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https://github.com/sui-feng-cb/AzurLaneAutoScript1.git
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Add: event shop
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guoh064
244
module/shop_event/item.py
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244
module/shop_event/item.py
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import re
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import cv2
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import numpy as np
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import module.config.server as server
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from module.base.utils import color_similarity_2d, color_similar, rgb2luma
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from module.logger import logger
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from module.ocr.ocr import Ocr, Digit
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from module.shop_event.selector import FILTER_REGEX
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from module.statistics.item import Item, ItemGrid
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ITEM_SHAPE = (63, 63)
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GRID_SHAPE = (152, 206)
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DELTA_PRICE_BACKGROUND = (14, 164)
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DELTA_ITEM = (45, 44, 45 + ITEM_SHAPE[0], 33 + ITEM_SHAPE[1])
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DELTA_AMOUNT = (13, 144, 136, 160)
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DELTA_PRICE = (28, 164, 128, 193)
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DELTA_TAG = (108, 30, 155, 52)
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COUNTER_COLOR = (106, 120, 131)
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COUNTER_THRESHOLD = 150
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PRICE_THRESHOLD = 230
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PRICE_BACKGROUND_COLOR = (61, 78, 91)
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if server.server == 'jp':
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COUNTER_LEFT_STRIP = 54
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elif server.server == 'en':
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COUNTER_LEFT_STRIP = 42
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else:
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COUNTER_LEFT_STRIP = 70
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class CounterOcr(Ocr):
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def __init__(self, buttons, lang='azur_lane', letter=(255, 255, 255), threshold=128,
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alphabet='0123456789/IDSB', name=None):
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super().__init__(buttons, lang=lang, letter=letter, threshold=threshold, alphabet=alphabet, name=name)
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def pre_process(self, image):
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mask = color_similarity_2d(image, (255, 255, 255))
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brightness = np.min(mask, axis=0)
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match = np.where(brightness < COUNTER_THRESHOLD)[0]
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if len(match):
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left = match[0] + COUNTER_LEFT_STRIP
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total = mask.shape[1]
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if left < total:
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image = image[:, left:]
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image = super().pre_process(image)
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return image
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def after_process(self, result):
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result = super().after_process(result)
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result = result.replace('I', '1').replace('D', '0').replace('S', '5')
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result = result.replace('B', '8')
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return result
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def ocr(self, image, direct_ocr=False):
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"""
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Do OCR on a counter, such as `14/15`, and returns 14, 15
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Args:
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image:
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direct_ocr:
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Returns:
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list[list[int]: [[current, total]].
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"""
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result_list = super().ocr(image, direct_ocr=direct_ocr)
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if isinstance(result_list, list):
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parsed = []
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for i in result_list:
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if not i or '/' not in i:
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logger.warning(f'Invalid OCR result format: {i}')
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parsed.append([0, 0])
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continue
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parts = i.split('/')
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if len(parts) != 2:
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logger.warning(f'Invalid counter format: {i}')
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parsed.append([0, 0])
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continue
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parsed.append([int(j) for j in parts])
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return parsed
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else:
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if not result_list or '/' not in result_list:
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logger.warning(f'Invalid OCR result: {result_list}')
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return [0, 0]
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parts = result_list.split('/')
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if len(parts) != 2:
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logger.warning(f'Invalid counter format: {result_list}')
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return [0, 0]
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return [int(i) for i in parts]
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class PriceOcr(Digit):
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def pre_process(self, image):
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mask = color_similarity_2d(image, PRICE_BACKGROUND_COLOR)
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brightness = np.min(mask, axis=0)
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match = np.where(brightness < PRICE_THRESHOLD)[0]
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if len(match):
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left = match[0] + 20
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total = mask.shape[1]
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if left < total:
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image = image[:, left:]
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image = super().pre_process(image)
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return image
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PRICE_OCR = PriceOcr([], letter=(221, 221, 221), threshold=128, name='Price_ocr')
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URPT_PRICE_IN_PT = 150 # 1 URpt costs 150 pt
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COIN_PRICE_IN_URPT = 1 # 1 Coin costs 1 URpt
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UR_SHIP_PRICES_IN_URPT = [200, 300] # UR Ships cost 200 or 300 URpt
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class EventShopItem(Item):
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IMAGE_SHAPE = ITEM_SHAPE
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.is_ship = False
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self._scroll_pos = None
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self.total_count = -1
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self.count = 1
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def __str__(self):
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name = f'{self.name}_x{self.amount}_{self.count}/{self.total_count}_{self.cost}_x{self.price}'
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if self.tag is not None:
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name = f'{name}_{self.tag}'
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return name
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def predict_valid(self):
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luma = rgb2luma(self.image)
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return np.mean(luma > 127) >= 0.2
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@property
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def scroll_pos(self):
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return self._scroll_pos
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@scroll_pos.setter
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def scroll_pos(self, value):
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self._scroll_pos = value
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def __eq__(self, other):
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return id(self) == id(other)
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def correct_name_and_cost(self):
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if self.price in UR_SHIP_PRICES_IN_URPT and self.total_count == 1:
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self.name = 'ShipUR'
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self.cost = 'URpt'
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self.is_ship = True
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elif self.price == COIN_PRICE_IN_URPT and self.total_count == 350:
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# URpt to Coin
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self.name = 'Coin'
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self.cost = 'URpt'
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else:
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self.cost = 'pt'
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if self.price == 2000:
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if self.total_count == 10:
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self.name = 'SkinBox'
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elif self.total_count == 4:
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self.name = 'Meta'
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else:
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self.name = 'EquipSSR'
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elif self.price == 8000:
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self.name = 'ShipSSR'
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self.is_ship = True
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elif self.price == 10000:
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self.name = 'EquipUR'
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elif self.price == URPT_PRICE_IN_PT and self.total_count == 500:
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self.name = 'URpt'
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elif self.name.isdigit():
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logger.warning(f'Unrecognized item with price {self.price} and total count {self.total_count}, '
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# f'defaulting to EquipSSR')
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f'saving image for analysis.')
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import os
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from module.base.utils import save_image
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os.mkdir('assets/shop/event/new_templates/') if not os.path.exists('assets/shop/event/new_templates/') else None
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save_image(self.image, f'assets/shop/event/new_templates/{self.name}.png')
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# self.name = 'EquipSSR'
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def predict_genre(self):
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self.group, self.sub_genre, self.tier = None, None, None
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# Can use regular expression to quickly populate
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# the new attributes
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name = self.name.lower()
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result = re.search(FILTER_REGEX, name)
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if result:
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self.group, self.sub_genre, self.tier = \
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[group.lower()
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if group is not None else None
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for group in result.groups()]
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class EventShopItemGrid(ItemGrid):
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item_class = EventShopItem
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def __init__(self,
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grids,
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templates,
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template_area=(0, 0, ITEM_SHAPE[0], ITEM_SHAPE[1]),
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amount_area=(31, 50, ITEM_SHAPE[0], ITEM_SHAPE[1]),
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cost_area=(DELTA_PRICE[0] - DELTA_ITEM[0], DELTA_PRICE[1] - DELTA_ITEM[1],
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DELTA_PRICE[2] - DELTA_ITEM[0], DELTA_PRICE[3] - DELTA_ITEM[1]),
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price_area=(DELTA_PRICE[0] - DELTA_ITEM[0], DELTA_PRICE[1] - DELTA_ITEM[1],
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DELTA_PRICE[2] - DELTA_ITEM[0], DELTA_PRICE[3] - DELTA_ITEM[1]),
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tag_area=(DELTA_TAG[0] - DELTA_ITEM[0], DELTA_TAG[1] - DELTA_ITEM[1],
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DELTA_TAG[2] - DELTA_ITEM[0], DELTA_TAG[3] - DELTA_ITEM[1]),
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counter_area=(DELTA_AMOUNT[0] - DELTA_ITEM[0], DELTA_AMOUNT[1] - DELTA_ITEM[1],
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DELTA_AMOUNT[2] - DELTA_ITEM[0], DELTA_AMOUNT[3] - DELTA_ITEM[1]),
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):
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super().__init__(grids, templates, template_area, amount_area, cost_area, price_area, tag_area)
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self.counter_ocr = CounterOcr([], letter=COUNTER_COLOR, name="CounterOcr")
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self.counter_area = counter_area
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self.price_ocr = PRICE_OCR
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def predict_tag(self, image):
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color = cv2.mean(np.array(image))[:3]
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if color_similar(color1=color, color2=(255, 72, 72), threshold=50):
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return 'unobtained'
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return None
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def predict(self, image, name=True, amount=True, cost=False, price=True, tag=True, counter=True, scroll_pos=None):
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super().predict(image, name=name, amount=amount, cost=cost, price=price, tag=tag)
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if counter and len(self.items):
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counter_list = [item.crop(self.counter_area) for item in self.items]
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counter_list = self.counter_ocr.ocr(counter_list, direct_ocr=True)
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for i, t in zip(self.items, counter_list):
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i.count, i.total_count = t
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if isinstance(scroll_pos, float) and len(self.items):
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for i in self.items:
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i.scroll_pos = scroll_pos
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for i in self.items:
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i.correct_name_and_cost()
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i.predict_genre()
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return self.items
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