86 lines
3.2 KiB
Python
86 lines
3.2 KiB
Python
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import os
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import random
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from disarming.parameters.hash_function import SeriesHash, SpecificityHash
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from objects.mine_models.mine import Mine
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from algorithms.learn.decision_tree.decision_tree import DecisionTree
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from algorithms.learn.neural_network.neural_network import NNType, NeuralNetwork
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SERIES_IMAGES_PATH = r"resources/data/neural_network/series/disarm"
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SPECIFICITY_IMAGES_PATH = r"resources/data/neural_network/specificity/disarm"
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class_to_series = \
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{SeriesHash[name].value[2]: SeriesHash[name].value[1] for name, _ in SeriesHash.__members__.items()}
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class_to_specificity = \
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{SpecificityHash[name].value[2]: SpecificityHash[name].value[1] for name, _ in SpecificityHash.__members__.items()}
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class DisarmingHandler:
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def __init__(self, mine: Mine):
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self.mine = mine
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self.mine_params = dict()
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self.series_img = None
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self.specificity_img = None
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self.recognized_series = None
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self.recognized_specificity = None
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self.correct_wire = None
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self.chosen_wire = None
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self._set_mine_params()
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self._set_correct_wire()
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def _set_mine_params(self):
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self.mine_params = self.mine.investigate()
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def _set_correct_wire(self):
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self.correct_wire = self.mine.wire
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def get_mine_params(self):
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return [self.mine_params["mine_type"], self.mine_params["weight"], self.mine_params["danger_cls"],
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self.mine_params["indicator"], self.mine_params["series"], self.mine_params["specificity"]]
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def pick_series_image(self):
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series_class = SeriesHash[self.mine_params["series"].upper().replace(" ", "_")].value[2]
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imgs_dir = os.path.join(SERIES_IMAGES_PATH, series_class)
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self.series_img = os.path.join(
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imgs_dir, random.choice([x for x in os.listdir(imgs_dir) if os.path.isfile(os.path.join(imgs_dir, x))]))
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return self.series_img
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def pick_specificity_image(self):
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specificity_class = SpecificityHash[self.mine_params["specificity"].upper().replace(" ", "_")].value[2]
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imgs_dir = os.path.join(SPECIFICITY_IMAGES_PATH, specificity_class)
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self.specificity_img = os.path.join(
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imgs_dir, random.choice([x for x in os.listdir(imgs_dir) if os.path.isfile(os.path.join(imgs_dir, x))]))
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return self.specificity_img
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def recognize_series(self):
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nn = NeuralNetwork(NNType.SERIES, load_from_file=True)
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answer, confidence = nn.get_answer(self.series_img)
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self.recognized_series = class_to_series[answer]
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return self.recognized_series, self.mine_params["series"] == self.recognized_series
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def recognize_specificity(self):
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nn = NeuralNetwork(NNType.SPECIFICITY, load_from_file=True)
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answer, confidence = nn.get_answer(self.specificity_img)
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self.recognized_specificity = class_to_specificity[answer]
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return self.recognized_specificity, self.mine_params["specificity"] == self.recognized_specificity
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def choose_wire(self):
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dt = DecisionTree(load_from_file=True)
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self.chosen_wire = dt.get_answer(self.mine_params)[0]
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return self.chosen_wire
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def defuse(self):
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return self.mine.disarm(self.chosen_wire)
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