Initial Dansori character workspace

tools/relax_leesori_v2_waist.py

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