implemented new disarming in auto mode
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@ -1,17 +1,18 @@
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import os
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import json
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from matplotlib import pyplot
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from joblib import dump, load
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from sklearn import tree
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from sklearn.feature_extraction import DictVectorizer
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from objects.mines.disarming.mine_parameters import MineParameters
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from objects.mines.disarming.parameter_json import generate_data
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from disarming.parameters.mine_parameters import MineParameters
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from disarming.parameters.parameter_json import generate_data
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class DecisionTree:
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def __init__(self, clf_source: str = None, vec_source: str = None):
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if clf_source is not None and vec_source is not None:
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def __init__(self, load_from_file: bool = False):
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if load_from_file:
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clf_source = r"algorithms/learn/decision_tree/decision_tree.joblib"
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vec_source = r"algorithms/learn/decision_tree/dict_vectorizer.joblib"
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self.load(clf_source, vec_source)
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else:
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self.clf = None
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@ -48,8 +49,8 @@ class DecisionTree:
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# fig.savefig("decistion_tree.png")
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def save(self):
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dump(self.clf, 'decision_tree.joblib')
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dump(self.vec, 'dict_vectorizer.joblib')
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dump(self.clf, r'algorithms/learn/decision_tree/decision_tree.joblib')
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dump(self.vec, r'algorithms/learn/decision_tree/dict_vectorizer.joblib')
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def load(self, clf_file, vec_file):
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self.clf = load(clf_file)
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@ -84,7 +85,7 @@ class DecisionTree:
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if __name__ == "__main__":
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# generate_data("training_set.txt", 12000)
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generate_data("training_set.txt", 12000)
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decision_tree = DecisionTree()
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decision_tree.build("training_set.txt", 15)
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decision_tree.test()
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@ -16,14 +16,16 @@ class NNType(Enum):
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class NeuralNetwork:
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def __init__(self, network_type: NNType, saved_model_path=None, classes_path=None, img_height=180, img_width=180):
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def __init__(self, network_type: NNType, load_from_file: bool = False, img_height=180, img_width=180):
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self.type = network_type
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self.training_data_dir = pathlib.Path(fr"../../../resources/data/neural_network/{self.type.value[0]}/train")
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self.img_height = img_height
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self.img_width = img_width
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if saved_model_path is not None and classes_path is not None:
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if load_from_file:
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saved_model_path = fr"algorithms/learn/neural_network/{self.type.value[0]}/saved_model.h5"
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classes_path = fr"algorithms/learn/neural_network/{self.type.value[0]}/saved_model_classes.joblib"
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self.load(saved_model_path, classes_path)
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else:
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self.model = None
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@ -113,10 +115,8 @@ class NeuralNetwork:
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predictions = self.model.predict(img_array)
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score = tf.nn.softmax(predictions[0])
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print(
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"This image most likely belongs to {} with a {:.2f} percent confidence."
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.format(self.class_names[np.argmax(score)], 100 * np.max(score))
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)
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# returns class, condifdence
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return self.class_names[np.argmax(score)], 100 * np.max(score)
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if __name__ == "__main__":
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@ -128,7 +128,7 @@ if __name__ == "__main__":
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# neural_network.save()
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# Loading a model from file:
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neural_network = NeuralNetwork(NNType.SPECIFICITY, r"specificity/saved_model.h5", r"specificity/saved_model_classes.joblib")
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neural_network = NeuralNetwork(NNType.SPECIFICITY, load_from_file=True)
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# Test
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image = r"../../../resources/data/neural_network/specificity/disarm/tanks/1-35-British-Tank-FV-214-Conqueror-MK-II-Amusing-Hobby-35A027-AH-35A027_b_0.JPG"
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@ -3,9 +3,9 @@ import pygame
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import project_constants as const
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from assets import asset_constants as asset
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from objects.mines.mine_models.standard_mine import StandardMine
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from objects.mines.mine_models.chained_mine import ChainedMine
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from objects.mines.mine_models.time_mine import TimeMine
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from objects.mine_models.standard_mine import StandardMine
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from objects.mine_models.chained_mine import ChainedMine
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from objects.mine_models.time_mine import TimeMine
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# ================================= #
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85
disarming/disarming_handler.py
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85
disarming/disarming_handler.py
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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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@ -23,14 +23,10 @@ class DangerClassHash(Enum):
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class SeriesHash(Enum):
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TCH_2990TONER = 128, "TCH 2990toner"
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TCH_2990INKJET = 110, "TCH 2990inkjet"
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TVY_2400H = 100, "TVY 2400h"
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SWX_5000 = 80, "SWX 5000"
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SWX_4000 = 50, "SWX 4000"
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WORKHORSE_3200 = 30, "WORKHORSE 3200"
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FX_500 = 15, "FX 500"
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TVY_2400 = 0, "TVY 2400"
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TCH_2990TONER = 128, "TCH 2990toner", "T"
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SWX_5000 = 80, "SWX 5000", "S"
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WORKHORSE_3200 = 30, "WORKHORSE 3200", "W"
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FX_500 = 15, "FX 500", "F"
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class IndicatorHash(Enum):
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@ -42,13 +38,9 @@ class IndicatorHash(Enum):
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class SpecificityHash(Enum):
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ANTI_AIRCRAFT = 55, "anti aircraft"
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ANTI_PERSONNEL = 43, "anti personnel"
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DEPTH_MINE = 37, "depth mine"
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ANTI_TANK = 26, "anti tank"
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PROXIMITY_MINE = 18, "proximity mine"
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PRESSURE_MINE = 9, "pressure mine"
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FRAGMENTATION_MINE = 0, "fragmentation mine"
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ANTI_AIRCRAFT = 55, "anti aircraft", "planes"
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DEPTH_MINE = 37, "depth mine", "ships"
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ANTI_TANK = 26, "anti tank", "tanks"
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class WeightHash(Enum):
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@ -1,5 +1,5 @@
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import random
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from objects.mines.disarming import hash_function as hf
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from disarming.parameters import hash_function as hf
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class MineParameters:
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import json
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import objects.mines.disarming.mine_parameters as param
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import disarming.parameters.mine_parameters as param
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import os
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import project_constants as const
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# this module is self contained, used to generate a json file
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DIR_DATA = os.path.join(const.ROOT_DIR, "resources", "data")
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DIR_DATA = os.path.join(const.ROOT_DIR, "resources", "data", "decision_tree")
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# just to show, how mine parameters works
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import pygame
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import pygame_gui
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from project_constants import SCREEN_WIDTH, SCREEN_HEIGHT, V_NAME_OF_WINDOW
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from assets.asset_constants import ASSET_CONCRETE
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from objects.mine_models.mine import Mine
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from disarming.disarming_handler import DisarmingHandler
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# =========== #
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# == const == #
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@ -77,7 +79,7 @@ class SampleWindow:
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# main attributes
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self.running = True
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self.clock = pygame.time.Clock()
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self.manager = pygame_gui.UIManager(screen_size, 'theme.json') # TODO : change theme path
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self.manager = pygame_gui.UIManager(screen_size, 'disarming/theme.json') # TODO : change theme path
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# main attributes
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def gui():
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@ -501,49 +503,76 @@ class SampleWindow:
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# == run == #
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# ========= #
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def run(self):
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def run(self, mine: Mine):
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timed_event = pygame.USEREVENT + 1
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pygame.time.set_timer(timed_event, 5000)
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step = 0
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handler = DisarmingHandler(mine)
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while self.running:
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time_delta = self.clock.tick(60) / 1000.0
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keystate = pygame.key.get_pressed()
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# all events except QUIT are for testing
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for event in pygame.event.get():
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if event.type == pygame.QUIT:
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self.running = False
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if keystate[pygame.K_a]:
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self.show_params("hello", "yello", "gel", "jello", "yum", "hello")
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if keystate[pygame.K_s]:
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self.show_series("workhorse 3200wx", True)
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if keystate[pygame.K_d]:
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self.show_spec("anti aircraft", False)
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if keystate[pygame.K_f]:
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self.show_cable_calculated("red")
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if keystate[pygame.K_g]:
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self.show_cable_chosen("blue")
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if keystate[pygame.K_q]:
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self.show_pic_series(ASSET_CONCRETE)
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if keystate[pygame.K_w]:
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self.show_pic_spec(ASSET_CONCRETE)
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if event.type == timed_event:
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if step == 0:
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params = handler.get_mine_params()
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self.show_params(params[0], params[1], params[2], params[3], params[4], params[5])
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elif step == 1:
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self.show_cable_calculated(handler.correct_wire)
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elif step == 2:
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# TODO:
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img = pygame.transform.scale(
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pygame.image.load(handler.pick_series_image()),
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(1080, 1080)
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)
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self.show_pic_series(img)
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elif step == 3:
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# TODO:
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img = pygame.transform.scale(
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pygame.image.load(handler.pick_specificity_image()),
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(60, 60)
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)
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self.show_pic_spec(img)
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elif step == 4:
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answer, is_correct = handler.recognize_series()
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self.show_series(answer, is_correct)
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elif step == 5:
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answer, is_correct = handler.recognize_specificity()
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self.show_spec(answer, is_correct)
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elif step == 6:
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self.show_cable_chosen(handler.choose_wire())
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else:
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self.running = False
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step += 1
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self.manager.update(time_delta)
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self.window_surface.blit(self.background, (0, 0))
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self.manager.draw_ui(self.window_surface)
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pygame.display.update()
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return handler.defuse()
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def disarming_popup():
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def disarming_popup(mine: Mine = None):
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# run the pop-up
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app = SampleWindow()
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app.run()
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result = app.run(mine)
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# bring display back to normal
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pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))
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pygame.display.set_caption(V_NAME_OF_WINDOW)
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return result
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if __name__ == '__main__':
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pygame.init()
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2
game.py
2
game.py
@ -7,7 +7,7 @@ from algorithms.search import a_star
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from minefield import Minefield
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from objects.mines.mine_models.time_mine import TimeMine
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from objects.mine_models.time_mine import TimeMine
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from ui.ui_components_manager import UiComponentsManager
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from ui.text_box import TextBox
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@ -7,9 +7,9 @@ import project_constants as const
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from objects.tile import Tile
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# import mine models
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from objects.mines.mine_models.standard_mine import StandardMine
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from objects.mines.mine_models.time_mine import TimeMine
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from objects.mines.mine_models.chained_mine import ChainedMine
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from objects.mine_models.standard_mine import StandardMine
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from objects.mine_models.time_mine import TimeMine
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from objects.mine_models.chained_mine import ChainedMine
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class JsonGenerator:
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@ -1,6 +1,6 @@
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import project_constants as const
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from objects import tile as tl, agent as ag
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from objects.mines.mine_models.time_mine import TimeMine
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from objects.mine_models.time_mine import TimeMine
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import json_generator as jg
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@ -3,6 +3,8 @@ from assets import asset_constants as asset
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import json
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from time import sleep
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from pygame import transform
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import disarming.popup as popup
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from algorithms.learn.decision_tree.decision_tree import DecisionTree
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@ -21,21 +23,16 @@ class Agent:
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self.row, self.column = int(self.row), int(self.column)
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self.position = [self.row, self.column]
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self.on_screen_coordinates = const.get_tile_coordinates(tuple(self.position))
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self.decision_tree = DecisionTree(const.ROOT_DIR + "/algorithms/learn/decision_tree/decision_tree.joblib",
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const.ROOT_DIR + "/algorithms/learn/decision_tree/dict_vectorizer.joblib")
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self.direction = const.Direction(data["agents_initial_state"]["direction"])
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self.rotation_angle = -const.Direction(self.direction).value * 90
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self.going_forward = False
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self.rotating_left = False
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self.rotating_right = False
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def defuse_a_mine(self, mine):
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mine_params = mine.investigate()
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chosen_wire = self.decision_tree.get_answer(mine_params)
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# TODO temporarily printing chosen wire
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print("agent's chosen wire: " + str(chosen_wire[0]))
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sleep(3)
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return mine.disarm(chosen_wire)
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@staticmethod
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def defuse_a_mine(mine):
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is_success = popup.disarming_popup(mine)
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return is_success
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def update_and_draw(self, window, delta_time, minefield):
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self.update(delta_time, minefield)
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@ -2,7 +2,7 @@
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from __future__ import annotations
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from .mine import Mine
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from objects.mines.disarming.hash_function import TypeHash
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from disarming.parameters.hash_function import TypeHash
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class ChainedMine(Mine):
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@ -4,7 +4,7 @@ from abc import ABC, abstractmethod
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# type hints
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from typing import Tuple
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from objects.mines.disarming.mine_parameters import MineParameters
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from disarming.parameters.mine_parameters import MineParameters
|
||||
|
||||
# Mine cannot be instantiated
|
||||
# all abstract methods must be implemented in derived classes
|
||||
@ -34,7 +34,4 @@ class Mine(ABC):
|
||||
del mine_parameters["wire"]
|
||||
self.wire = wire
|
||||
|
||||
# TODO temporarily printing parameters and right wire
|
||||
print("parameters:", mine_parameters, "\nright wire: " + wire)
|
||||
|
||||
return mine_parameters
|
||||
@ -1,5 +1,5 @@
|
||||
from .mine import Mine
|
||||
from objects.mines.disarming.hash_function import TypeHash
|
||||
from disarming.parameters.hash_function import TypeHash
|
||||
|
||||
|
||||
class StandardMine(Mine):
|
||||
@ -1,5 +1,5 @@
|
||||
from .mine import Mine
|
||||
from objects.mines.disarming.hash_function import TypeHash
|
||||
from disarming.parameters.hash_function import TypeHash
|
||||
|
||||
|
||||
class TimeMine(Mine):
|
||||
File diff suppressed because it is too large
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Reference in New Issue
Block a user