diff --git a/.idea/misc.xml b/.idea/misc.xml
index 812ab5a..dfd527a 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -3,5 +3,5 @@
-
+
\ No newline at end of file
diff --git a/.idea/sztuczna.iml b/.idea/sztuczna.iml
index d0876a7..c24009a 100644
--- a/.idea/sztuczna.iml
+++ b/.idea/sztuczna.iml
@@ -1,8 +1,10 @@
-
-
+
+
+
+
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..35eb1dd
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/Animals/animal.py b/Animals/animal.py
index a4a40bc..d889ba9 100644
--- a/Animals/animal.py
+++ b/Animals/animal.py
@@ -1,17 +1,31 @@
+import random
import pygame
from abc import abstractmethod
class Animal:
- def __init__(self, x, y,name, image, food_image, food, environment, adult=False,):
+
+ def choose_picture(self, name):
+ ran = random.randint(0, 1)
+ if ran == 0:
+ path = f'images/{name}.png'
+ return path
+ else:
+ path = f'images/{name}2.png'
+ return path
+
+ def __init__(self, x, y,name, image_path, food_image, food, environment, activity, ill=False, adult=False,):
self.x = x - 1
self.y = y - 1
self.name = name
- self.image = image
+ self.image_path = image_path
+ self.image = pygame.image.load(image_path)
self.adult = adult
self.food = food
self.food_image = food_image
self._feed = 0
- self.environment = environment #hot/cold/medium
+ self.environment = environment # hot/cold/medium
+ self.activity = activity # diurnal/nocturnal
+ self.ill = ill
def draw(self, screen, grid_size):
if self.adult:
@@ -30,9 +44,9 @@ class Animal:
exclamation_image = pygame.transform.scale(exclamation_image, (int(grid_size * 0.45), int(grid_size * 0.45)))
screen.blit(exclamation_image, (x * grid_size, y * grid_size))
- def draw_food(self, screen, grid_size, x, y):
+ def draw_food(self, screen, grid_size, x, y,food_image):
scale = 0.45
- food_image = pygame.image.load(self.food_image)
+ food_image = pygame.image.load(food_image)
if(self.adult):
y = y + 1
@@ -40,9 +54,32 @@ class Animal:
food_image = pygame.transform.scale(food_image, (int(grid_size * scale), int(grid_size * scale)))
screen.blit(food_image, (x * grid_size, (y + 1) * grid_size - int(grid_size * scale)))
- @abstractmethod
- def feed(self):
- pass
+ def is_ill(self):
+ chance = random.randint(1, 100)
+ if chance >= 90:
+ return True
+ else: return False
+
+ def draw_illness(self, screen, grid_size, x, y):
+ scale = 0.45
+ illness_image = pygame.image.load('images/ill.png')
+ y = y
+
+ if self.adult:
+ x = x + 1
+ y = y
+ scale = 0.7
+
+ x_blit = x * grid_size + (grid_size - int(grid_size * scale))
+ illness_image = pygame.transform.scale(illness_image, (int(grid_size * scale), int(grid_size * scale)))
+ screen.blit(illness_image, (x_blit, y * grid_size))
+
+ def draw_snack(self, screen, grid_size, x, y):
+ exclamation_image = pygame.image.load(self.food_image)
+ exclamation_image = pygame.transform.scale(exclamation_image, (int(grid_size * 0.45), int(grid_size * 0.45)))
+ screen.blit(exclamation_image, (x * grid_size, y * grid_size))
+ pygame.display.update()
+ pygame.time.wait(700)
@abstractmethod
def getting_hungry(self):
diff --git a/Animals/animals.py b/Animals/animals.py
index b228a52..4823cd9 100644
--- a/Animals/animals.py
+++ b/Animals/animals.py
@@ -3,6 +3,8 @@ from giraffe import Giraffe
from penguin import Penguin
from parrot import Parrot
from bear import Bear
+from owl import Owl
+from bat import Bat
def create_animals():
giraffe1 = Giraffe(0, 0, adult=True)
@@ -26,22 +28,35 @@ def create_animals():
elephant5 = Elephant(0, 0)
parrot1 = Parrot(0, 0)
parrot2 = Parrot(0, 0)
- parrot3 = Parrot(0, 0)
- parrot4 = Parrot(0, 0)
- parrot5 = Parrot(0, 0)
+ owl1 = Owl(0, 0)
+ owl2 = Owl(0, 0)
+ bat1 = Bat(0, 0)
+ bat2 = Bat(0, 0)
Animals = [giraffe1, giraffe2, giraffe3, giraffe4, giraffe5,
bear1, bear2, bear3, bear4, bear5,
elephant1, elephant2, elephant3, elephant4, elephant5,
penguin1, penguin2, penguin3, penguin4,
- parrot1, parrot2, parrot3, parrot4, parrot5]
+ parrot1, parrot2, owl1, owl2, bat1, bat2]
return Animals
def draw_Animals(Animals, const):
for Animal in Animals:
Animal.draw(const.screen, const.GRID_SIZE)
- if Animal.feed() == 'True':
- Animal.draw_exclamation(const.screen, const.GRID_SIZE, Animal.x, Animal.y)
- else:
- Animal.draw_food(const.screen,const.GRID_SIZE,Animal.x,Animal.y)
\ No newline at end of file
+
+ hunger_level = Animal.getting_hungry(const)
+
+ if hunger_level >= 9:
+ food_image = 'images/empty_bowl.png'
+ elif hunger_level >= 8:
+ food_image = 'images/almost_empty.png'
+ elif hunger_level >= 5:
+ food_image = 'images/half_bowl.png'
+ else:
+ food_image = 'images/full_bowl.png'
+
+ Animal.draw_food(const.screen, const.GRID_SIZE, Animal.x, Animal.y, food_image)
+
+ if Animal.ill:
+ Animal.draw_illness(const.screen, const.GRID_SIZE, Animal.x, Animal.y)
\ No newline at end of file
diff --git a/Animals/bat.py b/Animals/bat.py
new file mode 100644
index 0000000..83dc661
--- /dev/null
+++ b/Animals/bat.py
@@ -0,0 +1,26 @@
+from animal import Animal
+import pygame
+from datetime import datetime
+
+class Bat(Animal):
+ def __init__(self, x, y, adult=False):
+ name = 'bat'
+ image_path = self.choose_picture(name)
+ environment = "medium"
+ food_image = 'images/grains.png'
+ parrot_food = 'grains'
+ activity = 'nocturnal'
+ super().__init__(x, y,name, image_path, food_image,parrot_food, environment, adult)
+ self._starttime = datetime.now()
+
+ def getting_hungry(self, const):
+ checktime = datetime.now()
+ delta = checktime - self._starttime
+ minutes_passed = delta.total_seconds() / (25)
+ self._starttime = checktime
+
+ if const.IS_NIGHT and self._feed < 10:
+ self._feed += minutes_passed
+ self._feed = min(self._feed, 10)
+
+ return self._feed
\ No newline at end of file
diff --git a/Animals/bear.py b/Animals/bear.py
index 3eb5c65..2950042 100644
--- a/Animals/bear.py
+++ b/Animals/bear.py
@@ -4,27 +4,24 @@ from datetime import datetime
class Bear(Animal):
def __init__(self, x, y, adult=False):
- Bear_image = pygame.image.load('images/bear.png')
name = 'bear'
+ image_path = self.choose_picture(name)
environment = "cold"
+ activity = 'nocturnal'
+ ill = self.is_ill()
bear_food = 'meat'
food_image = 'images/meat.png'
- super().__init__(x, y,name, Bear_image, food_image,bear_food,environment, adult)
+ super().__init__(x, y,name, image_path, food_image,bear_food,environment, activity, ill, adult)
self._starttime = datetime.now()
-
-
- def feed(self):
- self.getting_hungry()
- if self._feed < 2:
- return 'False'
- else:
- return 'True'
+ def getting_hungry(self, const):
-
- def getting_hungry(self):
checktime = datetime.now()
delta = checktime - self._starttime
- minutes_passed = delta.total_seconds() / 60
- self._feed += minutes_passed
- self._starttime = checktime
\ No newline at end of file
+ minutes_passed = delta.total_seconds() / (45)
+ self._starttime = checktime
+
+ if const.IS_NIGHT and self._feed < 10 and const.season != "winter":
+ self._feed += minutes_passed
+ self._feed = min(self._feed, 10)
+ return self._feed
\ No newline at end of file
diff --git a/Animals/elephant.py b/Animals/elephant.py
index 4e63bfb..701b40d 100644
--- a/Animals/elephant.py
+++ b/Animals/elephant.py
@@ -2,13 +2,13 @@ from animal import Animal
import pygame
from datetime import datetime
-
-
class Elephant(Animal):
def __init__(self, x, y, adult=False):
- Elephant_image = pygame.image.load('images/elephant.png')
name = 'elephant'
+ image_path = self.choose_picture(name)
environment = "hot"
+ activity = 'diurnal'
+ ill = self.is_ill()
if adult:
elephant_food = 'leavs'
food_image = 'images/leaves.png'
@@ -16,22 +16,16 @@ class Elephant(Animal):
elephant_food = 'milk'
food_image = 'images/milk.png'
- super().__init__(x, y,name, Elephant_image, food_image,elephant_food, environment, adult)
+ super().__init__(x, y,name, image_path, food_image,elephant_food, environment, activity, ill, adult)
self._starttime = datetime.now()
-
-
-
- def feed(self):
- self.getting_hungry()
- if self._feed < 0.3:
- return 'False'
- else:
- return 'True'
-
-
- def getting_hungry(self):
+
+ def getting_hungry(self, const):
checktime = datetime.now()
delta = checktime - self._starttime
- minutes_passed = delta.total_seconds() / 60
- self._feed += minutes_passed
- self._starttime = checktime
\ No newline at end of file
+ minutes_passed = delta.total_seconds() / (90)
+ self._starttime = checktime
+
+ if not const.IS_NIGHT and self._feed < 10:
+ self._feed += minutes_passed
+ self._feed = min(self._feed, 10)
+ return self._feed
\ No newline at end of file
diff --git a/Animals/giraffe.py b/Animals/giraffe.py
index f5c191f..93479bb 100644
--- a/Animals/giraffe.py
+++ b/Animals/giraffe.py
@@ -2,31 +2,25 @@ from animal import Animal
import pygame
from datetime import datetime
-
-
class Giraffe(Animal):
def __init__(self, x, y, adult=False):
- Giraffe_image = pygame.image.load('images/giraffe.png')
name = 'giraffe'
+ image_path = self.choose_picture(name)
environment = "hot"
+ activity = 'diurnal'
+ ill = self.is_ill()
food_image = 'images/leaves.png'
giraffe_food = 'leaves'
- super().__init__(x, y,name, Giraffe_image, food_image,giraffe_food, environment, adult)
+ super().__init__(x, y, name, image_path, food_image,giraffe_food, environment, activity, ill, adult)
self._starttime = datetime.now()
-
-
- def feed(self):
- self.getting_hungry()
- if self._feed < 0.8:
- return 'False'
- else:
- return 'True'
-
-
- def getting_hungry(self):
+ def getting_hungry(self, const):
checktime = datetime.now()
delta = checktime - self._starttime
- minutes_passed = delta.total_seconds() / 60
- self._feed += minutes_passed
- self._starttime = checktime
\ No newline at end of file
+ minutes_passed = delta.total_seconds() / (60)
+ self._starttime = checktime
+
+ if not const.IS_NIGHT and self._feed < 10:
+ self._feed += minutes_passed
+ self._feed = min(self._feed, 10)
+ return self._feed
\ No newline at end of file
diff --git a/Animals/owl.py b/Animals/owl.py
new file mode 100644
index 0000000..fb6125f
--- /dev/null
+++ b/Animals/owl.py
@@ -0,0 +1,26 @@
+from animal import Animal
+import pygame
+from datetime import datetime
+
+class Owl(Animal):
+ def __init__(self, x, y, adult=False):
+ name = 'owl'
+ image_path = self.choose_picture(name)
+ environment = "medium"
+ food_image = 'images/grains.png'
+ parrot_food = 'grains'
+ activity = 'nocturnal'
+ super().__init__(x, y,name, image_path, food_image,parrot_food, environment, adult)
+ self._starttime = datetime.now()
+
+ def getting_hungry(self, const):
+ checktime = datetime.now()
+ delta = checktime - self._starttime
+ minutes_passed = delta.total_seconds() / (50)
+ self._starttime = checktime
+
+ if const.IS_NIGHT and self._feed < 10:
+ self._feed += minutes_passed
+ self._feed = min(self._feed, 10)
+
+ return self._feed
\ No newline at end of file
diff --git a/Animals/parrot.py b/Animals/parrot.py
index 1873b53..e675e8d 100644
--- a/Animals/parrot.py
+++ b/Animals/parrot.py
@@ -2,31 +2,25 @@ from animal import Animal
import pygame
from datetime import datetime
-
-
class Parrot(Animal):
def __init__(self, x, y, adult=False):
- Parrot_image = pygame.image.load('images/parrot.png')
name = 'parrot'
+ image_path = self.choose_picture(name)
environment = "medium"
+ activity = 'diurnal'
+ ill = self.is_ill()
food_image = 'images/grains.png'
parrot_food = 'grains'
- super().__init__(x, y,name, Parrot_image, food_image,parrot_food, environment, adult)
+ super().__init__(x, y, name, image_path, food_image, parrot_food, environment, activity, ill, adult)
self._starttime = datetime.now()
-
-
- def feed(self):
- self.getting_hungry()
- if self._feed < 1.5:
- return 'False'
- else:
- return 'True'
-
-
- def getting_hungry(self):
+ def getting_hungry(self, const):
checktime = datetime.now()
delta = checktime - self._starttime
- minutes_passed = delta.total_seconds() / 60
- self._feed += minutes_passed
- self._starttime = checktime
\ No newline at end of file
+ minutes_passed = delta.total_seconds() / (30)
+ self._starttime = checktime
+
+ if not const.IS_NIGHT and self._feed < 10:
+ self._feed += minutes_passed
+ self._feed = min(self._feed, 10)
+ return self._feed
\ No newline at end of file
diff --git a/Animals/penguin.py b/Animals/penguin.py
index 830f176..33a885c 100644
--- a/Animals/penguin.py
+++ b/Animals/penguin.py
@@ -2,31 +2,25 @@ from animal import Animal
import pygame
from datetime import datetime
-
-
class Penguin(Animal):
def __init__(self, x, y, adult=False):
- Penguin_image = pygame.image.load('images/penguin.png')
name = 'penguin'
+ image_path = self.choose_picture(name)
environment = "cold"
+ activity = 'diurnal'
+ ill = self.is_ill()
food_image = 'images/fish.png'
penguin_food = 'fish'
- super().__init__(x, y,name, Penguin_image, food_image,penguin_food,environment, adult)
+ super().__init__(x, y, name, image_path, food_image, penguin_food, environment, activity, ill, adult)
self._starttime = datetime.now()
-
-
- def feed(self):
- self.getting_hungry()
- if self._feed < 2:
- return 'False'
- else:
- return 'True'
-
-
- def getting_hungry(self):
+ def getting_hungry(self, const):
checktime = datetime.now()
delta = checktime - self._starttime
- minutes_passed = delta.total_seconds() / 60
- self._feed += minutes_passed
- self._starttime = checktime
\ No newline at end of file
+ minutes_passed = delta.total_seconds() / (25)
+ self._starttime = checktime
+
+ if not const.IS_NIGHT and self._feed < 10:
+ self._feed += minutes_passed
+ self._feed = min(self._feed, 10)
+ return self._feed
\ No newline at end of file
diff --git a/agent.py b/agent.py
index 19734b3..176c697 100644
--- a/agent.py
+++ b/agent.py
@@ -1,6 +1,23 @@
import pygame
+import random
+from constants import Constants
from state_space_search import is_border, is_obstacle
+from night import draw_night
+from decision_tree import feed_decision
+from constants import Constants
+from classification import AnimalClassifier
+const = Constants()
+
+classes = [
+ "bat",
+ "bear",
+ "elephant",
+ "giraffe",
+ "owl",
+ "parrot",
+ "penguin"
+]
class Agent:
def __init__(self, istate, image_path, grid_size):
self.istate = istate
@@ -8,8 +25,10 @@ class Agent:
self.grid_size = grid_size
self.image= pygame.image.load(image_path)
self.image = pygame.transform.scale(self.image, (grid_size, grid_size))
+ self._dryfood = 0
+ self._wetfood = 0
- def draw(self, screen, grid_size):
+ def draw(self, const):
# Obróć obrazek zgodnie z kierunkiem
if self.direction == 'E':
self.image= pygame.image.load('images/agent4.png')
@@ -19,19 +38,21 @@ class Agent:
self.image= pygame.image.load('images/agent3.png')
else: # direction == 'N'
self.image= pygame.image.load('images/agent2.png')
- self.image = pygame.transform.scale(self.image, (grid_size, grid_size))
- screen.blit(self.image, (self.x * self.grid_size, self.y * self.grid_size))
+ self.image = pygame.transform.scale(self.image, (const.GRID_SIZE, const.GRID_SIZE))
+ const.screen.blit(self.image, (self.x * self.grid_size, self.y * self.grid_size))
- def handle_event(self, event, max_x, max_y, animals, obstacles):
+ if const.IS_NIGHT: draw_night(const)
+
+ def handle_event(self, event, max_x, max_y, animals, obstacles,const):
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_UP:
- self.move('Go Forward', max_x, max_y, obstacles, animals)
+ self.move('Go Forward', max_x, max_y, obstacles, animals,const)
elif event.key == pygame.K_LEFT:
- self.move('Turn Left', max_x, max_y, obstacles, animals)
+ self.move('Turn Left', max_x, max_y, obstacles, animals,const)
elif event.key == pygame.K_RIGHT:
- self.move('Turn Right', max_x, max_y, obstacles, animals)
+ self.move('Turn Right', max_x, max_y, obstacles, animals,const)
- def move(self, action, max_x, max_y, obstacles, animals, goal):
+ def move(self, action, max_x, max_y, obstacles, animals, goal,const):
if action == 'Go Forward':
new_x, new_y = self.x, self.y
if self.direction == 'N':
@@ -54,13 +75,54 @@ class Agent:
self.direction = {'N': 'E', 'E': 'S', 'S': 'W', 'W': 'N'}[self.direction]
self.istate = (self.x, self.y, self.direction)
- feed_animal(self, animals, goal)
+ feed_animal(self, animals, goal,const)
+ take_food(self)
-def feed_animal(self, animals, goal):
+def feed_animal(self, animals, goal,const):
goal_x, goal_y = goal
+ neuron = AnimalClassifier('./model/best_model.pth', classes)
if self.x == goal_x and self.y == goal_y:
for animal in animals:
if animal.x == goal_x and animal.y == goal_y:
- if animal.feed() == 'True':
- animal._feed = 0
- print(animal.name, "fed with", animal.food)
\ No newline at end of file
+ if (animal.activity == 'nocturnal' and const.IS_NIGHT) or (animal.activity == 'diurnal' and not(const.IS_NIGHT)):
+ activity_time = True
+ else:
+ activity_time = False
+ guests = random.randint(1, 15)
+ guess = neuron.classify(animal.image_path)
+ if guess == animal.name:
+ print(f"I'm sure this is {guess} and i give it {animal.food} as a snack")
+ animal.draw_snack(const.screen, const.GRID_SIZE, animal.x, animal.y)
+ else:
+ print(f"I was wrong, this is not a {guess} but a {animal.name}")
+ decision = feed_decision(animal.adult, activity_time, animal.ill, const.season, guests, animal._feed, self._dryfood, self._wetfood)
+ if decision != [1]:
+ if decision == [2]:
+ if animal.getting_hungry(const=Constants()) < self._wetfood :
+ self._wetfood -= animal._feed
+ animal._feed = 0
+ else:
+ animal._feed -= self._wetfood
+ self._wetfood = 0
+ print(animal.name, "fed with wet food")
+ else:
+ if animal.getting_hungry(const=Constants()) < self._dryfood :
+ self._dryfood -= animal._feed
+ animal._feed = 0
+ else:
+ animal._feed -= self._dryfood
+ self._dryfood = 0
+ print(animal.name, "fed with dry food")
+ print("Current wet food level: ", self._wetfood)
+ print("Current dry food level: ", self._dryfood)
+ else: print(animal.name, " not fed")
+
+
+def take_food(self):
+ house_x = 3
+ house_y = 1
+ if self.x == house_x and self.y == house_y:
+ if self._dryfood < 1 or self._wetfood < 1:
+ self._dryfood = 50
+ self._wetfood = 50
+ print("Agent took food and current food level is", self._dryfood, self._wetfood)
diff --git a/classification.py b/classification.py
new file mode 100644
index 0000000..5545b7d
--- /dev/null
+++ b/classification.py
@@ -0,0 +1,47 @@
+import torch
+import torchvision.transforms as transforms
+import PIL.Image as Image
+
+class AnimalClassifier:
+ def __init__(self, model_path, classes, image_size=224, mean=None, std=None):
+ self.classes = classes
+ self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ self.model = torch.load(model_path, map_location=torch.device('cpu'))
+ self.model = self.model.to(self.device)
+ self.model = self.model.eval()
+ self.image_size = image_size
+ self.mean = mean if mean is not None else [0.5164, 0.5147, 0.4746]
+ self.std = std if std is not None else [0.2180, 0.2126, 0.2172]
+ self.image_transforms = transforms.Compose([
+ transforms.Resize((self.image_size, self.image_size)),
+ transforms.ToTensor(),
+ transforms.Normalize(torch.Tensor(self.mean), torch.Tensor(self.std))
+ ])
+
+ def classify(self, image_path):
+ image = Image.open(image_path)
+
+ if image.mode == 'RGBA':
+ image = image.convert('RGB')
+
+ image = self.image_transforms(image).float()
+ image = image.unsqueeze(0).to(self.device)
+
+ with torch.no_grad():
+ output = self.model(image)
+
+ _, predicted = torch.max(output.data, 1)
+
+ return self.classes[predicted.item()]
+
+classes = [
+ "bat",
+ "bear",
+ "elephant",
+ "giraffe",
+ "owl",
+ "parrot",
+ "penguin"
+]
+
+
diff --git a/constants.py b/constants.py
index 2796685..e3623c0 100644
--- a/constants.py
+++ b/constants.py
@@ -1,15 +1,26 @@
+import random
import pygame
+import time
class Constants:
def __init__(self):
self.BLACK = (0, 0, 0)
self.RED = (255, 0, 0)
- self.GRID_SIZE = 50
+ self.GRID_SIZE = 65
self.GRID_WIDTH = 30
self.GRID_HEIGHT = 15
self.WINDOW_SIZE = (self.GRID_WIDTH * self.GRID_SIZE, self.GRID_HEIGHT * self.GRID_SIZE)
self.background_image = pygame.transform.scale(pygame.image.load('images/tło.jpg'), self.WINDOW_SIZE)
+ self.IS_NIGHT = False
+ self.TIME_CHANGE = time.time() + 60
+
+ self.season = random.choice(["spring", "summer", "autumn", "winter"])
+
+ self.SIZE = 224
+ self.mean = [0.5164, 0.5147, 0.4746]
+ self.std = [0.2180, 0.2126, 0.2172]
+
def init_pygame(const):
pygame.init()
const.screen = pygame.display.set_mode(const.WINDOW_SIZE)
\ No newline at end of file
diff --git a/dane.csv b/dane.csv
new file mode 100644
index 0000000..3125c80
--- /dev/null
+++ b/dane.csv
@@ -0,0 +1,313 @@
+adult,active_time,ill,season,guests,hunger,wet_food,dry_food,decision
+True,True,True,spring,12,4.0462,37.63803,5.60819,2
+True,False,False,spring,9,2.89229,34.37597,27.75948,1
+False,True,True,summer,15,2.4002,45.06447,41.50999,3
+True,False,False,summer,5,0.73248,46.1058,33.44281,1
+True,False,False,summer,12,0.62973,49.99647,41.07699,1
+True,False,False,winter,9,7.0889,46.16796,45.904,2
+False,True,False,summer,2,9.07977,22.08011,20.53507,3
+False,False,True,winter,11,3.5635,14.75823,43.46342,2
+False,False,True,winter,9,8.03113,20.6384,30.81177,2
+True,True,True,summer,0,0.01966,28.27203,3.37575,1
+False,False,False,autumn,12,8.27518,5.91931,1.10505,2
+False,False,True,summer,1,5.058,11.01892,48.04589,3
+False,True,False,winter,9,5.64777,17.19678,12.20864,1
+True,True,True,summer,0,6.86046,19.03315,47.13198,3
+True,False,True,winter,13,0.42516,12.62312,19.15853,1
+True,False,True,summer,14,4.46951,33.59537,47.343,1
+True,True,False,autumn,11,6.49386,0.64398,1.0515,1
+False,False,False,winter,10,1.9354,22.80015,26.01165,1
+True,False,False,summer,7,3.55716,1.91094,41.82462,1
+False,True,True,winter,8,9.01072,15.89341,9.10422,2
+False,True,True,spring,4,4.64513,10.39766,7.57955,2
+True,False,False,autumn,2,4.11019,45.85039,30.34183,1
+True,True,True,spring,8,7.09003,6.92577,24.77301,3
+True,True,False,spring,6,4.12955,47.91516,12.84722,1
+True,True,True,spring,10,2.70845,1.0623,42.6941,3
+True,True,True,spring,15,4.34025,6.89381,3.03923,2
+True,False,False,autumn,5,7.24464,31.34759,6.44719,1
+True,True,True,summer,6,3.8924,18.08964,18.77435,1
+False,False,True,spring,13,4.86962,48.62578,6.05301,2
+False,True,False,spring,11,1.60186,31.34926,14.41007,2
+False,True,False,autumn,2,4.19839,19.58116,28.63919,1
+False,True,True,autumn,6,1.50346,25.88282,24.97486,1
+False,True,False,spring,7,2.67431,17.05587,24.7939,1
+True,True,True,spring,7,1.72828,38.17304,24.3685,1
+False,True,True,summer,12,7.55322,26.52797,42.24754,3
+False,True,False,summer,5,8.74993,30.39317,4.5676,2
+False,True,False,summer,5,7.42862,48.73014,5.94891,2
+True,False,False,spring,4,9.3662,12.90556,41.31266,3
+False,True,False,autumn,8,4.72785,0.1623,19.48377,1
+False,False,False,winter,14,6.49349,29.52121,21.87192,1
+True,False,False,summer,14,1.57899,26.90783,33.03528,1
+False,True,False,summer,3,6.21725,3.60564,23.51978,1
+True,True,True,winter,4,4.18565,25.24209,11.02056,1
+False,False,True,autumn,8,6.5479,39.16107,4.75438,2
+False,False,False,summer,13,2.14609,39.97177,13.98651,1
+True,True,False,winter,9,7.43651,10.09353,15.70939,3
+False,False,True,autumn,5,7.85569,40.47073,49.75818,2
+True,False,True,summer,1,9.03492,23.44692,20.0026,3
+False,True,True,autumn,8,2.36724,42.81768,21.34668,2
+False,False,True,summer,15,6.8222,15.2733,15.14799,3
+True,False,True,summer,4,8.63882,41.36166,7.98981,2
+False,True,False,autumn,12,0.48943,8.67832,40.4952,1
+False,False,False,autumn,3,3.0489,14.81219,8.32707,1
+False,True,True,winter,6,0.41014,49.94757,12.61713,1
+True,False,True,winter,12,5.55017,0.98544,10.25287,1
+True,False,False,summer,11,9.92135,6.80759,48.0665,3
+False,True,True,winter,2,7.14394,11.41862,18.29288,2
+False,True,True,winter,10,1.39924,36.41807,42.95548,2
+False,True,False,summer,1,0.95193,46.53851,25.70391,1
+False,True,False,winter,4,1.44658,6.61497,31.58116,1
+False,True,False,autumn,10,7.9404,26.82316,49.5898,2
+True,False,False,spring,5,5.14951,32.36345,11.32114,1
+True,True,False,summer,5,7.55665,49.12578,29.32983,3
+True,False,True,autumn,3,3.80646,11.86722,35.43034,1
+False,False,False,winter,12,3.65822,0.14026,19.18031,1
+True,False,False,spring,8,8.58384,46.33595,11.52974,2
+True,False,False,summer,3,6.34127,22.66891,19.15813,1
+False,True,False,spring,13,7.86448,8.85557,40.03913,3
+True,True,False,spring,13,8.89316,49.89548,21.04525,2
+True,True,False,autumn,9,0.91339,36.35922,16.09576,1
+False,False,False,summer,3,7.36268,28.50462,29.52973,1
+True,True,True,spring,3,6.1319,37.71758,33.50616,1
+False,True,True,spring,0,0.77228,42.89976,19.19004,1
+True,False,True,autumn,3,7.73055,20.87865,37.18248,1
+False,False,False,summer,1,8.30392,34.47046,8.77926,2
+True,True,False,summer,8,2.96562,17.50839,23.22476,1
+True,True,True,winter,4,2.27279,20.58575,32.17293,1
+True,False,True,spring,7,6.14608,34.46015,17.22245,1
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+False,True,False,spring,14,9.56618,48.49473,46.37651,2
+True,False,True,spring,10,9.33146,47.99213,35.92519,2
+False,True,False,spring,6,0.47396,37.45415,36.87019,1
+True,False,False,summer,12,7.61463,4.36339,36.07375,3
+True,True,True,autumn,3,6.42039,4.90383,43.29857,3
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+False,False,False,spring,0,3.76673,1.64674,34.50649,1
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+True,False,False,summer,12,5.68671,49.18777,22.53807,3
+False,False,False,winter,0,0.86765,41.28704,33.77284,1
+False,False,True,summer,10,5.97643,36.23669,48.32615,2
+False,True,True,autumn,7,1.76947,38.34692,13.28679,1
+True,True,True,summer,13,8.63622,16.14861,44.91355,3
+True,False,False,winter,11,8.48481,37.52722,47.76888,2
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+False,False,False,winter,7,0.53067,13.47003,18.45329,1
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+True,True,False,spring,12,5.19071,47.53107,34.68942,2
+True,False,False,summer,9,6.9442,46.79146,13.92798,2
+True,False,False,autumn,1,3.09242,18.02023,11.03004,1
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+True,True,True,autumn,1,9.98637,27.57982,15.82173,2
+False,True,True,summer,9,8.25408,13.10493,27.07596,3
+False,False,False,spring,15,1.9089,33.25115,44.57492,1
+True,False,False,autumn,12,6.65534,38.00972,20.31047,2
+False,True,True,autumn,1,0.01592,6.24929,15.51308,1
+False,True,True,winter,13,1.24017,36.88006,16.50894,1
+False,True,True,winter,13,6.1878,15.18876,9.02381,2
+True,True,True,winter,5,5.45157,13.27868,39.39805,2
+True,False,True,spring,5,2.82881,28.62319,24.03077,1
+False,False,True,spring,2,4.8246,41.45269,48.89539,2
+True,False,False,spring,8,9.3343,39.02018,45.01066,3
+True,True,True,autumn,4,1.8456,2.94366,37.44996,1
+False,True,True,summer,14,0.95333,4.57964,26.37633,1
+False,False,True,autumn,13,9.84087,24.03819,41.72097,2
+True,False,False,autumn,9,3.70617,32.70115,1.69105,1
+True,True,True,spring,12,6.77783,6.67976,20.46179,3
+False,False,True,summer,15,7.15829,31.24546,10.37666,3
+False,True,True,summer,1,2.28393,18.13299,34.38756,3
+True,False,True,autumn,7,3.96302,39.84093,47.0172,1
+True,False,False,summer,1,0.65085,20.20581,14.96995,1
+True,False,False,winter,2,9.24331,26.34543,30.5147,2
+True,False,False,summer,3,2.86309,15.56342,1.04324,1
+True,True,True,autumn,8,5.71809,24.41045,48.78273,2
+True,True,True,autumn,8,5.71809,24.41045,48.78273,2
+True,True,True,winter,8,5.71809,0.99999,48.78273,3
+True,True,True,winter,8,5.71809,1.98675,48.79993,3
+True,True,False,winter,8,7.71809,1.02345,38.78273,3
+True,False,False,summer,1,4.03947,33.84073,6.48891,1
+True,False,False,winter,4,2.46471,24.07929,17.77792,1
+False,False,False,winter,9,3.66415,23.68306,24.43865,1
+True,True,True,autumn,8,5.71809,24.41045,48.78273,2
+False,True,True,spring,3,5.3009,34.83862,6.75862,2
+True,False,True,winter,8,6.23275,8.1183,19.6922,3
+False,True,True,autumn,2,4.21016,11.24334,34.98395,3
+True,True,False,autumn,0,2.79424,13.25106,5.69617,1
+True,True,False,winter,15,2.43843,14.61703,49.57393,2
+True,True,True,summer,8,2.28654,20.9895,5.64007,1
+False,False,False,autumn,1,2.5607,26.85209,47.10784,1
+True,True,True,winter,14,1.56638,18.02703,7.05011,1
+False,False,False,winter,13,3.86632,28.9884,20.1928,1
+True,False,False,summer,13,6.37654,34.3833,34.53892,3
+True,False,False,summer,13,6.37654,34.3833,34.53892,3
+True,False,False,spring,11,8.35634,0.00000,34.53892,3
+True,False,True,summer,13,9.37654,34.3833,0.00000,2
+True,False,False,winter,13,7.77754,34.3833,0.00000,2
+True,True,True,summer,5,8.10422,7.6617,23.41017,3
\ No newline at end of file
diff --git a/decision_tree.py b/decision_tree.py
new file mode 100644
index 0000000..b42ffea
--- /dev/null
+++ b/decision_tree.py
@@ -0,0 +1,48 @@
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier, plot_tree
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+import matplotlib.pyplot as plt
+headers = ['adult','active_time','ill','season','guests','hunger','wet_food','dry_food']
+# Wczytanie danych
+data = pd.read_csv('dane.csv', header=0)
+X = data[headers]
+Y = data['decision']
+X = pd.get_dummies(data=X, columns=['season'])
+clf = DecisionTreeClassifier(max_depth=6)
+X1, X2, Y1, Y2 = train_test_split(X, Y, train_size=0.8)
+clf = clf.fit(X1, Y1)
+pred = clf.predict(X2)
+accuracy = accuracy_score(Y2, pred)
+print("Dokładność:", accuracy)
+
+
+
+#zapisanie drzewa do pliku
+plt.figure(figsize=(50,30))
+plot_tree(clf, filled=True, feature_names=X.columns.tolist(), class_names=['nie karmi', 'karmi mokrą karmą', 'karmi suchą karmą'])
+plt.savefig('tree.png')
+# dane do decyzji
+def feed_decision(adult,active_time,ill,season,guests,hunger,dry_food,wet_food):
+
+ X_new = pd.DataFrame({
+ 'adult': [adult],
+ 'active_time': [active_time],
+ 'ill': [ill],
+ 'season': [season],
+ 'guests':[guests],
+ 'hunger': [hunger],
+ 'wet_food': [wet_food],
+ 'dry_food': [dry_food]
+})
+ X_new = pd.get_dummies(X_new)
+ missing_columns = set(X.columns) - set(X_new)
+ for col in missing_columns:
+ X_new[col] = False
+ X_new = X_new.reindex(columns=X.columns, fill_value=0)
+ print("Atrybuty zwierzęcia:", adult,active_time,ill,season,guests,hunger,wet_food,dry_food)
+ return (clf.predict(X_new))
+
+
+
+
diff --git a/draw.py b/draw.py
index f032281..df5df3e 100644
--- a/draw.py
+++ b/draw.py
@@ -11,4 +11,12 @@ def draw_grid(const):
for y in range(0, const.GRID_HEIGHT * const.GRID_SIZE, const.GRID_SIZE):
for x in range(0, const.GRID_WIDTH * const.GRID_SIZE, const.GRID_SIZE):
rect = pygame.Rect(x, y, const.GRID_SIZE, const.GRID_SIZE)
- pygame.draw.rect(const.screen, const.BLACK, rect, 1)
\ No newline at end of file
+ pygame.draw.rect(const.screen, const.BLACK, rect, 1)
+
+def draw_house(const):
+ X = 2
+ Y = 0
+ image_path = 'images/house.png'
+ image_surface = pygame.image.load(image_path) # Wczytanie obrazka do obiektu Surface
+ scaled_image = pygame.transform.scale(image_surface, (const.GRID_SIZE * 2, const.GRID_SIZE * 2))
+ const.screen.blit(scaled_image, (X * const.GRID_SIZE, Y * const.GRID_SIZE))
\ No newline at end of file
diff --git a/enclosure.py b/enclosure.py
index 723e243..da237a8 100644
--- a/enclosure.py
+++ b/enclosure.py
@@ -1,6 +1,5 @@
import pygame
-
class Enclosure:
def __init__(self, x1, y1, x2, y2, gate1, gate2, type, imageH, imageV, imageGate):
self.x1 = x1 - 1
@@ -15,6 +14,7 @@ class Enclosure:
self.imageH = imageH
self.imageV = imageV
self.imageGate = imageGate
+ self.animals = set()
def gatebuild(self, screen, grid_size):
self.imageGate = pygame.transform.scale(self.imageGate, (grid_size, grid_size))
@@ -56,10 +56,10 @@ def create_enclosures():
gate = pygame.image.load('images/gate.png')
en1 = Enclosure(0, 5, 9, 11, (9, 6), (4, 11), "hot", fenceH, fenceV, gate) # Lewa klatka
- en2 = Enclosure(13, 0, 29, 3, (16, 3), (27, 3), 'medium', fenceH, fenceV, gate) # Górna klatka
- en3 = Enclosure(11, 5, 16, 11, (12, 5), (16, 8), 'cold', fenceH, fenceV, gate) # Środkowa klatka
- en4 = Enclosure(19, 5, 31, 11, (23, 5), (25, 11), 'hot', fenceH, fenceV, gate) # Prawa klatka
- en5 = Enclosure(4, 13, 28, 16, (12, 13), (20, 13), 'cold', fenceH, fenceV, gate) # Dolna klatka
+ en2 = Enclosure(4, 13, 28, 16, (12, 13), (20, 13), 'cold', fenceH, fenceV, gate) # Dolna klatka
+ en3 = Enclosure(19, 5, 31, 11, (23, 5), (25, 11), 'hot', fenceH, fenceV, gate) # Prawa klatka
+ en4 = Enclosure(11, 5, 16, 11, (12, 5), (16, 8), 'cold', fenceH, fenceV, gate) # Środkowa klatka
+ en5 = Enclosure(13, 0, 29, 3, (16, 3), (27, 3), 'medium', fenceH, fenceV, gate) # Górna klatka
Enclosures = [en1, en2, en3, en4, en5]
diff --git a/genetics.py b/genetics.py
new file mode 100644
index 0000000..22dea52
--- /dev/null
+++ b/genetics.py
@@ -0,0 +1,148 @@
+from state_space_search import graphsearch, generate_cost_map
+import random
+
+# Parametry algorytmu genetycznego
+POPULATION_SIZE = 700
+MUTATION_RATE = 0.01
+NUM_GENERATIONS = 600
+
+# Generowanie początkowej populacji
+def generate_individual(animals):
+ return random.sample(animals, len(animals))
+
+def generate_population(animals, size):
+ return [generate_individual(animals) for _ in range(size)]
+
+# Obliczanie odległości między zwierzetami
+def calculate_distance(animal1, animal2):
+ x1, y1 = animal1
+ x2, y2 = animal2
+ return abs(x1 - x2) + abs(y1 - y2) # Odległość Manhattana
+
+def calculate_total_distance(animals):
+ total_distance = 0
+ for i in range(len(animals) - 1):
+ total_distance += calculate_distance(animals[i], animals[i+1])
+ total_distance += calculate_distance(animals[-1], animals[0]) # Zamknięcie cyklu
+ return total_distance
+
+# Selekcja rodziców za pomocą metody ruletki
+def select_parents(population, num_parents):
+ fitness_scores = [1 / calculate_total_distance(individual) for individual in population]
+ total_fitness = sum(fitness_scores)
+ selection_probs = [fitness / total_fitness for fitness in fitness_scores]
+
+ parents = random.choices(population, weights=selection_probs, k=num_parents)
+ return parents
+
+# Krzyżowanie rodziców (OX,Davis)
+def crossover(parent1, parent2):
+ child1 = [None] * len(parent1)
+ child2 = [None] * len(parent1)
+ start_index = random.randint(0, len(parent1) - 1)
+ end_index = random.randint(start_index, len(parent1) - 1)
+ child1[start_index:end_index+1] = parent1[start_index:end_index+1]
+ child2[start_index:end_index+1] = parent2[start_index:end_index+1]
+
+ # Uzupełnienie brakujących zwierząt z drugiego rodzica
+ for i in range(len(parent1)):
+ if parent2[i] not in child1:
+ for j in range(len(parent2)):
+ if child1[j] is None:
+ child1[j] = parent2[i]
+ break
+
+ for i in range(len(parent1)):
+ if parent1[i] not in child2:
+ for j in range(len(parent1)):
+ if child2[j] is None:
+ child2[j] = parent1[i]
+ break
+
+ return child1, child2
+
+# Mutacja: zamiana dwóch losowych zwierząt z prawdopodobieństwem MUTATION_RATE
+def mutate(individual):
+ if random.random() < MUTATION_RATE:
+ index1, index2 = random.sample(range(len(individual)), 2)
+ individual[index1], individual[index2] = individual[index2], individual[index1]
+
+# Algorytm genetyczny
+def genetic_algorithm(animals):
+ population = generate_population(animals, POPULATION_SIZE)
+
+ for generation in range(NUM_GENERATIONS):
+ # Selekcja rodziców
+ parents = select_parents(population, POPULATION_SIZE // 2)
+
+ # Krzyżowanie i tworzenie nowej populacji
+ next_generation = []
+ for i in range(0, len(parents), 2):
+ parent1 = parents[i]
+ if i + 1 < len(parents):
+ parent2 = parents[i + 1]
+ else:
+ parent2 = parents[0]
+ child1, child2 = crossover(parent1, parent2)
+ next_generation.extend([child1, child2])
+
+ # Mutacja nowej populacji
+ for individual in next_generation:
+ mutate(individual)
+
+ # Zastąpienie starej populacji nową
+ population = next_generation
+
+ # Znalezienie najlepszego osobnika
+ best_individual = min(population, key=calculate_total_distance)
+
+ return best_individual
+
+# def calculate_distance(start, goal, max_x, max_y, obstacles, cost_map):
+# istate = (start[0], start[1], 'N') # Zakładamy, że zaczynamy od kierunku północnego
+# actions, cost = graphsearch(istate, goal, max_x, max_y, obstacles, cost_map)
+# return cost
+
+# def calculate_total_distance(animals, max_x, max_y, obstacles, cost_map):
+# total_distance = 0
+# for i in range(len(animals) - 1):
+# total_distance += calculate_distance(animals[i], animals[i+1], max_x, max_y, obstacles, cost_map)
+# total_distance += calculate_distance(animals[-1], animals[0], max_x, max_y, obstacles, cost_map) # Zamknięcie cyklu
+# return total_distance
+
+# # Selekcja rodziców za pomocą metody ruletki
+# def select_parents(population, num_parents, max_x, max_y, obstacles, cost_map):
+# fitness_scores = [1 / calculate_total_distance(individual, max_x, max_y, obstacles, cost_map) for individual in population]
+# total_fitness = sum(fitness_scores)
+# selection_probs = [fitness / total_fitness for fitness in fitness_scores]
+
+# parents = random.choices(population, weights=selection_probs, k=num_parents)
+# return parents
+
+
+# def genetic_algorithm(animals, max_x, max_y, obstacles, cost_map):
+# population = generate_population(animals, POPULATION_SIZE)
+
+# for generation in range(NUM_GENERATIONS):
+# # Selekcja rodziców
+# parents = select_parents(population, POPULATION_SIZE // 2, max_x, max_y, obstacles, cost_map)
+
+# # Krzyżowanie i tworzenie nowej populacji
+# next_generation = []
+# for i in range(0, len(parents), 2):
+# parent1 = parents[i]
+# parent2 = parents[i + 1]
+# child1, child2 = crossover(parent1, parent2)
+# next_generation.extend([child1, child2])
+
+# # Mutacja nowej populacji
+# for individual in next_generation:
+# mutate(individual)
+
+# # Zastąpienie starej populacji nową
+# population = next_generation
+
+# # Znalezienie najlepszego osobnika
+# best_individual = min(population, key=lambda individual: calculate_total_distance(individual, max_x, max_y, obstacles, cost_map))
+
+# return best_individual
\ No newline at end of file
diff --git a/images/almost_empty.png b/images/almost_empty.png
new file mode 100644
index 0000000..f6b404d
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diff --git a/images/bat.png b/images/bat.png
new file mode 100644
index 0000000..2dd676a
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diff --git a/images/bat2.png b/images/bat2.png
new file mode 100644
index 0000000..3a6d087
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diff --git a/images/bear2.png b/images/bear2.png
new file mode 100644
index 0000000..75d8b71
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diff --git a/images/elephant.png b/images/elephant.png
index 86c15e1..6a9125c 100644
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diff --git a/images/elephant2.png b/images/elephant2.png
new file mode 100644
index 0000000..532d6b6
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diff --git a/images/empty_bowl.png b/images/empty_bowl.png
new file mode 100644
index 0000000..975e256
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diff --git a/images/full_bowl.png b/images/full_bowl.png
new file mode 100644
index 0000000..9f45a3b
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diff --git a/images/giraffe.png b/images/giraffe.png
index 8247a89..cbfe5a2 100644
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diff --git a/images/giraffe2.png b/images/giraffe2.png
new file mode 100644
index 0000000..c1b0b63
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diff --git a/images/half_bowl.png b/images/half_bowl.png
new file mode 100644
index 0000000..992ebab
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diff --git a/images/house.png b/images/house.png
new file mode 100644
index 0000000..9195278
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diff --git a/images/ill.png b/images/ill.png
new file mode 100644
index 0000000..fb97cbd
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diff --git a/images/owl.png b/images/owl.png
new file mode 100644
index 0000000..039dbd0
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diff --git a/images/owl2.png b/images/owl2.png
new file mode 100644
index 0000000..5a836c1
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diff --git a/images/parrot2.png b/images/parrot2.png
new file mode 100644
index 0000000..99bc134
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diff --git a/images/penguin2.png b/images/penguin2.png
new file mode 100644
index 0000000..d0eea2d
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diff --git a/images/tłojesień.jpg b/images/tłojesień.jpg
new file mode 100644
index 0000000..c9215a5
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diff --git a/images/tłowiosna.jpg b/images/tłowiosna.jpg
new file mode 100644
index 0000000..7e17fa2
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diff --git a/images/tłozima.jpg b/images/tłozima.jpg
new file mode 100644
index 0000000..8c49b50
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diff --git a/main.py b/main.py
index 8246fde..738317f 100644
--- a/main.py
+++ b/main.py
@@ -1,7 +1,7 @@
import random
import pygame
import sys
-import sys
+
sys.path.append('./Animals')
from animals import create_animals, draw_Animals
from agent import Agent
@@ -10,7 +10,10 @@ from spawner import Spawner
from state_space_search import graphsearch, generate_cost_map
from terrain_obstacle import create_obstacles, draw_Terrain_Obstacles
from constants import Constants, init_pygame
-from draw import draw_goal, draw_grid
+from draw import draw_goal, draw_grid, draw_house
+from season import draw_background
+from night import change_time
+from genetics import genetic_algorithm
const = Constants()
init_pygame(const)
@@ -75,42 +78,81 @@ def main():
actions = []
clock = pygame.time.Clock()
spawned = False
+ route = False
+
+ # # Lista zawierająca klatki do odwiedzenia
+ # enclosures_to_visit = Enclosures.copy()
+ # current_enclosure_index = -1 # Indeks bieżącej klatki
+ # actions_to_compare_list = [] # Lista zawierająca ścieżki do porównania
+ # goals_to_compare_list = list() # Lista zawierająca cele do porównania
while True:
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
sys.exit()
- agent.handle_event(event, const.GRID_WIDTH, const.GRID_HEIGHT, Animals, obstacles)
+ agent.handle_event(event, const.GRID_WIDTH, const.GRID_HEIGHT, Animals, obstacles,const)
- const.screen.blit(const.background_image, (0, 0))
- draw_grid(const)
+ change_time(const)
+ draw_background(const)
draw_enclosures(Enclosures, const)
draw_gates(Enclosures, const)
-
+ draw_house(const)
+
if not spawned:
spawn_all_animals()
spawn_obstacles()
cost_map = generate_cost_map(Animals, Terrain_Obstacles)
for animal in Animals:
- animal._feed = 2 # Ustawienie, aby zwierzę było głodne
+ # animal._feed = 0
+ animal._feed = random.randint(0, 10)
spawned = True
+
+ if not route:
+ animals = [(animal.x, animal.y) for animal in Animals]
+ best_route = genetic_algorithm(animals)
+ route = True
draw_Animals(Animals, const)
draw_Terrain_Obstacles(Terrain_Obstacles, const)
- agent.draw(const.screen, const.GRID_SIZE)
+ agent.draw(const)
pygame.display.flip()
clock.tick(10)
if actions:
action = actions.pop(0)
- agent.move(action, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, Animals, goal)
+ agent.move(action, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, Animals, goal,const)
pygame.time.wait(200)
else:
- animal = random.choice(Animals)
- goal = (animal.x, animal.y)
- draw_goal(const, goal)
- actions = graphsearch(agent.istate, goal, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, cost_map)
+ if agent._dryfood > 1 and agent._wetfood > 1 :
+ # if not goals_to_compare_list:
+ # current_enclosure_index = (current_enclosure_index + 1) % len(enclosures_to_visit)
+ # current_enclosure = enclosures_to_visit[current_enclosure_index]
+
+ # for animal in current_enclosure.animals:
+ # goal = (animal.x, animal.y)
+ # goals_to_compare_list.append(goal)
+
+ # actions_to_compare = graphsearch(agent.istate, goal, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, cost_map)
+ # actions_to_compare_list.append((actions_to_compare, goal))
+
+ # chosen_path_and_goal = min(actions_to_compare_list, key=lambda x: len(x[0]))
+ # goal = chosen_path_and_goal[1]
+ # draw_goal(const, goal)
+
+ # # Usuń wybrany element z listy
+ # actions_to_compare_list.remove(chosen_path_and_goal)
+ # goals_to_compare_list.remove(goal)
+ goal = best_route.pop(0)
+ best_route.append(goal)
+ draw_goal(const, goal)
+
+ actions, cost = graphsearch(agent.istate, goal, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, cost_map)
+
+ else:
+ goal = (3,1)
+ draw_goal(const, goal)
+ actions, cost = graphsearch(agent.istate, goal, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, cost_map)
if __name__ == "__main__":
main()
\ No newline at end of file
diff --git a/model/best_model.pth b/model/best_model.pth
new file mode 100644
index 0000000..c308654
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diff --git a/model/data/train/bat/0CUH9CM2IF4Z.jpg b/model/data/train/bat/0CUH9CM2IF4Z.jpg
new file mode 100644
index 0000000..966ac67
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diff --git a/model/model.py b/model/model.py
new file mode 100644
index 0000000..b36904a
--- /dev/null
+++ b/model/model.py
@@ -0,0 +1,129 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision.datasets
+from torchvision import datasets, transforms, models
+from torch.utils.data import DataLoader
+
+
+def set_device():
+ if torch.cuda.is_available():
+ device = 'cuda'
+ else:
+ device = 'cpu'
+ return torch.device(device)
+
+
+train_dataset_path = './data/train'
+test_dataset_path = './data/val'
+number_of_classes = 7
+
+SIZE = 224
+mean = [0.5164, 0.5147, 0.4746]
+std = [0.2180, 0.2126, 0.2172]
+
+train_transforms = transforms.Compose([
+ transforms.Resize((SIZE, SIZE)),
+ transforms.RandomHorizontalFlip(),
+ transforms.RandomRotation(10),
+ transforms.ToTensor(),
+ transforms.Normalize(torch.Tensor(mean), torch.Tensor(std))
+])
+
+test_transforms = transforms.Compose([
+ transforms.Resize((SIZE, SIZE)),
+ transforms.ToTensor(),
+ transforms.Normalize(torch.Tensor(mean), torch.Tensor(std))
+])
+
+train_dataset = torchvision.datasets.ImageFolder(root=train_dataset_path, transform=train_transforms)
+test_dataset = torchvision.datasets.ImageFolder(root=test_dataset_path, transform=test_transforms)
+
+train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
+test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)
+
+resnet18_model = models.resnet18(weights=None)
+num_ftrs = resnet18_model.fc.in_features
+resnet18_model.fc = nn.Linear(num_ftrs, number_of_classes)
+device = set_device()
+resnet18_model = resnet18_model.to(device)
+loss_fn = nn.CrossEntropyLoss()
+
+optimizer = optim.SGD(resnet18_model.parameters(), lr=0.001, momentum=0.9, weight_decay=0.003)
+
+
+def save_checkpoint(model, epoch, optimizer, best_acc):
+ state = {
+ 'epoch': epoch + 1,
+ 'model': model.state_dict(),
+ 'best accuracy': best_acc,
+ 'optimizer': optimizer.state_dict()
+ }
+ torch.save(state, 'model_best_checkpoint.pth.tar')
+def train_nn(model, train_loader, test_loader, criterion, optimizer, n_epochs):
+ device = set_device()
+ best_acc = 0
+
+ for epoch in range(n_epochs):
+ print("Epoch number %d " % (epoch + 1))
+ model.train()
+ running_loss = 0.0
+ running_correct = 0.0
+ total = 0
+
+ for data in train_loader:
+ images, labels = data
+ images = images.to(device)
+ labels = labels.to(device)
+ total += labels.size(0)
+
+ optimizer.zero_grad()
+ outputs = model(images)
+ _, predicted = torch.max(outputs.data, 1)
+ loss = criterion(outputs, labels)
+ loss.backward()
+ optimizer.step()
+
+ running_loss += loss.item()
+ running_correct += (labels == predicted).sum().item()
+
+ epoch_loss = running_loss/len(train_loader)
+ epoch_acc = 100 * running_correct / total
+ print(f"Training dataset. Got {running_correct} out of {total} images correctly ({epoch_acc}). Epoch loss: {epoch_loss}")
+
+ test_data_acc = evaluate_model_on_test_set(model, test_loader)
+
+ if test_data_acc > best_acc:
+ best_acc = test_data_acc
+ save_checkpoint(model, epoch, optimizer, best_acc)
+
+ print("Finished")
+ return model
+def evaluate_model_on_test_set(model, test_loader):
+ model.eval()
+ predicted_correctly_on_epoch = 0
+ total = 0
+ device = set_device()
+
+ with torch.no_grad():
+ for data in test_loader:
+ images, labels = data
+ images = images.to(device)
+ labels = labels.to(device)
+ total += labels.size(0)
+
+ outputs = model(images)
+ _, predicted = torch.max(outputs.data, 1)
+
+ predicted_correctly_on_epoch += (predicted == labels).sum().item()
+
+ epoch_acc = 100 * predicted_correctly_on_epoch / total
+ print(f"Testing dataset. Got {predicted_correctly_on_epoch} out of {total} images correctly ({epoch_acc})")
+ return epoch_acc
+
+
+train_nn(resnet18_model, train_loader, test_loader, loss_fn, optimizer, n_epochs=30)
+
+checkpoint = torch.load('model_best_checkpoint.pth.tar')
+resnet18_model.load_state_dict(checkpoint['model'])
+torch.save(resnet18_model, 'best_model.pth')
\ No newline at end of file
diff --git a/model/model_best_checkpoint.pth.tar b/model/model_best_checkpoint.pth.tar
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index 0000000..e00de45
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diff --git a/night.py b/night.py
new file mode 100644
index 0000000..cc831fd
--- /dev/null
+++ b/night.py
@@ -0,0 +1,19 @@
+import time
+import pygame
+
+DAY_LENGTH = 90 # Długość dnia w sekundach
+
+def draw_night(const):
+ overlay = pygame.Surface(const.WINDOW_SIZE)
+ overlay.fill((0, 0, 0))
+ overlay.set_alpha(128) # Ustawienie przezroczystości (0 - całkowicie przeźroczyste, 255 - nieprzeźroczyste)
+ const.screen.blit(overlay, (0, 0))
+
+def change_time(const):
+ current_time = time.time()
+
+ # Sprawdzamy, czy nadszedł czas zmiany pory dnia
+ if current_time >= const.TIME_CHANGE:
+ # Zmieniamy porę dnia
+ const.IS_NIGHT = not const.IS_NIGHT # Jeśli było dzień, teraz będzie noc, i odwrotnie
+ const.TIME_CHANGE = current_time + DAY_LENGTH
\ No newline at end of file
diff --git a/season.py b/season.py
new file mode 100644
index 0000000..4268ef7
--- /dev/null
+++ b/season.py
@@ -0,0 +1,11 @@
+import pygame
+
+def draw_background(const):
+ season_images = {
+ "spring": "images/tłowiosna.jpg",
+ "summer": "images/tło.jpg",
+ "autumn": "images/tłojesień.jpg",
+ "winter": "images/tłozima.jpg"
+ }
+ background_image = pygame.transform.scale(pygame.image.load(season_images[const.season]), const.WINDOW_SIZE)
+ const.screen.blit(background_image, (0, 0))
\ No newline at end of file
diff --git a/spawner.py b/spawner.py
index 29f89eb..04b0849 100644
--- a/spawner.py
+++ b/spawner.py
@@ -9,6 +9,8 @@ class Spawner:
# Wyrażenie listowe filtrujące tylko te wybiegi, które pasują do środowiska zwierzęcia
enclosure = random.choice(self.enclosures)
+ enclosure.animals.add(self.entity) # Przydzielenie zwierzęcia do wybiegu
+
while True:
if self.entity.adult:
self.entity.x = random.randint(enclosure.x1+1, enclosure.x2-2)
@@ -21,7 +23,7 @@ class Spawner:
break
def spawn_terrain_obstacles(self, blocked1, blocked2, taken, grid_width, grid_height):
- blocked1 = blocked1 | {(8,5),(3,10),(15,2),(26,2),(11,4),(15,7),(22,4),(24,10),(11,12),(19,12)}
+ blocked1 = blocked1 | {(2,0),(3,0),(2,1),(3,1),(8,5),(3,10),(15,2),(26,2),(11,4),(15,7),(22,4),(24,10),(11,12),(19,12)}
while True:
self.entity.x = random.randint(0, grid_width - 1)
self.entity.y = random.randint(0, grid_height - 1)
diff --git a/state_space_search.py b/state_space_search.py
index 7aae41e..0f1ea91 100644
--- a/state_space_search.py
+++ b/state_space_search.py
@@ -40,7 +40,7 @@ def graphsearch(istate, goal, max_x, max_y, obstacles, cost_map):
state, _, _ = node
if goaltest(state, goal):
- return build_action_sequence(node)
+ return build_action_sequence(node), current_cost(node, cost_map)
explored.add(state)
@@ -61,7 +61,7 @@ def graphsearch(istate, goal, max_x, max_y, obstacles, cost_map):
else:
break
- return False
+ return False, float('inf')
def is_state_in_queue(state, queue):
for _, (s, _, _) in queue.queue:
@@ -111,9 +111,9 @@ def generate_cost_map(Animals, Terrain_Obstacles, cost_map={}):
for animal in Animals:
if animal.adult:
- cost_map[(animal.x + 1, animal.y + 1)] = adult_animal_cost
- cost_map[(animal.x + 1, animal.y)] = adult_animal_cost
- cost_map[(animal.x, animal.y + 1)] = adult_animal_cost
+ # cost_map[(animal.x + 1, animal.y + 1)] = adult_animal_cost
+ # cost_map[(animal.x + 1, animal.y)] = adult_animal_cost
+ # cost_map[(animal.x, animal.y + 1)] = adult_animal_cost
cost_map[(animal.x, animal.y)] = adult_animal_cost
else:
cost_map[(animal.x, animal.y)] = baby_animal_cost
@@ -124,4 +124,5 @@ def generate_cost_map(Animals, Terrain_Obstacles, cost_map={}):
else:
cost_map[(terrain_obstacle.x , terrain_obstacle.y )] = bush_cost
- return cost_map
\ No newline at end of file
+ return cost_map
+
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