156 lines
5.3 KiB
Python
156 lines
5.3 KiB
Python
# from collections import deque
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from queue import PriorityQueue
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import matplotlib.pyplot as plt
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from neural import *
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from path_algorithms.a_star import a_star
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# from path_algorithms.bfs import bfs
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from rubbish import *
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from tree import evaluate_values, trash_selection
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from truck import Truck
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from surface import *
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from PIL import Image
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from genetic import genetic
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RESOLUTION = 900
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SIZE = 60
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pygame.init()
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screen = pygame.display.set_mode([RESOLUTION, RESOLUTION])
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truck = Truck(screen)
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surface_list = []
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rubbish_list = []
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refused_rubbish_list = []
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# x and y are swapped on display in pygame
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# matrix for display
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matrix = [[0, 1, 1, 2, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 3, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 3, 0, 0, 1, 2, 3, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 3, 0, 2, 1, 5, 3, 0, 5, 0, 0, 0, 0, 0],
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[0, 0, 0, 5, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],
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[3, 3, 3, 0, 0, 0, 2, 5, 0, 5, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
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]
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for i in range(15):
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for j in range(15):
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if matrix[i][j] == 0:
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surface_list.append(Grass(screen, j * 60, i * 60))
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if matrix[i][j] == 1:
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surface_list.append(Sand(screen, j * 60, i * 60))
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if matrix[i][j] == 2:
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surface_list.append(Rock(screen, j * 60, i * 60))
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if matrix[i][j] == 3:
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surface_list.append(Water(screen, j * 60, i * 60))
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if matrix[i][j] == 5:
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surface_list.append(Grass(screen, j * 60, i * 60))
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rubbish_list.append(Rubbish(screen, j * 60, i * 60))
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path = []
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X,y = create_training_data()
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model = learn_neural_network(X,y)
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gen = [(truck.y / 60, truck.x / 60)]
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fl = 0
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length = []
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finalLength = []
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order = []
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while True:
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pygame.time.delay(500)
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# drawing on screen
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for i in surface_list:
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i.draw_surface()
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for i in rubbish_list:
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i.draw_rubbish()
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for i in refused_rubbish_list:
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i.draw_rubbish()
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truck.draw_truck()
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# finding order to collect rubbish
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if fl == 0:
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for item in rubbish_list:
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print(item.y / 60, item.x / 60, end='\n')
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gen.append((item.y / 60, item.x / 60))
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for item1 in range(len(gen)):
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for item2 in range(len(gen)):
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if item1 < item2:
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length.append(len(a_star(surface_list, gen[item2]).tree_search(PriorityQueue(), gen[item1], 'R')))
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else:
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length.append(0)
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finalLength.append(length)
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length = []
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fl = 1
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for i in range(len(finalLength)):
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for j in range(len(finalLength)):
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if i > j:
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finalLength[i][j] = finalLength[j][i]
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for i in range(len(finalLength)):
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for j in range(len(finalLength)):
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print(finalLength[i][j], end=',')
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print('')
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print(finalLength)
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order = genetic(finalLength).search()
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order = list(map(int, order))
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order.pop(0)
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for j in range(len(order)):
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order[j] -= 1
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# finding a path to rubbish
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if order and not path:
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start = (truck.y / 60, truck.x / 60)
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direction = truck.direction
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currentRubbish = rubbish_list[order[0]]
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endpoint = (currentRubbish.y / 60, currentRubbish.x / 60)
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# path = bfs(surface_list, endpoint).tree_search(deque(), start, direction)
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path = a_star(surface_list, endpoint).tree_search(PriorityQueue(), start, direction)
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# do an action
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if path:
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action = path.pop(0)
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if action == 'M':
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truck.move()
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else:
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truck.change_direction(action)
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# the decision that takes what to do with the garbage
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if not path and order:
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number = np.random.randint(2077)
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path_img = "images/bbb"
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img = Image.open(path_img+'/'+str(number)+'.jpg')
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img.show()
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prediction = predict(model,path_img+'/'+str(number)+'.jpg')
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result(prediction)
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data = rubbish_list[order[0]].data_for_decision_tree()
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print(f'----------\n'
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f'Characteristics of the garbage we met:\n'
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f'Weight:{data[0]}\nDensity:{data[1]}\n'
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f'Fragility:{data[2]}\nMaterial:{data[3]}\n'
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f'Size:{data[4]}\nDegradability:{data[4]}\n'
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f'Renewability:{data[5]}\n'
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f'----------')
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decision = trash_selection(evaluate_values(data))
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if decision == [0]:
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print('We refused this rubbish because of bad characteristics')
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rubbish_list[order[0]].rubbish_refused()
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refused_rubbish_list.append(rubbish_list[order[0]])
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else:
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print('We take this rubbish because of good characteristics')
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order.pop(0)
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pygame.display.flip()
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for event in pygame.event.get():
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if event.type == pygame.QUIT:
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exit()
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