129 lines
5.4 KiB
Python
129 lines
5.4 KiB
Python
import pygame
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import random
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from constants import Constants
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from state_space_search import is_border, is_obstacle
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from night import draw_night
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from decision_tree import feed_decision
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from constants import Constants
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from classification import AnimalClassifier
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const = Constants()
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classes = [
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"bat",
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"bear",
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"elephant",
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"giraffe",
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"owl",
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"parrot",
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"penguin"
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]
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class Agent:
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def __init__(self, istate, image_path, grid_size):
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self.istate = istate
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self.x, self.y, self.direction = istate
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self.grid_size = grid_size
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self.image= pygame.image.load(image_path)
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self.image = pygame.transform.scale(self.image, (grid_size, grid_size))
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self._dryfood = 0
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self._wetfood = 0
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def draw(self, const):
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# Obróć obrazek zgodnie z kierunkiem
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if self.direction == 'E':
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self.image= pygame.image.load('images/agent4.png')
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elif self.direction == 'S':
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self.image= pygame.image.load('images/agent1.png')
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elif self.direction == 'W':
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self.image= pygame.image.load('images/agent3.png')
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else: # direction == 'N'
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self.image= pygame.image.load('images/agent2.png')
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self.image = pygame.transform.scale(self.image, (const.GRID_SIZE, const.GRID_SIZE))
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const.screen.blit(self.image, (self.x * self.grid_size, self.y * self.grid_size))
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if const.IS_NIGHT: draw_night(const)
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def handle_event(self, event, max_x, max_y, animals, obstacles,const):
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if event.type == pygame.KEYDOWN:
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if event.key == pygame.K_UP:
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self.move('Go Forward', max_x, max_y, obstacles, animals,const)
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elif event.key == pygame.K_LEFT:
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self.move('Turn Left', max_x, max_y, obstacles, animals,const)
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elif event.key == pygame.K_RIGHT:
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self.move('Turn Right', max_x, max_y, obstacles, animals,const)
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def move(self, action, max_x, max_y, obstacles, animals, goal,const):
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if action == 'Go Forward':
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new_x, new_y = self.x, self.y
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if self.direction == 'N':
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new_y -= 1
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elif self.direction == 'E':
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new_x += 1
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elif self.direction == 'S':
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new_y += 1
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elif self.direction == 'W':
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new_x -= 1
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# Sprawdź, czy nowe położenie mieści się w granicach kraty i nie jest przeszkodą
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if is_border(new_x, new_y, max_x, max_y) and not(is_obstacle(new_x, new_y, obstacles)):
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self.x, self.y = new_x, new_y
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elif action == 'Turn Left':
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self.direction = {'N': 'W', 'W': 'S', 'S': 'E', 'E': 'N'}[self.direction]
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elif action == 'Turn Right':
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self.direction = {'N': 'E', 'E': 'S', 'S': 'W', 'W': 'N'}[self.direction]
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self.istate = (self.x, self.y, self.direction)
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feed_animal(self, animals, goal,const)
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take_food(self)
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def feed_animal(self, animals, goal,const):
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goal_x, goal_y = goal
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neuron = AnimalClassifier('./model/best_model.pth', classes)
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if self.x == goal_x and self.y == goal_y:
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for animal in animals:
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if animal.x == goal_x and animal.y == goal_y:
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if (animal.activity == 'nocturnal' and const.IS_NIGHT) or (animal.activity == 'diurnal' and not(const.IS_NIGHT)):
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activity_time = True
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else:
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activity_time = False
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guests = random.randint(1, 15)
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guess = neuron.classify(animal.image_path)
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if guess == animal.name:
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print(f"I'm sure this is {guess} and i give it {animal.food} as a snack")
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animal.draw_snack(const.screen, const.GRID_SIZE, animal.x, animal.y)
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else:
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print(f"I was wrong, this is not a {guess} but a {animal.name}")
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decision = feed_decision(animal.adult, activity_time, animal.ill, const.season, guests, animal._feed, self._dryfood, self._wetfood)
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if decision != [1]:
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if decision == [2]:
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if animal.getting_hungry(const=Constants()) < self._wetfood :
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self._wetfood -= animal._feed
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animal._feed = 0
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else:
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animal._feed -= self._wetfood
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self._wetfood = 0
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print(animal.name, "fed with wet food")
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else:
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if animal.getting_hungry(const=Constants()) < self._dryfood :
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self._dryfood -= animal._feed
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animal._feed = 0
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else:
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animal._feed -= self._dryfood
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self._dryfood = 0
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print(animal.name, "fed with dry food")
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print("Current wet food level: ", self._wetfood)
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print("Current dry food level: ", self._dryfood)
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else: print(animal.name, " not fed")
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def take_food(self):
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house_x = 3
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house_y = 1
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if self.x == house_x and self.y == house_y:
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if self._dryfood < 1 or self._wetfood < 1:
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self._dryfood = 50
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self._wetfood = 50
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print("Agent took food and current food level is", self._dryfood, self._wetfood)
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