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Author SHA1 Message Date
Wiktor Szynaka
a146880414 srodowisko 2023-03-11 08:23:30 +01:00
Wiktor Szynaka
ffaedf6979 Merge branch 'master' of https://git.wmi.amu.edu.pl/s473616/sztuczna_inteligencja_2023_smieciarka into test 2023-03-11 08:21:09 +01:00
Wiktor Szynaka
8f9266fd45 test 2023-03-06 12:05:54 +01:00
13580 changed files with 38 additions and 1199 deletions

3
.idea/.gitignore vendored
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# Default ignored files
/shelf/
/workspace.xml

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# sztuczna_inteligencja_2023_smieciarka
Symulacja inteligentnej śmieciarki. Śmieciarka zbiera śmieci z kubłów wystawionych przez mieszkańców, samodzielnie je segreguje i zawozi na wysypisko.

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from enum import Enum
class AgentActionType (Enum):
MOVE_FORWARD = 0
TURN_LEFT = 1
TURN_RIGHT = 2
UNKNOWN = None

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from enum import Enum
class AgentOrientation (Enum):
UP = 0
RIGHT = 1
DOWN = 2
LEFT = 3

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from agentOrientation import AgentOrientation
from typing import Tuple
class AgentState:
orientation: AgentOrientation
position: Tuple[int, int]
def __init__(self, position: Tuple[int, int], orientation: AgentOrientation) -> None:
self.orientation = orientation
self.position = position

165
bfs.py
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from agentState import AgentState
from typing import Dict, Tuple, List
from city import City
from gridCellType import GridCellType
from agentActionType import AgentActionType
from agentOrientation import AgentOrientation
from queue import Queue, PriorityQueue
from turnCar import turn_left_orientation, turn_right_orientation
class Successor: # klasa reprezentuje sukcesora, stan i akcję którą można po nim podjąć
def __init__(self, state: AgentState, action: AgentActionType, cost: int, predicted_cost: int) -> None:
self.state = state
self.action = action
self.cost = cost
self.predicted_cost = predicted_cost
class SuccessorList: # lista sukcesorów, czyli możliwych ścieżek po danym stanie
succ_list: list[Successor]
def __init__(self, succ_list: list[Successor]) -> None:
self.succ_list = succ_list
def __gt__(self, other):
return self.succ_list[-1].predicted_cost > other.succ_list[-1].predicted_cost
def __lt__(self, other):
return self.succ_list[-1].predicted_cost < other.succ_list[-1].predicted_cost
def find_path_to_nearest_can(startState: AgentState, grid: Dict[Tuple[int, int], GridCellType], city: City) -> List[
AgentActionType]: # znajduje ścieżkę do najbliższego kosza na smieci
visited: List[AgentState] = []
queue: PriorityQueue[SuccessorList] = PriorityQueue() # kolejka priorytetowa przechodująca listę sukcesorów
queue.put(SuccessorList([Successor(startState, AgentActionType.UNKNOWN, 0, _heuristics(startState.position, city))]))
while not queue.empty(): # dopóki kolejka nie jest pusta, pobiera z niej aktualny element
current = queue.get()
previous = current.succ_list[-1]
visited.append(previous.state)
if is_state_success(previous.state, grid): # jeśli ostatni stan w liście jest stanem końcowym (agent dotarł do śmietnika)
return extract_actions(current)
successors = get_successors(previous, grid, city)
for s in successors:
already_visited = False
for v in visited:
if v.position == s.state.position and v.orientation == s.state.orientation:
already_visited = True
break
if already_visited:
continue
if is_state_valid(s.state, grid):
new_list = current.succ_list.copy()
new_list.append(s)
queue.put(SuccessorList(new_list))
return []
def extract_actions(successors: SuccessorList) -> list[AgentActionType]: # wyodrębnienie akcji z listy sukcesorów, z pominięciem uknown
output: list[AgentActionType] = []
for s in successors.succ_list:
if s.action != AgentActionType.UNKNOWN:
output.append(s.action)
return output
def get_successors(succ: Successor, grid: Dict[Tuple[int, int], GridCellType], city: City) -> List[Successor]:
result: List[Successor] = [] # generuje następników dla danego stanu,
turn_left_cost = 1 + succ.cost
turn_left_state = AgentState(succ.state.position, turn_left_orientation(succ.state.orientation))
turn_left_heuristics = _heuristics(succ.state.position, city)
result.append(
Successor(turn_left_state, AgentActionType.TURN_LEFT, turn_left_cost, turn_left_cost + turn_left_heuristics))
turn_right_cost = 1 + succ.cost
turn_right_state = AgentState(succ.state.position, turn_right_orientation(succ.state.orientation))
turn_right_heuristics = _heuristics(succ.state.position, city)
result.append(
Successor(turn_right_state, AgentActionType.TURN_RIGHT, turn_right_cost,
turn_right_cost + turn_right_heuristics))
state_succ = move_forward_succ(succ, city, grid)
if state_succ is not None:
result.append(state_succ)
return result
def move_forward_succ(succ: Successor, city: City, grid: Dict[Tuple[int, int], GridCellType]) -> Successor:
position = get_next_cell(succ.state)
if position is None:
return None
cost = get_cost_for_action(AgentActionType.MOVE_FORWARD, grid[position]) + succ.cost
predicted_cost = cost + _heuristics(position, city)
new_state = AgentState(position, succ.state.orientation)
return Successor(new_state, AgentActionType.MOVE_FORWARD, cost, predicted_cost)
def get_next_cell(state: AgentState) -> Tuple[int, int]:
x, y = state.position
orientation = state.orientation
if orientation == AgentOrientation.UP:
if y - 1 < 1:
return None
return x, y - 1
elif orientation == AgentOrientation.DOWN:
if y + 1 > 27:
return None
return x, y + 1
elif orientation == AgentOrientation.LEFT:
if x - 1 < 1:
return None
return x - 1, y
elif x + 1 > 27:
return None
else:
return x + 1, y
def is_state_success(state: AgentState, grid: Dict[Tuple[int, int], GridCellType]) -> bool:
next_cell = get_next_cell(state)
try:
return grid[next_cell] == GridCellType.GARBAGE_CAN # agent dotarł do śmietnika
except KeyError:
return False
def get_cost_for_action(action: AgentActionType, cell_type: GridCellType) -> int:
if action in [AgentActionType.TURN_LEFT, AgentActionType.TURN_RIGHT]:
return 1
if cell_type == GridCellType.SPEED_BUMP and action == AgentActionType.MOVE_FORWARD:
return -10000
if action == AgentActionType.MOVE_FORWARD:
return 3
def is_state_valid(state: AgentState, grid: Dict[Tuple[int, int], GridCellType]) -> bool:
try:
return grid[state.position] == GridCellType.STREET_HORIZONTAL or grid[
state.position] == GridCellType.STREET_VERTICAL or grid[state.position] == GridCellType.SPEED_BUMP
except KeyError:
return False
def _heuristics(position: Tuple[int, int], city: City):
min_distance: int = 300
found_nonvisited: bool = False
for can in city.cans:
if can.is_visited:
continue
found_nonvisited = True
distance = 3 * (abs(position[0] - can.position[0]) + abs(position[1] - can.position[1]))
if distance < min_distance:
min_distance = distance
if found_nonvisited:
return min_distance
return -1

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city.py
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from typing import List, Dict, Tuple
from garbageCan import GarbageCan
from speedBump import SpeedBump
from street import Street
from gameContext import GameContext
class City:
cans: List[GarbageCan]
bumps: List[SpeedBump]
streets: List[Street]
cans_dict: Dict[Tuple[int, int], GarbageCan] = {}
def __init__(self) -> None:
self.cans = []
self.streets = []
self.bumps = []
def add_can(self, can: GarbageCan) -> None:
self.cans.append(can)
self.cans_dict[can.position] = can
def add_street(self, street: Street) -> None:
self.streets.append(street)
def add_bump(self, bump: SpeedBump) -> None:
self.streets.append(bump)
def render_city(self, game_context: GameContext) -> None:
self._render_streets(game_context)
self._render_nodes(game_context)
self._render_bumps(game_context)
def _render_streets(self, game_context: GameContext) -> None:
for street in self.streets:
street.render(game_context)
def _render_nodes(self, game_context: GameContext) -> None:
for node in self.cans:
node.render(game_context)
def _render_bumps(self, game_context: GameContext) -> None:
for bump in self.bumps:
bump.render(game_context)

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from typing import Tuple, List, Dict
import pygame
from PIL import Image
from gridCellType import GridCellType
class GameContext:
dust_car_speed = 20
dust_car_position_x = 0
dust_car_position_y = 0
dust_car_pygame = None
dust_car_pil = None
canvas = None
_cell_size: int = 30
city = None
grid: Dict[Tuple[int, int], GridCellType] = {}
dust_car = None
landfill = None
def __init__(self) -> None:
self._init_grid()
def _init_grid(self) -> None:
for i in range(1, 28):
for j in range(1, 28):
self.grid[(i, j)] = GridCellType.NOTHING
def render_in_cell(self, cell: Tuple[int, int], img_path: str):
img = Image.open(img_path)
pygame_img = pygame.image.frombuffer(img.tobytes(), (self._cell_size,self._cell_size), 'RGB')
start_x = (cell[0] - 1) * self._cell_size
start_y = (cell[1] - 1) * self._cell_size
self.canvas.blit(pygame_img, (start_x, start_y))

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import pygame
from gameContext import GameContext
def handle_game_event(event, game_context: GameContext):
pass
def handle_game_event(event):
return

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from enum import Enum
class GarbageType(Enum):
PAPER = 0,
PLASTIC_AND_METAL = 1
GLASS = 3
BIO = 4
MIXED = 5
class Garbage:
img: str
shape: str
flexibility: str
does_smell: str
weight: str
size: str
color: str
softness: str
does_din: str
def __init__(self, img: str, shape: str, flexibility: str, does_smell: str, weight: str, size: str, color: str, softness: str, does_din: str) -> None:
self.img = img
self.shape = shape
self.flexibility = flexibility
self.does_smell = does_smell
self.weight = weight
self.size = size
self.color = color
self.softness = softness
self.does_din = does_din
class RecognizedGarbage:
garbage_type: GarbageType
src: Garbage
def __init__(self, src: Garbage, garbage_type: GarbageType) -> None:
self.garbage_type = garbage_type
self.src = src

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from garbage import Garbage
from typing import List, Tuple
from gameContext import GameContext
from gridCellType import GridCellType
class GarbageCan:
position: Tuple[int, int]
garbage: List[Garbage]
is_visited: bool
def __init__(self, position: Tuple[int, int]) -> None:
self.position = position
self.garbage = []
self.is_visited = False
def add_garbage(self, garbage: Garbage) -> None:
self.garbage.append(garbage)
def remove_garbage(self, garbage: Garbage) -> None:
self.garbage.remove(garbage)
def render(self, game_context: GameContext) -> None:
game_context.render_in_cell(self.position, "imgs/container.png")
game_context.grid[self.position] = GridCellType.GARBAGE_CAN

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from typing import List, Tuple
from agentOrientation import AgentOrientation
from garbage import GarbageType, RecognizedGarbage
from gameContext import GameContext
class GarbageTruck:
position: Tuple[int, int]
paper: List[RecognizedGarbage]
plastic_and_metal: List[RecognizedGarbage]
glass: List[RecognizedGarbage]
bio: List[RecognizedGarbage]
mixed: List[RecognizedGarbage]
orientation: AgentOrientation = AgentOrientation.RIGHT
def __init__(self, position: Tuple[int, int]) -> None:
self.position = position
self.paper = []
self.plastic_and_metal = []
self.glass = []
self.bio = []
self.mixed = []
def sort_garbage(self, RecognizedGarbage) -> None:
if RecognizedGarbage.garbage_type == GarbageType.PAPER:
self.paper.append(RecognizedGarbage)
elif RecognizedGarbage.garbage_type == GarbageType.PLASTIC_AND_METAL:
self.plastic_and_metal.append(RecognizedGarbage)
elif RecognizedGarbage.garbage_type == GarbageType.GLASS:
self.glass.append(RecognizedGarbage)
elif RecognizedGarbage.garbage_type == GarbageType.BIO:
self.bio.append(RecognizedGarbage)
elif RecognizedGarbage.garbage_type == GarbageType.MIXED:
self.mixed.append(RecognizedGarbage)
def render(self, game_context: GameContext) -> None:
path = None
if self.orientation == AgentOrientation.LEFT:
path = 'imgs/dust_car_left.png'
elif self.orientation == AgentOrientation.RIGHT:
path = 'imgs/dust_car_right.png'
elif self.orientation == AgentOrientation.UP:
path = 'imgs/dust_car_up.png'
elif self.orientation == AgentOrientation.DOWN:
path = 'imgs/dust_car_down.png'
game_context.render_in_cell(self.position, path)

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from enum import Enum
class GridCellType(Enum):
NOTHING = 0
STREET_VERTICAL = 1
STREET_HORIZONTAL = 2
GARBAGE_CAN = 3
VISITED_GARBAGE_CAN = 4
LANDFILL = 5
SPEED_BUMP = 6
UNKNOWN = None

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from typing import Tuple
from gameContext import GameContext
from garbage import RecognizedGarbage
from gridCellType import GridCellType
class Landfill:
position: Tuple[int, int] = []
paper: list[RecognizedGarbage]
plastic_and_metal: list[RecognizedGarbage] = []
glass: list[RecognizedGarbage] = []
bio: list[RecognizedGarbage] = []
mixed: list[RecognizedGarbage] = []
def __init__(self, position: Tuple[int, int]) -> None:
self.position = position
def add_paper(self, paper: list[RecognizedGarbage]) -> None:
for p in paper:
self.paper.append(p)
def add_plastic_and_metal(self, plastic_and_metal: list[RecognizedGarbage]) -> None:
for p in plastic_and_metal:
self.plastic_and_metal.append(p)
def add_glass(self, glass: list[RecognizedGarbage]) -> None:
for g in glass:
self.glass.append(g)
def add_paper(self, bio: list[RecognizedGarbage]) -> None:
for b in bio:
self.bio.append(b)
def add_mixed(self, mixed: list[RecognizedGarbage]) -> None:
for m in mixed:
self.mixed.append(m)
def render(self, game_context: GameContext) -> None:
game_context.render_in_cell(self.position, 'imgs/landfill.png')
game_context.grid[self.position] = GridCellType.LANDFILL

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import os
from trainingData import TrainingData
from sklearn import tree
import joblib
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import numpy as np
def _read_training_data() -> TrainingData:
attributes = []
classes = []
location = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
file = open(os.path.join(location, 'training_data.csv'))
lines = file.readlines()[1:]
file.close()
for line in lines:
actual_row = line.replace('\n', '')
values = actual_row.split(',')
line_attributes = values[:-1]
line_class = values[-1]
attributes.append(line_attributes)
classes.append(line_class.strip())
return TrainingData(attributes, classes)
def _attributes_to_floats(attributes: list[str]) -> list[float]:
output: list[float] = []
if attributes[0] == 'Longitiudonal':
output.append(0)
elif attributes[0] == 'Round':
output.append(1)
elif attributes[0] == 'Flat':
output.append(2)
elif attributes[0] == 'Irregular':
output.append(3)
if attributes[1] == 'Low':
output.append(0)
elif attributes[1] == 'Medium':
output.append(1)
elif attributes[1] == 'High':
output.append(2)
if attributes[2] == "Yes":
output.append(0)
else:
output.append(1)
if attributes[3] == 'Low':
output.append(0)
elif attributes[3] == 'Medium':
output.append(1)
elif attributes[3] == 'High':
output.append(2)
if attributes[4] == 'Low':
output.append(0)
elif attributes[4] == 'Medium':
output.append(1)
elif attributes[4] == 'High':
output.append(2)
if attributes[5] == 'Transparent':
output.append(0)
elif attributes[5] == 'Light':
output.append(1)
elif attributes[5] == 'Dark':
output.append(2)
elif attributes[5] == "Colorful":
output.append(3)
if attributes[6] == 'Low':
output.append(0)
elif attributes[6] == 'Medium':
output.append(1)
elif attributes[6] == 'High':
output.append(2)
if attributes[7] == "Yes":
output.append(0)
else:
output.append(1)
return output
trainning_data = _read_training_data()
X = trainning_data.attributes
Y = trainning_data.classes
model = tree.DecisionTreeClassifier()
encoded = [_attributes_to_floats(x) for x in X]
dtc = model.fit(encoded, Y)
joblib.dump(model, 'model.pkl')

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Shape,Flexibility,DoesSmell,Weight,Size,Color,Softness,DoesDin
Irregular,High,No,High,Medium,Dark,Low,Yes
Longitiudonal,Low,No,Low,Low,Light,Medium,Yes
Longitiudonal,Medium,No,Medium,Low,Dark,High,No
Longitiudonal,Low,No,Medium,Low,Dark,High,Yes
Round,Low,Yes,High,High,Transparent,High,Yes
Irregular,Medium,Yes,High,Low,Transparent,Medium,No
Longitiudonal,Medium,Yes,Low,High,Colorful,Medium,Yes
Longitiudonal,Low,No,Low,Medium,Dark,Medium,Yes
Flat,Medium,Yes,High,Low,Transparent,Low,Yes
Irregular,Medium,Yes,High,Medium,Dark,Low,No
Longitiudonal,High,No,Low,High,Colorful,Low,Yes
Round,Medium,No,Medium,Medium,Dark,Low,No
Longitiudonal,Medium,No,Medium,Medium,Transparent,High,No
Flat,Medium,Yes,Low,Low,Light,Medium,No
Flat,Medium,Yes,Medium,High,Light,Medium,No
Flat,Low,No,High,Low,Dark,High,No
Longitiudonal,Medium,Yes,High,High,Dark,Low,Yes
Flat,Low,Yes,Low,Low,Transparent,Low,No
Flat,Low,No,Medium,Low,Colorful,Low,No
Longitiudonal,Low,Yes,High,Medium,Transparent,Low,No
Longitiudonal,Low,No,Medium,High,Dark,Low,Yes
Irregular,Medium,No,Medium,Medium,Light,Low,Yes
Longitiudonal,High,No,High,High,Colorful,Low,No
Flat,Low,No,Low,Low,Dark,High,No
Flat,Low,Yes,Low,High,Dark,Low,Yes
Irregular,Medium,Yes,High,High,Dark,Low,No
Flat,High,No,High,Low,Dark,Medium,Yes
Longitiudonal,High,Yes,Low,Medium,Colorful,Low,Yes
1 Shape Flexibility DoesSmell Weight Size Color Softness DoesDin
2 Irregular High No High Medium Dark Low Yes
3 Longitiudonal Low No Low Low Light Medium Yes
4 Longitiudonal Medium No Medium Low Dark High No
5 Longitiudonal Low No Medium Low Dark High Yes
6 Round Low Yes High High Transparent High Yes
7 Irregular Medium Yes High Low Transparent Medium No
8 Longitiudonal Medium Yes Low High Colorful Medium Yes
9 Longitiudonal Low No Low Medium Dark Medium Yes
10 Flat Medium Yes High Low Transparent Low Yes
11 Irregular Medium Yes High Medium Dark Low No
12 Longitiudonal High No Low High Colorful Low Yes
13 Round Medium No Medium Medium Dark Low No
14 Longitiudonal Medium No Medium Medium Transparent High No
15 Flat Medium Yes Low Low Light Medium No
16 Flat Medium Yes Medium High Light Medium No
17 Flat Low No High Low Dark High No
18 Longitiudonal Medium Yes High High Dark Low Yes
19 Flat Low Yes Low Low Transparent Low No
20 Flat Low No Medium Low Colorful Low No
21 Longitiudonal Low Yes High Medium Transparent Low No
22 Longitiudonal Low No Medium High Dark Low Yes
23 Irregular Medium No Medium Medium Light Low Yes
24 Longitiudonal High No High High Colorful Low No
25 Flat Low No Low Low Dark High No
26 Flat Low Yes Low High Dark Low Yes
27 Irregular Medium Yes High High Dark Low No
28 Flat High No High Low Dark Medium Yes
29 Longitiudonal High Yes Low Medium Colorful Low Yes

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