397 lines
14 KiB
Python
397 lines
14 KiB
Python
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 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=5) # 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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if card_count == 0:
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logger.warning("Recommend card detection timeout but no card found.")
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timeout_cd.reset()
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continue
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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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logger.debug('start break_cd')
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break_cd.reset().start()
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if break_cd.expired():
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logger.info("End condition met. do_cards finished.")
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break
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else:
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logger.debug('reset break_cd')
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break_cd.stop()
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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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R.InPurodyuusu.T,
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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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if not cards:
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logger.info("No skill cards left. Retrying...")
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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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R.InPurodyuusu.T,
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])
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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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x, y, w, h = R.InPurodyuusu.BoxCardLetter
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img = img[y:y+h, x:x+w]
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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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if __name__ == '__main__':
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import cv2
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img = cv2.imread(r'E:\GithubRepos\KotonesAutoAssistant.worktrees\dev\kotonebot-resource\sprites\jp\in_purodyuusu\produce_exam_1.png')
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print(skill_card_count(img)) |