P1 서연 리노컷 아트 원본과 자산 파이프라인
- 아트 디렉션 v3 스타일 프레임·프롬프트(소유자 선택: 리노컷) - P1 정면 원화·얼굴 없는 기본형, 분할 레이어, 원화 픽셀 입술·턱 조각, 모티프 스프라이트, 소유자 기준 이미지 - 파이프라인 스크립트(분할·얼굴 음영·눈썹 중심선·입술 결·턱 조각·게시)와 manifest 검사 수치 - MediaPipe 모델과 재생성 가능한 진단 PNG는 무시하고 README에 받는 곳을 적었다
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Yun Chan
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docs/avatar-art/p1-linocut/scripts/build_layers.py
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docs/avatar-art/p1-linocut/scripts/build_layers.py
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"""P1 서연 리노컷 리그 덩어리 레이어(body/head/hairFront) 빌드 스크립트.
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raw/*.png (초록 배경 위 codex exec 생성본) -> 크로마키 -> 기준 이미지 위상상관 정렬
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-> layers/*.png(투명 PNG) + preview/*.png + manifest.json(layers 섹션).
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실행: <venv>/python.exe build_layers.py
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"""
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from __future__ import annotations
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import json
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import sys
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from pathlib import Path
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import numpy as np
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from PIL import Image
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from scipy.ndimage import gaussian_filter
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ROOT = Path(__file__).resolve().parents[1]
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BASE_DIR = ROOT / "base"
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RAW_DIR = ROOT / "raw"
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LAYERS_DIR = ROOT / "layers"
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PREVIEW_DIR = ROOT / "preview"
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MANIFEST_PATH = ROOT / "manifest.json"
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CREAM_BG = (0xEE, 0xE5, 0xD3)
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# AGENTS.md §4.2 알파 정제 임계치
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ALPHA_LO, ALPHA_HI = 35, 205
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FEATHER_SIGMA = 0.6 # ~1px 페더
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# 크로마키(HSV 기반) 튜닝값. #00ff00 배경 기준.
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HUE_TARGET_DEG = 120.0
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HUE_WINDOW_DEG = 40.0
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SAT_LO, SAT_HI = 0.15, 0.5
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VAL_LO, VAL_HI = 0.15, 0.5
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GREEN_RESIDUE_MARGIN = 30 # G > R+margin && G > B+margin
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def rgb_to_hsv_np(rgb: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
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r, g, b = rgb[..., 0], rgb[..., 1], rgb[..., 2]
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maxc = np.max(rgb, axis=-1)
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minc = np.min(rgb, axis=-1)
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v = maxc
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delta = maxc - minc
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s = np.where(maxc > 0, delta / np.where(maxc == 0, 1, maxc), 0.0)
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safe_delta = np.where(delta == 0, 1, delta)
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rc = (maxc - r) / safe_delta
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gc = (maxc - g) / safe_delta
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bc = (maxc - b) / safe_delta
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h = np.zeros_like(maxc)
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h = np.where(maxc == r, (bc - gc), h)
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h = np.where(maxc == g, 2.0 + rc - bc, h)
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h = np.where(maxc == b, 4.0 + gc - rc, h)
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h = (h / 6.0) % 1.0
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h = np.where(delta == 0, 0.0, h)
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return h, s, v
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def chroma_key(rgb_u8: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
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"""RGB(0-255) 배열을 받아 (despill된 RGB uint8, 정제된 알파 uint8)를 반환."""
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rgb = rgb_u8.astype(np.float64) / 255.0
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h, s, v = rgb_to_hsv_np(rgb)
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hue_deg = h * 360.0
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hue_dist = np.abs(hue_deg - HUE_TARGET_DEG)
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hue_dist = np.minimum(hue_dist, 360.0 - hue_dist)
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hue_component = np.clip(1.0 - hue_dist / HUE_WINDOW_DEG, 0.0, 1.0)
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sat_component = np.clip((s - SAT_LO) / (SAT_HI - SAT_LO), 0.0, 1.0)
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val_component = np.clip((v - VAL_LO) / (VAL_HI - VAL_LO), 0.0, 1.0)
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green_score = hue_component * sat_component * val_component
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alpha_raw = (1.0 - green_score) * 255.0
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alpha_refined = np.clip((alpha_raw - ALPHA_LO) * 255.0 / (ALPHA_HI - ALPHA_LO), 0, 255)
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alpha_feathered = gaussian_filter(alpha_refined, sigma=FEATHER_SIGMA)
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alpha_feathered = np.clip(alpha_feathered, 0, 255)
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r = rgb_u8[..., 0].astype(np.float64)
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g = rgb_u8[..., 1].astype(np.float64)
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b = rgb_u8[..., 2].astype(np.float64)
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g_despill = np.minimum(g, np.maximum(r, b))
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despilled_rgb = np.stack([r, g_despill, b], axis=-1)
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return despilled_rgb.astype(np.uint8), alpha_feathered.astype(np.uint8)
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def load_and_normalize(path: Path, canvas_size: tuple[int, int]) -> tuple[np.ndarray, dict]:
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im = Image.open(path).convert("RGB")
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src_w, src_h = im.size
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tgt_w, tgt_h = canvas_size
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report = {"srcSize": [src_w, src_h], "targetSize": [tgt_w, tgt_h], "resized": False}
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if (src_w, src_h) != (tgt_w, tgt_h):
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src_ratio = src_w / src_h
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tgt_ratio = tgt_w / tgt_h
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ratio_diff_pct = abs(src_ratio - tgt_ratio) / tgt_ratio * 100.0
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report["srcRatio"] = src_ratio
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report["targetRatio"] = tgt_ratio
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report["ratioDiffPct"] = ratio_diff_pct
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if ratio_diff_pct > 1.0:
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raise SystemExit(
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f"[중단] {path.name}: 종횡비 차이 {ratio_diff_pct:.3f}% > 1% "
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f"(src={src_w}x{src_h}, target={tgt_w}x{tgt_h}) — 보고 후 정지."
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)
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im = im.resize((tgt_w, tgt_h), Image.LANCZOS)
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report["resized"] = True
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return np.array(im), report
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def phase_correlate(mask_a: np.ndarray, mask_b: np.ndarray) -> tuple[int, int]:
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"""mask_a를 mask_b에 맞추기 위한 정수 (dx, dy) 오프셋을 반환한다.
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mask_a를 (dy行, dx열)만큼 이동시키면 mask_b와 정렬된다."""
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a = mask_a.astype(np.float64)
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b = mask_b.astype(np.float64)
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fa = np.fft.fft2(a)
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fb = np.fft.fft2(b)
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cross = fa * np.conj(fb)
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denom = np.abs(cross)
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denom[denom == 0] = 1e-12
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r = np.fft.ifft2(cross / denom)
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r = np.abs(r)
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peak = np.unravel_index(np.argmax(r), r.shape)
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dy, dx = peak
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h, w = a.shape
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if dy > h // 2:
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dy -= h
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if dx > w // 2:
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dx -= w
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# 교차 위상 스펙트럼 peak는 -d(이동량)에서 나타난다(이산 이동 정리) — 부호 반전해 반환.
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return int(-dx), int(-dy)
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def shift_rgba(rgb: np.ndarray, alpha: np.ndarray, dx: int, dy: int) -> tuple[np.ndarray, np.ndarray]:
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h, w = alpha.shape
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out_rgb = np.zeros_like(rgb)
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out_alpha = np.zeros_like(alpha)
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src_x0, src_x1 = max(0, -dx), min(w, w - dx)
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src_y0, src_y1 = max(0, -dy), min(h, h - dy)
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dst_x0, dst_x1 = max(0, dx), min(w, w + dx)
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dst_y0, dst_y1 = max(0, dy), min(h, h + dy)
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out_rgb[dst_y0:dst_y1, dst_x0:dst_x1] = rgb[src_y0:src_y1, src_x0:src_x1]
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out_alpha[dst_y0:dst_y1, dst_x0:dst_x1] = alpha[src_y0:src_y1, src_x0:src_x1]
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return out_rgb, out_alpha
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def pad_to_canvas(mask: np.ndarray, canvas_w: int, canvas_h: int) -> np.ndarray:
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src_h, src_w = mask.shape
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if (src_w, src_h) == (canvas_w, canvas_h):
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return mask
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out = np.zeros((canvas_h, canvas_w), dtype=mask.dtype)
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h = min(src_h, canvas_h)
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w = min(src_w, canvas_w)
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out[:h, :w] = mask[:h, :w]
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return out
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def build_body_ref_mask(base_front_rgb: np.ndarray) -> np.ndarray:
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h, w, _ = base_front_rgb.shape
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lum = base_front_rgb.astype(np.float64).mean(axis=2)
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y0 = int(0.62 * h)
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mask = np.zeros((h, w), dtype=bool)
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mask[y0:, :] = lum[y0:, :] < 90
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return mask
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def build_head_ref_mask(base_faceless_rgb: np.ndarray) -> np.ndarray:
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h, w, _ = base_faceless_rgb.shape
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bg = np.array([233.0, 226.0, 207.0])
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diff = np.sqrt(((base_faceless_rgb.astype(np.float64) - bg) ** 2).sum(axis=2))
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y1 = int(0.735 * h)
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mask = np.zeros((h, w), dtype=bool)
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mask[:y1, :] = diff[:y1, :] > 25
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return mask
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def build_hair_front_ref_mask(base_front_rgb: np.ndarray) -> np.ndarray:
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h, w, _ = base_front_rgb.shape
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lum = base_front_rgb.astype(np.float64).mean(axis=2)
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yy, xx = np.mgrid[0:h, 0:w]
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cx, cy = w * 0.5, h * 0.365
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rx, ry = w * 0.30, h * 0.34
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oval = ((xx - cx) / rx) ** 2 + ((yy - cy) / ry) ** 2 <= 1.0
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dark = lum < 90
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return oval & dark
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def alpha_bbox(alpha: np.ndarray, threshold: int = 1) -> list[int] | None:
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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 green_residue_count(rgb: np.ndarray, alpha: np.ndarray) -> int:
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r = rgb[..., 0].astype(np.int32)
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g = rgb[..., 1].astype(np.int32)
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b = rgb[..., 2].astype(np.int32)
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mask = (alpha > 0) & (g > r + GREEN_RESIDUE_MARGIN) & (g > b + GREEN_RESIDUE_MARGIN)
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return int(mask.sum())
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def composite_over(base_rgba: np.ndarray, layer_rgb: np.ndarray, layer_alpha: np.ndarray) -> np.ndarray:
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out = base_rgba.astype(np.float64).copy()
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a = (layer_alpha.astype(np.float64) / 255.0)[..., None]
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out[..., :3] = layer_rgb.astype(np.float64) * a + out[..., :3] * (1 - a)
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out[..., 3] = 255.0 * a[..., 0] + out[..., 3] * (1 - a[..., 0])
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return out
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def main() -> int:
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LAYERS_DIR.mkdir(parents=True, exist_ok=True)
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PREVIEW_DIR.mkdir(parents=True, exist_ok=True)
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base_front = np.array(Image.open(BASE_DIR / "base-front.png").convert("RGB"))
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base_faceless = np.array(Image.open(BASE_DIR / "base-faceless.png").convert("RGB"))
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canvas_h, canvas_w = base_front.shape[0], base_front.shape[1]
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canvas_size = (canvas_w, canvas_h)
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print(f"기준 캔버스: {canvas_w}x{canvas_h}")
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ref_masks = {
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"body": build_body_ref_mask(base_front),
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"head": pad_to_canvas(build_head_ref_mask(base_faceless), canvas_w, canvas_h),
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"hairFront": build_hair_front_ref_mask(base_front),
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}
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for k, m in ref_masks.items():
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Image.fromarray((m * 255).astype(np.uint8)).save(PREVIEW_DIR / f"refmask-{k}.png")
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layer_specs = [
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("body", "body.png"),
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("head", "head.png"),
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("hairFront", "hair-front.png"),
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]
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layers_report = []
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results = {}
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for layer_id, filename in layer_specs:
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raw_path = RAW_DIR / filename
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rgb, norm_report = load_and_normalize(raw_path, canvas_size)
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despilled_rgb, alpha = chroma_key(rgb)
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layer_mask = alpha > 127
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ref_mask = ref_masks[layer_id]
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dx0, dy0 = phase_correlate(layer_mask, ref_mask)
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shifted_rgb, shifted_alpha = shift_rgba(despilled_rgb, alpha, dx0, dy0)
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shifted_mask = shifted_alpha > 127
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dx1, dy1 = phase_correlate(shifted_mask, ref_mask)
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residual_exceeds = abs(dx1) > 1 or abs(dy1) > 1
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out = np.dstack([shifted_rgb, shifted_alpha]).astype(np.uint8)
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out_path = LAYERS_DIR / f"{layer_id}.png"
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Image.fromarray(out, "RGBA").save(out_path)
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bbox = alpha_bbox(shifted_alpha)
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opaque_pixels = int((shifted_alpha == 255).sum())
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green_residue = green_residue_count(shifted_rgb, shifted_alpha)
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entry = {
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"id": layer_id,
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"file": f"layers/{layer_id}.png",
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"sourceRaw": f"raw/{filename}",
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"rawNormalize": norm_report,
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"alphaBBox": bbox,
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"opaquePixels": opaque_pixels,
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"greenResidue": green_residue,
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"alignOffsetBefore": [dx0, dy0],
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"alignOffsetAfter": [dx1, dy1],
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"residualExceeds1px": residual_exceeds,
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}
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layers_report.append(entry)
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results[layer_id] = (shifted_rgb, shifted_alpha)
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print(
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f"[{layer_id}] normalize={norm_report} offsetBefore=({dx0},{dy0}) "
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f"offsetAfter=({dx1},{dy1}) bbox={bbox} opaque={opaque_pixels} "
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f"greenResidue={green_residue}"
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)
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if residual_exceeds:
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print(f" [경고] {layer_id} 잔여 오프셋이 ±1px를 초과했다: ({dx1},{dy1})")
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canvas_rgba = np.zeros((canvas_h, canvas_w, 4), dtype=np.float64)
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canvas_rgba[..., 0] = CREAM_BG[0]
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canvas_rgba[..., 1] = CREAM_BG[1]
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canvas_rgba[..., 2] = CREAM_BG[2]
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canvas_rgba[..., 3] = 255.0
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for layer_id in ("body", "head", "hairFront"):
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rgb, alpha = results[layer_id]
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canvas_rgba = composite_over(canvas_rgba, rgb, alpha)
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composite = canvas_rgba.astype(np.uint8)
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composite_img = Image.fromarray(composite, "RGBA")
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composite_img.save(PREVIEW_DIR / "composite-faceless.png")
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base_faceless_img = Image.open(BASE_DIR / "base-faceless.png").convert("RGBA")
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bf_w, bf_h = base_faceless_img.size
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cmp_w, cmp_h = composite_img.size
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diff_w, diff_h = min(bf_w, cmp_w), min(bf_h, cmp_h)
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size_note = None
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if (bf_w, bf_h) != (cmp_w, cmp_h):
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size_note = (
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f"base-faceless.png({bf_w}x{bf_h})와 composite({cmp_w}x{cmp_h}) 크기가 달라 "
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f"좌상단 기준 {diff_w}x{diff_h} 교차 영역만 비교했다."
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)
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print(f"[안내] {size_note}")
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composite_arr = np.array(composite_img.convert("RGB"))[0:diff_h, 0:diff_w].astype(np.float64)
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base_arr = np.array(base_faceless_img.convert("RGB"))[0:diff_h, 0:diff_w].astype(np.float64)
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full_diff = np.abs(composite_arr - base_arr).mean(axis=2)
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mean_abs_diff_full = float(full_diff.mean())
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head_ref = ref_masks["head"][0:diff_h, 0:diff_w]
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mean_abs_diff_face = float(full_diff[head_ref].mean()) if head_ref.any() else None
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hair_edge = ref_masks["hairFront"][0:diff_h, 0:diff_w]
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from scipy.ndimage import binary_dilation, binary_erosion
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dilated = binary_dilation(hair_edge, iterations=6)
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eroded = binary_erosion(hair_edge, iterations=6)
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hair_outline_band = dilated & ~eroded
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mean_abs_diff_hair_outline = (
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float(full_diff[hair_outline_band].mean()) if hair_outline_band.any() else None
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)
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side_by_side = Image.new("RGB", (diff_w * 2 + 20, diff_h), CREAM_BG)
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side_by_side.paste(base_faceless_img.convert("RGB").crop((0, 0, diff_w, diff_h)), (0, 0))
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side_by_side.paste(composite_img.convert("RGB").crop((0, 0, diff_w, diff_h)), (diff_w + 20, 0))
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side_by_side.save(PREVIEW_DIR / "compare.png")
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print(
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f"합성 차이: 전체={mean_abs_diff_full:.3f} 얼굴영역={mean_abs_diff_face} "
|
||||
f"머리윤곽영역={mean_abs_diff_hair_outline}"
|
||||
)
|
||||
|
||||
manifest = {}
|
||||
if MANIFEST_PATH.exists():
|
||||
manifest = json.loads(MANIFEST_PATH.read_text(encoding="utf-8"))
|
||||
|
||||
manifest["schemaVersion"] = "vignette.avatar.v3.layers.v1"
|
||||
manifest["persona"] = "P1"
|
||||
manifest["canvas"] = {"w": canvas_w, "h": canvas_h}
|
||||
manifest["base"] = {
|
||||
"front": "base/base-front.png",
|
||||
"faceless": "base/base-faceless.png",
|
||||
"facelessSize": list(base_faceless_img.size),
|
||||
}
|
||||
manifest["layers"] = layers_report
|
||||
manifest["composite"] = {
|
||||
"meanAbsDiff": {
|
||||
"full": mean_abs_diff_full,
|
||||
"face": mean_abs_diff_face,
|
||||
"hairOutline": mean_abs_diff_hair_outline,
|
||||
},
|
||||
"sizeNote": size_note,
|
||||
}
|
||||
MANIFEST_PATH.write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8")
|
||||
print(f"manifest.json 저장: {MANIFEST_PATH}")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
Loading…
Add table
Add a link
Reference in a new issue