Initial Dansori character workspace

2026-07-04 10:34:46 +09:00
commit 5a419816ff
2480 changed files with 38692 additions and 0 deletions
@@ -0,0 +1,92 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+from PIL import Image, ImageFilter
+
+
+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"
+QA = PUPPET / "qa_source_waist_patch_black.png"
+AI_PATCH = Path(
+    r"C:\Users\eKeerar\.codex\generated_images\019f277b-18ad-7800-8b99-e77ae92de4bd"
+    r"\ig_0f4c5e4720cfe26e016a47e80d6f9c8191ba3b82a1c165cf39.png"
+)
+
+
+def alpha_bbox(im: Image.Image) -> tuple[int, int, int, int]:
+    return im.getchannel("A").getbbox() or (0, 0, im.width, im.height)
+
+
+def subject_bbox_on_checker(im: Image.Image) -> tuple[int, int, int, int]:
+    arr = np.array(im.convert("RGB"))
+    mask = arr.max(axis=2) < 225
+    ys, xs = np.where(mask)
+    return int(xs.min()), int(ys.min()), int(xs.max() + 1), int(ys.max() + 1)
+
+
+def map_box(
+    src_box: tuple[int, int, int, int],
+    src_bbox: tuple[int, int, int, int],
+    ref_bbox: tuple[int, int, int, int],
+) -> tuple[int, int, int, int]:
+    sx0, sy0, sx1, sy1 = src_bbox
+    rx0, ry0, rx1, ry1 = ref_bbox
+    sw, sh = sx1 - sx0, sy1 - sy0
+    rw, rh = rx1 - rx0, ry1 - ry0
+    x0, y0, x1, y1 = src_box
+    return (
+        int(rx0 + ((x0 - sx0) / sw) * rw),
+        int(ry0 + ((y0 - sy0) / sh) * rh),
+        int(rx0 + ((x1 - sx0) / sw) * rw),
+        int(ry0 + ((y1 - sy0) / sh) * rh),
+    )
+
+
+def main() -> None:
+    base = Image.open(BACKUP if BACKUP.exists() else SOURCE).convert("RGBA")
+    ref = Image.open(AI_PATCH).convert("RGBA")
+
+    src_bbox = alpha_bbox(base)
+    ref_bbox = subject_bbox_on_checker(ref)
+
+    # Torso-only region: below crop-top chest, through waist and top waistband.
+    src_patch_box = (86, 306, 282, 505)
+    ref_patch_box = map_box(src_patch_box, src_bbox, ref_bbox)
+
+    patch = ref.crop(ref_patch_box).resize(
+        (src_patch_box[2] - src_patch_box[0], src_patch_box[3] - src_patch_box[1]),
+        Image.Resampling.LANCZOS,
+    )
+
+    mask = Image.new("L", patch.size, 0)
+    px = mask.load()
+    w, h = patch.size
+    for y in range(h):
+        for x in range(w):
+            nx = abs((x / max(1, w - 1)) * 2 - 1)
+            ny = abs((y / max(1, h - 1)) * 2 - 1)
+            edge = max(nx, ny)
+            value = int(max(0.0, min(1.0, (1.0 - edge) / 0.36)) * 210)
+            # Keep the upper chest and far lower pants transition conservative.
+            if y < 28:
+                value = int(value * (y / 28))
+            if y > h - 28:
+                value = int(value * ((h - y) / 28))
+            px[x, y] = value
+    mask = mask.filter(ImageFilter.GaussianBlur(5))
+
+    out = base.copy()
+    out.alpha_composite(Image.composite(patch, out.crop(src_patch_box), mask), (src_patch_box[0], src_patch_box[1]))
+    out.save(SOURCE)
+
+    black = Image.new("RGBA", out.size, (16, 16, 18, 255))
+    black.alpha_composite(out)
+    black.convert("RGB").save(QA)
+
+
+if __name__ == "__main__":
+    main()