Initial Dansori EQ workspace

tools/make_transparent.py

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+"""
+Make the (opaque, near-white) background of the mascot cutouts transparent,
+WITHOUT punching holes in the character's white clothing.
+
+Method: flag near-white pixels, then keep only the near-white regions that are
+CONNECTED TO THE IMAGE BORDER (that's the background). Interior whites (hoodie /
+tee), enclosed by the character's outline, are preserved. A tiny alpha blur
+feathers the edge to avoid a white halo.
+
+Usage:
+    python tools/make_transparent.py <file_or_dir> [<file_or_dir> ...]
+Originals are backed up to Characters/_opaque_backup/ (once) before overwrite.
+"""
+import sys, os, glob
+import numpy as np
+from PIL import Image
+from scipy import ndimage
+
+WHITE_THRESH = 236      # min channel value to be considered "near white"
+FEATHER = 0.8           # gaussian sigma for alpha edge feather (px)
+
+BACKUP_ROOT = os.path.join("Characters", "_opaque_backup")
+
+
+def process(path: str):
+    im = Image.open(path).convert("RGBA")
+    arr = np.array(im)
+    rgb = arr[:, :, :3].astype(np.int16)
+
+    # near-white mask: all channels high AND low chroma (avoids eating pale skin)
+    mn = rgb.min(axis=2)
+    mx = rgb.max(axis=2)
+    near_white = (mn >= WHITE_THRESH) & ((mx - mn) <= 12)
+
+    # connected components of near-white; keep those touching the border = background
+    labels, n = ndimage.label(near_white)
+    border_labels = set(labels[0, :]) | set(labels[-1, :]) | set(labels[:, 0]) | set(labels[:, -1])
+    border_labels.discard(0)
+    bg = np.isin(labels, list(border_labels))
+
+    alpha = np.where(bg, 0, 255).astype(np.float32)
+    if FEATHER > 0:
+        alpha = ndimage.gaussian_filter(alpha, sigma=FEATHER)
+    arr[:, :, 3] = np.clip(alpha, 0, 255).astype(np.uint8)
+
+    # backup once
+    rel = os.path.relpath(path, "Characters") if path.replace("\\", "/").startswith("Characters") else os.path.basename(path)
+    bpath = os.path.join(BACKUP_ROOT, rel)
+    os.makedirs(os.path.dirname(bpath), exist_ok=True)
+    if not os.path.exists(bpath):
+        Image.open(path).save(bpath)
+
+    Image.fromarray(arr, "RGBA").save(path)
+    covered = 100.0 * bg.mean()
+    print(f"  {os.path.basename(path)}  bg removed {covered:4.1f}%")
+
+
+def collect(target):
+    if os.path.isdir(target):
+        return sorted(glob.glob(os.path.join(target, "*.png")))
+    return [target]
+
+
+if __name__ == "__main__":
+    targets = sys.argv[1:] or ["Characters/sori", "Characters/dan", "Characters/duo"]
+    for t in targets:
+        files = collect(t)
+        if files:
+            print(t)
+        for f in files:
+            process(f)
+    print("done.")