59 lines
5.4 KiB
Python
59 lines
5.4 KiB
Python
import astar
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import cart
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import definitions
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import graph
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import image_slicer
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import map
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import neuralnetwork
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import os
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import plant
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import pygame
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import station
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import treelearn
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pygame.display.set_caption("Smart Cart")
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def main():
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#tworzenie podstawowych obiektów
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map1 = map.Map([])
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map1.create_base_map()
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move_list = ["rotate_left", "move", "move", "move", "move", "move", "move", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "move", "move", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move"] #początkowe ruchy
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amount_of_seeds_dict = {"beetroot": definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE, "carrot": definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE, "potato": definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE, "wheat": definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE}
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collected_plants_dict = {"beetroot": 0, "carrot": 0, "potato": 0, "wheat": 0}
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fertilizer_dict = {"beetroot": definitions.CART_FERTILIZER, "carrot": definitions.CART_FERTILIZER, "potato": definitions.CART_FERTILIZER, "wheat": definitions.CART_FERTILIZER}
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station1 = station.Station(collected_plants_dict)
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cart1 = cart.Cart(amount_of_seeds_dict, collected_plants_dict, definitions.CART_DIRECTION_WEST, fertilizer_dict, definitions.CART_FUEL, definitions.CART_WATER_LEVEL, 0 * definitions.BLOCK_SIZE, 0 * definitions.BLOCK_SIZE)
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cart1_rect = pygame.Rect(cart1.get_x(), cart1.get_y(), definitions.BLOCK_SIZE, definitions.BLOCK_SIZE)
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clock = pygame.time.Clock()
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tree = treelearn.treelearn() #tworzenie drzewa decyzyjnego
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decision = [0] #początkowa decyzja o braku powrotu do stacji (0)
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classes, model = neuralnetwork.create_neural_network() #uczenie sieci neuronowej
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random_movement = False
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run = True
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while run: #pętla główna programu
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clock.tick(definitions.FPS)
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for event in pygame.event.get():
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if event.type == pygame.QUIT:
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run = False
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map1.draw_window(cart1, cart1_rect)
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if not move_list: #jeżeli są jakieś ruchy do wykonania w move_list
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pygame.image.save(pygame.display.get_surface(), os.path.join('resources/neural_network/sliced/', 'screen.jpg')) #zrzut obecnego ekranu
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image_slicer.slice(os.path.join('resources/neural_network/sliced/', 'screen.jpg'), 100) #pocięcie ekranu na sto części
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os.remove('resources/neural_network/sliced/screen.jpg')
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if neuralnetwork.predfield(cart1.get_direction(), cart1.get_x() / definitions.BLOCK_SIZE, cart1.get_y() / definitions.BLOCK_SIZE, classes, model) is not False: #jeżeli istnieje jakaś dojrzała roślina
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random_movement = False
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istate = graph.Istate(cart1.get_direction(), cart1.get_x() / definitions.BLOCK_SIZE, cart1.get_y() / definitions.BLOCK_SIZE) #stan początkowy wózka (jego orientacja oraz jego aktualne miejsce)
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if decision == [0]: #jeżeli decyzja jest 0 (brak powrotu do stacji) to uprawiaj pole
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move_list = (astar.graphsearch([], astar.f, [], neuralnetwork.predfield(cart1.get_direction(), cart1.get_x() / definitions.BLOCK_SIZE, cart1.get_y() / definitions.BLOCK_SIZE, classes, model), istate, map1, graph.succ)) #lista z ruchami, które należy po kolei wykonać, astar
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else: #jeżeli decyzja jest 1 (powrót do stacji) to wróć do stacji uzupełnić zapasy
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move_list = (graph.graphsearch([], [], (0, 0), istate, graph.succ)) #lista z ruchami, które należy po kolei wykonać, graphsearch
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else:
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random_movement = True
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elif move_list: #jeżeli move_list nie jest pusta
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cart1.handle_movement(cart1_rect, move_list.pop(0)) #wykonaj kolejny ruch oraz zdejmij ten ruch z początku listy
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if random_movement is not False:
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cart1.handle_movement_random(cart1_rect) #wykonuj losowe ruchy
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cart1.do_work(cart1_rect, map1, station1) #wykonaj pracę na danym polu
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decision = treelearn.make_decision(cart1.get_all_amount_of_seeds(), cart1.get_all_collected_plants(), cart1.get_all_fertilizer(), cart1.get_fuel(), tree, cart1.get_water_level()) #podejmij decyzję czy wracać do stacji (0 : NIE, 1 : TAK)
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plant.Plant.grow_plants(map1) #zwiększ poziom dojrzałości roślin
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pygame.quit()
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if __name__ == "__main__":
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main() |