106 lines
5.2 KiB
Python
106 lines
5.2 KiB
Python
import json
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import os
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import re
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from difflib import get_close_matches
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# --- Paste hier die Model-Dateinamen rein (oder lies sie aus dem Ordner) ---
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MODEL_FILES = [
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"Ebisu.glb", "Enenra.glb", "Enenra2.glb", "Oboroguruma.glb", "Oiwa.glb", "Okiku.glb", "Okomeki.001.glb",
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"Okuninushi.glb", "Oni.glb", "Onryo.glb", "Oyamatsumi.001.glb", "Raijin.glb", "Rokurokubi.glb", "Ryujin.glb",
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"Sarutahiko_Okami.glb", "Shinigami.001.glb", "Shuten_Doji.glb", "Sojobo.glb", "Sojobo2.glb", "Susanoo.glb",
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"Takeminakata.glb", "Takeminakata2.001.glb", "Tanuki.glb", "Tengu.glb", "Tenjin.glb", "Tsukumogami.glb",
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"Tsukuyomi_No_Mikoto.glb", "Tsurube_Otoshi.glb", "Tsurube_Otoshi2.glb", "Tsurube_Otoshi3.glb", "Tsurube_Otoshi4.glb",
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"Ubume.glb", "Yama_Uba.glb", "Yama_Uba2.glb", "Yamata_No_Orichi.glb", "Yamawaro.glb", "Yatagarasu2.glb",
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"Yuki_Onna.glb", "Yurei.glb", "Abe_No_Seimei.glb", "Abura_Akago.glb", "Abura_Sumashi.glb", "Abura_Sumashi2.glb",
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"Aka_Manto.glb", "Akaname.glb", "Akateko2.glb", "Akkorokamui.glb", "Akuchu.glb", "Amabie2.glb", "Amanojaku.glb",
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"Amaterasu.glb", "Ame_No_Uzume.001.glb", "Amenominakanushi.glb", "Aoandon.001.glb", "Aoandon2.001.glb",
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"Ashiari_Yashiki.glb", "Ashinaga_Tenaga2.glb", "Azukiarai.glb", "Azukibabaa.glb", "Azukihakari.glb",
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"Bake_Kujira.glb", "Bake_Kujira2.glb", "Bake_Kujira3.glb", "Bakezori.glb", "Baku.glb", "Basan.glb",
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"Benzaiten.glb", "Betobeto_San.glb", "Bishamonten.glb", "Biwa_Bokuboku.glb", "Chochin_Obake.glb", "Daidarabotchi.glb",
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"Daikokuten+Text.glb", "Daikokuten.glb", "Fujin.glb", "Funayurei.glb", "Furaribi.glb", "Futakuchi_Onna.glb",
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"Gaki.glb", "Gashadokuro.glb", "Hachiman.glb", "Hiderigami.001.glb", "Hitotsume_Kozo.glb", "Hoko.glb",
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"Inari_Okami.glb", "Ittan_Momen2.glb", "Izanagi_No_Mikoto.glb", "Izanami_No_Mikoto.glb", "Jikininki.glb",
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"Jorogumo3.glb", "Kamaitachi.glb", "Kamikiri.glb", "Kappa.glb", "Karakasa_Obake.glb", "Karakasa_Obake2.glb",
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"Kitsune.glb", "Kodama.glb", "Kudan.glb", "Mizushi.glb", "Mokumokuren.glb", "Mujina.glb", "Nekomata.glb",
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"Noppera_Bo.glb", "Nue.glb", "Nuppeppo2.glb", "Nurarihyon.glb", "Nure_Onna.glb", "Nurikabe.glb", "Nurikabe2.glb"
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]
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def normalize(s):
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s = s.lower()
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s = re.sub(r"[^a-z0-9]", "", s)
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s = s.replace("ou", "o") # YamatanoOrOchi vs Yamata_No_Orichi etc.
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return s
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def generate_candidates(spirit_name):
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# z.B. "Tsukuyomi (月読命)" => ["Tsukuyomi", "TsukuyomiNoMikoto", ...]
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# Extrahiere lateinische Namen und alle Wörter, splitte auf Sonderzeichen
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base = spirit_name.split()[0]
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latin = re.split(r"\s|\(|（", spirit_name)[0]
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# Alternativen, z.B. alles ohne Klammern, nur erstes Wort etc.
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candidates = [latin]
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candidates += [latin.replace("-", "_"), latin.replace("-", ""), latin.replace("_", ""), latin.title(), latin.upper()]
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# Häufig bei Kami: _No_Mikoto-Suffix
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if not latin.endswith("NoMikoto"):
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candidates.append(latin + "NoMikoto")
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candidates.append(latin + "_No_Mikoto")
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# Auch mal alles Klein, Snake, Camel
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return list(set([normalize(c) for c in candidates]))
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def find_best_model(spirit_name):
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candidates = generate_candidates(spirit_name)
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model_names = [f[:-4] for f in MODEL_FILES] # .glb weg
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normalized_models = [normalize(n) for n in model_names]
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# Alle Matches (ab Distanz <= 2 oder exaktes Substring-Match)
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results = []
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for c in candidates:
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for i, n in enumerate(normalized_models):
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dist = levenshtein(c, n)
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if dist <= 2 or c in n or n in c:
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results.append(MODEL_FILES[i])
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# Unique!
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results = sorted(list(set(results)))
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if not results:
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# Fuzzy best 3
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matches = get_close_matches(candidates[0], normalized_models, n=3, cutoff=0.6)
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models = [MODEL_FILES[normalized_models.index(m)] for m in matches]
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return models
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return results
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# Levenshtein-Distanz
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def levenshtein(a, b):
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if a == b: return 0
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if not a: return len(b)
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if not b: return len(a)
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v0 = list(range(len(b) + 1))
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v1 = [0] * (len(b) + 1)
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for i in range(len(a)):
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v1[0] = i + 1
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for j in range(len(b)):
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cost = 0 if a[i] == b[j] else 1
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v1[j + 1] = min(v1[j] + 1, v0[j + 1] + 1, v0[j] + cost)
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v0, v1 = v1, v0
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return v0[len(b)]
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# ---- Hauptlogik ----
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def main():
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with open("spirit_list.json", encoding="utf-8") as f:
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spirits = json.load(f)
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for spirit in spirits:
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if "Model URL" in spirit and spirit["Model URL"]:
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continue # already done
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name = spirit.get("Name", "")
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matches = find_best_model(name)
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if len(matches) == 1:
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spirit["Model URL"] = "/assets/models/spirits/" + matches[0]
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elif len(matches) > 1:
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print(f"\n[?] Mehrere mögliche Modelle für '{name}': {matches}")
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spirit["Model URL"] = "/assets/models/spirits/" + matches[0] # Default das erste, Handcheck empfohlen!
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else:
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print(f"[!] Kein Modell gefunden für '{name}'!")
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spirit["Model URL"] = ""
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with open("spirit_list_out.json", "w", encoding="utf-8") as f:
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json.dump(spirits, f, ensure_ascii=False, indent=2)
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print("\nFERTIG. Ergebnis: spirit_list_out.json")
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if __name__ == "__main__":
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main() |