134 lines
4.6 KiB
Python
134 lines
4.6 KiB
Python
import pygame
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import random
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import pandas as pd
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from sklearn import tree
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from sklearn.preprocessing import LabelEncoder
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import graphviz
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from astar import astar
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from state import State
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import time
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from garbage_truck import GarbageTruck
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from heuristicfn import heuristicfn
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pygame.init()
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WIDTH, HEIGHT = 800, 800
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window = pygame.display.set_mode((WIDTH, HEIGHT))
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pygame.display.set_caption("Intelligent Garbage Collector")
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AGENT_IMG = pygame.image.load("garbage-truck-nbg.png")
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AGENT = pygame.transform.scale(AGENT_IMG, (50, 50))
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DIRT_IMG = pygame.image.load("dirt.jpg")
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DIRT = pygame.transform.scale(DIRT_IMG, (50, 50))
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GRASS_IMG = pygame.image.load("grass.png")
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GRASS = pygame.transform.scale(GRASS_IMG, (50, 50))
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SAND_IMG = pygame.image.load("sand.jpeg")
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SAND = pygame.transform.scale(SAND_IMG, (50, 50))
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COBBLE_IMG = pygame.image.load("cobble.jpeg")
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COBBLE = pygame.transform.scale(COBBLE_IMG, (50, 50))
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FPS = 10
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FIELDCOUNT = 16
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FIELDWIDTH = 50
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class Agent:
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def __init__(self, rect, direction):
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self.rect = rect
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self.direction = direction
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def randomize_map(): # tworzenie mapy z losowymi polami
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field_array_1 = []
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field_array_2 = []
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field_priority = []
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for i in range(16):
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temp_priority = []
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for j in range(16):
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if i in (0, 1) and j in (0, 1):
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field_array_2.append(GRASS)
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temp_priority.append(1)
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else:
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prob = random.uniform(0, 100)
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if 0 <= prob <= 12:
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field_array_2.append(COBBLE)
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temp_priority.append(3)
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elif 12 < prob <= 24:
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field_array_2.append(SAND)
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temp_priority.append(2)
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else:
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field_array_2.append(GRASS)
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temp_priority.append(1)
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field_array_1.append(field_array_2)
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field_array_2 = []
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field_priority.append(temp_priority)
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return field_array_1, field_priority
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def draw_window(agent, fields, flip):
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if flip:
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direction = pygame.transform.flip(AGENT, True, False)
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else:
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direction = pygame.transform.flip(AGENT, False, False)
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for i in range(16):
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for j in range(16):
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window.blit(fields[i][j], (i * 50, j * 50))
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window.blit(direction, (agent.rect.x, agent.rect.y)) # wyswietlanie agenta
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pygame.display.update()
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def main():
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train_data = pd.read_csv('./data_set.csv')
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attributes = train_data.drop('collect', axis='columns')
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e_type = LabelEncoder()
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attributes['type_num'] = e_type.fit_transform(attributes['garbage_type'])
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attr_encoded = attributes.drop(['garbage_type'], axis='columns')
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attr_names = ['fuel','distance','space_occupied','days_since_last_collection','paid_on_time','odour_intensity','garbage_weight', 'garbage_type']
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label_names = ['collect', 'no-collect']
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label = train_data['collect']
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print(attr_encoded)
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print(label)
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classifier = tree.DecisionTreeClassifier()
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classifier.fit(attr_encoded, label)
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dot_data = tree.export_graphviz(classifier, out_file=None, feature_names=attr_names, class_names=label_names)
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graph = graphviz.Source(dot_data)
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graph.render('collect')
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clock = pygame.time.Clock()
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run = True
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x, y = [0, 0]
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agent = GarbageTruck(0, 0, pygame.Rect(x, y, 50, 50), 0) # tworzenie pola dla agenta
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fields, priority_array = randomize_map()
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final_x, final_y = [100, 300]
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while run:
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clock.tick(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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# keys_pressed = pygame.key.get_pressed()
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draw_window(agent, fields, False) # false = kierunek east (domyslny), true = west
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steps = astar(State(None, None, x, y, 'E', priority_array[0][0], heuristicfn(x, y, final_x, final_y)), final_x, final_y, priority_array)
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for interm in steps:
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if interm.action == 'LEFT':
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agent.turn_left()
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draw_window(agent, fields, True)
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elif interm.action == 'RIGHT':
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agent.turn_right()
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draw_window(agent, fields, False)
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elif interm.action == 'FORWARD':
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agent.forward()
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if agent.orientation == 0:
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draw_window(agent, fields, False)
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elif agent.orientation == 2:
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draw_window(agent, fields, True)
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else:
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draw_window(agent, fields, False)
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time.sleep(0.5)
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while True:
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pass
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pygame.quit()
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if __name__ == "__main__":
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main()
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