eKeerar
103 lines
3.3 KiB
Python
103 lines
3.3 KiB
Python
from __future__ import annotations
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from pathlib import Path
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from PIL import Image
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ROOT = Path(r"D:\Work_AI\Dansori")
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PUPPET = ROOT / "DansoriEQ" / "src" / "DansoriEQ.App" / "Assets" / "Characters" / "Puppets" / "LeeSoriV2"
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SOURCE = PUPPET / "leesori_v2_source.png"
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BACKUP = PUPPET / "leesori_v2_source_pre_waist_fix.png"
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AI_PATCH_BASE = PUPPET / "leesori_v2_source_ai_waist_patch_base.png"
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QA = PUPPET / "qa_source_waist_relaxed_black.png"
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WAIST_TARGET_TOP_WIDTH = 116
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def smoothstep(edge0: float, edge1: float, value: float) -> float:
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t = max(0.0, min(1.0, (value - edge0) / (edge1 - edge0)))
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return t * t * (3.0 - 2.0 * t)
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def is_skin(pixel: tuple[int, int, int, int]) -> bool:
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r, g, b, a = pixel
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return a > 180 and r > 145 and g > 85 and b > 55 and r > g > b and (r - b) > 45
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def relax_waist(im: Image.Image) -> Image.Image:
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src = im.convert("RGBA")
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dst = src.copy()
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w, h = src.size
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layer = src.copy()
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mask = Image.new("L", src.size, 0)
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# Only clean the side notches directly below the crop-top hem. This leaves
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# the belly, waistband, and pelvis pixels exactly as they are in the base.
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# The layer starts as a full source copy so feathering never blends against
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# transparent black.
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for y in range(350, 378):
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xs = [x for x in range(86, 280) if is_skin(src.getpixel((x, y)))]
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if not xs:
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continue
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left, right = min(xs), max(xs)
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width = right - left + 1
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target_width = max(width, int(WAIST_TARGET_TOP_WIDTH + (y - 350) * 0.16))
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extra = max(0, int(round((target_width - width) / 2)))
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if extra <= 0:
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continue
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vertical = smoothstep(350, 358, y) * (1.0 - smoothstep(370, 378, y))
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if vertical <= 0:
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continue
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sample_y = min(h - 1, y + 18)
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for dx in range(1, extra + 1):
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edge = 1.0 - dx / (extra + 1)
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amount = int(205 * vertical * edge)
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if amount <= 0:
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continue
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ldst = left - dx
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if 0 <= ldst < w:
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sample = src.getpixel((ldst, sample_y))
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if not is_skin(sample):
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sample = src.getpixel((min(left + 2, right), y))
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layer.putpixel((ldst, y), sample)
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mask.putpixel((ldst, y), max(mask.getpixel((ldst, y)), amount))
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rdst = right + dx
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if 0 <= rdst < w:
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sample = src.getpixel((rdst, sample_y))
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if not is_skin(sample):
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sample = src.getpixel((max(right - 2, left), y))
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layer.putpixel((rdst, y), sample)
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mask.putpixel((rdst, y), max(mask.getpixel((rdst, y)), amount))
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return Image.composite(layer, dst, mask)
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def blend(base: tuple[int, int, int, int], overlay: tuple[int, int, int, int], amount: int) -> tuple[int, int, int, int]:
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t = amount / 255.0
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return tuple(int(round(base[i] * (1.0 - t) + overlay[i] * t)) for i in range(4))
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def main() -> None:
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if not BACKUP.exists():
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BACKUP.write_bytes(SOURCE.read_bytes())
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if not AI_PATCH_BASE.exists():
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AI_PATCH_BASE.write_bytes(SOURCE.read_bytes())
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fixed = relax_waist(Image.open(AI_PATCH_BASE))
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fixed.save(SOURCE)
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black = Image.new("RGBA", fixed.size, (16, 16, 18, 255))
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black.alpha_composite(fixed)
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black.convert("RGB").save(QA)
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if __name__ == "__main__":
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main()
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