142 lines
5.2 KiB
Python
142 lines
5.2 KiB
Python
import os
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from PIL import Image, ImageDraw, ImageFont
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from nonebot import logger
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from functools import reduce
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from .img_utils import png_resize
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from .param import round_data, interval_table
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filepath = os.path.dirname(__file__).replace("\\", "/")
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# async def avatar_cache(avatar_url, player_id):
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# if avatar_url:
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# try:
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# # 添加10s超时判断，如果超时直接使用默认头像
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# res = BytesIO(requests.get(avatar_url, timeout=10).content)
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# avatar = Image.open(res).convert('RGBA')
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# avatar.save(f'{filepath}/avatar_cache/{player_id}.png', 'PNG')
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# return avatar
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# except requests.exceptions.RequestException as e:
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# logger.warning(f"请求异常：{e}")
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# else:
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# return None
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#
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#
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# async def get_avatar(avatar_url, player_id):
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# img = Image.open(filepath + "/avatar_cache/0.png").convert('RGBA')
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# path = filepath + "/avatar_cache/"
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# try:
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# avatar_list = os.listdir(path)
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# if player_id in str(avatar_list):
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# logger.info(f"本地已存在{player_id}头像")
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# img = Image.open(f"{path}{player_id}.png").convert('RGBA')
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# else:
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# logger.info(f"未检测到{player_id}头像，缓存至本地")
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# out_put = filepath + f"/avatar_cache/{player_id}.png"
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# img = await avatar_cache(avatar_url, out_put)
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# except Exception as err:
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# logger.error(f"获取头像失败：{str(err)}")
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# return img
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async def get_best_weapon_and_best_vehicle(player_stat):
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weapon = sorted(player_stat["weapons"], key=lambda k: k['kills'], reverse=True)[0]
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vehicle = sorted(player_stat["vehicles"], key=lambda k: k['kills'], reverse=True)[0]
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return weapon, vehicle
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async def get_vehicle_destroyed(player_stat):
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vehicle = reduce(lambda acc, x: acc + x['destroyed'], player_stat, 0)
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return vehicle
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async def stats_calculator(player_stat, game):
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per500_data = round_data[game]
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# 场次（无直接数值反馈，采用胜利数+失败数）
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match_played = player_stat["wins"] + player_stat["loses"]
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kd = player_stat["killDeath"]
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kpm = player_stat["killsPerMinute"]
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kill_assists = player_stat["killAssists"]
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acc = float(player_stat["accuracy"].replace("%", ""))
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if game == "bf3":
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headshots = float(0)
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else:
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headshots = float(player_stat["headshots"].replace("%", ""))
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spm = player_stat["scorePerMinute"]
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revives = player_stat["revives"]
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# second_play = player_stat["secondsPlayed"]
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apr = kill_assists / match_played
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rpr = revives / match_played
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# SPM*秒数/60/场数=场均得分
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# dpr = score_perMinute * (second_play / 60) / match_played
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# 计算比值
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# kd
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ratio_kd = round(kd / per500_data["kd"], 2)
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# kpm
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ratio_kpm = round(kpm / per500_data["kpm"], 2)
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# 爆头率
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ratio_hs = round(headshots / 100 / per500_data["hs"], 2)
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# 场均救援
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ratio_rpr = round(rpr / per500_data["rpr"], 2)
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# 场均助攻
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ratio_apr = round(apr / per500_data["apr"], 2)
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# 分钟得分
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ratio_spm = round(spm / per500_data["spm"], 2)
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logger.info(f"雷达图数据")
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logger.info(f"生涯KD比值:{ratio_kd}")
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logger.info(f"生涯Kpm比值:{ratio_kpm}")
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logger.info(f"生涯准度比值:{ratio_hs}")
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logger.info(f"场均救援比值:{ratio_rpr}")
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logger.info(f"场均助攻比值:{ratio_apr}")
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logger.info(f"分钟得分比值:{ratio_spm}")
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stat_data = [ratio_kd, ratio_apr, ratio_hs, ratio_spm, ratio_kpm, ratio_rpr]
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# 血腥度 kd*0.5 + kpm *0.4 +hs*0.1
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player_blood = kd * 0.5 + kpm * 0.4 + headshots * 0.1
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ratio_blood = player_blood / per500_data["blood"]
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# 贡献度 场均助攻*0.4 + 场均救援*0.4 + spm*0.2
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player_dedication = rpr * 0.4 + apr * 0.4 + spm * 0.2
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ratio_dedication = player_dedication / per500_data["dedication"]
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# 全能度 血腥度*0.5 + 贡献度*0.5
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player_all_round = player_blood * 0.5 + player_dedication * 0.5
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ratio_all_round = player_all_round / per500_data["all_round"]
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logger.info(f"称号评级数据")
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logger.info(f"血腥度:{player_blood} 比值:{ratio_blood}")
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logger.info(f"贡献度:{player_dedication} 比值:{ratio_dedication}")
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logger.info(f"全能度:{player_all_round} 比值:{ratio_all_round}")
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level_designation = get_level_designation(ratio_blood, ratio_dedication, ratio_all_round)
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return stat_data, level_designation
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def get_level_designation(blood, dedication, all_round):
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feature_type = ""
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final_max = max([blood, dedication, all_round])
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if final_max == blood:
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feature_type = "blood"
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elif final_max == dedication:
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feature_type = "dedication"
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elif final_max == all_round:
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feature_type = "all_round"
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logger.info(f"最高能力:{feature_type}")
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logger.info(f"最高能力值:{final_max}")
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final_info = get_feature(final_max, feature_type)
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return final_info
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def get_feature(value, feature_type):
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"""根据输入值和数值类别，返回对应特征值"""
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for (low, high), features in interval_table.items():
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if low <= value < high:
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return features[feature_type]
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raise ValueError(f"数值 {value} 不在任何区间内")
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