feat(task): 支持培育中四五张卡片情况的检测
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import logging
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from functools import partial
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from typing import Callable, NamedTuple, Literal
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import cv2
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import numpy as np
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from cv2.typing import MatLike
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from .. import R
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from ..common import conf
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from ..game_ui import dialog
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from ..util.trace import trace
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from kotonebot.util import crop
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from kotonebot import action, Interval, Countdown, device, image, sleep, ocr, contains, use_screenshot, color, Rect
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class SkillCard(NamedTuple):
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available: bool
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rect: Rect
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class CardPosInfo(NamedTuple):
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x: int
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y: int
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w: int
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h: int
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type: Literal[0, 1, 2, 3, 4, 10] # 0~4=卡片下标，10=SKIP
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class CardDetectResult(NamedTuple):
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type: int
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"""
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点击的卡片类型。
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0=第一张卡片，1=第二张卡片，2=第三张卡片，3=第四张卡片，10=SKIP。
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"""
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score: float
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"""总分数"""
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left_score: float
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"""左边分数"""
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right_score: float
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"""右边分数"""
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top_score: float
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"""上边分数"""
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bottom_score: float
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"""下边分数"""
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rect: Rect
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logger = logging.getLogger(__name__)
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# TODO: 硬编码。需要想办法移动到资源文件中
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CARD_SIZE = (192, 252) # 卡片大小 w, h
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CARD_Y = 883 # 卡片 Y 坐标
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# 只有一张卡的情况
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CARD_START_X_1 = 264
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CARD_DELTA_X_1 = 0
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# 两张卡的情况
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CARD_START_X_2 = 156 # 第一张卡的起始 X
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CARD_DELTA_X_2 = 24 # 间隔距离 = card_x_2 - card_x_1 - card_width
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# 三张卡的情况
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CARD_START_X_3 = 47
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CARD_DELTA_X_3 = 25
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# 四张卡的情况
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CARD_START_X_4 = 17
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CARD_DELTA_X_4 = -27
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# 五张卡的情况
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CARD_START_X_5 = 17
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CARD_DELTA_X_5 = -68
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# SKIP 按钮
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SKIP_CARD_BUTTON = CardPosInfo(621, 739, 85, 85, 10)
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def calc_card_position(card_count: int):
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w, h = CARD_SIZE
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if card_count == 1:
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result = [
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CardPosInfo(x=CARD_START_X_1, y=CARD_Y, w=w, h=h, type=0)
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]
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elif card_count == 2:
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delta = CARD_DELTA_X_2 + w
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result = [
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CardPosInfo(x=CARD_START_X_2, y=CARD_Y, w=w, h=h, type=0),
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CardPosInfo(x=CARD_START_X_2 + delta, y=CARD_Y, w=w, h=h, type=1),
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]
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elif card_count == 3:
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delta = CARD_DELTA_X_3 + w
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result = [
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CardPosInfo(x=CARD_START_X_3, y=CARD_Y, w=w, h=h, type=0),
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CardPosInfo(x=CARD_START_X_3 + delta, y=CARD_Y, w=w, h=h, type=1),
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CardPosInfo(x=CARD_START_X_3 + delta * 2, y=CARD_Y, w=w, h=h, type=2),
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]
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elif card_count == 4:
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delta = CARD_DELTA_X_4 + w
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result = [
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CardPosInfo(x=CARD_START_X_4, y=CARD_Y, w=w, h=h, type=0),
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CardPosInfo(x=CARD_START_X_4 + delta, y=CARD_Y, w=w, h=h, type=1),
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CardPosInfo(x=CARD_START_X_4 + delta * 2, y=CARD_Y, w=w, h=h, type=2),
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CardPosInfo(x=CARD_START_X_4 + delta * 3, y=CARD_Y, w=w, h=h, type=3),
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]
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elif card_count == 5:
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delta = CARD_DELTA_X_5 + w
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result = [
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CardPosInfo(x=CARD_START_X_5, y=CARD_Y, w=w, h=h, type=0),
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CardPosInfo(x=CARD_START_X_5 + delta, y=CARD_Y, w=w, h=h, type=1),
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CardPosInfo(x=CARD_START_X_5 + delta * 2, y=CARD_Y, w=w, h=h, type=2),
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CardPosInfo(x=CARD_START_X_5 + delta * 3, y=CARD_Y, w=w, h=h, type=3),
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CardPosInfo(x=CARD_START_X_5 + delta * 4, y=CARD_Y, w=w, h=h, type=4),
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]
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else:
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raise ValueError(f'不支持 {card_count} 张手牌')
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return result
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@action('打牌', screenshot_mode='manual')
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def do_cards(
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threshold_predicate: Callable[[int, CardDetectResult], bool],
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end_predicate: Callable[[], bool]
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):
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"""
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循环打出推荐卡，直到考试/练习结束
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前置条件：考试/练习页面\n
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结束状态：考试/练习结束的一瞬间
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:param threshold_predicate: 推荐卡检测阈值判断函数
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:param end_predicate: 结束条件判断函数
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"""
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it = Interval(seconds=1/30)
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timeout_cd = Countdown(sec=120).start() # 推荐卡检测超时计时器
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break_cd = Countdown(sec=3).start() # 满足结束条件计时器
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no_card_cd = Countdown(sec=4) # 无手牌计时器
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detect_card_count_cd = Countdown(sec=4).start() # 刷新检测手牌数量间隔
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tries = 1
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card_count = -1
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while True:
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device.click(0, 0)
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img = device.screenshot()
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it.wait()
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# 技能卡自选移动对话框
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if image.find(R.InPurodyuusu.IconTitleSkillCardMove):
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if handle_skill_card_move():
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sleep(4) # 等待卡片刷新
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continue
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# 技能卡效果无法发动对话框
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if image.find(R.Common.ButtonIconCheckMark):
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logger.info("Confirmation dialog detected")
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device.click()
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sleep(4) # 等待卡片刷新
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continue
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# 更新卡片数量
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if card_count == -1 or detect_card_count_cd.expired():
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detect_card_count_cd.reset()
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card_count = skill_card_count(img)
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logger.debug("Current card count: %d", card_count)
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# 处理手牌
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if card_count == 0:
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# 处理本回合已无剩余手牌的情况
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# TODO: 使用模板匹配而不是 OCR，提升速度
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no_card_cd.start()
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no_remaining_card = ocr.find(contains("0枚"), rect=R.InPurodyuusu.BoxNoSkillCard)
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if no_remaining_card and no_card_cd.expired():
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logger.debug('No remaining card detected. Skip this turn.')
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# TODO: HARD CODEDED
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SKIP_POSITION = (621, 739, 85, 85)
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device.click(SKIP_POSITION)
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no_card_cd.reset()
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continue
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else:
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if handle_recommended_card(
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card_count=card_count,
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threshold_predicate=threshold_predicate,
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img=img
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):
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logger.info("Handle recommended card success with %d tries", tries)
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sleep(4.5)
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tries = 0
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timeout_cd.reset()
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continue
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else:
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tries += 1
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# 检测超时（防止一直卡在检测）
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if timeout_cd.expired():
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logger.info("Recommend card detection timed out. Click first card.")
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card_rects = calc_card_position(card_count)
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card_rect = card_rects[0]
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device.double_click(card_rect[:4])
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timeout_cd.reset()
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# 结束条件
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if card_count == 0 and end_predicate():
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if not break_cd.started:
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break_cd.start()
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if break_cd.expired():
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break
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else:
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break_cd.reset()
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logger.info("CLEAR/PERFECT not found. Practice finished.")
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@action("技能卡移动")
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def handle_skill_card_move():
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"""
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前置条件：技能卡移动对话框\n
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结束状态：对话框结束瞬间
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"""
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cards = image.find_all_multi([
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R.InPurodyuusu.A,
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R.InPurodyuusu.M
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])
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if not cards:
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logger.info("No skill cards found")
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return False
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it = Interval()
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cd = Countdown(sec=3)
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while True:
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device.screenshot()
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# 判断对话框是否关闭
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# 已关闭，开始计时
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if not image.find(R.InPurodyuusu.IconTitleSkillCardMove):
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cd.start()
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if cd.expired():
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logger.info("Skill card move dialog closed.")
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break
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# 没有，要继续选择并确定
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else:
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cd.reset()
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card = cards.pop()
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device.double_click(card)
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sleep(1)
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dialog.yes()
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it.wait()
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logger.debug("Handle skill card move finished.")
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@action('获取当前卡牌信息', screenshot_mode='manual-inherit')
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def obtain_cards(img: MatLike | None = None):
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img = use_screenshot(img)
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cards_rects = image.find_all_multi([
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R.InPurodyuusu.A,
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R.InPurodyuusu.M,
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R.InPurodyuusu.T
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])
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logger.info("Current cards: %s", len(cards_rects))
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cards = []
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for result in cards_rects:
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available = color.find('#7a7d7d', rect=result.rect) is None
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cards.append(SkillCard(available=available, rect=result.rect))
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return cards
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def handle_recommended_card(
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card_count: int, timeout: float = 7,
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threshold_predicate: Callable[[int, CardDetectResult], bool] = lambda _, __: True,
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*,
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img: MatLike | None = None,
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):
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result = detect_recommended_card(card_count, threshold_predicate, img=img)
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if result is not None:
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device.double_click(result)
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return result
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return None
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@action('获取当前卡片数量', screenshot_mode='manual-inherit')
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def skill_card_count(img: MatLike | None = None):
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"""获取当前持有的技能卡数量"""
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img = use_screenshot(img)
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img = crop(img, y1=0.83, y2=0.90)
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count = image.raw().count(img, R.InPurodyuusu.A)
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count += image.raw().count(img, R.InPurodyuusu.M)
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count += image.raw().count(img, R.InPurodyuusu.T)
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logger.info("Current skill card count: %d", count)
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return count
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def detect_recommended_card(
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card_count: int,
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threshold_predicate: Callable[[int, CardDetectResult], bool],
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*,
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img: MatLike | None = None,
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):
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"""
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识别推荐卡片
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前置条件：练习或考试中\n
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结束状态：-
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:param card_count: 卡片数量(2-4)
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:param threshold_predicate: 阈值判断函数
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:return: 执行结果。若返回 None，表示未识别到推荐卡片。
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"""
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YELLOW_LOWER = np.array([20, 100, 100])
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YELLOW_UPPER = np.array([30, 255, 255])
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GLOW_EXTENSION = 15
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cards = calc_card_position(card_count)
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cards.append(SKIP_CARD_BUTTON)
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img = use_screenshot(img)
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original_image = img.copy()
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results: list[CardDetectResult] = []
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for x, y, w, h, return_value in cards:
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outer = (max(0, x - GLOW_EXTENSION), max(0, y - GLOW_EXTENSION))
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# 裁剪出检测区域
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glow_area = img[outer[1]:y + h + GLOW_EXTENSION, outer[0]:x + w + GLOW_EXTENSION]
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area_h = glow_area.shape[0]
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area_w = glow_area.shape[1]
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glow_area[GLOW_EXTENSION:area_h-GLOW_EXTENSION, GLOW_EXTENSION:area_w-GLOW_EXTENSION] = 0
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# 过滤出目标黄色
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glow_area = cv2.cvtColor(glow_area, cv2.COLOR_BGR2HSV)
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yellow_mask = cv2.inRange(glow_area, YELLOW_LOWER, YELLOW_UPPER)
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# 分割出每一边
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left_border = yellow_mask[:, 0:GLOW_EXTENSION]
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right_border = yellow_mask[:, area_w-GLOW_EXTENSION:area_w]
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top_border = yellow_mask[0:GLOW_EXTENSION, :]
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bottom_border = yellow_mask[area_h-GLOW_EXTENSION:area_h, :]
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y_border_pixels = area_h * GLOW_EXTENSION
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x_border_pixels = area_w * GLOW_EXTENSION
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# 计算每一边的分数
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left_score = np.count_nonzero(left_border) / y_border_pixels
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right_score = np.count_nonzero(right_border) / y_border_pixels
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top_score = np.count_nonzero(top_border) / x_border_pixels
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bottom_score = np.count_nonzero(bottom_border) / x_border_pixels
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result = (left_score + right_score + top_score + bottom_score) / 4
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results.append(CardDetectResult(
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return_value,
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result,
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left_score,
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right_score,
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top_score,
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bottom_score,
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(x, y, w, h)
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))
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img = original_image.copy()
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# cv2.imshow(f"card detect {return_value}", cv2.cvtColor(glow_area, cv2.COLOR_HSV2BGR))
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# cv2.namedWindow(f"card detect {return_value}", cv2.WINDOW_NORMAL)
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# cv2.moveWindow(f"card detect {return_value}", 100 + (return_value % 3) * 300, 100 + (return_value // 3) * 300)
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# cv2.waitKey(1)
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filtered_results = list(filter(partial(threshold_predicate, card_count), results))
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if not filtered_results:
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max_result = max(results, key=lambda x: x.score)
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logger.info("Max card detect result (discarded): value=%d score=%.4f borders=(%.4f, %.4f, %.4f, %.4f)",
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max_result.type,
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max_result.score,
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max_result.left_score,
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max_result.right_score,
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max_result.top_score,
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max_result.bottom_score
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)
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return None
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filtered_results.sort(key=lambda x: x.score, reverse=True)
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logger.info("Max card detect result: value=%d score=%.4f borders=(%.4f, %.4f, %.4f, %.4f)",
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filtered_results[0].type,
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filtered_results[0].score,
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filtered_results[0].left_score,
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filtered_results[0].right_score,
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filtered_results[0].top_score,
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filtered_results[0].bottom_score
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)
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# 跟踪检测结果
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if conf().trace.recommend_card_detection:
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x, y, w, h = filtered_results[0].rect
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cv2.rectangle(original_image, (x, y), (x+w, y+h), (0, 0, 255), 3)
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trace('rec-card', original_image, {
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'card_count': card_count,
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'type': filtered_results[0].type,
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'score': filtered_results[0].score,
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'borders': (
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filtered_results[0].left_score,
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filtered_results[0].right_score,
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filtered_results[0].top_score,
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filtered_results[0].bottom_score
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)
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})
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return filtered_results[0]
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@ -1,26 +1,19 @@
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import time
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import logging
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from typing_extensions import deprecated, assert_never
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from itertools import cycle
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from typing import Generic, Iterable, Literal, NamedTuple, Callable, Generator, TypeVar, ParamSpec, cast
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import cv2
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import numpy as np
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from cv2.typing import MatLike
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from typing_extensions import assert_never
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from typing import Literal, NamedTuple
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from .. import R
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from ..actions import loading
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from ..game_ui import WhiteFilter
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from ..actions.scenes import at_home
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from ..util.trace import trace
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from ..game_ui import WhiteFilter, dialog
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from .cards import do_cards, CardDetectResult
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from ..actions.commu import handle_unread_commu
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from ..common import ProduceAction, RecommendCardDetectionMode, conf
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from kotonebot.errors import UnrecoverableError
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from kotonebot.backend.context.context import use_screenshot
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from kotonebot.util import Countdown, Interval, cropped
|
||||
from kotonebot.backend.dispatch import DispatcherContext
|
||||
from ..common import ProduceAction, RecommendCardDetectionMode, conf
|
||||
from ..produce.common import until_acquisition_clear, commut_event, fast_acquisitions
|
||||
from kotonebot.util import Countdown, Interval, crop, cropped, Stopwatch
|
||||
from kotonebot.backend.dispatch import DispatcherContext, SimpleDispatcher
|
||||
from kotonebot import ocr, device, contains, image, regex, action, sleep, color, Rect, wait
|
||||
from kotonebot import ocr, device, contains, image, regex, action, sleep, Rect, wait
|
||||
from ..produce.non_lesson_actions import (
|
||||
enter_allowance, allowance_available,
|
||||
study_available, enter_study,
|
||||
|
|
@ -28,34 +21,8 @@ from ..produce.non_lesson_actions import (
|
|||
outing_available, enter_outing
|
||||
)
|
||||
|
||||
class SkillCard(NamedTuple):
|
||||
available: bool
|
||||
rect: Rect
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
ActionType = None | Literal['lesson', 'rest']
|
||||
CARD_POSITIONS_1 = [
|
||||
# 格式：(x, y, w, h, return_value)
|
||||
(264, 883, 192, 252, 0)
|
||||
]
|
||||
CARD_POSITIONS_2 = [
|
||||
(156, 883, 192, 252, 0),
|
||||
(372, 883, 192, 252, 1),
|
||||
# delta_x = 216, delta_x-width = 24
|
||||
]
|
||||
CARD_POSITIONS_3 = [
|
||||
(47, 883, 192, 252, 0), # 左卡片 (x, y, w, h)
|
||||
(264, 883, 192, 252, 1), # 中卡片
|
||||
(481, 883, 192, 252, 2) # 右卡片
|
||||
# delta_x = 217, delta_x-width = 25
|
||||
]
|
||||
CARD_POSITIONS_4 = [
|
||||
(17, 883, 192, 252, 0),
|
||||
(182, 883, 192, 252, 1),
|
||||
(346, 883, 192, 252, 2),
|
||||
(511, 883, 192, 252, 3),
|
||||
# delta_x = 165, delta_x-width = -27
|
||||
]
|
||||
|
||||
@action('执行 SP 课程')
|
||||
def handle_sp_lesson():
|
||||
|
|
@ -150,232 +117,6 @@ def handle_recommended_action(final_week: bool = False) -> ProduceAction | None:
|
|||
device.double_click(image.expect(template))
|
||||
return recommended
|
||||
|
||||
class CardDetectResult(NamedTuple):
|
||||
type: int
|
||||
"""
|
||||
点击的卡片类型。
|
||||
|
||||
0=第一张卡片，1=第二张卡片，2=第三张卡片，3=第四张卡片，10=SKIP。
|
||||
"""
|
||||
score: float
|
||||
"""总分数"""
|
||||
left_score: float
|
||||
"""左边分数"""
|
||||
right_score: float
|
||||
"""右边分数"""
|
||||
top_score: float
|
||||
"""上边分数"""
|
||||
bottom_score: float
|
||||
"""下边分数"""
|
||||
rect: Rect
|
||||
|
||||
def detect_recommended_card(
|
||||
card_count: int,
|
||||
threshold_predicate: Callable[[CardDetectResult], bool],
|
||||
*,
|
||||
img: MatLike | None = None,
|
||||
):
|
||||
"""
|
||||
识别推荐卡片
|
||||
|
||||
前置条件：练习或考试中\n
|
||||
结束状态：-
|
||||
|
||||
:param card_count: 卡片数量(2-4)
|
||||
:param threshold_predicate: 阈值判断函数
|
||||
:return: 执行结果。若返回 None，表示未识别到推荐卡片。
|
||||
"""
|
||||
YELLOW_LOWER = np.array([20, 100, 100])
|
||||
YELLOW_UPPER = np.array([30, 255, 255])
|
||||
SKIP_POSITION = (621, 739, 85, 85, 10)
|
||||
GLOW_EXTENSION = 15
|
||||
|
||||
if card_count == 4:
|
||||
logger.info("4 cards detected. Currently not supported. Use 1st card.")
|
||||
return CardDetectResult(
|
||||
type=0,
|
||||
score=1,
|
||||
left_score=1,
|
||||
right_score=1,
|
||||
top_score=1,
|
||||
bottom_score=1,
|
||||
rect=(17, 883, 192, 252)
|
||||
)
|
||||
|
||||
if card_count == 5:
|
||||
logger.info("5 cards detected. Currently not supported. Use 1st card.")
|
||||
return CardDetectResult(
|
||||
type=0,
|
||||
score=1,
|
||||
left_score=1,
|
||||
right_score=1,
|
||||
top_score=1,
|
||||
bottom_score=1,
|
||||
rect=R.InPurodyuusu.BoxLessonCards5_1
|
||||
)
|
||||
|
||||
if card_count == 1:
|
||||
cards = CARD_POSITIONS_1
|
||||
elif card_count == 2:
|
||||
cards = CARD_POSITIONS_2
|
||||
elif card_count == 3:
|
||||
cards = CARD_POSITIONS_3
|
||||
elif card_count == 4:
|
||||
cards = CARD_POSITIONS_4
|
||||
else:
|
||||
raise ValueError(f"Unsupported card count: {card_count}")
|
||||
cards.append(SKIP_POSITION)
|
||||
|
||||
image = use_screenshot(img)
|
||||
original_image = image.copy()
|
||||
results: list[CardDetectResult] = []
|
||||
for x, y, w, h, return_value in cards:
|
||||
outer = (max(0, x - GLOW_EXTENSION), max(0, y - GLOW_EXTENSION))
|
||||
# 裁剪出检测区域
|
||||
glow_area = image[outer[1]:y + h + GLOW_EXTENSION, outer[0]:x + w + GLOW_EXTENSION]
|
||||
area_h = glow_area.shape[0]
|
||||
area_w = glow_area.shape[1]
|
||||
glow_area[GLOW_EXTENSION:area_h-GLOW_EXTENSION, GLOW_EXTENSION:area_w-GLOW_EXTENSION] = 0
|
||||
|
||||
# 过滤出目标黄色
|
||||
glow_area = cv2.cvtColor(glow_area, cv2.COLOR_BGR2HSV)
|
||||
yellow_mask = cv2.inRange(glow_area, YELLOW_LOWER, YELLOW_UPPER)
|
||||
|
||||
# 分割出每一边
|
||||
left_border = yellow_mask[:, 0:GLOW_EXTENSION]
|
||||
right_border = yellow_mask[:, area_w-GLOW_EXTENSION:area_w]
|
||||
top_border = yellow_mask[0:GLOW_EXTENSION, :]
|
||||
bottom_border = yellow_mask[area_h-GLOW_EXTENSION:area_h, :]
|
||||
y_border_pixels = area_h * GLOW_EXTENSION
|
||||
x_border_pixels = area_w * GLOW_EXTENSION
|
||||
|
||||
# 计算每一边的分数
|
||||
left_score = np.count_nonzero(left_border) / y_border_pixels
|
||||
right_score = np.count_nonzero(right_border) / y_border_pixels
|
||||
top_score = np.count_nonzero(top_border) / x_border_pixels
|
||||
bottom_score = np.count_nonzero(bottom_border) / x_border_pixels
|
||||
|
||||
result = (left_score + right_score + top_score + bottom_score) / 4
|
||||
results.append(CardDetectResult(
|
||||
return_value,
|
||||
result,
|
||||
left_score,
|
||||
right_score,
|
||||
top_score,
|
||||
bottom_score,
|
||||
(x, y, w, h)
|
||||
))
|
||||
|
||||
filtered_results = list(filter(threshold_predicate, results))
|
||||
if not filtered_results:
|
||||
max_result = max(results, key=lambda x: x.score)
|
||||
logger.info("Max card detect result (discarded): value=%d score=%.4f borders=(%.4f, %.4f, %.4f, %.4f)",
|
||||
max_result.type,
|
||||
max_result.score,
|
||||
max_result.left_score,
|
||||
max_result.right_score,
|
||||
max_result.top_score,
|
||||
max_result.bottom_score
|
||||
)
|
||||
return None
|
||||
filtered_results.sort(key=lambda x: x.score, reverse=True)
|
||||
logger.info("Max card detect result: value=%d score=%.4f borders=(%.4f, %.4f, %.4f, %.4f)",
|
||||
filtered_results[0].type,
|
||||
filtered_results[0].score,
|
||||
filtered_results[0].left_score,
|
||||
filtered_results[0].right_score,
|
||||
filtered_results[0].top_score,
|
||||
filtered_results[0].bottom_score
|
||||
)
|
||||
# 跟踪检测结果
|
||||
if conf().trace.recommend_card_detection:
|
||||
x, y, w, h = filtered_results[0].rect
|
||||
cv2.rectangle(original_image, (x, y), (x+w, y+h), (0, 0, 255), 3)
|
||||
trace('rec-card', original_image, {
|
||||
'card_count': card_count,
|
||||
'type': filtered_results[0].type,
|
||||
'score': filtered_results[0].score,
|
||||
'borders': (
|
||||
filtered_results[0].left_score,
|
||||
filtered_results[0].right_score,
|
||||
filtered_results[0].top_score,
|
||||
filtered_results[0].bottom_score
|
||||
)
|
||||
})
|
||||
return filtered_results[0]
|
||||
|
||||
def handle_recommended_card(
|
||||
card_count: int, timeout: float = 7,
|
||||
threshold_predicate: Callable[[CardDetectResult], bool] = lambda _: True,
|
||||
*,
|
||||
img: MatLike | None = None,
|
||||
):
|
||||
result = detect_recommended_card(card_count, threshold_predicate, img=img)
|
||||
if result is not None:
|
||||
device.double_click(result)
|
||||
return result
|
||||
return None
|
||||
|
||||
|
||||
@action('获取当前卡片数量', screenshot_mode='manual-inherit')
|
||||
def skill_card_count(img: MatLike | None = None):
|
||||
"""获取当前持有的技能卡数量"""
|
||||
img = use_screenshot(img)
|
||||
img = crop(img, y1=0.83, y2=0.90)
|
||||
count = image.raw().count(img, R.InPurodyuusu.A)
|
||||
count += image.raw().count(img, R.InPurodyuusu.M)
|
||||
count += image.raw().count(img, R.InPurodyuusu.T)
|
||||
logger.info("Current skill card count: %d", count)
|
||||
return count
|
||||
|
||||
|
||||
Yield = TypeVar('Yield')
|
||||
Send = TypeVar('Send')
|
||||
Return = TypeVar('Return')
|
||||
P = ParamSpec('P')
|
||||
class GeneratorWrapper(Iterable[Yield], Generic[P, Yield, Send, Return]):
|
||||
def __init__(
|
||||
self,
|
||||
generator_func: Callable[P, Generator[Yield, Send, Return]],
|
||||
*args: P.args,
|
||||
**kwargs: P.kwargs
|
||||
):
|
||||
self.generator_func = generator_func
|
||||
self.generator = generator_func(*args, **kwargs)
|
||||
self.args = args
|
||||
self.kwargs = kwargs
|
||||
|
||||
def __iter__(self):
|
||||
return self
|
||||
|
||||
def __call__(self):
|
||||
return next(self.generator)
|
||||
|
||||
def reset(self):
|
||||
self.generator = self.generator_func(*self.args, **self.kwargs)
|
||||
|
||||
def loop(self) -> Return:
|
||||
while True:
|
||||
try:
|
||||
next(self.generator)
|
||||
except StopIteration as e:
|
||||
return cast(Return, e.value)
|
||||
|
||||
@action('获取当前卡牌信息', screenshot_mode='manual-inherit')
|
||||
def obtain_cards(img: MatLike | None = None):
|
||||
img = use_screenshot(img)
|
||||
cards_rects = image.find_all_multi([
|
||||
R.InPurodyuusu.A,
|
||||
R.InPurodyuusu.M,
|
||||
R.InPurodyuusu.T
|
||||
])
|
||||
logger.info("Current cards: %s", len(cards_rects))
|
||||
cards = []
|
||||
for result in cards_rects:
|
||||
available = color.find('#7a7d7d', rect=result.rect) is None
|
||||
cards.append(SkillCard(available=available, rect=result.rect))
|
||||
return cards
|
||||
|
||||
|
||||
@action('等待进入行动场景')
|
||||
def until_action_scene(week_first: bool = False):
|
||||
|
|
@ -416,139 +157,6 @@ def until_exam_scene():
|
|||
while ocr.find(regex("合格条件|三位以上")) is None and not is_exam_scene():
|
||||
until_acquisition_clear()
|
||||
|
||||
|
||||
@action("技能卡移动")
|
||||
def handle_skill_card_move():
|
||||
"""
|
||||
前置条件：技能卡移动对话框\n
|
||||
结束状态：对话框结束瞬间
|
||||
"""
|
||||
cards = image.find_all_multi([
|
||||
R.InPurodyuusu.A,
|
||||
R.InPurodyuusu.M
|
||||
])
|
||||
if not cards:
|
||||
logger.info("No skill cards found")
|
||||
return False
|
||||
|
||||
it = Interval()
|
||||
cd = Countdown(sec=3)
|
||||
while True:
|
||||
device.screenshot()
|
||||
# 判断对话框是否关闭
|
||||
# 已关闭，开始计时
|
||||
if not image.find(R.InPurodyuusu.IconTitleSkillCardMove):
|
||||
cd.start()
|
||||
if cd.expired():
|
||||
logger.info("Skill card move dialog closed.")
|
||||
break
|
||||
# 没有，要继续选择并确定
|
||||
else:
|
||||
cd.reset()
|
||||
card = cards.pop()
|
||||
device.double_click(card)
|
||||
sleep(1)
|
||||
dialog.yes()
|
||||
|
||||
it.wait()
|
||||
logger.debug("Handle skill card move finished.")
|
||||
|
||||
@action('打牌', screenshot_mode='manual')
|
||||
def do_cards(
|
||||
threshold_predicate: Callable[[CardDetectResult], bool],
|
||||
end_predicate: Callable[[], bool]
|
||||
):
|
||||
"""
|
||||
循环打出推荐卡，直到考试/练习结束
|
||||
|
||||
前置条件：考试/练习页面\n
|
||||
结束状态：考试/练习结束的一瞬间
|
||||
|
||||
:param threshold_predicate: 推荐卡检测阈值判断函数
|
||||
:param end_predicate: 结束条件判断函数
|
||||
"""
|
||||
it = Interval(seconds=1/30)
|
||||
timeout_cd = Countdown(sec=120).start() # 推荐卡检测超时计时器
|
||||
break_cd = Countdown(sec=3).start() # 满足结束条件计时器
|
||||
no_card_cd = Countdown(sec=4) # 无手牌计时器
|
||||
detect_card_count_cd = Countdown(sec=4).start() # 刷新检测手牌数量间隔
|
||||
tries = 1
|
||||
card_count = -1
|
||||
|
||||
while True:
|
||||
device.click(0, 0)
|
||||
img = device.screenshot()
|
||||
it.wait()
|
||||
|
||||
# 技能卡自选移动对话框
|
||||
if image.find(R.InPurodyuusu.IconTitleSkillCardMove):
|
||||
if handle_skill_card_move():
|
||||
sleep(4) # 等待卡片刷新
|
||||
continue
|
||||
# 技能卡效果无法发动对话框
|
||||
if image.find(R.Common.ButtonIconCheckMark):
|
||||
logger.info("Confirmation dialog detected")
|
||||
device.click()
|
||||
sleep(4) # 等待卡片刷新
|
||||
continue
|
||||
|
||||
# 更新卡片数量
|
||||
if card_count == -1 or detect_card_count_cd.expired():
|
||||
detect_card_count_cd.reset()
|
||||
card_count = skill_card_count(img)
|
||||
logger.debug("Current card count: %d", card_count)
|
||||
# 处理手牌
|
||||
if card_count == 0:
|
||||
# 处理本回合已无剩余手牌的情况
|
||||
# TODO: 使用模板匹配而不是 OCR，提升速度
|
||||
no_card_cd.start()
|
||||
no_remaining_card = ocr.find(contains("0枚"), rect=R.InPurodyuusu.BoxNoSkillCard)
|
||||
if no_remaining_card and no_card_cd.expired():
|
||||
logger.debug('No remaining card detected. Skip this turn.')
|
||||
# TODO: HARD CODEDED
|
||||
SKIP_POSITION = (621, 739, 85, 85)
|
||||
device.click(SKIP_POSITION)
|
||||
no_card_cd.reset()
|
||||
continue
|
||||
else:
|
||||
if handle_recommended_card(
|
||||
card_count=card_count,
|
||||
threshold_predicate=threshold_predicate,
|
||||
img=img
|
||||
):
|
||||
logger.info("Handle recommended card success with %d tries", tries)
|
||||
sleep(4.5)
|
||||
tries = 0
|
||||
timeout_cd.reset()
|
||||
continue
|
||||
else:
|
||||
tries += 1
|
||||
# 检测超时（防止一直卡在检测）
|
||||
if timeout_cd.expired():
|
||||
logger.info("Recommend card detection timed out. Click first card.")
|
||||
if card_count == 1:
|
||||
card_rect = CARD_POSITIONS_1[0]
|
||||
elif card_count == 2:
|
||||
card_rect = CARD_POSITIONS_2[0]
|
||||
elif card_count == 3:
|
||||
card_rect = CARD_POSITIONS_3[0]
|
||||
elif card_count == 4:
|
||||
card_rect = CARD_POSITIONS_4[0]
|
||||
else:
|
||||
raise ValueError("Invalid card count: %d" % card_count)
|
||||
device.double_click(card_rect[:4])
|
||||
timeout_cd.reset()
|
||||
# 结束条件
|
||||
if card_count == 0 and end_predicate():
|
||||
if not break_cd.started:
|
||||
break_cd.start()
|
||||
if break_cd.expired():
|
||||
break
|
||||
else:
|
||||
break_cd.reset()
|
||||
|
||||
logger.info("CLEAR/PERFECT not found. Practice finished.")
|
||||
|
||||
@action('执行练习', screenshot_mode='manual')
|
||||
def practice():
|
||||
"""
|
||||
|
|
@ -559,7 +167,7 @@ def practice():
|
|||
"""
|
||||
logger.info("Practice started")
|
||||
|
||||
def threshold_predicate(result: CardDetectResult):
|
||||
def threshold_predicate(card_count: int, result: CardDetectResult):
|
||||
border_scores = (result.left_score, result.right_score, result.top_score, result.bottom_score)
|
||||
is_strict_mode = conf().produce.recommend_card_detection_mode == RecommendCardDetectionMode.STRICT
|
||||
if is_strict_mode:
|
||||
|
|
@ -592,36 +200,40 @@ def exam(type: Literal['mid', 'final']):
|
|||
"""
|
||||
logger.info("Exam started")
|
||||
|
||||
def threshold_predicate(result: CardDetectResult):
|
||||
def threshold_predicate(card_count: int, result: CardDetectResult):
|
||||
is_strict_mode = conf().produce.recommend_card_detection_mode == RecommendCardDetectionMode.STRICT
|
||||
total = lambda t: result.score >= t
|
||||
def borders(t):
|
||||
# 卡片数量小于三时无遮挡，以及最后一张卡片也总是无遮挡
|
||||
if card_count <= 3 or (result.type == card_count - 1):
|
||||
return (
|
||||
result.left_score >= t
|
||||
and result.right_score >= t
|
||||
and result.top_score >= t
|
||||
and result.bottom_score >= t
|
||||
)
|
||||
# 其他情况下，卡片的右侧会被挡住，并不会发光
|
||||
else:
|
||||
return (
|
||||
result.left_score >= t
|
||||
and result.top_score >= t
|
||||
and result.bottom_score >= t
|
||||
)
|
||||
|
||||
if is_strict_mode:
|
||||
if type == 'final':
|
||||
return (
|
||||
result.score >= 0.4
|
||||
and result.left_score >= 0.2
|
||||
and result.right_score >= 0.2
|
||||
and result.top_score >= 0.2
|
||||
and result.bottom_score >= 0.2
|
||||
)
|
||||
return total(0.4) and borders(0.2)
|
||||
else:
|
||||
return (
|
||||
result.score >= 0.10
|
||||
and result.left_score >= 0.01
|
||||
and result.right_score >= 0.01
|
||||
and result.top_score >= 0.01
|
||||
and result.bottom_score >= 0.01
|
||||
)
|
||||
return total(0.10) and borders(0.01)
|
||||
else:
|
||||
if type == 'final':
|
||||
return (
|
||||
result.score >= 0.4
|
||||
and result.left_score >= 0.2
|
||||
and result.right_score >= 0.2
|
||||
and result.top_score >= 0.2
|
||||
and result.bottom_score >= 0.2
|
||||
)
|
||||
if result.type == 10: # SKIP
|
||||
return total(0.4) and borders(0.02)
|
||||
else:
|
||||
return total(0.2) and borders(0.02)
|
||||
else:
|
||||
return result.score >= 0.10
|
||||
return total(0.10) and borders(0.01)
|
||||
|
||||
# 关于上面阈值的解释：
|
||||
# 所有阈值均指卡片周围的“黄色度”，
|
||||
# score 指卡片四边的平均黄色度阈值，
|
||||
|
|
|
|||
Loading…
Reference in New Issue