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Author SHA1 Message Date
10ac917df3 Merge pull request 'Genetic-algorythm' (#29) from Genetic-algorythm into main
Reviewed-on: #29
Reviewed-by: Nastassia Zhuravel <naszhu@st.amu.edu.pl>
2023-06-16 15:13:36 +02:00
Mateusz Dokowicz
a404e70058 mutations geneticAlg 2023-06-16 03:22:30 +02:00
Mateusz Dokowicz
74fd6d9263 crossover working version 2023-06-16 00:48:59 +02:00
Mateusz Dokowicz
8b1e390e6b Improvement on code + path creation 2023-06-15 16:46:08 +02:00
Mateusz Dokowicz
af7027a90f neural network config 2023-06-14 19:59:23 +02:00
10 changed files with 462 additions and 95 deletions

3
.gitignore vendored
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@ -4,4 +4,5 @@
__pycache__
#PyCharm
.idea/
.idea/
AI_brain/model.h5

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@ -0,0 +1,281 @@
import copy
import random
import configparser
import math
import pygame
from domain.entities.entity import Entity
config = configparser.ConfigParser()
config.read("config.ini")
from domain.world import World
from AI_brain.rotate_and_go_aStar import RotateAndGoAStar, State
from random import randint
hits = 0
misses = 0
class Cashed_sub_paths(dict):
def __init__(self):
super().__init__()
def __missing__(self, key):
self[key] = Cashed_sub_paths()
return self[key]
class Cashed_sub_path:
def __init__(self, sub_path: list[str] = [], distance: int = 0):
self.sub_path = sub_path
self.distance = distance
steps_distance_cashed: dict[tuple[int, int], Cashed_sub_path] = Cashed_sub_paths()
class Path:
def __init__(self):
self.walk = []
self.permutation = []
self.real_path = []
self.distance = 0
def random_walk(self, dusts: list[Entity]):
permutation = generate_random_permutation(len(dusts))
self.permutation = permutation
self.walk = addStartAndStation(permutation)
def calculate_distance(self, world: World):
distance = 0
for i in range(len(self.walk) - 1):
next_distance, next_real_path = self.step_distance(
self.walk[i], self.walk[i + 1], world
)
distance += next_distance
# BUG this part is not working and is not used, B.1 must be resolved
self.real_path = self.real_path + ["DEFAULT_ROTATION"] + next_real_path
self.distance = distance
def step_distance(
self, from_id: int, to_id: int, world: World
) -> tuple[int, list[str]]:
global hits, misses
if (from_id, to_id) in steps_distance_cashed:
hits += 1
distance = steps_distance_cashed[(from_id, to_id)].distance
sub_path = steps_distance_cashed[(from_id, to_id)].sub_path
return distance, sub_path
misses += 1
path_searcher = RotateAndGoAStar(
world,
self.getPosition(from_id, world.dustList),
self.getPosition(to_id, world.dustList),
)
path_searcher.search()
steps_distance_cashed[(from_id, to_id)] = Cashed_sub_path(
path_searcher.actions, path_searcher.cost
)
# BUG B.1 inverse path
inverse_sub_path = path_searcher.actions.copy()
steps_distance_cashed[(to_id, from_id)] = Cashed_sub_path(
inverse_sub_path, path_searcher.cost
)
return path_searcher.cost, path_searcher.actions
def inverse_sub_path(sub_path: list[str]) -> list[str]:
sub_path.reverse()
for command in sub_path:
command.replace("RL", "RR")
command.replace("RR", "RR")
def getPosition(
self,
number: int,
dustList: list[Entity],
) -> State:
if number == -1:
dock_start_x, dock_start_y = config.get(
"CONSTANT", "DockStationStartPosition"
).split(",")
dock_start_x, dock_start_y = int(dock_start_x), int(dock_start_y)
return State(dock_start_x, dock_start_y)
if number == -2:
vacuum_start_x, vacuum_start_y = config.get(
"CONSTANT", "RobotStartPosition"
).split(",")
vacuum_start_x, vacuum_start_y = int(vacuum_start_x), int(vacuum_start_y)
return State(vacuum_start_x, vacuum_start_y)
return State(dustList[number].x, dustList[number].y)
def get_real_path(self, world: World):
full_path = []
for index_place in range(len(self.walk) - 1):
path_searcher = RotateAndGoAStar(
world,
self.getPosition(self.walk[index_place], world.dustList),
self.getPosition(self.walk[index_place + 1], world.dustList),
)
path_searcher.search()
full_path = full_path + ["DEFAULT_ROTATION"] + path_searcher.actions
self.real_path = full_path
def generate_random_permutation(n):
# Create a list of numbers from 1 to n
numbers = list(range(0, n))
# Shuffle the list using the random.shuffle function
random.shuffle(numbers)
return numbers
# BUG solution: inverse direction at the last step
def addStartAndStation(permutation: list[int]):
frequency = math.ceil(100 / config.getint("CONSTANT", "BananaFilling"))
numer_of_stops = math.ceil(len(permutation) / frequency)
walk = permutation.copy()
for i in range(1, numer_of_stops):
walk.insert((frequency + 1) * i - 1, -1)
walk.insert(len(walk), -1)
walk.insert(0, -2)
return walk
class GeneticAlgorytm:
def __init__(self, world: World):
self.world = world
self.population_size = config.getint("GENETIC_ALGORITHM", "PopulationSize")
self.mutation_probability = config.getfloat(
"GENETIC_ALGORITHM", "MutationProbability"
)
self.iteration_number = config.getint("GENETIC_ALGORITHM", "IterationNumber")
self.descendants_number = config.getint(
"GENETIC_ALGORITHM", "DescendantsNumber"
)
self.dusts = world.dustList
self.doc_station = world.doc_station
self.paths: list[Path] = []
self.checked_permutations = {}
self.best_path = None
self.best_distance = math.inf
self.best_real_path = []
def generate_population(self):
for i in range(self.population_size):
path = Path()
path.random_walk(self.dusts)
self.checked_permutations[tuple(path.permutation)] = True
path.calculate_distance(self.world)
self.paths.append(path)
def print_top(self):
print(
"Best path: ",
self.best_path.walk,
"Distance: ",
self.best_path.distance,
)
for path in self.paths[1:]:
print(path.walk, path.distance)
def evaluate_population(self):
self.paths.sort(key=lambda x: x.distance, reverse=False)
self.best_distance = self.paths[0].distance
self.best_path = self.paths[0]
for path in self.paths[self.population_size :]:
del self.checked_permutations[tuple(path.permutation)]
self.paths = self.paths[: self.population_size]
def create_child(self, parent1: Path, parent2: Path) -> Path:
child = Path()
child.permutation = parent1.permutation[: len(parent1.permutation) // 2]
# Add missing items from parent2 in the order they appear
for item in parent2.permutation:
if item not in child.permutation:
child.permutation.append(item)
child.walk = addStartAndStation(child.permutation)
child.calculate_distance(self.world)
return child
def run(self):
self.generate_population()
for i in range(self.iteration_number):
self.crossover()
self.evaluate_population()
self.best_real_path = self.paths[0].get_real_path(self.world)
print(hits, (misses + hits))
print(hits / (misses + hits))
def mutate(self, mutant: Path) -> Path:
random_number = randint(0, len(mutant.permutation) - 1)
random_number2 = random_number
while random_number == random_number2:
random_number2 = randint(0, len(mutant.permutation) - 1)
mutant.permutation[random_number], mutant.permutation[random_number2] = (
mutant.permutation[random_number2],
mutant.permutation[random_number],
)
if tuple(mutant.permutation) in self.checked_permutations:
return self.mutate(mutant)
mutant.walk = addStartAndStation(mutant.permutation)
mutant.calculate_distance(self.world)
return mutant
def crossover(self):
for i in range(self.descendants_number):
parent1 = self.paths[random.randint(0, self.population_size - 1)]
parent2 = self.paths[random.randint(0, self.population_size - 1)]
child = self.create_child(parent1, parent2)
while tuple(child.permutation) in self.checked_permutations:
parent1 = self.paths[random.randint(0, self.population_size - 1)]
parent2 = self.paths[random.randint(0, self.population_size - 1)]
child = self.create_child(parent1, parent2)
self.checked_permutations[tuple(child.permutation)] = True
self.paths.append(child)
mutant = Path()
mutant.permutation = child.permutation.copy()
mutant = self.mutate(mutant)
self.checked_permutations[tuple(mutant.permutation)] = True
self.paths.append(mutant)
self.evaluate_population()

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@ -5,18 +5,19 @@ from tensorflow import keras
import cv2
import random
#You can download model from https://uam-my.sharepoint.com/:f:/g/personal/pavbia_st_amu_edu_pl/EmBHjnETuk5LiCZS6xk7AnIBNsnffR3Sygf8EX2bhR1w4A
#Change the path to model + to datasets (string 12 + strings 35,41,47,53)
# You can download model from https://uam-my.sharepoint.com/:f:/g/personal/pavbia_st_amu_edu_pl/EmBHjnETuk5LiCZS6xk7AnIBNsnffR3Sygf8EX2bhR1w4A
# Change the path to model + to datasets (string 12 + strings 35,41,47,53)
class VacuumRecognizer:
model = keras.models.load_model('AI_brain\model.h5') #Neuron model path
model = keras.models.load_model("AI_brain\model.h5") # Neuron model path
def recognize(self, image_path) -> str:
class_names = ['Banana', 'Cat', 'Earings', 'Plant']
class_names = ["Banana", "Cat", "Earings", "Plant"]
img = cv2.imread(image_path, flags=cv2.IMREAD_GRAYSCALE)
cv2.waitKey(0)
img = (np.expand_dims(img, 0))
img = np.expand_dims(img, 0)
predictions = self.model.predict(img)[0].tolist()
@ -31,31 +32,43 @@ class VacuumRecognizer:
return class_names[predictions.index(max(predictions))]
def get_random_dir(self, type) -> str:
if type == 'Plant':
plant_image_paths = 'C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Plant' #Plant dataset path
if type == "Plant":
plant_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Plant" # Plant dataset path
plant_dirs = os.listdir(plant_image_paths)
full_path = plant_image_paths + '\\' + plant_dirs[random.randint(0, len(plant_dirs)-1)]
full_path = (
plant_image_paths
+ "\\"
+ plant_dirs[random.randint(0, len(plant_dirs) - 1)]
)
print(full_path)
return full_path
elif type == 'Earings':
earnings_image_paths = 'C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Earings' #Earings dataset path
elif type == "Earings":
earnings_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Earings" # Earings dataset path
earning_dirs = os.listdir(earnings_image_paths)
full_path = earnings_image_paths + '\\' + earning_dirs[random.randint(0, len(earning_dirs)-1)]
full_path = (
earnings_image_paths
+ "\\"
+ earning_dirs[random.randint(0, len(earning_dirs) - 1)]
)
print(full_path)
return full_path
elif type == 'Banana':
banana_image_paths = 'C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Banana' #Banana dataset path
elif type == "Banana":
banana_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Banana" # Banana dataset path
banana_dirs = os.listdir(banana_image_paths)
full_path = banana_image_paths + '\\' + banana_dirs[random.randint(0, len(banana_dirs)-1)]
full_path = (
banana_image_paths
+ "\\"
+ banana_dirs[random.randint(0, len(banana_dirs) - 1)]
)
print(full_path)
return full_path
elif type == 'Cat':
cat_image_paths = 'C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Cat' #Cat dataset path
elif type == "Cat":
cat_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Cat" # Cat dataset path
cat_dir = os.listdir(cat_image_paths)
#For testing the neuron model
'''image_paths = []
# For testing the neuron model
"""image_paths = []
image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Banana')
image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Cat')
image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Earings')
@ -65,4 +78,4 @@ uio = VacuumRecognizer()
for image_path in image_paths:
dirs = os.listdir(image_path)
for i in range(3):
print(uio.recognize(image_path + '\\' + dirs[random.randint(0, len(dirs)-1)]))'''
print(uio.recognize(image_path + '\\' + dirs[random.randint(0, len(dirs)-1)]))"""

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@ -3,11 +3,10 @@ from domain.world import World
class State:
def __init__(self, x, y, direction=(1, 0), entity=None):
def __init__(self, x: int, y: int, direction=(1, 0), entity=None):
self.x = x
self.y = y
self.direction = direction
def __hash__(self):
return hash((self.x, self.y))
@ -19,7 +18,7 @@ class State:
and self.direction == other.direction
)
def heuristic(self, goal_state):
def heuristic(self, goal_state) -> int:
return abs(self.x - goal_state.x) + abs(self.y - goal_state.y)
@ -53,19 +52,19 @@ class RotateAndGoAStar:
self.enqueued_states = set()
self.explored = set()
self.actions = []
self.cost = 0
def get_g_score(self, state):
def get_g_score(self, state) -> int:
return self.world.get_cost(state.x, state.y)
def search(self):
heapq.heappush(
self.fringe, Node(self.start_state, 0, self.goal_state)
)
heapq.heappush(self.fringe, Node(self.start_state, 0, self.goal_state))
while self.fringe:
elem = heapq.heappop(self.fringe)
elem: Node = heapq.heappop(self.fringe)
if self.is_goal(elem.state):
self.actions = action_sequence(elem)
self.cost = elem.g_score
return True
self.explored.add(elem.state)
@ -73,7 +72,7 @@ class RotateAndGoAStar:
if state in self.explored:
continue
new_g_score = new_g_score = elem.g_score + self.world.get_cost(state.x, state.y)
new_g_score = elem.g_score + self.world.get_cost(state.x, state.y)
if state not in self.enqueued_states:
next_node = Node(state, new_g_score, self.goal_state)
next_node.action = action
@ -84,12 +83,12 @@ class RotateAndGoAStar:
for node in self.fringe:
if node.state == state:
node.g_score = new_g_score
node.f_score = (
new_g_score + node.state.heuristic(self.goal_state)
node.f_score = new_g_score + node.state.heuristic(
self.goal_state
)
node.parent = elem
node.action = action
heapq.heapify(self.fringe)
heapq.heapify(self.fringe)
break
return False
@ -102,12 +101,12 @@ class RotateAndGoAStar:
next_x = state.x + state.direction[0]
next_y = state.y + state.direction[1]
if self.world.accepted_move(next_x, next_y):
new_successors.append(
("GO", State(next_x, next_y, state.direction))
)
new_successors.append(("GO", State(next_x, next_y, state.direction)))
return new_successors
def is_goal(self, state: State) -> bool:
return (
state.x == self.goal_state.x
and state.y == self.goal_state.y )
return state.x == self.goal_state.x and state.y == self.goal_state.y
def number_of_moves_forward(self) -> int:
go_count = self.actions.count("GO")
return go_count

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@ -4,9 +4,23 @@ movement = robot
#accept: human, robot
[CONSTANT]
NumberOfBananas = 10
NumberOfEarrings = 3
NumberOfBananas = 15
NumberOfEarrings = 0
NumberOfPlants = 5
BananaFilling = 25
RobotStartPosition = 5, 5
DockStationStartPosition = 5, 6
#9,8
[NEURAL_NETWORK]
is_nural_network_off = True
is_neural_network_off = True
[AI_BRAIN]
mode = full_clean
#accept: full_clean, to_station
[GENETIC_ALGORITHM]
PopulationSize = 20
DescendantsNumber = 6
MutationProbability = 0.3
IterationNumber = 1_000

View File

@ -21,12 +21,13 @@ class VacuumMoveCommand(Command):
if not self.world.accepted_move(end_x, end_y):
return
tmp = self.world.is_garbage_at(end_x, end_y)
if len(tmp) > 0:
for t in tmp:
garbage = self.world.garbage_at(end_x, end_y)
if len(garbage) > 0:
for item in garbage:
if self.vacuum.get_container_filling() < 100:
self.vacuum.increase_container_filling()
self.world.dust[end_x][end_y].remove(t)
self.world.delete_entities_at_Of_type(item.x, item.y, item.type)
self.world.dust[end_x][end_y].remove(item)
if self.world.is_docking_station_at(end_x, end_y):
self.vacuum.dump_trash()

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@ -1,5 +1,9 @@
from domain.entities.entity import Entity
from domain.world import World
import configparser
config = configparser.ConfigParser()
config.read("config.ini")
class Vacuum(Entity):
@ -11,7 +15,7 @@ class Vacuum(Entity):
self.container_filling = 0
def increase_container_filling(self) -> None:
self.container_filling += 5
self.container_filling += config.getint("CONSTANT", "BananaFilling")
def dump_trash(self) -> None:
self.container_filling = 0

View File

@ -8,7 +8,9 @@ class World:
self.width = width
self.height = height
self.dust = [[[] for j in range(height)] for i in range(width)]
self.dustList: list[Entity] = []
self.obstacles = [[[] for j in range(height)] for i in range(width)]
self.entity = [[[] for j in range(height)] for i in range(width)]
self.vacuum = None
self.cat = None
@ -19,8 +21,10 @@ class World:
self.doc_station = entity
elif entity.type == "PEEL":
self.dust[entity.x][entity.y].append(entity)
self.dustList.append(Entity(entity.x, entity.y, "PEEL"))
elif entity.type == "EARRING":
self.dust[entity.x][entity.y].append(entity)
self.dustList.append(Entity(entity.x, entity.y, "EARRING"))
elif entity.type == "VACUUM":
self.vacuum = entity
elif entity.type == "CAT":
@ -29,10 +33,27 @@ class World:
else:
self.obstacles[entity.x][entity.y].append(entity)
self.entity[entity.x][entity.y].append(entity)
def is_entity_at(
self,
x: int,
y: int,
) -> bool:
if len(self.entity[x][y]) > 0:
return True
return False
def delete_entities_at_Of_type(self, x: int, y: int, type: str):
entities = self.entity[x][y]
for entity in entities:
if entity.type == type:
entities.remove(entity)
def is_obstacle_at(self, x: int, y: int) -> bool:
return bool(self.obstacles[x][y])
def is_garbage_at(self, x: int, y: int):
def garbage_at(self, x: int, y: int) -> list[Entity]:
if len(self.dust[x][y]) == 0:
return []
return [i for i in self.dust[x][y] if evaluate([i.properties])[0] == 1]
@ -54,5 +75,5 @@ class World:
return True
def get_cost(self, x, y):
def get_cost(self, x, y) -> float:
return self.costs[x][y]

113
main.py
View File

@ -3,6 +3,9 @@ from random import randint
import pygame
import configparser
config = configparser.ConfigParser()
config.read("config.ini")
from domain.commands.random_cat_move_command import RandomCatMoveCommand
from domain.commands.vacuum_move_command import VacuumMoveCommand
from domain.entities.cat import Cat
@ -13,17 +16,16 @@ from domain.entities.earring import Earring
from domain.entities.docking_station import Doc_Station
from domain.world import World
from view.renderer import Renderer
from AI_brain.image_recognition import VacuumRecognizer
from AI_brain.genetic_algorytm import GeneticAlgorytm, Path
if not config.getboolean("NEURAL_NETWORK", "is_neural_network_off"):
from AI_brain.image_recognition import VacuumRecognizer
# from AI_brain.movement import GoAnyDirectionBFS, State
# from AI_brain.rotate_and_go_bfs import RotateAndGoBFS, State
from AI_brain.rotate_and_go_aStar import RotateAndGoAStar, State
config = configparser.ConfigParser()
config.read("config.ini")
class Main:
def __init__(self):
tiles_x = 10
@ -51,24 +53,38 @@ class Main:
def run_robot(self):
self.renderer.render(self.world)
start_state = State(self.world.vacuum.x, self.world.vacuum.y)
end_state = State(self.world.doc_station.x, self.world.doc_station.y)
if config["AI_BRAIN"]["mode"] == "to_station":
start_state = State(self.world.vacuum.x, self.world.vacuum.y)
end_state = State(self.world.doc_station.x, self.world.doc_station.y)
# path_searcher = GoAnyDirectionBFS(self.world, start_state, end_state)
# path_searcher = RotateAndGoBFS(self.world, start_state, end_state)
path_searcher = RotateAndGoAStar(self.world, start_state, end_state)
if not path_searcher.search():
print("No solution")
path_searcher = RotateAndGoAStar(self.world, start_state, end_state)
if not path_searcher.search():
print("No solution")
exit(0)
print(path_searcher.actions)
print(path_searcher.cost)
robot_actions = path_searcher.actions
elif config["AI_BRAIN"]["mode"] == "full_clean":
genetic_searcher = GeneticAlgorytm(self.world)
genetic_searcher.run()
genetic_searcher.print_top()
robot_actions = genetic_searcher.best_path.real_path
else:
print("Wrong mode")
exit(0)
path_searcher.actions.reverse()
while self.running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
self.running = False
if len(path_searcher.actions) > 0:
action_direction = path_searcher.actions.pop()
if len(robot_actions) > 0:
action_direction = robot_actions.pop(0)
# self.handle_action1(action_direction)
self.handle_action2(action_direction)
@ -113,6 +129,8 @@ class Main:
self.world.vacuum.direction[1],
-self.world.vacuum.direction[0],
)
elif action == "DEFAULT_ROTATION":
self.world.vacuum.direction = (1, 0)
def process_input(self):
for event in pygame.event.get():
@ -145,35 +163,59 @@ class Main:
def generate_world(tiles_x: int, tiles_y: int) -> World:
if config.getboolean("NEURAL_NETWORK", "is_nural_network_off"):
if config.getboolean("NEURAL_NETWORK", "is_neural_network_off"):
world = World(tiles_x, tiles_y)
for _ in range(config.getint("CONSTANT", "NumberOfBananas")):
temp_x = randint(0, tiles_x - 1)
temp_y = randint(0, tiles_y - 1)
world.add_entity(Garbage(temp_x, temp_y))
world.vacuum = Vacuum(1, 1)
world.doc_station = Doc_Station(9, 8)
x, y = config.get("CONSTANT", "RobotStartPosition").split(",")
x, y = int(x), int(y)
world.vacuum = Vacuum(x, y)
x, y = config.get("CONSTANT", "DockStationStartPosition").split(",")
x, y = int(x), int(y)
world.doc_station = Doc_Station(x, y)
if config.getboolean("APP", "cat"):
world.cat = Cat(7, 8)
world.add_entity(world.cat)
world.add_entity(world.doc_station)
world.add_entity(world.vacuum)
world.add_entity(Entity(2, 8, "PLANT1"))
world.add_entity(Entity(4, 1, "PLANT1"))
world.add_entity(Entity(3, 4, "PLANT2"))
world.add_entity(Entity(8, 8, "PLANT2"))
world.add_entity(Entity(9, 3, "PLANT3"))
world.add_entity(Earring(9, 7))
world.add_entity(Earring(5, 5))
world.add_entity(Earring(4, 6))
numberOfEarrings = config.getint("CONSTANT", "NumberOfEarrings")
for _ in range(numberOfEarrings):
temp_x = randint(0, tiles_x - 1)
temp_y = randint(0, tiles_y - 1)
while world.is_entity_at(temp_x, temp_y):
temp_x = randint(0, tiles_x - 1)
temp_y = randint(0, tiles_y - 1)
world.add_entity(Earring(temp_x, temp_y))
for _ in range(config.getint("CONSTANT", "NumberOfBananas")):
temp_x = randint(0, tiles_x - 1)
temp_y = randint(0, tiles_y - 1)
while world.is_entity_at(temp_x, temp_y):
temp_x = randint(0, tiles_x - 1)
temp_y = randint(0, tiles_y - 1)
world.add_entity(Garbage(temp_x, temp_y))
else:
def world_adder(x,y,object,style=None):
def world_adder(x, y, object, style=None):
print(object)
if object == 'Plant':
if object == "Plant":
world.add_entity(Entity(x, y, f"PLANT{randint(1, 3)}"))
if object == 'Earings':
if object == "Earings":
world.add_entity(Earring(x, y))
if object == 'Banana':
if object == "Banana":
world.add_entity(Garbage(temp_x, temp_y))
if object == 'Cat' and config.getboolean("APP", "cat"):
if object == "Cat" and config.getboolean("APP", "cat"):
world.add_entity(Cat(x, y))
neural_network = VacuumRecognizer()
@ -188,28 +230,27 @@ def generate_world(tiles_x: int, tiles_y: int) -> World:
for _ in range(config.getint("CONSTANT", "NumberOfPlants")):
temp_x = randint(0, tiles_x - 1)
temp_y = randint(0, tiles_y - 1)
path = VacuumRecognizer.get_random_dir(neural_network,'Plant')
path = VacuumRecognizer.get_random_dir(neural_network, "Plant")
world_adder(temp_x, temp_y, neural_network.recognize(path))
for _ in range(config.getint("CONSTANT", "NumberOfEarrings")):
temp_x = randint(0, tiles_x - 1)
temp_y = randint(0, tiles_y - 1)
path = VacuumRecognizer.get_random_dir(neural_network,'Earings')
path = VacuumRecognizer.get_random_dir(neural_network, "Earings")
world_adder(temp_x, temp_y, neural_network.recognize(path))
for _ in range(config.getint("CONSTANT", "NumberOfBananas")):
temp_x = randint(0, tiles_x - 1)
temp_y = randint(0, tiles_y - 1)
path = VacuumRecognizer.get_random_dir(neural_network,'Banana')
path = VacuumRecognizer.get_random_dir(neural_network, "Banana")
world_adder(temp_x, temp_y, neural_network.recognize(path))
for x in range(world.width):
for y in range(world.height):
if world.is_garbage_at(x, y):
if world.garbage_at(x, y):
world.costs[x][y] = 1
else:
world.costs[x][y] = 10
world.costs[x][y] = 2
return world

View File

@ -94,7 +94,7 @@ class Renderer:
self.tile_height + self.tile_height / 4,
),
),
"EARRING": pygame.transform.scale(
"EARRING": pygame.transform.scale(
pygame.image.load("media/sprites/earrings.webp"),
(
self.tile_width + self.tile_width / 4,
@ -115,16 +115,8 @@ class Renderer:
self.render_board()
for x in range(world.width):
for y in range(world.height):
for entity in world.dust[x][y]:
for entity in world.entity[x][y]:
self.draw_entity(entity)
for x in range(world.width):
for y in range(world.height):
for entity in world.obstacles[x][y]:
self.draw_entity(entity)
self.draw_entity(world.vacuum)
self.draw_entity(world.doc_station)
if config.getboolean("APP", "cat"):
self.draw_entity(world.cat)
pygame.display.update()
def line(self, x_1, y_1, x_2, y_2, color=None):