"""
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.")