import json import lzma import math import os import pickle import re import shutil from datetime import datetime import cv2 import numpy as np from PIL import Image, ImageDraw, ImageFont from skimage.feature import hog from sklearn.neighbors import KNeighborsClassifier from mower.utils.character_recognize import fast_keypoints from mower.utils.image import loadimg, thres2 from mower.utils.vector import sa def 加载json(file_path): with open(file_path, "r", encoding="utf-8") as f: return json.load(f) class Arknights数据处理器: def __init__(self): self.当前时间戳 = datetime.now().timestamp() 仓库_1 = "ArknightsGameResource/gamedata/excel" 仓库_2 = "ArknightsGameData/zh_CN/gamedata/excel" 仓库_3 = "ArknightsResource/gamedata/excel" # noqa: F841 self.物品表 = 加载json(f"./{仓库_2}/item_table.json") self.干员表 = 加载json(f"./{仓库_2}/character_table.json") self.阿米娅表 = 加载json(f"./{仓库_1}/char_patch_table.json") self.抽卡表 = 加载json(f"./{仓库_2}/gacha_table.json") self.关卡表 = 加载json(f"./{仓库_2}/stage_table.json") self.活动表 = 加载json(f"./{仓库_2}/activity_table.json") self.基建表 = 加载json(f"./{仓库_2}/building_data.json") self.游戏变量 = 加载json(f"./{仓库_2}/gamedata_const.json") self.模组 = 加载json(f"./{仓库_2}/uniequip_table.json") self.战斗技能 = 加载json(f"./{仓库_2}/skill_table.json") self.装仓库物品的字典 = {"NORMAL": [], "CONSUME": [], "MATERIAL": []} self.常驻关卡 = 加载json("mower/data/stage_data.json") self.所有buff = [] self.限定十连 = self.抽卡表["limitTenGachaItem"] self.联动十连 = self.抽卡表["linkageTenGachaItem"] self.普通十连 = self.抽卡表["normalGachaItem"] self.所有卡池 = self.限定十连 + self.联动十连 + self.普通十连 self.总掉落表 = [] def 添加物品至前端文件(self): """和 数据处理器.输出_活动关卡和掉落() 有联动 ， 读取活动关卡提供了额外的图片 活动代币""" def 检查图标代码匹配(目标图标代码, 物品类型, 分类类型, 排序代码): 匹配结果 = False for 池子限时物品 in self.所有卡池: if ( 池子限时物品["itemId"] == 目标图标代码 and self.当前时间戳 > 池子限时物品["endTime"] ): 匹配结果 = True break 分割部分 = 目标图标代码.split("_") if len(分割部分) == 2 and 分割部分[0].endswith("recruitment10"): 匹配结果 = True if len(分割部分) == 6 and int(分割部分[5]) < 2023: 匹配结果 = True if len(分割部分) == 3 and 目标图标代码.startswith("uni"): 匹配结果 = True if len(分割部分) == 3 and 目标图标代码.startswith("voucher_full"): 匹配结果 = True if 目标图标代码 == "ap_supply_lt_60": 匹配结果 = True 抽卡 = self.抽卡表.get("gachaPoolClient", []) for 卡池 in 抽卡: if 卡池["LMTGSID"] == 目标图标代码 and self.当前时间戳 > int( 卡池["endTime"] ): 匹配结果 = True if 分类类型 == "NONE": 匹配结果 = True if 排序代码 <= 0: 匹配结果 = True if 分类类型 == "NONE": for item in self.总掉落表: if 目标图标代码 == item["id"]: 匹配结果 = False return 匹配结果 self.物品_名称_输出用 = {} self.物品_名称_模型用 = {} if not os.path.exists("./ui/public/depot/EXP.webp"): png_image = Image.open("./ArknightsGameResource/item/EXP_PLAYER.png") png_image.save("./ui/public/depot/EXP.webp", "WEBP") for 物品代码, 物品数据 in self.物品表["items"].items(): 中文名称 = 物品数据.get("name", "") 图标代码 = 物品数据.get("iconId", "") 排序代码 = 物品数据.get("sortId", "") 分类类型 = 物品数据.get("classifyType", "") 物品类型 = 物品数据.get("itemType", "") 源文件路径 = f"./ArknightsGameResource/item/{图标代码}.png" 排除开关 = False 排除开关 = 检查图标代码匹配(图标代码, 物品类型, 分类类型, 排序代码) if not 排除开关: if os.path.exists(源文件路径): 目标文件路径 = f"./ui/public/depot/{中文名称}.webp" if not os.path.exists(目标文件路径): png_image = Image.open(源文件路径) png_image.save(目标文件路径, "WEBP") if 分类类型 != "NONE": self.装仓库物品的字典[分类类型].append( [目标文件路径, 源文件路径] ) templist = [物品代码, 图标代码, 中文名称, 分类类型, 排序代码] self.物品_名称_输出用[物品代码] = templist self.物品_名称_模型用[中文名称] = templist self.物品_名称_模型用[物品代码] = templist print(f"复制 {源文件路径} 到 {目标文件路径}") with open("./mower/data/key_mapping.json", "w", encoding="utf8") as json_file: json.dump(self.物品_名称_输出用, json_file, ensure_ascii=False, indent=4) print() def 干员列表(self): """读取干员列表并输出至./mower/data/agent.json""" 干员_名称列表 = {} for 干员代码, 干员数据 in self.干员表.items(): if not 干员数据["itemObtainApproach"]: continue 干员名 = 干员数据["name"] 干员_名称列表[干员名] = { "key": 干员代码, "profession": 干员数据["profession"], "name": 干员数据["appellation"], } 干员头像路径 = f"./ArknightsGameResource/avatar/{干员代码}.png" 目标路径 = f"./ui/public/avatar/{干员数据['name']}.webp" print(f"{干员名}: {干员代码}") png_image = Image.open(干员头像路径) png_image.save(目标路径, "WEBP") 干员_名称列表 = dict(sorted(干员_名称列表.items())) with open("./mower/data/agent.json", "w", encoding="utf-8") as f: json.dump(干员_名称列表, f, ensure_ascii=False, indent=4) def 读取卡池(self): """读取当前开放的卡池,暂无输出""" 抽卡 = self.抽卡表.get("gachaPoolClient", []) 卡池类型映射 = { "SINGLE": "单人池", "LIMITED": "限定池", "NORM": "普通池", "CLASSIC": "中坚池", "CLASSIC_ATTAIN": "跨年中坚池", "LINKAGE": "联动池", "ATTAIN": "跨年池", "FESCLASSIC": "中坚甄选", } for 项 in 抽卡: 卡池名称 = 项.get("gachaPoolName") 开始时间戳 = 项.get("openTime") 结束时间戳 = 项.get("endTime") 卡池类型代码 = 项.get("gachaPoolId") 卡池出人 = 项.get("dynMeta") if self.当前时间戳 < 结束时间戳: 卡池类型 = 卡池类型映射.get(卡池类型代码.split("_")[0], 卡池类型代码) if 卡池类型代码.split("_")[1] == "ATTAIN": 卡池类型 = "跨年中坚池" if 卡池名称 == "适合多种场合的强力干员": 卡池名称 = 卡池类型 开始时间 = datetime.fromtimestamp(开始时间戳) 结束时间 = datetime.fromtimestamp(结束时间戳 + 1) print("卡池名称:", 卡池名称) print("卡池类型:", 卡池类型) if 卡池类型 == "中坚池": print( 卡池出人["main6RarityCharId"], 卡池出人["sub6RarityCharId"], 卡池出人["rare5CharList"], ) if self.当前时间戳 > 开始时间戳: print("正在进行") print("卡池结束时间:", 结束时间) else: print("卡池开始时间:", 开始时间) print("卡池结束时间:", 结束时间) print(卡池类型代码) print() def 输出_活动关卡和掉落(self): """读取活动关卡及掉落物并写入./ui/src/pages/stage_data/event_data.json""" 关卡 = self.关卡表["stageValidInfo"] 还未结束的非常驻关卡 = { 键: 值 for 键, 值 in 关卡.items() if 值["endTs"] != -1 and 值["endTs"] > self.当前时间戳 } 还未结束的非常驻关卡 = dict(sorted(还未结束的非常驻关卡.items())) for 键, _ in 还未结束的非常驻关卡.items(): 关卡代码 = self.关卡表["stages"][键]["code"] if 键.endswith("#f#"): 关卡代码 += " 突袭" 关卡名称 = self.关卡表["stages"][键]["name"] 关卡结束时间戳 = 还未结束的非常驻关卡[键]["endTs"] 关卡消耗理智 = self.关卡表["stages"][键]["apCost"] # 关卡结束时间 = datetime.fromtimestamp(还未结束的非常驻关卡[键]["endTs"] + 1) 关卡掉落表 = self.关卡表["stages"][键]["stageDropInfo"][ "displayDetailRewards" ] self.总掉落表.extend(关卡掉落表) 关卡掉落 = {} 突袭首次掉落 = [ self.物品表.get("items", {}).get(item["id"], {}).get("name", item["id"]) for item in 关卡掉落表 if item["dropType"] == 1 ] 常规掉落 = [ self.物品表.get("items", {}).get(item["id"], {}).get("name", item["id"]) for item in 关卡掉落表 if item["dropType"] == 2 ] 特殊掉落 = [ self.物品表.get("items", {}).get(item["id"], {}).get("name", item["id"]) for item in 关卡掉落表 if item["dropType"] == 3 ] 额外物资 = [ self.物品表.get("items", {}).get(item["id"], {}).get("name", item["id"]) for item in 关卡掉落表 if item["dropType"] == 4 ] 首次掉落 = [ self.物品表.get("items", {}).get(item["id"], {}).get("name", item["id"]) for item in 关卡掉落表 if item["dropType"] == 8 ] 关卡掉落 = { "突袭首次掉落": 突袭首次掉落, "常规掉落": 常规掉落, "首次掉落": 首次掉落, "特殊掉落": 特殊掉落, "额外物资": 额外物资, } self.常驻关卡.append( { "id": 关卡代码, "id_key":键, "name": 关卡名称, "drop": 关卡掉落, "end": 关卡结束时间戳, "理智消耗": 关卡消耗理智, "周一": 1, "周二": 1, "周三": 1, "周四": 1, "周五": 1, "周六": 1, "周日": 1, } ) unkey = 0 for item in self.常驻关卡: item["key"] = unkey unkey += 1 with open( "./ui/src/pages/stage_data/event_data.json", "w", encoding="utf-8" ) as f: json.dump(self.常驻关卡, f, ensure_ascii=False, indent=4) def load_recruit_data(self): recruit_data = {} recruit_result_data = { 4: [], 3: [], 2: [], 1: [], -1: [], } # for 干员代码, 干员数据 in self.干员表.items(): # print(干员代码,干员数据) recruit_list = self.抽卡表["recruitDetail"].replace("\\n<@rc.eml>", "") recruit_list = recruit_list.replace("\\n", "") recruit_list = recruit_list.replace("\r", "") recruit_list = recruit_list.replace("★", "") recruit_list = recruit_list.replace("<@rc.eml>", "") recruit_list = recruit_list.replace("", "") recruit_list = recruit_list.replace("/", "") recruit_list = recruit_list.replace(" ", "\n") recruit_list = recruit_list.replace("--------------------", "") recruit_list = recruit_list.replace("<@rc.title>公开招募说明", "") recruit_list = recruit_list.replace("<@rc.em>※稀有职业需求招募说明※", "") recruit_list = recruit_list.replace( "<@rc.em>当职业需求包含高级资深干员，且招募时限为9小时时，招募必得6星干员", "", ) recruit_list = recruit_list.replace( "<@rc.em>当职业需求包含资深干员同时不包含高级资深干员，且招募时限为9小时，则该次招募必得5星干员", "", ) recruit_list = recruit_list.replace("<@rc.subtitle>※全部可能出现的干员※", "") recruit_list = recruit_list.replace("绿色高亮的不可寻访干员，可以在此招募", "") recruit_list = recruit_list.split("\n") profession = { "MEDIC": "医疗干员", "WARRIOR": "近卫干员", "SPECIAL": "特种干员", "SNIPER": "狙击干员", "CASTER": "术师干员", "TANK": "重装干员", "SUPPORT": "辅助干员", "PIONEER": "先锋干员", } for 干员代码, 干员数据 in self.干员表.items(): 干员名 = 干员数据["name"] if 干员数据["profession"] not in profession: continue if 干员名 in recruit_list: tag = 干员数据["tagList"] # 数据中稀有度从0-5 if 干员数据["rarity"].startswith("TIER_"): 干员数据["rarity"] = int(干员数据["rarity"][-1]) - 1 干员数据["rarity"] = 干员数据["rarity"] + 1 if len(干员名) <= 4: recruit_result_data[len(干员名)].append(干员代码) else: recruit_result_data[-1].append(干员代码) if 干员数据["rarity"] == 5: tag.append("资深干员") elif 干员数据["rarity"] == 6: tag.append("高级资深干员") if 干员数据["position"] == "MELEE": tag.append("近战位") elif 干员数据["position"] == "RANGED": tag.append("远程位") tag.append(profession[干员数据["profession"]]) recruit_data[干员代码] = { "name": 干员名, "stars": 干员数据["rarity"], "tags": 干员数据["tagList"], } print( "{} stars：{} tags:{}".format( 干员名, 干员数据["rarity"], 干员数据["tagList"] ) ) print("载入公招干员数据{}个".format(len(recruit_data))) with open("./mower/data/recruit.json", "w", encoding="utf-8") as f: json.dump(recruit_data, f, ensure_ascii=False, indent=4) with open("./mower/data/recruit_result.json", "w", encoding="utf-8") as f: json.dump(recruit_result_data, f, ensure_ascii=False, indent=4) def load_recruit_template(self): # !/usr/bin/env python3 template = {} with open("./mower/data/recruit.json", "r", encoding="utf-8") as f: recruit_operators = json.load(f) font = ImageFont.truetype("FZDYSK.TTF", 120) print(len(recruit_operators)) for operator in recruit_operators: im = Image.new(mode="RGBA", size=(1920, 1080)) draw = ImageDraw.Draw(im) draw.text((0, 0), recruit_operators[operator]["name"], font=font) im = im.crop(im.getbbox()) im = cv2.cvtColor(np.asarray(im), cv2.COLOR_RGB2GRAY) im = cv2.erode(im, np.ones((5, 5))) template[operator] = im with lzma.open("mower/static/recruit_result.pkl", "wb") as f: pickle.dump(template, f) def load_recruit_tag(self): with open("./mower/data/recruit.json", "r", encoding="utf-8") as f: recruit_agent = json.load(f) font = ImageFont.truetype("mower/fonts/SourceHanSansCN-Medium.otf", 30) recruit_tag = ["资深干员", "高级资深干员"] recruit_tag_template = {} for x in recruit_agent.values(): recruit_tag += x["tags"] recruit_tag = list(set(recruit_tag)) for tag in sorted(recruit_tag): im = Image.new(mode="RGBA", color=(49, 49, 49), size=(215, 70)) W, H = im.size draw = ImageDraw.Draw(im) _, _, w, h = draw.textbbox((0, 0), tag, font=font) draw.text(((W - w) / 2, (H - h) / 2 - 5), tag, font=font) recruit_tag_template[tag] = cv2.cvtColor( np.array(im.crop(im.getbbox())), cv2.COLOR_RGB2BGR ) with lzma.open("./mower/static/recruit.pkl", "wb") as f: pickle.dump(recruit_tag_template, f) def 输出_公招资源(self): self.load_recruit_data() self.load_recruit_template() self.load_recruit_tag() def 训练仓库的knn模型(self, 模板文件夹, 模型保存路径): def 提取特征点(模板): 模板 = 模板[40:173, 40:173] hog_features = hog( 模板, orientations=18, pixels_per_cell=(8, 8), cells_per_block=(2, 2), block_norm="L2-Hys", transform_sqrt=True, channel_axis=2, ) return hog_features def 加载图片特征点_标签(模板类型): 特征点列表 = [] 标签列表 = [] for [目标文件路径, 源文件路径] in self.装仓库物品的字典[模板类型]: 模板 = cv2.imread(源文件路径) 模板 = cv2.resize(模板, (213, 213)) 特征点 = 提取特征点(模板) 特征点列表.append(特征点) 标签列表.append(self.物品_名称_模型用[目标文件路径[18:-5]][2]) return 特征点列表, 标签列表 def 训练knn模型(images, labels): knn_classifier = KNeighborsClassifier( weights="distance", n_neighbors=1, n_jobs=1 ) knn_classifier.fit(images, labels) return knn_classifier def 保存knn模型(classifier, filename): with lzma.open(filename, "wb") as f: pickle.dump(classifier, f) 模板特征点, 模板标签 = 加载图片特征点_标签(模板文件夹) knn模型 = 训练knn模型(模板特征点, 模板标签) 保存knn模型(knn模型, 模型保存路径) def 输出_扫仓库模型(self): """输出扫描仓库的Knn模型 和添加物品至前端文件() 有联动 ， 添加物品提供了分类的图片位置""" self.训练仓库的knn模型("NORMAL", "./mower/static/NORMAL.pkl") self.训练仓库的knn模型("CONSUME", "./mower/static/CONSUME.pkl") self.训练仓库的knn模型("MATERIAL", "./mower/static/MATERIAL.pkl") def 输出_在房间内的干员名的模型(self): font = ImageFont.truetype("mower/fonts/SourceHanSansCN-Medium.otf", 37) data = {} kernel = np.ones((12, 12), np.uint8) with open("./mower/data/agent.json", "r", encoding="utf-8") as f: agent_list = json.load(f) for operator in sorted(agent_list, key=lambda x: len(x), reverse=True): img = Image.new(mode="L", size=(400, 100)) draw = ImageDraw.Draw(img) draw.text((50, 20), operator, fill=(255,), font=font) img = np.array(img, dtype=np.uint8) img = thres2(img, 200) dilation = cv2.dilate(img, kernel, iterations=1) contours, _ = cv2.findContours( dilation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE ) rect = map(lambda c: cv2.boundingRect(c), contours) x, y, w, h = sorted(rect, key=lambda c: c[0])[0] img = img[y : y + h, x : x + w] tpl = np.zeros((46, 265), dtype=np.uint8) tpl[: img.shape[0], : img.shape[1]] = img # cv2.imwrite(f"/home/zhao/Desktop/data/{operator}.png", tpl) data[operator] = tpl with lzma.open("mower/static/operator_room.model", "wb") as f: pickle.dump(data, f) def avatar_and_portrait(self, save_img=False): mapping = {} # char_285_medic2 -> Lancet-2 for name, data in self.干员表.items(): mapping[name] = data["name"] for name in self.阿米娅表["infos"]["char_002_amiya"]["tmplIds"]: mapping[name] = "阿米娅" def extract(name, scope): data_path = f"./ArknightsGameResource/{name}" file_list = os.listdir(data_path) h, w = loadimg(os.path.join(data_path, file_list[0]), True).shape total = len(file_list) col = math.ceil(math.sqrt(total * h / w)) row = math.ceil(total / col) full = np.zeros((row * h, col * w), dtype=np.uint8) mask = full.copy() name_list = [] i = 0 for filename in file_list: x = (i % col) * w y = (i // col) * h mx, my = sa(scope, (x, y)) # filename: char_285_medic2_1.png for j, k in mapping.items(): # j: char_285_medic2 # k: Lancet-2 if filename.startswith(j): full[y : y + h, x : x + w] = loadimg( os.path.join(data_path, filename), True ) cv2.rectangle(mask, mx, my, (255,), -1) name_list.append(k) i += 1 break if save_img: disp = cv2.bitwise_and(full, mask) cv2.imwrite(f"{name}.png", disp) kp, des = fast_keypoints(full, mask) pt = [p.pt for p in kp] data = {"w": w, "h": h, "col": col, "name": name_list, "pt": pt, "des": des} with lzma.open(f"./mower/static/{name}.pkl", "wb") as f: pickle.dump(data, f) extract("avatar", ((20, 51), (160, 146))) extract("portrait", ((20, 115), (160, 190))) def 获得干员基建描述(self): buff描述 = self.基建表["buffs"] buff_table = {} for buff名称, 相关buff in buff描述.items(): buff_table[buff名称] = [ 相关buff["buffName"], 相关buff["description"], 相关buff["roomType"], 相关buff["buffCategory"], 相关buff["skillIcon"], 相关buff["buffColor"], 相关buff["textColor"], ] 干员技能列表 = [] name_key = 0 for 角色id, 相关buff in self.基建表["chars"].items(): 干员技能字典 = { "key": 0, "name": "", "span": 0, "child_skill": [], } 干员技能字典["name"] = self.干员表[角色id]["name"] skill_key = 0 name_key += 1 干员技能字典["key"] = name_key for item in 相关buff["buffChar"]: skill_level = 0 if item["buffData"] != []: for item2 in item["buffData"]: 干员技能详情 = {} 干员技能详情["skill_key"] = skill_key 干员技能详情["skill_level"] = skill_level skill_level += 1 干员技能详情["phase_level"] = ( f'精{item2["cond"]["phase"][-1]} {item2["cond"]["level"]}级' ) 干员技能详情["skillname"] = buff_table[item2["buffId"]][0] text = buff_table[item2["buffId"]][1] pattern = r"<\$(.*?)>" matches = re.findall(pattern, text) ex_string = [] 干员技能详情["buffer"] = False 干员技能详情["buffer_des"] = [] if matches: 干员技能详情["buffer"] = True ex_string = list( set([match.replace(".", "_") for match in matches]) ) ex_string.sort() 干员技能详情["buffer_des"] = ex_string self.所有buff.extend(ex_string) 干员技能详情["des"] = text 干员技能详情["roomType"] = roomType[ buff_table[item2["buffId"]][2] ] 干员技能详情["buffCategory"] = buff_table[item2["buffId"]][3] 干员技能详情["skillIcon"] = buff_table[item2["buffId"]][4] 干员技能详情["buffColor"] = buff_table[item2["buffId"]][5] 干员技能详情["textColor"] = buff_table[item2["buffId"]][6] 干员技能字典["child_skill"].append(干员技能详情) 干员技能详情 = [] 干员技能字典["span"] = len(干员技能字典["child_skill"]) skill_key += 1 干员技能列表.append(干员技能字典.copy()) 干员技能列表 = sorted(干员技能列表, key=lambda x: (-x["key"])) # print(干员技能列表) with open( "./ui/src/pages/basement_skill/skill.json", "w", encoding="utf-8" ) as f: json.dump(干员技能列表, f, ensure_ascii=False, indent=4) def 转换输出基建buff(self): buff_table = {} pattern = r"<\$(.*?)>" for item in self.游戏变量["termDescriptionDict"]: matches = re.findall( pattern, self.游戏变量["termDescriptionDict"][item]["description"] ) matches = [match.replace(".", "_") for match in matches] dict1 = self.游戏变量["termDescriptionDict"][item] dict1["buffer"] = [] if item.startswith("cc") and matches: dict1["buffer"] = matches buff_table[item.replace(".", "_")] = dict1 with open( "./ui/src/pages/basement_skill/buffer.json", "w", encoding="utf-8" ) as f: json.dump(buff_table, f, ensure_ascii=False, indent=4) def 添加基建技能图标(self): # 源目录和目标目录 source_dir = "./ArknightsGameResource/building_skill" destination_dir = "./ui/public/building_skill" # 创建目标目录（如果不存在） os.makedirs(destination_dir, exist_ok=True) # 遍历源目录中的所有文件 for root, dirs, files in os.walk(source_dir): for file in files: if file.endswith(".png"): src_file_path = os.path.join(root, file) # 修改文件扩展名为 .webp dest_file_name = os.path.splitext(file)[0] + ".webp" dest_file_path = os.path.join(destination_dir, dest_file_name) if not os.path.exists(dest_file_path): with Image.open(src_file_path) as img: img.save(dest_file_path, "webp") print(f"转换: {src_file_path} 到 {dest_file_path}") else: print(f"跳过: {dest_file_path} 已存在") def 输出_前端基建资源(self): self.获得干员基建描述() self.转换输出基建buff() self.添加基建技能图标() def levels(self): levels_json = 加载json("./ArknightsGameResource/levels.json") with lzma.open("mower/static/levels.pkl", "wb") as f: pickle.dump(levels_json, f) def 读取模组(self): _ = [] for item in self.模组["missionList"]: _.append(self.模组["missionList"][item]["template"]) template_list = list({}.fromkeys(_).keys()) print(template_list) def 输出_干员战斗技能图标(self): for 干员代码, 干员数据 in self.干员表.items(): if not 干员数据["itemObtainApproach"]: continue 干员名 = 干员数据["name"] print(干员名) for 技能数, item in enumerate(干员数据["skills"]): 技能图标名 = self.战斗技能[item["skillId"]]["iconId"] or item["skillId"] 干员技能路径 = ( f"./ArknightsGameResource/skill/skill_icon_{技能图标名}.png" ) os.makedirs( f"./mower/static/avatar_skill/{干员数据['name']}", exist_ok=True ) 目标路径 = ( f"./mower/static/avatar_skill/{干员数据['name']}/{技能数+1}.png" ) shutil.copy(干员技能路径, 目标路径) print(目标路径) roomType = { "POWER": "发电站", "DORMITORY": "宿舍", "MANUFACTURE": "制造站", "MEETING": "会客室", "WORKSHOP": "加工站", "TRADING": "贸易站", "HIRE": "人力办公室", "TRAINING": "训练室", "CONTROL": "中枢", } if __name__ == "__main__": 数据处理器 = Arknights数据处理器() 数据处理器.干员列表() 数据处理器.读取卡池() 数据处理器.输出_活动关卡和掉落() 数据处理器.添加物品至前端文件() # 显示在仓库里的物品 数据处理器.输出_扫仓库模型() 数据处理器.输出_在房间内的干员名的模型() 数据处理器.avatar_and_portrait() 数据处理器.输出_前端基建资源() 数据处理器.输出_干员战斗技能图标() 数据处理器.输出_公招资源() 数据处理器.levels() 数据处理器.读取模组()