
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subtract_shapes=[((292, 282, 608, 386), "round", 34)],
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feather=1.5,
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mode="dark",
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dark_hi=178,
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dark_lo=75,
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)
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parts["hair-left"] = save_masked_neutral(
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neutral,
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"hair-left",
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[
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((168, 155, 318, 522), "round", 54),
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((242, 430, 392, 522), "round", 42),
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],
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subtract_shapes=[
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((270, 228, 430, 538), "ellipse", 48),
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((292, 282, 425, 390), "round", 32),
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],
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feather=1.5,
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mode="dark",
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dark_hi=178,
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dark_lo=75,
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)
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parts["hair-right"] = save_masked_neutral(
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neutral,
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"hair-right",
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[
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((582, 155, 732, 522), "round", 54),
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((508, 430, 658, 522), "round", 42),
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],
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subtract_shapes=[
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((470, 228, 630, 538), "ellipse", 48),
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((475, 282, 608, 390), "round", 32),
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],
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feather=1.5,
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mode="dark",
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dark_hi=178,
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dark_lo=75,
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)
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# Ear files are kept as small static compatibility layers; the faceless base
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# already contains the ears in their approved position.
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parts["ear-left"] = save_masked_neutral(
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neutral,
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"ear-left",
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[((285, 318, 337, 392), "ellipse", 20)],
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feather=2,
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)
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parts["ear-right"] = save_masked_neutral(
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neutral,
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"ear-right",
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[((563, 318, 615, 392), "ellipse", 20)],
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feather=2,
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)
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parts["brow-neutral"] = save_sheet_part(
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sheet,
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"brow-neutral",
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(600, 424, 848, 482),
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(312, 288, 588, 326),
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matte_lo=10,
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matte_hi=42,
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)
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parts["eyes-neutral"] = save_sheet_part(
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sheet,
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"eyes-neutral",
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(594, 458, 856, 548),
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(306, 314, 594, 376),
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matte_lo=10,
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matte_hi=44,
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)
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parts["nose-neutral"] = save_masked_neutral(
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neutral,
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"nose-neutral",
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[((414, 385, 488, 426), "ellipse", 18)],
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feather=2,
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)
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parts["mouth-neutral"] = save_masked_neutral(
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neutral,
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"mouth-neutral",
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[((382, 414, 518, 468), "ellipse", 20)],
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feather=2,
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)
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parts["eyelid-closed"] = save_sheet_part(
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sheet,
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"eyelid-closed",
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(592, 552, 856, 620),
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(306, 315, 594, 369),
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matte_lo=11,
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matte_hi=46,
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)
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parts["mouth-sad"] = save_sheet_part(
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sheet,
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"mouth-sad",
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(888, 520, 1016, 586),
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(383, 414, 517, 468),
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matte_lo=10,
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matte_hi=44,
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)
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parts["mouth-warm"] = save_sheet_part(
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sheet,
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"mouth-warm",
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(888, 596, 1016, 662),
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(383, 414, 517, 468),
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matte_lo=10,
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matte_hi=44,
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)
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parts["mouth-open"] = save_sheet_part(
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sheet,
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"mouth-open",
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||||
(888, 672, 1016, 752),
|
||||
(380, 407, 520, 475),
|
||||
matte_lo=10,
|
||||
matte_hi=44,
|
||||
)
|
||||
|
||||
for variant in ["sad", "tired", "anxious", "warm", "startled"]:
|
||||
duplicate("brow-neutral", f"brow-{variant}")
|
||||
duplicate("eyes-neutral", f"eyes-{variant}")
|
||||
duplicate("mouth-sad", "mouth-tired")
|
||||
duplicate("mouth-sad", "mouth-anxious")
|
||||
duplicate("mouth-open", "mouth-startled")
|
||||
|
||||
preview = make_preview(parts)
|
||||
preview.save(PREVIEW, optimize=True)
|
||||
preview.save(LEGACY_PREVIEW, optimize=True)
|
||||
print(f"preview {PREVIEW}")
|
||||
print(f"legacy-preview {LEGACY_PREVIEW}")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
Loading…
Add table
Add a link
Reference in a new issue