490 lines
15 KiB
Python
490 lines
15 KiB
Python
from __future__ import annotations
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import asyncio
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from copy import deepcopy
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from enum import Enum
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import math
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from typing import TYPE_CHECKING
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from app.config import settings
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from app.log import logger
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from app.models.beatmap import BeatmapAttributes
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from app.models.mods import APIMod, parse_enum_to_str
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from app.models.score import GameMode
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from osupyparser import HitObject, OsuFile
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from osupyparser.osu.objects import Slider
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from redis.asyncio import Redis
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from sqlmodel import col, exists, select
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from sqlmodel.ext.asyncio.session import AsyncSession
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try:
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import rosu_pp_py as rosu
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except ImportError:
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raise ImportError(
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"rosu-pp-py is not installed. "
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"Please install it.\n"
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" Official: uv add rosu-pp-py\n"
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" ppy-sb: uv add git+https://github.com/ppy-sb/rosu-pp-py.git"
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)
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if TYPE_CHECKING:
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from app.database.score import Score
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from app.fetcher import Fetcher
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def clamp[T: int | float](n: T, min_value: T, max_value: T) -> T:
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if n < min_value:
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return min_value
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elif n > max_value:
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return max_value
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else:
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return n
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def calculate_beatmap_attribute(
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beatmap: str,
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gamemode: GameMode | None = None,
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mods: int | list[APIMod] | list[str] = 0,
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) -> BeatmapAttributes:
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map = rosu.Beatmap(content=beatmap)
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if gamemode is not None:
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map.convert(gamemode.to_rosu(), mods) # pyright: ignore[reportArgumentType]
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diff = rosu.Difficulty(mods=mods).calculate(map)
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return BeatmapAttributes(
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star_rating=diff.stars,
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max_combo=diff.max_combo,
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aim_difficulty=diff.aim,
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aim_difficult_slider_count=diff.aim_difficult_slider_count,
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speed_difficulty=diff.speed,
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speed_note_count=diff.speed_note_count,
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slider_factor=diff.slider_factor,
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aim_difficult_strain_count=diff.aim_difficult_strain_count,
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speed_difficult_strain_count=diff.speed_difficult_strain_count,
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mono_stamina_factor=diff.stamina,
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)
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async def calculate_pp(score: "Score", beatmap: str, session: AsyncSession) -> float:
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from app.database.beatmap import BannedBeatmaps
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if settings.suspicious_score_check:
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beatmap_banned = (
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await session.exec(select(exists()).where(col(BannedBeatmaps.beatmap_id) == score.beatmap_id))
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).first()
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if beatmap_banned:
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return 0
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try:
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is_suspicious = is_suspicious_beatmap(beatmap)
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if is_suspicious:
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session.add(BannedBeatmaps(beatmap_id=score.beatmap_id))
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logger.warning(f"Beatmap {score.beatmap_id} is suspicious, banned")
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return 0
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except Exception:
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logger.exception(f"Error checking if beatmap {score.beatmap_id} is suspicious")
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# 使用线程池执行计算密集型操作以避免阻塞事件循环
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loop = asyncio.get_event_loop()
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def _calculate_pp_sync():
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map = rosu.Beatmap(content=beatmap)
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mods = deepcopy(score.mods.copy())
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parse_enum_to_str(int(score.gamemode), mods)
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map.convert(score.gamemode.to_rosu(), mods) # pyright: ignore[reportArgumentType]
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perf = rosu.Performance(
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mods=mods,
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lazer=True,
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accuracy=clamp(score.accuracy * 100, 0, 100),
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combo=score.max_combo,
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large_tick_hits=score.nlarge_tick_hit or 0,
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slider_end_hits=score.nslider_tail_hit or 0,
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small_tick_hits=score.nsmall_tick_hit or 0,
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n_geki=score.ngeki,
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n_katu=score.nkatu,
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n300=score.n300,
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n100=score.n100,
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n50=score.n50,
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misses=score.nmiss,
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)
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return perf.calculate(map)
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# 在线程池中执行计算
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attrs = await loop.run_in_executor(None, _calculate_pp_sync)
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pp = attrs.pp
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# mrekk bp1: 2048pp; ppy-sb top1 rxbp1: 2198pp
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if settings.suspicious_score_check and ((attrs.difficulty.stars > 25 and score.accuracy < 0.8) or pp > 3000):
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logger.warning(
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f"User {score.user_id} played {score.beatmap_id} "
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f"(star={attrs.difficulty.stars}) with {pp=} "
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f"acc={score.accuracy}. The score is suspicious and return 0pp"
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f"({score.id=})"
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)
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return 0
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return pp
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async def pre_fetch_and_calculate_pp(
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score: "Score", session: AsyncSession, redis: Redis, fetcher: "Fetcher"
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) -> tuple[float, bool]:
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"""
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优化版PP计算：预先获取beatmap文件并使用缓存
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"""
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from app.database.beatmap import BannedBeatmaps
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beatmap_id = score.beatmap_id
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# 快速检查是否被封禁
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if settings.suspicious_score_check:
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beatmap_banned = (
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await session.exec(select(exists()).where(col(BannedBeatmaps.beatmap_id) == beatmap_id))
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).first()
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if beatmap_banned:
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return 0, False
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# 异步获取beatmap原始文件，利用已有的Redis缓存机制
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try:
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beatmap_raw = await fetcher.get_or_fetch_beatmap_raw(redis, beatmap_id)
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except Exception as e:
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logger.error(f"Failed to fetch beatmap {beatmap_id}: {e}")
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return 0, False
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# 在获取文件的同时，可以检查可疑beatmap
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if settings.suspicious_score_check:
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try:
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# 将可疑检查也移到线程池中执行
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def _check_suspicious():
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return is_suspicious_beatmap(beatmap_raw)
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loop = asyncio.get_event_loop()
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is_sus = await loop.run_in_executor(None, _check_suspicious)
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if is_sus:
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session.add(BannedBeatmaps(beatmap_id=beatmap_id))
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logger.warning(f"Beatmap {beatmap_id} is suspicious, banned")
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return 0, True
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except Exception:
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logger.exception(f"Error checking if beatmap {beatmap_id} is suspicious")
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# 调用已优化的PP计算函数
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return await calculate_pp(score, beatmap_raw, session), True
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async def batch_calculate_pp(
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scores_data: list[tuple["Score", int]], session: AsyncSession, redis, fetcher
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) -> list[float]:
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"""
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批量计算PP：适用于重新计算或批量处理场景
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Args:
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scores_data: [(score, beatmap_id), ...] 的列表
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Returns:
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对应的PP值列表
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"""
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import asyncio
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from app.database.beatmap import BannedBeatmaps
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if not scores_data:
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return []
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# 提取所有唯一的beatmap_id
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unique_beatmap_ids = list({beatmap_id for _, beatmap_id in scores_data})
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# 批量检查被封禁的beatmap
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banned_beatmaps = set()
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if settings.suspicious_score_check:
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banned_results = await session.exec(
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select(BannedBeatmaps.beatmap_id).where(col(BannedBeatmaps.beatmap_id).in_(unique_beatmap_ids))
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)
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banned_beatmaps = set(banned_results.all())
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# 并发获取所有需要的beatmap原始文件
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async def fetch_beatmap_safe(beatmap_id: int) -> tuple[int, str | None]:
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if beatmap_id in banned_beatmaps:
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return beatmap_id, None
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try:
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content = await fetcher.get_or_fetch_beatmap_raw(redis, beatmap_id)
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return beatmap_id, content
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except Exception as e:
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logger.error(f"Failed to fetch beatmap {beatmap_id}: {e}")
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return beatmap_id, None
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# 并发获取所有beatmap文件
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fetch_tasks = [fetch_beatmap_safe(bid) for bid in unique_beatmap_ids]
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fetch_results = await asyncio.gather(*fetch_tasks, return_exceptions=True)
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# 构建beatmap_id -> content的映射
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beatmap_contents = {}
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for result in fetch_results:
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if isinstance(result, tuple):
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beatmap_id, content = result
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beatmap_contents[beatmap_id] = content
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# 为每个score计算PP
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pp_results = []
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for score, beatmap_id in scores_data:
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beatmap_content = beatmap_contents.get(beatmap_id)
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if beatmap_content is None:
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pp_results.append(0.0)
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continue
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try:
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pp = await calculate_pp(score, beatmap_content, session)
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pp_results.append(pp)
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except Exception as e:
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logger.error(f"Failed to calculate PP for score {score.id}: {e}")
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pp_results.append(0.0)
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return pp_results
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# https://osu.ppy.sh/wiki/Gameplay/Score/Total_score
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def calculate_level_to_score(n: int) -> float:
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if n <= 100:
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return 5000 / 3 * (4 * n**3 - 3 * n**2 - n) + 1.25 * 1.8 ** (n - 60)
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else:
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return 26931190827 + 99999999999 * (n - 100)
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# https://github.com/ppy/osu-queue-score-statistics/blob/4bdd479530408de73f3cdd95e097fe126772a65b/osu.Server.Queues.ScoreStatisticsProcessor/Processors/TotalScoreProcessor.cs#L70-L116
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def calculate_score_to_level(total_score: int) -> float:
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to_next_level = [
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30000,
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100000,
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210000,
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360000,
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550000,
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780000,
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1050000,
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1360000,
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1710000,
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2100000,
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2530000,
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3000000,
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3510000,
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4060000,
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4650000,
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5280000,
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5950000,
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6660000,
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7410000,
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8200000,
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9030000,
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9900000,
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10810000,
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11760000,
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12750000,
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13780000,
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14850000,
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15960000,
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17110000,
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18300000,
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19530000,
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20800000,
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22110000,
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23460000,
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24850000,
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26280000,
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27750000,
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29260000,
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30810000,
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32400000,
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34030000,
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35700000,
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37410000,
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39160000,
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40950000,
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42780000,
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44650000,
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46560000,
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48510000,
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50500000,
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52530000,
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54600000,
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56710000,
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58860000,
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61050000,
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63280000,
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65550000,
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67860000,
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70210001,
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72600001,
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75030002,
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77500003,
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80010006,
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82560010,
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85150019,
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87780034,
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90450061,
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93160110,
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95910198,
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98700357,
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101530643,
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104401157,
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107312082,
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110263748,
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113256747,
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116292144,
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119371859,
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122499346,
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125680824,
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128927482,
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132259468,
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135713043,
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139353477,
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143298259,
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147758866,
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153115959,
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160054726,
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169808506,
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184597311,
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208417160,
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248460887,
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317675597,
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439366075,
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655480935,
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1041527682,
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1733419828,
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2975801691,
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5209033044,
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9225761479,
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99999999999,
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99999999999,
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99999999999,
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99999999999,
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99999999999,
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99999999999,
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99999999999,
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99999999999,
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99999999999,
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99999999999,
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99999999999,
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99999999999,
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99999999999,
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99999999999,
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99999999999,
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99999999999,
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]
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remaining_score = total_score
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level = 0.0
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while remaining_score > 0:
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next_level_requirement = to_next_level[min(len(to_next_level) - 1, round(level))]
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level += min(1, remaining_score / next_level_requirement)
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remaining_score -= next_level_requirement
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return level + 1
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# https://osu.ppy.sh/wiki/Performance_points/Weighting_system
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def calculate_pp_weight(index: int) -> float:
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return math.pow(0.95, index)
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def calculate_weighted_pp(pp: float, index: int) -> float:
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return calculate_pp_weight(index) * pp if pp > 0 else 0.0
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def calculate_weighted_acc(acc: float, index: int) -> float:
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return calculate_pp_weight(index) * acc if acc > 0 else 0.0
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# 大致算法来自 https://github.com/MaxOhn/rosu-pp/blob/main/src/model/beatmap/suspicious.rs
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class Threshold(int, Enum):
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# 谱面异常常量
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NOTES_THRESHOLD = 500000 # 除 taiko 以外任何模式的物件数量
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TAIKO_THRESHOLD = 30000 # taiko 模式下的物量限制
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NOTES_PER_1S_THRESHOLD = 200 # 3000 BPM
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NOTES_PER_10S_THRESHOLD = 500 # 600 BPM
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# 这个尺寸已经是常规游玩区域大小的 4 倍了 …… 如果不合适那另说吧
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NOTE_POSX_THRESHOLD = 512 # x: [-512,512]
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NOTE_POSY_THRESHOLD = 384 # y: [-384,384]
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POS_ERROR_THRESHOLD = 1280 * 50 # 超过这么多个物件（包括滑条控制点）的位置有问题就毙掉
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SLIDER_REPEAT_THRESHOLD = 5000
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def too_dense(hit_objects: list[HitObject], per_1s: int, per_10s: int) -> bool:
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per_1s = max(1, per_1s)
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per_10s = max(1, per_10s)
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for i in range(0, len(hit_objects)):
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if len(hit_objects) > i + per_1s:
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if hit_objects[i + per_1s].start_time - hit_objects[i].start_time < 1000:
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return True
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elif len(hit_objects) > i + per_10s:
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if hit_objects[i + per_10s].start_time - hit_objects[i].start_time < 10000:
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return True
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return False
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def slider_is_sus(hit_objects: list[HitObject]) -> bool:
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for obj in hit_objects:
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if isinstance(obj, Slider):
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flag_repeat = obj.repeat_count > Threshold.SLIDER_REPEAT_THRESHOLD
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flag_pos = int(
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obj.pos.x > Threshold.NOTE_POSX_THRESHOLD
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or obj.pos.x < 0
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or obj.pos.y > Threshold.NOTE_POSY_THRESHOLD
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or obj.pos.y < 0
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)
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for point in obj.points:
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flag_pos += int(
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point.x > Threshold.NOTE_POSX_THRESHOLD
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or point.x < 0
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or point.y > Threshold.NOTE_POSY_THRESHOLD
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or point.y < 0
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)
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if flag_pos or flag_repeat:
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return True
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return False
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def is_2b(hit_objects: list[HitObject]) -> bool:
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for i in range(0, len(hit_objects) - 1):
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if hit_objects[i] == hit_objects[i + 1].start_time:
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return True
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return False
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def is_suspicious_beatmap(content: str) -> bool:
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osufile = OsuFile(content=content.encode("utf-8")).parse_file()
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if osufile.hit_objects[-1].start_time - osufile.hit_objects[0].start_time > 24 * 60 * 60 * 1000:
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return True
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if osufile.mode == int(GameMode.TAIKO):
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if len(osufile.hit_objects) > Threshold.TAIKO_THRESHOLD:
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return True
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elif len(osufile.hit_objects) > Threshold.NOTES_THRESHOLD:
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return True
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match osufile.mode:
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case int(GameMode.OSU):
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return (
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too_dense(
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osufile.hit_objects,
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Threshold.NOTES_PER_1S_THRESHOLD,
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Threshold.NOTES_PER_10S_THRESHOLD,
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)
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or slider_is_sus(osufile.hit_objects)
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or is_2b(osufile.hit_objects)
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)
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case int(GameMode.TAIKO):
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return too_dense(
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osufile.hit_objects,
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Threshold.NOTES_PER_1S_THRESHOLD * 2,
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Threshold.NOTES_PER_10S_THRESHOLD * 2,
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) or is_2b(osufile.hit_objects)
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case int(GameMode.FRUITS):
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return slider_is_sus(osufile.hit_objects) or is_2b(osufile.hit_objects)
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case int(GameMode.MANIA):
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keys_per_hand = max(1, int(osufile.cs / 2))
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per_1s = Threshold.NOTES_PER_1S_THRESHOLD * keys_per_hand
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per_10s = Threshold.NOTES_PER_10S_THRESHOLD * keys_per_hand
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return too_dense(osufile.hit_objects, per_1s, per_10s)
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return False
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