integrating neural network with project
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@ -35,14 +35,14 @@ VERTICAL_NUM_OF_FIELDS = 3
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HORIZONTAL_NUM_OF_FIELDS = 3
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```
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\
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#####4.1 Save generated map:
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####4.1 Save generated map:
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```bash
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python main.py --save-map
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```
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Map will be saved in maps directory.
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Generated filename: map-uuid
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#####4.2 Load map
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####4.2 Load map
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```bash
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python main.py --load-map=name_of_map
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```
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@ -42,21 +42,21 @@ class App:
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def keys_pressed_handler(self):
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keys = pygame.key.get_pressed()
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if keys[pygame.K_m]:
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self.__tractor.move()
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print(self.__tractor)
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if keys[pygame.K_w]:
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self.__tractor.move()
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print(self.__tractor)
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if keys[pygame.K_n]:
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self.__bot_is_running.set()
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self.__tractor.harvest_checked_fields_handler(self.__bot_is_running)
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if keys[pygame.K_h]:
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self.__tractor.harvest()
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if keys[pygame.K_v]:
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self.__tractor.sow()
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if keys[pygame.K_n]:
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if keys[pygame.K_j]:
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self.__tractor.hydrate()
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if keys[pygame.K_f]:
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@ -7,6 +7,9 @@ class BaseField:
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def __init__(self, img_path: str):
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self._img_path = img_path
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def get_img_path(self):
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return self._img_path
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def draw_field(self, screen: pygame.Surface, pos_x: int,
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pos_y: int, is_centered: bool = False,
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size: tuple = None, angle: float = 0.0) -> None:
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113
app/neural_network.py
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113
app/neural_network.py
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@ -0,0 +1,113 @@
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#!/usr/bin/python3
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import os
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from tensorflow.keras.models import Sequential, save_model, load_model
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from tensorflow.keras.layers import Dense, Flatten, Conv2D
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from tensorflow.keras.losses import sparse_categorical_crossentropy
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from tensorflow.keras.optimizers import Adam
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from tensorflow.keras.preprocessing.image import ImageDataGenerator
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from tensorflow import keras as k
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import numpy as np
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from app.base_field import BaseField
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from config import *
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class NeuralNetwork:
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def __init__(self):
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# Model config
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self.batch_size = 25
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self.img_width, self.img_height, self.img_num_channels = 25, 25, 3
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self.loss_function = sparse_categorical_crossentropy
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self.no_classes = 7
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self.no_epochs = 40
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self.optimizer = Adam()
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self.verbosity = 1
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# Determine shape of the data
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self.input_shape = (self.img_width, self.img_height, self.img_num_channels)
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# labels
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self.labels = ["cabbage", "carrot", "corn", "lettuce", "paprika", "potato", "tomato"]
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def init_model(self):
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if not self.model_dir_is_empty():
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# Load the model
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self.model = load_model(
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os.path.join(RESOURCE_DIR, "saved_model"),
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custom_objects=None,
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compile=True
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)
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else:
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# Create the model
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self.model = Sequential()
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self.model.add(Conv2D(16, kernel_size=(5, 5), activation='relu', input_shape=self.input_shape))
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self.model.add(Conv2D(32, kernel_size=(5, 5), activation='relu'))
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self.model.add(Conv2D(64, kernel_size=(5, 5), activation='relu'))
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self.model.add(Conv2D(128, kernel_size=(5, 5), activation='relu'))
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self.model.add(Flatten())
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self.model.add(Dense(16, activation='relu'))
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self.model.add(Dense(self.no_classes, activation='softmax'))
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# Display a model summary
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self.model.summary()
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def load_images(self):
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# Create a generator
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self.train_datagen = ImageDataGenerator(
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rescale=1. / 255
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)
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self.train_datagen = self.train_datagen.flow_from_directory(
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TRAINING_SET_DIR,
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save_to_dir=ADAPTED_IMG_DIR,
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save_format='jpeg',
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batch_size=self.batch_size,
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target_size=(25, 25),
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class_mode='sparse')
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def train(self):
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self.model.compile(loss=self.loss_function,
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optimizer=self.optimizer,
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metrics=['accuracy'])
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# Start training
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self.model.fit(
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self.train_datagen,
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epochs=self.no_epochs,
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shuffle=False)
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def predict(self, field: BaseField) -> str:
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print(field.get_img_path())
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# corn_img_path = os.path.join(RESOURCE_DIR,'corn.png')
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loaded_image = k.preprocessing.image.load_img(field.get_img_path(),
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target_size=(
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self.img_width, self.img_height, self.img_num_channels))
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# convert to array and resample dividing by 255
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img_array = k.preprocessing.image.img_to_array(loaded_image) / 255.
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# add sample dimension. the predictor is expecting (1, CHANNELS, IMG_WIDTH, IMG_HEIGHT)
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img_np_array = np.expand_dims(img_array, axis=0)
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# print(img_np_array)
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predictions = self.model.predict(img_np_array)
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prediction = np.argmax(predictions[0])
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label = self.labels[prediction]
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print(f'Ground truth: {type(field).__name__} - Prediction: {label}')
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return label
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def model_dir_is_empty(self) -> bool:
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if len(os.listdir(MODEL_DIR)) == 0:
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return True
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return False
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def check(self, field: BaseField) -> str:
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self.load_images()
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self.init_model()
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prediction = self.predict(field)
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# Saving model
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if not self.model_dir_is_empty():
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save_model(self.model, MODEL_DIR)
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return prediction
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@ -11,6 +11,7 @@ from typing import Union
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from app.base_field import BaseField
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from app.board import Board
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from app.neural_network import NeuralNetwork
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from app.utils import get_class
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from app.fields import CROPS, PLANTS, Crops, Sand, Clay, Field
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from config import *
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@ -28,6 +29,7 @@ class Tractor(BaseField):
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self.__board = board
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self.__harvested_corps = []
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self.__fuel = 10
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self.__neural_network = None
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def draw(self, screen: pygame.Surface) -> None:
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self.draw_field(screen, self.__pos_x + FIELD_SIZE / 2, self.__pos_y + FIELD_SIZE / 2,
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@ -185,7 +187,7 @@ class Tractor(BaseField):
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def run_bot(self, moves: list[tuple[str, str]], is_running: threading.Event) -> None:
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print(moves)
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print(f"Length of Moves {len(moves)}") #- {3 ** len(moves)}")
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print(f"Length of Moves {len(moves)}") # - {3 ** len(moves)}")
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while len(moves) > 0:
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movement, action = moves.pop(0)
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# do action
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@ -203,6 +205,12 @@ class Tractor(BaseField):
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time.sleep(1)
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# move
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self.move_or_rotate(movement)
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time.sleep(TIME_OF_MOVING)
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is_running.clear()
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def move_or_rotate(self, movement: str):
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print(f"Move {movement}")
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if movement == M_GO_FORWARD:
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self.move()
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@ -211,9 +219,6 @@ class Tractor(BaseField):
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elif movement == M_ROTATE_RIGHT:
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self.rotate_right()
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time.sleep(TIME_OF_MOVING)
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is_running.clear()
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@staticmethod
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def move_is_correct(x: int, y: int, direction: float) -> Union[(int, int), None]:
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pos_x = x * FIELD_SIZE
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@ -261,3 +266,26 @@ class Tractor(BaseField):
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obj = get_class("app.fields", choosen_type)
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board.get_fields()[x][y] = obj()
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return obj()
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def harvest_checked_fields_handler(self, is_running: threading.Event):
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thread = threading.Thread(target=self.harvest_checked_fields, args=(is_running,), daemon=True)
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thread.start()
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def harvest_checked_fields(self, is_running: threading.Event):
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moves = [M_GO_FORWARD, M_ROTATE_LEFT, M_ROTATE_RIGHT]
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distribution=[0.6,0.2,0.2]
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while True:
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field = self.get_field_from_board()
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self.__neural_network = NeuralNetwork()
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prediction = self.__neural_network.check(field)
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if prediction.capitalize() in CROPS:
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self.harvest()
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break
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chosen_move = random.choices(moves,distribution)
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self.move_or_rotate(chosen_move[0])
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time.sleep(1)
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is_running.clear()
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@ -12,7 +12,8 @@ __all__ = (
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'A_SOW', 'A_HARVEST', 'A_HYDRATE', 'A_FERTILIZE', 'A_DO_NOTHING',
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'D_NORTH', 'D_EAST', 'D_SOUTH', 'D_WEST',
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'VALUE_OF_CROPS', 'VALUE_OF_PLANT', 'VALUE_OF_SAND', 'VALUE_OF_CLAY',
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'MAP_FILE_NAME', 'JSON','SAVE_MAP', 'LOAD_MAP'
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'MAP_FILE_NAME', 'JSON', 'SAVE_MAP', 'LOAD_MAP',
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'TRAINING_SET_DIR', 'TEST_SET_DIR', 'ADAPTED_IMG_DIR', 'MODEL_DIR'
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)
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# Board settings:
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@ -26,11 +27,15 @@ HEIGHT = VERTICAL_NUM_OF_FIELDS * FIELD_SIZE
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FPS = 10
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CAPTION = 'Tractor'
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# Path
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# Paths
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BASE_DIR = os.path.dirname(__file__)
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RESOURCE_DIR = os.path.join(BASE_DIR, 'resources')
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MAP_DIR = os.path.join(BASE_DIR, 'maps')
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MAP_FILE_NAME = 'map'
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TRAINING_SET_DIR = os.path.join(RESOURCE_DIR, 'smaller_train')
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TEST_SET_DIR = os.path.join(RESOURCE_DIR, 'smaller_test')
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ADAPTED_IMG_DIR = os.path.join(RESOURCE_DIR, "adapted-images")
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MODEL_DIR = os.path.join(RESOURCE_DIR, 'saved_model')
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# Picture format
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PNG = "png"
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BIN
requirements.txt
BIN
requirements.txt
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@ -9,5 +9,5 @@
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“root.layer-4"_tf_keras_layer*é{"name": "flatten", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "shared_object_id": 13, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 1, "axes": {}}, "shared_object_id": 26}}2
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Êroot.layer_with_weights-4"_tf_keras_layer*“{"name": "dense", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 16, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 14}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 15}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 16, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 10368}}, "shared_object_id": 27}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 10368]}}2
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Êroot.layer_with_weights-5"_tf_keras_layer*“{"name": "dense_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 7, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 17}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 18}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 19, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 16}}, "shared_object_id": 28}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16]}}2
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¹croot.keras_api.metrics.0"_tf_keras_metric*‚{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 29}2
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ódroot.keras_api.metrics.1"_tf_keras_metric*¼{"class_name": "MeanMetricWrapper", "name": "accuracy", "dtype": "float32", "config": {"name": "accuracy", "dtype": "float32", "fn": "sparse_categorical_accuracy"}, "shared_object_id": 22}2
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¹froot.keras_api.metrics.0"_tf_keras_metric*‚{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 29}2
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ógroot.keras_api.metrics.1"_tf_keras_metric*¼{"class_name": "MeanMetricWrapper", "name": "accuracy", "dtype": "float32", "config": {"name": "accuracy", "dtype": "float32", "fn": "sparse_categorical_accuracy"}, "shared_object_id": 22}2
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