def main():
parser = argparse.ArgumentParser(description="LSTM CRF implementation")
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
set_seed(opt, conf.seed)
trains = reader.read_txt(conf.train_file, conf.train_num)
devs = reader.read_txt(conf.dev_file, conf.dev_num)
tests = reader.read_txt(conf.test_file, conf.test_num)
if conf.context_emb != ContextEmb.none:
print('Loading the ELMo vectors for all datasets.')
conf.context_emb_size = load_elmo_vec(conf.train_file + "." + conf.context_emb.name + ".vec", trains)
load_elmo_vec(conf.dev_file + "." + conf.context_emb.name + ".vec", devs)
load_elmo_vec(conf.test_file + "." + conf.context_emb.name + ".vec", tests)
conf.build_label_idx(trains)
conf.build_word_idx(trains, devs, tests)
conf.build_emb_table()
conf.map_insts_ids(trains)
conf.map_insts_ids(devs)
conf.map_insts_ids(tests)
print("num chars: " + str(conf.num_char))
# print(str(config.char2idx))
print("num words: " + str(len(conf.word2idx)))
# print(config.word2idx)
train_model(conf, conf.num_epochs, trains, devs, tests)
def main():
parser = argparse.ArgumentParser(description="LSTM CRF implementation")
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
set_seed(opt, conf.seed)
trains = reader.read_txt(conf.train_file, conf.train_num)
devs = reader.read_txt(conf.dev_file, conf.dev_num)
tests = reader.read_txt(conf.test_file, conf.test_num)
if conf.context_emb != ContextEmb.none:
logging.info('[Data Info] Loading the ELMo vectors for all datasets.')
conf.context_emb_size = load_elmo_vec(
conf.train_file + "." + conf.context_emb.name + ".vec", trains)
load_elmo_vec(conf.dev_file + "." + conf.context_emb.name + ".vec",
devs)
load_elmo_vec(conf.test_file + "." + conf.context_emb.name + ".vec",
tests)
# conf.use_iobes(trains + devs + tests)
conf.build_label_idx(trains + devs + tests)
conf.build_word_idx(trains, devs, tests)
conf.build_emb_table()
conf.map_insts_ids(devs + tests)
logging.info("[Data Info] num chars: " + str(conf.num_char))
logging.info("[Data Info] num words: " + str(len(conf.word2idx)))
logging.info(
f"[Data Info] Removing {conf.entity_keep_ratio*100}% of entities from the training set"
)
logging.info("[Data Info] Removing the entities")
## it will return the set of removed entities (for debug purpose)
_ = remove_entites(trains, conf)
# logging.info(f"entities removed: {span_set}")
conf.map_insts_ids(trains)
random.shuffle(trains)
for inst in trains:
inst.is_prediction = [False] * len(inst.input)
if conf.variant == "soft":
inst.marginals = np.full((len(inst.input), conf.label_size), -1e10)
for pos, label in enumerate(inst.output):
if label == conf.O:
inst.is_prediction[pos] = True
if conf.variant == "soft":
inst.marginals[pos, conf.label2idx[label]] = 0
num_insts_in_fold = math.ceil(len(trains) / conf.num_folds)
trains = [
trains[i * num_insts_in_fold:(i + 1) * num_insts_in_fold]
for i in range(conf.num_folds)
]
train_model(config=conf,
train_insts=trains,
dev_insts=devs,
test_insts=tests)
def main():
parser = argparse.ArgumentParser(description="LSTM CRF implementation")
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
set_seed(opt, conf.seed)
if "ontonotes" in conf.train_file:
trains = reader.read_conll(conf.train_file, conf.train_num)
devs = reader.read_conll(conf.dev_file, conf.dev_num)
tests = reader.read_conll(conf.test_file, conf.test_num)
else:
trains = reader.read_txt(conf.train_file, conf.train_num)
if conf.typing_model:
devs = reader.read_txt_with_extraction(conf.dev_file,
conf.dev_extraction,
conf.dev_num)
tests = reader.read_txt_with_extraction(conf.test_file,
conf.test_extraction,
conf.test_num)
else:
devs = reader.read_txt(conf.dev_file, conf.dev_num)
tests = reader.read_txt(conf.test_file, conf.test_num)
if conf.context_emb != ContextEmb.none:
print('Loading the ELMo vectors for all datasets.')
conf.context_emb_size = load_elmo_vec(
conf.train_file + "." + conf.context_emb.name + ".vec", trains)
load_elmo_vec(conf.dev_file + "." + conf.context_emb.name + ".vec",
devs)
load_elmo_vec(conf.test_file + "." + conf.context_emb.name + ".vec",
tests)
conf.use_iobes(trains + devs + tests)
conf.build_label_idx(trains + devs + tests)
conf.build_word_idx(trains, devs, tests)
conf.build_emb_table()
conf.map_insts_ids(trains + devs + tests)
if conf.typing_model:
"""
Building mapping, for example: {B-per: [B-per, B-org, B-misc], O: O, I-org: [I-per, I-org]}
Will be used when creating the mask
"""
conf.typing_map = build_type_id_mapping(conf)
print("num chars: " + str(conf.num_char))
# print(str(config.char2idx))
print("num words: " + str(len(conf.word2idx)))
# print(config.word2idx)
train_model(conf, conf.num_epochs, trains, devs, tests)
def main():
parser = argparse.ArgumentParser()
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
dataset, max_length, label_length = reader.read_trigger_txt(
conf.trigger_file, -1)
reader.merge_labels(dataset)
trains = reader.read_txt(conf.train_all_file, conf.train_num)
devs = reader.read_txt(conf.dev_file, conf.dev_num)
tests = reader.read_txt(conf.test_file, conf.test_num)
print(len(dataset))
if conf.context_emb == ContextEmb.bert:
print('Loading the BERT vectors for all datasets.')
conf.context_emb_size = load_bert_vec(
conf.trigger_file + "." + conf.context_emb.name + ".vec", dataset)
# setting for data
conf.use_iobes(trains)
conf.use_iobes(dataset)
conf.use_iobes(devs)
conf.use_iobes(tests)
conf.optimizer = opt.trig_optimizer
conf.build_label_idx(dataset)
conf.build_word_idx(trains, devs, tests)
conf.build_emb_table()
conf.map_insts_ids(dataset)
conf.map_insts_ids(trains)
conf.map_insts_ids(devs)
conf.map_insts_ids(tests)
dataset = reader.trigger_percentage(dataset, conf.percentage)
encoder = SoftMatcher(conf, label_length)
trainer = SoftMatcherTrainer(encoder, conf, devs, tests)
# matching module training
random.shuffle(dataset)
trainer.train_model(conf.num_epochs_soft, dataset)
logits, predicted, triggers = trainer.get_triggervec(dataset)
# all the trigger vectors, trigger type, string name of the trigger
triggers_remove = remove_duplicates(logits, predicted, triggers, dataset)
numbers = int(len(trains) * (1 - opt.unlabeled_percentage))
print("number of train instances : ", numbers)
initial_trains = trains[:numbers]
unlabeled_x = trains[numbers:]
for data in unlabeled_x:
data.output_ids = None
# sequence labeling module self-training
random.shuffle(dataset)
inference = SoftSequence(conf, encoder)
sequence_trainer = SoftSequenceTrainer(inference, conf, devs, tests,
triggers_remove)
sequence_trainer.self_training(conf.num_epochs, dataset, unlabeled_x)
def __init__(self, device: str, keyboard: Queue):
"""
device: 如果是 USB 连接，则为 adb devices 的返回结果；如果是模拟器，则为模拟器的控制 URL 。
"""
self.d = u2.connect(device)
self.config = Reader()
self.upgrade_iter_round = 0
self.keyboard = keyboard
self.command_mode = False
# 检查 uiautomator
if not self.d.uiautomator.running():
self.d.reset_uiautomator()
def __init__(self, device: str, keyboard: Queue):
"""
device: 如果是 USB 连接，则为 adb devices 的返回结果；如果是模拟器，则为模拟器的控制 URL 。
"""
self.d = u2.connect(device)
self.config = Reader()
self.upgrade_iter_round = 0
self.keyboard = keyboard
self.command_mode = False
self._check_uiautomator()
self.time_start_working = time.time()
self.refresh_times = 0
self.delivered_times = 0
def main():
parser = argparse.ArgumentParser(description="LSTM CRF implementation")
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
set_seed(opt, conf.seed)
trains = reader.read_txt(conf.train_file, conf.train_num)
devs = reader.read_txt(conf.dev_file, conf.dev_num)
tests = reader.read_txt(conf.test_file, conf.test_num)
if conf.static_context_emb != ContextEmb.none:
print('Loading the static ELMo vectors for all datasets.')
conf.context_emb_size = load_elmo_vec(
conf.train_file + "." + conf.static_context_emb.name + ".vec",
trains)
load_elmo_vec(
conf.dev_file + "." + conf.static_context_emb.name + ".vec", devs)
load_elmo_vec(
conf.test_file + "." + conf.static_context_emb.name + ".vec",
tests)
conf.use_iobes(trains + devs + tests)
conf.build_label_idx(trains + devs + tests)
if conf.embedder_type == "normal":
conf.build_word_idx(trains, devs, tests)
conf.build_emb_table()
conf.map_insts_ids(trains)
conf.map_insts_ids(devs)
conf.map_insts_ids(tests)
print("[Data Info] num chars: " + str(conf.num_char))
# print(str(conf.char2idx))
print("[Data Info] num words: " + str(len(conf.word2idx)))
# print(config.word2idx)
else:
"""
If we use the pretrained model from transformers
we need to use the pretrained tokenizer
"""
print(
colored(
f"[Data Info] Tokenizing the instances using '{conf.embedder_type}' tokenizer",
"red"))
tokenize_instance(
context_models[conf.embedder_type]["tokenizer"].from_pretrained(
conf.embedder_type), trains + devs + tests, conf.label2idx)
train_model(conf, conf.num_epochs, trains, devs, tests)
def main():
parser = argparse.ArgumentParser(description="LSTM CRF implementation")
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
#set_seed(opt, conf.seed)
trains = reader.read_txt(conf.train_file, conf.train_num)
devs = reader.read_txt(conf.dev_file, conf.dev_num)
tests = reader.read_txt(conf.test_file, conf.test_num)
if conf.context_emb != ContextEmb.none:
print('[Data Info] Loading the ELMo vectors for all datasets.')
conf.context_emb_size = load_elmo_vec(
conf.train_file + "." + conf.context_emb.name + ".vec", trains)
load_elmo_vec(conf.dev_file + "." + conf.context_emb.name + ".vec",
devs)
load_elmo_vec(conf.test_file + "." + conf.context_emb.name + ".vec",
tests)
conf.use_iobes(trains + devs + tests)
conf.use_iobes_gold(trains)
conf.build_label_idx(trains + devs + tests)
conf.build_word_idx(trains, devs, tests)
conf.build_emb_table()
conf.map_insts_ids(devs + tests)
print("[Data Info] num chars: " + str(conf.num_char))
print("[Data Info] num words: " + str(len(conf.word2idx)))
conf.map_insts_ids(trains)
conf.get_gold_label_ids(trains)
random.shuffle(trains)
for inst in trains:
inst.is_prediction = [False] * len(inst.input)
for pos, label in enumerate(inst.output):
if label == conf.O:
inst.is_prediction[pos] = True
num_insts_in_fold = math.ceil(len(trains) / conf.num_folds)
trains = [
trains[i * num_insts_in_fold:(i + 1) * num_insts_in_fold]
for i in range(conf.num_folds)
]
train_model(config=conf,
train_insts=trains,
dev_insts=devs,
test_insts=tests)
def main():
parser = argparse.ArgumentParser(description="LSTM CRF implementation")
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
set_seed(opt, conf.seed)
trains = reader.read_txt(conf.train_file, conf.train_num, conf.category)
devs = reader.read_txt(conf.dev_file, conf.dev_num, conf.category)
tests = reader.read_txt(conf.test_file, conf.test_num, conf.category)
if conf.context_emb not in [ContextEmb.none, ContextEmb.mbert]:
print('Loading the ELMo vectors for all datasets.')
conf.context_emb_size = load_elmo_vec(
conf.train_file + "." + conf.context_emb.name + ".vec", trains)
load_elmo_vec(conf.dev_file + "." + conf.context_emb.name + ".vec",
devs)
load_elmo_vec(conf.test_file + "." + conf.context_emb.name + ".vec",
tests)
conf.use_iobes(trains)
conf.use_iobes(devs)
conf.use_iobes(tests)
conf.build_label_idx(trains + devs + tests)
if conf.context_emb == ContextEmb.mbert:
from tokenizers import BertWordPieceTokenizer
conf.bert_path = f'data/{conf.dataset}/distilbert-base-uncased'
tokenizer = BertWordPieceTokenizer(f'{conf.bert_path}/vocab.txt',
lowercase=True)
conf.map_tokens_ids(trains, tokenizer)
conf.map_tokens_ids(devs, tokenizer)
conf.map_tokens_ids(tests, tokenizer)
else:
conf.build_word_idx(trains, devs, tests)
conf.build_emb_table()
conf.map_insts_ids(trains)
conf.map_insts_ids(devs)
conf.map_insts_ids(tests)
print("num chars: " + str(conf.num_char))
# print(str(config.char2idx))
print("num words: " + str(len(conf.word2idx)))
# print(config.word2idx)
train_model(conf, conf.num_epochs, trains, devs, tests)
def start(self):
"""
启动脚本，请确保已进入游戏页面。
"""
while True:
# 检查是否有键盘事件
if not self._need_continue():
self.schedule_keyboard.put('')
self.p.join()
break
msg = self.pipe.get()
if isinstance(msg, bytes) or msg.startswith(CONFIG_PREFIX):
msg = msg[len(CONFIG_PREFIX):] if not isinstance(
msg, bytes) else msg
self.config = Reader.from_string(msg)
elif msg.startswith(METHOD_PREFIX):
msg = msg[len(METHOD_PREFIX):]
method_list = msg.split(METHOD_SEP)
method_list = sorted(
method_list,
key=lambda item: METHOD_ORDER.get(item, float('inf')))
# 简单粗暴的方式，处理 “XX之光” 的荣誉显示。
# 当然，也可以使用图像探测的模式。
self.d.click(550, 1650)
logger.info(f'Scheduled method: [{", ".join(method_list)}]')
for method_name in method_list:
methodcaller(method_name)(self)
logger.info('Sub process end')
def main():
parser = argparse.ArgumentParser(description="LSTM CRF implementation")
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
setSeed(opt, conf.seed)
trains = reader.read_txt(conf.train_file, conf.train_num, True)
devs = reader.read_txt(conf.dev_file, conf.dev_num, False)
tests = reader.read_txt(conf.test_file, conf.test_num, False)
if conf.context_emb != ContextEmb.none:
print('Loading the elmo vectors for all datasets.')
conf.context_emb_size = reader.load_elmo_vec(
conf.train_file + "." + conf.context_emb.name + ".vec", trains)
reader.load_elmo_vec(
conf.dev_file + "." + conf.context_emb.name + ".vec", devs)
reader.load_elmo_vec(
conf.test_file + "." + conf.context_emb.name + ".vec", tests)
conf.use_iobes(trains)
conf.use_iobes(devs)
conf.use_iobes(tests)
conf.build_label_idx(trains)
conf.build_word_idx(trains, devs, tests)
conf.build_emb_table()
ids_train = conf.map_insts_ids(trains)
ids_dev = conf.map_insts_ids(devs)
ids_test = conf.map_insts_ids(tests)
print("num chars: " + str(conf.num_char))
# print(str(config.char2idx))
print("num words: " + str(len(conf.word2idx)))
# print(config.word2idx)
if opt.mode == "train":
learn_from_insts(conf, conf.num_epochs, trains, devs, tests)
else:
## Load the trained model.
test_model(conf, tests)
# pass
print(opt.mode)
def main():
logging.info("Transformer implementation")
parser = argparse.ArgumentParser(
description="Transformer CRF implementation")
opt = parse_arguments_t(parser)
conf = Config(opt)
conf.train_file = conf.dataset + "/train.txt"
conf.dev_file = conf.dataset + "/valid.txt"
os.environ['CUDA_VISIBLE_DEVICES'] = opt.device_num
# data reader
reader = Reader(conf.digit2zero)
set_seed(opt, conf.seed)
# set logger
utils.set_logger(os.path.join(conf.model_folder, 'train.log'))
# params
for k in opt.__dict__:
logging.info(k + ": " + str(opt.__dict__[k]))
# read trains/devs
logging.info("\n")
logging.info("Loading the datasets...")
trains = reader.read_txt(conf.train_file, conf.train_num)
devs = reader.read_txt(conf.dev_file, conf.dev_num)
logging.info("Building label idx ...")
# build label2idx and idx2label
conf.build_label_idx(trains + devs)
random.shuffle(trains)
# set the prediction flag, if is_prediction is False, we will not update this label.
for inst in trains:
inst.is_prediction = [False] * len(inst.input)
for pos, label in enumerate(inst.output):
if label == conf.O:
inst.is_prediction[pos] = True
# dividing the data into 2 parts(num_folds default to 2)
num_insts_in_fold = math.ceil(len(trains) / conf.num_folds)
trains = [
trains[i * num_insts_in_fold:(i + 1) * num_insts_in_fold]
for i in range(conf.num_folds)
]
train_model(config=conf, train_insts=trains, dev_insts=devs)
def __init__(self, device: str, keyboard: Queue):
"""
device: 如果是 USB 连接，则为 adb devices 的返回结果；如果是模拟器，则为模拟器的控制 URL 。
"""
self.d = u2.connect(device)
self.config = Reader()
self.upgrade_iter_round = 0
self.keyboard = keyboard
self.schedule_keyboard = Queue()
self.pipe = Queue()
self.p = Process(target=make_scheduler,
args=(self.schedule_keyboard, self.pipe))
self.p.start()
class Automator:
def __init__(self, device: str, keyboard: Queue):
"""
device: 如果是 USB 连接，则为 adb devices 的返回结果；如果是模拟器，则为模拟器的控制 URL 。
"""
self.d = u2.connect(device)
self.config = Reader()
self.upgrade_iter_round = 0
self.keyboard = keyboard
self.command_mode = False
self._check_uiautomator()
self.time_start_working = time.time()
self.refresh_times = 0
self.delivered_times = 0
def _need_continue(self):
if not self.keyboard.empty():
# 不在命令模式下时才接受回车暂停
if not self.command_mode:
txt = self.keyboard.get()
if txt == prop.END:
logger.info('End')
return False
logger.info('Pause')
txt = self.keyboard.get()
if txt == prop.END:
logger.info('End')
return False
# 判断是否输入命令
elif txt.split(' ')[0] == prop.RUN:
# 若输入了命令则进行解析
self._interpreter(txt.split(' ')[1:])
return True
else:
logger.info('Restart')
return True
else:
return True
def _interpreter(self, cmd):
"""
cmd: 用户输入的命令
"""
# logger.info(txt.split(' ')[1:])
op = cmd[0]
# 命令 - 升至 x 级
if op == prop.UPGRADE_TO:
try:
target_level = int(cmd[1])
except Exception:
logger.warn("Invalid number. Ignored.")
else:
self._upgrade_to(target_level)
# 命令 - 升级 x 次
elif op == prop.UPGRADE_TIMES:
try:
input_num = int(cmd[1])
except Exception:
logger.warn("Invalid number. Ignored.")
else:
self._upgrade_times(input_num)
# 命令 - 命令模式
elif op == prop.COMMAND_MODE:
if len(cmd) == 2 and cmd[1] == 'on':
self.command_mode = True
logger.info('Enter command mode.')
elif len(cmd) == 2 and cmd[1] == 'off':
self.command_mode = False
logger.info('Exit command mode.')
self._return_main_area()
else:
logger.warn("Unknown parameter. Ignored.")
# 命令 - 拆红包
elif op == prop.UNPACK:
if len(cmd) == 3 and cmd[1] in ['s', 'm', 'l']:
try:
input_num = int(cmd[2])
except Exception:
logger.warn("Invalid number. Ignored.")
else:
self._unpack_times(cmd[1], input_num)
logger.info('Unpack complete.')
else:
logger.warn("Unknown parameter. Ignored.")
elif op == prop.OPEN_ALBUM:
try:
input_num = int(cmd[1])
except Exception:
logger.warn("Invalid number. Ignored.")
else:
self._open_albums(input_num)
logger.info('Open complete.')
elif op == prop.SUMMARY:
self._print_summary()
# 无法识别命令
else:
logger.warn("Unknown command. Ignored.")
if not self.command_mode:
logger.info('Restart')
def start(self):
"""
启动脚本，请确保已进入游戏页面。
"""
tmp_upgrade_last_time = time.time()
logger.info("Start Working")
while True:
# 检查是否有键盘事件
if not self._need_continue():
break
# 进入命令模式后不继续执行常规操作
if self.command_mode:
continue
# 更新配置文件
self.config.refresh()
if self.config.debug_mode:
logger.info("Debug mode")
# 重启游戏法
# self._refresh_train_by_restart()
# 重连 wifi 法
# self._refresh_train_by_reconnect()
# 是否检测货物
if self.config.detect_goods:
logger.info('-' * 30)
logger.info("Start matching goods")
# 获取当前屏幕快照
screen = self._safe_screenshot()
# 判断是否出现货物。
has_goods = False
refresh_flag = False
for target in self.config.goods_2_building_seq.keys():
has_goods |= self._match_target(screen, target)
# 如果需要刷新火车并且已送过目标货物
if has_goods and self.config.refresh_train:
refresh_flag = True
logger.info("All target goods delivered.")
# 如果需要刷新火车并且未送过目标货物
elif self.config.refresh_train:
for target in self.config.goods_2_building_seq_excpet_target.keys(
):
if UIMatcher.match(screen, target) is not None:
has_goods = True
break
if has_goods:
refresh_flag = True
logger.info("Train detected with no target goods.")
else:
logger.info("Train not detected.")
if refresh_flag:
# 刷新火车
logger.info("Refresh train.")
logger.info("-" * 30)
self.refresh_times += 1
if not self._refresh_train_by_restart():
# 重启不成功（超时）时中止脚本
logger.warn("Timed out waiting for restart!")
break
else:
logger.info("End matching")
# 简单粗暴的方式，处理 “XX之光” 的荣誉显示。
# 当然，也可以使用图像探测的模式。
self.d.click(550, 1650)
# 滑动屏幕，收割金币。
logger.info("Collect coins")
self._swipe()
# 自动升级建筑
tmp_upgrade_interval = time.time() - tmp_upgrade_last_time
if tmp_upgrade_interval >= self.config.upgrade_interval_sec:
if self.config.upgrade_building is True:
self._auto_upgrade_building()
tmp_upgrade_last_time = time.time()
else:
logger.info(
f"Left {round(self.config.upgrade_interval_sec - tmp_upgrade_interval, 2)}s to upgrade"
)
time.sleep(self.config.swipe_interval_sec)
self._print_summary()
logger.info('Sub process end')
def _swipe(self):
"""
滑动屏幕，收割金币。
"""
for i in range(3):
# 横向滑动，共 3 次。
sx, sy = self._get_position(i * 3 + 1)
ex, ey = self._get_position(i * 3 + 3)
self.d.swipe(sx, sy, ex, ey)
@staticmethod
def _get_position(key):
"""
获取指定建筑的屏幕位置。
###7#8#9#
##4#5#6##
#1#2#3###
"""
return prop.BUILDING_POS.get(key)
def _get_target_position(self, target: TargetType):
"""
获取货物要移动到的屏幕位置。
"""
return self._get_position(self.config.goods_2_building_seq.get(target))
def _match_target(self, screen, target: TargetType):
"""
探测货物，并搬运货物。
"""
# 由于 OpenCV 的模板匹配有时会智障，故我们探测次数实现冗余。
counter = 6
logged = False
while counter != 0:
counter = counter - 1
# 使用 OpenCV 探测货物。
result = UIMatcher.match(screen, target)
# 若无探测到，终止对该货物的探测。
# 实现冗余的原因：返回的货物屏幕位置与实际位置存在偏差，导致移动失效
if result is None:
break
rank = result[-1]
result = result[:2]
sx, sy = result
# 获取货物目的地的屏幕位置。
ex, ey = self._get_target_position(target)
if not logged:
self.delivered_times += 1
logger.info(f"Detect {target} at ({sx},{sy}), rank: {rank}")
logged = True
# 搬运货物。
self.d.swipe(sx, sy, ex, ey)
# 侧面反映检测出货物
return logged
def _auto_upgrade_building(self):
"""
按顺序升级建筑
"""
logger.info("Start upgrade buildings")
self.d.click(*prop.BUILDING_DETAIL_BTN)
time.sleep(0.5)
for pos in self.config.upgrade_building_list:
self.d.click(*self._get_position(pos))
time.sleep(0.5)
self.d.click(*prop.BUILDING_UPGRADE_BTN)
time.sleep(0.5)
self.d.click(*prop.BUILDING_DETAIL_BTN)
logger.info("Upgrade complete")
def _upgrade_to(self, target_level):
"""
target_level: 目标等级
升至 target_level 级
利用 Tesseract 识别当前等级后点击升级按钮 target_level - 当前等级次
"""
screen = self._safe_screenshot()
screen = UIMatcher.pre_building_panel(screen)
tmp = UIMatcher.cut(screen, prop.BUILDING_INFO_PANEL_LEVEL_POS,
(120, 50))
# import cv2
# cv2.imwrite("./tmp/screen.jpg", screen)
tmp = UIMatcher.plain(tmp)
tmp = UIMatcher.fill_color(tmp)
tmp = UIMatcher.plain(tmp)
txt = UIMatcher.image_to_txt(tmp, plus='-l chi_sim --psm 7')
txt = UIMatcher.normalize_txt(txt)
try:
cur_level = int(txt)
logger.info(f'Current level -> {cur_level}')
except Exception:
logger.warning(f'Current level -> {txt}')
return
click_times = target_level - cur_level
self._upgrade_times(click_times)
def _upgrade_times(self, click_times: int):
"""
click_times: 点击/升级次数
执行点击升级按钮的操作 click_times 次
"""
# assert(times >= 0)
while click_times > 0:
click_times -= 1
bx, by = prop.BUILDING_INFO_PANEL_UPGRADE_BTN
self.d.click(bx, by)
time.sleep(0.015)
logger.info("Upgrade complete")
# 非命令模式下完成操作后返回主界面以继续常规流程
if not self.command_mode:
self._return_main_area()
def _return_main_area(self):
"""
通过点击两次导航栏内建设按钮来回到主界面
"""
time.sleep(0.5)
tx, ty = prop.CONSTRUCT_BTN
self.d.click(tx, ty)
time.sleep(0.1)
self.d.click(tx, ty)
time.sleep(0.5)
def _unpack_times(self, pack_type, num: int):
"""
开红包 num 个
"""
# 红包标题栏坐标 开红包后点这里直到开完这个红包
tx, ty = prop.REDPACKET_TITLE_POS
if pack_type == 'm':
bx, by = prop.REDPACKET_BTN_M
t = 12
elif pack_type == 'l':
bx, by = prop.REDPACKET_BTN_L
t = 24
else:
bx, by = prop.REDPACKET_BTN_S
t = 6
self.d.click(bx, by)
time.sleep(1)
while num > 1:
num -= 1
self.d.press("enter")
time.sleep(0.08)
time.sleep(1)
# 防止意外多点几下 例如升星或开出史诗
for _ in range(t):
self.d.click(tx, ty)
time.sleep(0.25)
if not self.command_mode:
self._return_main_area()
def _open_albums(self, num: int):
"""
开相册 num 个
"""
self.d.click(*prop.ALBUM_BTN)
time.sleep(1)
while num > 1:
num -= 1
self.d.press("enter")
time.sleep(0.08)
time.sleep(1)
for _ in range(4):
self.d.click(*prop.REDPACKET_TITLE_POS)
time.sleep(0.5)
if not self.command_mode:
self._return_main_area()
def _is_good_to_go(self):
"""
检测是否有排行图标来判断是否进入了游戏界面
"""
screen = self._safe_screenshot()
return UIMatcher.match(screen, TargetType.Rank_btn) is not None
def _refresh_train_by_restart(self):
"""
通过重启游戏的方法来刷新火车
全程用时大约在 20s 左右
qq 账号测试不用授权 20s 左右
"""
time_before_restart = time.time()
self.d.app_stop("com.tencent.jgm")
self.d.app_start("com.tencent.jgm", activity=".MainActivity")
time.sleep(5)
good_to_go = False
try_times = 0
while not good_to_go:
try_times += 1
if self._is_good_to_go():
good_to_go = True
logger.info(
f"Refresh train costs {round(time.time() - time_before_restart, 2)}s."
)
elif try_times >= 60:
return False
else:
time.sleep(1)
return True
def _refresh_train_by_reconnect(self):
"""
通过关闭开启 wifi 的方法刷新火车
要重新登陆+授权 暂时弃用
"""
self.d.press("home")
time.sleep(0.5)
logger.info("Wifi disable.")
logger.info(self.d.adb_shell("svc wifi disable"))
time.sleep(0.5)
logger.info("Wifi enable.")
logger.info(self.d.adb_shell("svc wifi enable"))
time.sleep(5)
self.d.app_start("com.tencent.jgm", activity=".MainActivity")
def _check_uiautomator(self):
"""
检查 uiautomator 运行状态
"""
if not self.d.uiautomator.running():
self.d.reset_uiautomator()
def _safe_screenshot(self):
"""
防止执行 screenshot 时报错终止
"""
self._check_uiautomator()
return self.d.screenshot(format="opencv")
def _print_summary(self):
logger.info('-' * 30)
pass_time = time.time() - self.time_start_working
logger.info(
f"本次启动运行了 {int(pass_time // 3600)} 小时 {int(pass_time % 3600 // 60)} 分钟 {round(pass_time % 60, 2)} 秒"
)
logger.info(
f"重启了 {self.refresh_times} 次， 检测到 {self.delivered_times} 车厢目标货物（非总送货次数）"
)
logger.info('-' * 30)
help="+ hard / soft matching") # soft, hard
parser.add_argument('--percentage',
type=int,
default=100,
help="how much percentage of training dataset to use")
args = parser.parse_args()
for k in args.__dict__:
print(k + ": " + str(args.__dict__[k]))
return args
parser = argparse.ArgumentParser()
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
dataset, max_length, label_length = reader.read_trigger_txt(
conf.trigger_file, -1)
reader.merge_labels(dataset)
devs = reader.read_txt(conf.dev_file, conf.dev_num)
tests = reader.read_txt(conf.test_file, conf.test_num)
print(len(dataset))
if conf.context_emb == ContextEmb.bert:
print('Loading the BERT vectors for all datasets.')
conf.context_emb_size = load_bert_vec(
conf.trigger_file + "." + conf.context_emb.name + ".vec", dataset)
# setting for data
conf.use_iobes(dataset)
conf.use_iobes(devs)
class Scheduler:
def __init__(self, keyboard: Queue, pipe: Queue):
self.config = Reader()
self.keyboard = keyboard
self.pipe = pipe
def _generate_do_2_time(self):
res = {}
for config_name in CONFIG_NAME_2_METHOD_NAME.keys():
res[config_name] = set()
return res
# 判断哪些任务需要重新放回调度队列，有可能出现关闭一个任务后重启，而此时这个任务将会饿死，因为没有trigger
def _generate_restart_list(self, do_2_time: dict, interval_map: dict):
res = []
for config_name in interval_map.keys():
if interval_map[config_name] > 0 and len(
do_2_time[config_name]) == 0:
res.append(config_name)
return res
def _add_time_2_do(self, time_2_do: dict, new_time: int, do: str):
if new_time not in time_2_do:
time_2_do[new_time] = {do}
else:
time_2_do[new_time].add(do)
def run(self):
default_loop_interval = 5
loop_time = 0
# 记录哪个时间干哪些事（实际上只是塞队列）
time_2_do = {}
# 记录哪些事在哪些时候干
do_2_time = self._generate_do_2_time()
while self.keyboard.empty():
# 更新配置
self.config.refresh()
# self.pipe.put(f'{CONFIG_PREFIX}{self.config.to_string()}')
self.pipe.put(self.config.to_string())
interval_map = self.config.interval_map
pipe_msg = []
for config_name, interval in interval_map.items():
if interval <= 0:
interval_map.pop(config_name)
# 如果一个任务被置于<=0的值代表不希望被执行，那么同时需要把它移出任务队列
for _time in do_2_time[config_name]:
time_2_do.get(_time, set()).discard(config_name)
do_2_time[config_name] = set()
# print(interval_map)
if len(interval_map) == 0:
loop_interval = default_loop_interval
else:
loop_interval = list_gcd(interval_map.values())
to_do_set = time_2_do.get(loop_time, set()) | set(
self._generate_restart_list(do_2_time, interval_map))
# print(to_do_set)
for config_name in to_do_set:
# 塞入调度队列
pipe_msg.append(CONFIG_NAME_2_METHOD_NAME[config_name])
# 计算这个任务下一次何时调度
next_time = interval_map[
config_name] / loop_interval + loop_time
# 更新time_2_do
self._add_time_2_do(time_2_do, next_time, config_name)
# 更新do_2_time
do_2_time[config_name].discard(loop_time)
do_2_time[config_name].add(next_time)
# 把当前时间的任务队列销掉，节省内存
time_2_do.pop(loop_time, '')
# print(pipe_msg)
if len(pipe_msg) != 0:
pipe_msg = f"{METHOD_PREFIX}{METHOD_SEP.join(pipe_msg)}"
self.pipe.put(pipe_msg)
loop_time += 1
time.sleep(loop_interval)
class Automator:
def __init__(self, device: str, keyboard: Queue):
"""
device: 如果是 USB 连接，则为 adb devices 的返回结果；如果是模拟器，则为模拟器的控制 URL 。
"""
self.d = u2.connect(device)
self.config = Reader()
self.upgrade_iter_round = 0
self.keyboard = keyboard
self.command_mode = False
# 检查 uiautomator
if not self.d.uiautomator.running():
self.d.reset_uiautomator()
def _need_continue(self):
if not self.keyboard.empty():
# 不在命令模式下时才接受回车暂停
if not self.command_mode:
txt = self.keyboard.get()
# logger.info('txt1:' + txt)
if txt == prop.END:
logger.info('End')
return False
logger.info('Pause')
txt = self.keyboard.get()
# logger.info('txt2:' + txt)
if txt == prop.END:
logger.info('End')
return False
# 判断是否输入命令
elif txt.split(' ')[0] == prop.RUN:
# logger.info(txt.split(' ')[1:])
cmd = txt.split(' ')[1]
# 命令 - 升至 x 级
if cmd == prop.UPGRADE_TO:
try:
target_level = int(txt.split(' ')[2])
except Exception:
logger.warn("Invalid number. Ignored.")
else:
self._upgrade_to(target_level)
# logger.info('target_level: ' + str(target_level))
# 命令 - 升级 x 次
elif cmd == prop.UPGRADE_TIMES:
try:
input_num = int(txt.split(' ')[2])
except Exception:
logger.warn("Invalid number. Ignored.")
else:
self._upgrade_times(input_num)
# 命令 - 命令模式
elif cmd == prop.COMMAND_MODE:
if txt.split(' ')[2] == 'on':
self.command_mode = True
logger.info('Enter command mode.')
elif txt.split(' ')[2] == 'off':
self.command_mode = False
logger.info('Exit command mode.')
self._return_main_area()
else:
logger.warn("Unknown parameter. Ignored.")
# 无法识别命令
else:
logger.warn("Unknown command. Ignored.")
if not self.command_mode:
logger.info('Restart')
return True
else:
logger.info('Restart')
return True
else:
return True
def start(self):
"""
启动脚本，请确保已进入游戏页面。
"""
tmp_upgrade_last_time = time.time()
logger.info("Start Working")
while True:
# 检查是否有键盘事件
if not self._need_continue():
break
# 进入命令模式后不继续执行常规操作
if self.command_mode:
continue
# 更新配置文件
self.config.refresh()
# 在下午五点以后再开始拿火车，收益最大化
# if datetime.now().hour > 17:
if True:
logger.info("Start matching goods")
# 获取当前屏幕快照
screen = self.d.screenshot(format="opencv")
# 判断是否出现货物。
has_goods = False
for target in self.config.goods_2_building_seq.keys():
has_goods |= self._match_target(screen, target)
if has_goods:
UIMatcher.write(screen)
# pass
logger.info("End matching")
# 简单粗暴的方式，处理 “XX之光” 的荣誉显示。
# 当然，也可以使用图像探测的模式。
self.d.click(550, 1650)
# 滑动屏幕，收割金币。
# logger.info("swipe")
self._swipe()
# 升级建筑
tmp_upgrade_interval = time.time() - tmp_upgrade_last_time
if tmp_upgrade_interval >= self.config.upgrade_interval_sec:
if self.config.upgrade_type_is_assign is True:
self._assigned_uprade()
else:
self._upgrade()
tmp_upgrade_last_time = time.time()
else:
logger.info(
f"Left {round(self.config.upgrade_interval_sec - tmp_upgrade_interval, 2)}s to upgrade"
)
time.sleep(self.config.swipe_interval_sec)
logger.info('Sub process end')
def _swipe(self):
"""
滑动屏幕，收割金币。
"""
for i in range(3):
# 横向滑动，共 3 次。
sx, sy = self._get_position(i * 3 + 1)
ex, ey = self._get_position(i * 3 + 3)
self.d.swipe(sx, sy, ex, ey)
@staticmethod
def _get_position(key):
"""
获取指定建筑的屏幕位置。
###7#8#9#
##4#5#6##
#1#2#3###
"""
return prop.BUILDING_POS.get(key)
def _get_target_position(self, target: TargetType):
"""
获取货物要移动到的屏幕位置。
"""
return self._get_position(self.config.goods_2_building_seq.get(target))
def _match_target(self, screen, target: TargetType):
"""
探测货物，并搬运货物。
"""
# 由于 OpenCV 的模板匹配有时会智障，故我们探测次数实现冗余。
counter = 6
logged = False
while counter != 0:
counter = counter - 1
# 使用 OpenCV 探测货物。
result = UIMatcher.match(screen, target)
# 若无探测到，终止对该货物的探测。
# 实现冗余的原因：返回的货物屏幕位置与实际位置存在偏差，导致移动失效
if result is None:
break
rank = result[-1]
result = result[:2]
sx, sy = result
# 获取货物目的地的屏幕位置。
ex, ey = self._get_target_position(target)
if not logged:
logger.info(f"Detect {target} at ({sx},{sy}), rank: {rank}")
logged = True
# 搬运货物。
self.d.swipe(sx, sy, ex, ey)
# 侧面反映检测出货物
return logged
def __find_selected_building_seq(self):
selected_seq_list = elect(len(self.config.upgrade_order),
self.upgrade_iter_round)
tmp_set = set()
for order_seq in selected_seq_list:
tmp_set |= self.config.upgrade_order[order_seq]
res = []
for i, building in enumerate(self.config.building_pos):
if building in tmp_set:
res.append(i + 1)
if len(res) == 0:
return list(prop.BUILDING_POS.keys())
else:
return res
def _select_min_building(self):
screen = self.d.screenshot(format="opencv")
screen = UIMatcher.pre(screen)
min_level = float('inf')
min_building_seq = None
for key in self.__find_selected_building_seq():
pos = prop.BUILDING_LEVEL_POS[key]
tmp = UIMatcher.cut(screen, pos)
tmp = UIMatcher.plain(tmp)
tmp = UIMatcher.fill_color(tmp)
tmp = UIMatcher.plain(tmp)
txt = UIMatcher.image_to_txt(tmp, plus='-l chi_sim --psm 7')
txt = UIMatcher.normalize_txt(txt)
try:
level = int(txt)
logger.info(
f'{self.config.building_pos[key - 1]} tesser -> {level}')
except Exception:
logger.warning(
f'{self.config.building_pos[key - 1]} tesser -> {txt}')
continue
if level < min_level:
min_level = level
min_building_seq = key
# 一个屋子的等级都没拿到
if min_building_seq is None:
res = choice(list(prop.BUILDING_POS.keys()))
logger.warning(
f'No tesseract result, random to {self.config.building_pos[res - 1]}'
)
return res
else:
logger.info(
f'Minimum level is {min_level} from {self.config.building_pos[min_building_seq - 1]}'
)
return min_building_seq
def _upgrade(self):
logger.info("Start upgrading")
# 迭代次数加一
self.upgrade_iter_round += 1
self.d.click(*prop.BUILDING_DETAIL_BTN)
time.sleep(1)
need_upgrade_building_seq = self._select_min_building()
self.d.click(*self._get_position(need_upgrade_building_seq))
time.sleep(1)
self.d.long_click(prop.BUILDING_UPGRADE_BTN[0],
prop.BUILDING_UPGRADE_BTN[1],
self.config.upgrade_press_time_sec)
time.sleep(0.5)
self.d.click(*prop.BUILDING_DETAIL_BTN)
logger.info("Upgrade complete")
def _assigned_uprade(self):
logger.info("Start assigned upgrading")
self.d.click(*prop.BUILDING_DETAIL_BTN)
time.sleep(0.5)
self.d.click(*self._get_position(self.config.assigned_building_pos))
time.sleep(0.5)
self.d.long_click(prop.BUILDING_UPGRADE_BTN[0],
prop.BUILDING_UPGRADE_BTN[1],
self.config.upgrade_press_time_sec)
time.sleep(0.5)
self.d.click(*prop.BUILDING_DETAIL_BTN)
logger.info("Upgrade complete")
def _upgrade_to(self, target_level):
"""
target_level: 目标等级
升至 target_level 级
利用 Tesseract 识别当前等级后点击升级按钮 target_level - 当前等级次
"""
screen = self.d.screenshot(format="opencv")
screen = UIMatcher.pre_building_panel(screen)
tmp = UIMatcher.cut(screen, prop.BUILDING_INFO_PANEL_LEVEL_POS,
(120, 50))
# import cv2
# cv2.imwrite("./tmp/screen.jpg", screen)
tmp = UIMatcher.plain(tmp)
tmp = UIMatcher.fill_color(tmp)
tmp = UIMatcher.plain(tmp)
txt = UIMatcher.image_to_txt(tmp, plus='-l chi_sim --psm 7')
txt = UIMatcher.normalize_txt(txt)
try:
cur_level = int(txt)
logger.info(f'Current level -> {cur_level}')
except Exception:
logger.warning(f'Current level -> {txt}')
return
click_times = target_level - cur_level
self._upgrade_times(click_times)
def _upgrade_times(self, click_times: int):
"""
click_times: 点击/升级次数
执行点击升级按钮的操作 click_times 次
"""
# assert(times >= 0)
while click_times > 0:
click_times -= 1
bx, by = prop.BUILDING_INFO_PANEL_UPGRADE_BTN
self.d.click(bx, by)
time.sleep(0.015)
logger.info("Upgrade complete")
# 非命令模式下完成操作后返回主界面以继续常规流程
if not self.command_mode:
self._return_main_area()
def _return_main_area(self):
"""
通过点击两次导航栏内建设按钮来回到主界面
"""
time.sleep(0.5)
tx, ty = prop.CONSTRUCT_BTN
self.d.click(tx, ty)
time.sleep(0.1)
self.d.click(tx, ty)
time.sleep(0.5)
help="+ hard / soft matching") # soft, hard
parser.add_argument('--percentage',
type=int,
default=100,
help="how much percentage of training dataset to use")
args = parser.parse_args()
for k in args.__dict__:
print(k + ": " + str(args.__dict__[k]))
return args
parser = argparse.ArgumentParser()
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
dataset = reader.read_txt(conf.train_file, conf.dev_num)
devs = reader.read_txt(conf.dev_file, conf.dev_num)
tests = reader.read_txt(conf.test_file, conf.test_num)
print(len(dataset))
if conf.context_emb == ContextEmb.bert:
print('Loading the BERT vectors for all datasets.')
conf.context_emb_size = load_bert_vec(
conf.trigger_file + "." + conf.context_emb.name + ".vec", dataset)
# setting for data
conf.use_iobes(dataset)
conf.use_iobes(devs)
conf.use_iobes(tests)
conf.optimizer = opt.trig_optimizer
parser.add_argument('--bert_path', type=str, default="./BERTOverflow")
# parser.add_argument('--bert_path', type=str, default="./bert-base-cased")
parser.add_argument('--from_pretrain', type=bool, default=True)
parser.add_argument('--bert_embedding_size', type=int, default=768)
parser.add_argument('--rnn', type=str, default='lstm')
args = parser.parse_args()
for k in args.__dict__:
print(k + ": " + str(args.__dict__[k]))
return args
parser = argparse.ArgumentParser()
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
# train_file = 'data/annotated_ner_data/StackOverflow/train.txt'
# dev_file = 'data/annotated_ner_data/StackOverflow/dev.txt'
# test_file = 'data/annotated_ner_data/StackOverflow/test.txt'
# dataset = reader.read_txt(train_file, -1)
# devs = reader.read_txt(dev_file, -1)
# tests = reader.read_txt(test_file, -1)
dataset = reader.read_txt(conf.train_all_file, -1)
# devs = reader.read_txt(conf.dev_file, -1)
tests = reader.read_txt(conf.test_file, -1)
print(len(dataset))
# setting for data
conf.use_iobes(dataset)
class Automator:
def __init__(self, device: str, keyboard: Queue):
"""
device: 如果是 USB 连接，则为 adb devices 的返回结果；如果是模拟器，则为模拟器的控制 URL 。
"""
self.d = u2.connect(device)
self.config = Reader()
self.upgrade_iter_round = 0
self.keyboard = keyboard
self.command_mode = False
# 检查 uiautomator
if not self.d.uiautomator.running():
self.d.reset_uiautomator()
self.time_start_working = time.time()
self.refresh_times = 0
self.delivered_times = 0
def _need_continue(self):
if not self.keyboard.empty():
# 不在命令模式下时才接受回车暂停
if not self.command_mode:
txt = self.keyboard.get()
# logger.info('txt1:' + txt)
if txt == prop.END:
logger.info('End')
return False
logger.info('Pause')
txt = self.keyboard.get()
# logger.info('txt2:' + txt)
if txt == prop.END:
logger.info('End')
return False
# 判断是否输入命令
elif txt.split(' ')[0] == prop.RUN:
# logger.info(txt.split(' ')[1:])
cmd = txt.split(' ')[1]
# 命令 - 升至 x 级
if cmd == prop.UPGRADE_TO:
try:
target_level = int(txt.split(' ')[2])
except Exception:
logger.warn("Invalid number. Ignored.")
else:
self._upgrade_to(target_level)
# logger.info('target_level: ' + str(target_level))
# 命令 - 升级 x 次
elif cmd == prop.UPGRADE_TIMES:
try:
input_num = int(txt.split(' ')[2])
except Exception:
logger.warn("Invalid number. Ignored.")
else:
self._upgrade_times(input_num)
# 命令 - 命令模式
elif cmd == prop.COMMAND_MODE:
if txt.split(' ')[2] == 'on':
self.command_mode = True
logger.info('Enter command mode.')
elif txt.split(' ')[2] == 'off':
self.command_mode = False
logger.info('Exit command mode.')
self._return_main_area()
else:
logger.warn("Unknown parameter. Ignored.")
# 命令 - 拆红包
elif cmd == prop.UNPACK:
pack_type = txt.split(' ')[2]
if pack_type in ['s', 'm', 'l']:
try:
input_num = int(txt.split(' ')[3])
except Exception:
logger.warn("Invalid number. Ignored.")
else:
self._unpack_times(pack_type, input_num)
logger.info('Unpack complete.')
else:
logger.warn("Unknown parameter. Ignored.")
elif cmd == prop.OPEN_ALBUM:
try:
input_num = int(txt.split(' ')[2])
except Exception:
logger.warn("Invalid number. Ignored.")
else:
self._open_albums(input_num)
logger.info('Open complete.')
# 无法识别命令
else:
logger.warn("Unknown command. Ignored.")
if not self.command_mode:
logger.info('Restart')
return True
else:
logger.info('Restart')
return True
else:
return True
def start(self):
"""
启动脚本，请确保已进入游戏页面。
"""
tmp_upgrade_last_time = time.time()
logger.info("Start Working")
while True:
# 检查是否有键盘事件
if not self._need_continue():
logger.info('-' * 30)
pass_time = time.time() - self.time_start_working
logger.info(f"本次启动运行了 {int(pass_time // 3600)} 小时 {int(pass_time % 3600 // 60)} 分钟 {round(pass_time % 60, 2)} 秒")
logger.info(f"重启了 {self.refresh_times} 次， 检测到 {self.delivered_times} 次货物（非总送货次数）")
break
# 进入命令模式后不继续执行常规操作
if self.command_mode:
continue
# 更新配置文件
self.config.refresh()
if self.config.debug_mode:
None
# 重启游戏法
# self._refresh_train_by_restart()
# 重连 wifi 法
# self._refresh_train_by_reconnect()
# 是否检测货物
if self.config.detect_goods:
logger.info('-' * 30)
logger.info("Start matching goods")
# 获取当前屏幕快照
screen = self.d.screenshot(format="opencv")
# 判断是否出现货物。
has_goods = False
refresh_flag = False
for target in self.config.goods_2_building_seq.keys():
has_goods |= self._match_target(screen, target)
# 如果需要刷新火车并且已送过目标货物
if has_goods and self.config.refresh_train:
refresh_flag = True
logger.info("All target goods delivered.")
# 如果需要刷新火车并且未送过目标货物
elif self.config.refresh_train:
for target in self.config.goods_2_building_seq_excpet_target.keys():
if UIMatcher.match(screen, target) is not None:
has_goods = True
break
if has_goods:
refresh_flag = True
logger.info("Train detected with no target goods.")
else:
logger.info("Train not detected.")
if refresh_flag:
# 刷新火车
logger.info("Refresh train.")
logger.info("-" * 30)
self.refresh_times += 1
self._refresh_train_by_restart()
else:
logger.info("End matching")
# 简单粗暴的方式，处理 “XX之光” 的荣誉显示。
# 当然，也可以使用图像探测的模式。
self.d.click(550, 1650)
# 滑动屏幕，收割金币。
# logger.info("swipe")
self._swipe()
# 升级建筑
tmp_upgrade_interval = time.time() - tmp_upgrade_last_time
if tmp_upgrade_interval >= self.config.upgrade_interval_sec:
if self.config.upgrade_type_is_assign is True:
self._assigned_uprade()
else:
self._upgrade()
tmp_upgrade_last_time = time.time()
else:
logger.info(f"Left {round(self.config.upgrade_interval_sec - tmp_upgrade_interval, 2)}s to upgrade")
time.sleep(self.config.swipe_interval_sec)
logger.info('Sub process end')
def _swipe(self):
"""
滑动屏幕，收割金币。
"""
for i in range(3):
# 横向滑动，共 3 次。
sx, sy = self._get_position(i * 3 + 1)
ex, ey = self._get_position(i * 3 + 3)
self.d.swipe(sx, sy, ex, ey)
@staticmethod
def _get_position(key):
"""
获取指定建筑的屏幕位置。
###7#8#9#
##4#5#6##
#1#2#3###
"""
return prop.BUILDING_POS.get(key)
def _get_target_position(self, target: TargetType):
"""
获取货物要移动到的屏幕位置。
"""
return self._get_position(self.config.goods_2_building_seq.get(target))
def _match_target(self, screen, target: TargetType):
"""
探测货物，并搬运货物。
"""
# 由于 OpenCV 的模板匹配有时会智障，故我们探测次数实现冗余。
counter = 6
logged = False
while counter != 0:
counter = counter - 1
# 使用 OpenCV 探测货物。
result = UIMatcher.match(screen, target)
# 若无探测到，终止对该货物的探测。
# 实现冗余的原因：返回的货物屏幕位置与实际位置存在偏差，导致移动失效
if result is None:
break
rank = result[-1]
result = result[:2]
sx, sy = result
# 获取货物目的地的屏幕位置。
ex, ey = self._get_target_position(target)
if not logged:
self.delivered_times += 1
logger.info(f"Detect {target} at ({sx},{sy}), rank: {rank}")
logged = True
# 搬运货物。
self.d.swipe(sx, sy, ex, ey)
# 侧面反映检测出货物
return logged
def __find_selected_building_seq(self):
selected_seq_list = elect(len(self.config.upgrade_order), self.upgrade_iter_round)
tmp_set = set()
for order_seq in selected_seq_list:
tmp_set |= self.config.upgrade_order[order_seq]
res = []
for i, building in enumerate(self.config.building_pos):
if building in tmp_set:
res.append(i + 1)
if len(res) == 0:
return list(prop.BUILDING_POS.keys())
else:
return res
def _select_min_building(self):
screen = self.d.screenshot(format="opencv")
screen = UIMatcher.pre(screen)
min_level = float('inf')
min_building_seq = None
for key in self.__find_selected_building_seq():
pos = prop.BUILDING_LEVEL_POS[key]
tmp = UIMatcher.cut(screen, pos)
tmp = UIMatcher.plain(tmp)
tmp = UIMatcher.fill_color(tmp)
tmp = UIMatcher.plain(tmp)
txt = UIMatcher.image_to_txt(tmp, plus='-l chi_sim --psm 7')
txt = UIMatcher.normalize_txt(txt)
try:
level = int(txt)
logger.info(f'{self.config.building_pos[key - 1]} tesser -> {level}')
except Exception:
logger.warning(f'{self.config.building_pos[key - 1]} tesser -> {txt}')
continue
if level < min_level:
min_level = level
min_building_seq = key
# 一个屋子的等级都没拿到
if min_building_seq is None:
res = choice(list(prop.BUILDING_POS.keys()))
logger.warning(f'No tesseract result, random to {self.config.building_pos[res - 1]}')
return res
else:
logger.info(f'Minimum level is {min_level} from {self.config.building_pos[min_building_seq - 1]}')
return min_building_seq
def _upgrade(self):
logger.info("Start upgrading")
# 迭代次数加一
self.upgrade_iter_round += 1
self.d.click(*prop.BUILDING_DETAIL_BTN)
time.sleep(1)
need_upgrade_building_seq = self._select_min_building()
self.d.click(*self._get_position(need_upgrade_building_seq))
time.sleep(1)
self.d.long_click(prop.BUILDING_UPGRADE_BTN[0], prop.BUILDING_UPGRADE_BTN[1],
self.config.upgrade_press_time_sec)
time.sleep(0.5)
self.d.click(*prop.BUILDING_DETAIL_BTN)
logger.info("Upgrade complete")
def _assigned_uprade(self):
logger.info("Start assigned upgrading")
self.d.click(*prop.BUILDING_DETAIL_BTN)
time.sleep(0.5)
self.d.click(*self._get_position(self.config.assigned_building_pos))
time.sleep(0.5)
self.d.long_click(prop.BUILDING_UPGRADE_BTN[0], prop.BUILDING_UPGRADE_BTN[1],
self.config.upgrade_press_time_sec)
time.sleep(0.5)
self.d.click(*prop.BUILDING_DETAIL_BTN)
logger.info("Upgrade complete")
def _upgrade_to(self, target_level):
"""
target_level: 目标等级
升至 target_level 级
利用 Tesseract 识别当前等级后点击升级按钮 target_level - 当前等级次
"""
screen = self.d.screenshot(format="opencv")
screen = UIMatcher.pre_building_panel(screen)
tmp = UIMatcher.cut(screen, prop.BUILDING_INFO_PANEL_LEVEL_POS, (120, 50))
# import cv2
# cv2.imwrite("./tmp/screen.jpg", screen)
tmp = UIMatcher.plain(tmp)
tmp = UIMatcher.fill_color(tmp)
tmp = UIMatcher.plain(tmp)
txt = UIMatcher.image_to_txt(tmp, plus='-l chi_sim --psm 7')
txt = UIMatcher.normalize_txt(txt)
try:
cur_level = int(txt)
logger.info(f'Current level -> {cur_level}')
except Exception:
logger.warning(f'Current level -> {txt}')
return
click_times = target_level - cur_level
self._upgrade_times(click_times)
def _upgrade_times(self, click_times: int):
"""
click_times: 点击/升级次数
执行点击升级按钮的操作 click_times 次
"""
# assert(times >= 0)
while click_times > 0:
click_times -= 1
bx, by = prop.BUILDING_INFO_PANEL_UPGRADE_BTN
self.d.click(bx, by)
time.sleep(0.015)
logger.info("Upgrade complete")
# 非命令模式下完成操作后返回主界面以继续常规流程
if not self.command_mode:
self._return_main_area()
def _return_main_area(self):
"""
通过点击两次导航栏内建设按钮来回到主界面
"""
time.sleep(0.5)
tx, ty = prop.CONSTRUCT_BTN
self.d.click(tx, ty)
time.sleep(0.1)
self.d.click(tx, ty)
time.sleep(0.5)
def _unpack_times(self, pack_type, sum: int):
# 红包标题栏坐标 开红包后点这里直到开完这个红包
tx, ty = prop.REDPACKET_TITLE_POS
if pack_type == 'm':
bx, by = prop.REDPACKET_BTN_M
t = 6
elif pack_type == 'l':
bx, by = prop.REDPACKET_BTN_L
t = 12
else:
# logger.inf("暂不支持开小红包")
bx, by = prop.REDPACKET_BTN_S
t = 3
while sum > 0:
sum -= 1
self.d.click(bx, by)
time.sleep(0.5)
self.d.click(tx, ty)
time.sleep(0.5)
# 防止意外多点几下 例如升星或开出史诗
for i in range(t):
# logger.info(f"第{i}次点击")
self.d.click(tx, ty)
time.sleep(0.5)
def _open_albums(self, sum: int):
tx, ty = prop.ALBUM_BTN
bx, by = prop.REDPACKET_TITLE_POS
while sum > 0:
sum -= 1
self.d.click(tx, ty)
time.sleep(1)
for i in range(5):
logger.info(f"第{i}次点击")
self.d.click(bx, by)
time.sleep(0.5)
def _is_good_to_go(self):
screen = self.d.screenshot(format="opencv")
return UIMatcher.match(screen, TargetType.Rank_btn) is not None
def _refresh_train_by_restart(self):
"""
通过重启游戏的方法来刷新火车
全程用时大约在 20s 左右
qq 账号测试不用授权 20s 左右
"""
time_before_restart = time.time()
self.d.app_stop("com.tencent.jgm")
self.d.app_start("com.tencent.jgm", activity=".MainActivity")
time.sleep(5)
good_to_go = False
while not good_to_go:
if self._is_good_to_go():
good_to_go = True
logger.info(f"Refresh train costs {round(time.time() - time_before_restart, 2)}s.")
else:
time.sleep(1)
def _refresh_train_by_reconnect(self):
"""
通过关闭开启 wifi 的方法刷新火车
要重新登陆+授权 暂时弃用
"""
self.d.press("home")
time.sleep(0.5)
logger.info("Wifi disable.")
logger.info(self.d.adb_shell("svc wifi disable"))
time.sleep(0.5)
logger.info("Wifi enable.")
logger.info(self.d.adb_shell("svc wifi enable"))
time.sleep(5)
self.d.app_start("com.tencent.jgm", activity=".MainActivity")
def __init__(self, keyboard: Queue, pipe: Queue):
self.config = Reader()
self.keyboard = keyboard
self.pipe = pipe
