Merge pull request 'Algorytm_genetyczny' (#3) from Algorytm_genetyczny into master

Reviewed-on: s481865/Male_zoo#3

.gitignore

@@ -0,0 +1 @@
+*.pyc

.idea/misc.xml

@@ -3,5 +3,5 @@
   <component name="Black">
     <option name="sdkName" value="Python 3.9" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (pythonProject)" project-jdk-type="Python SDK" />
 </project>

.idea/sztuczna.iml

@@ -1,8 +1,10 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$" />
-    <orderEntry type="inheritedJdk" />
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
+    </content>
+    <orderEntry type="jdk" jdkName="Python 3.10 (pythonProject)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

.vscode/settings.json

@@ -0,0 +1,5 @@
+{
+    "python.analysis.extraPaths": [
+        "./Animals"
+    ]
+}

Animals/animal.py

@@ -0,0 +1,86 @@
+import random
+import pygame
+from abc import abstractmethod
+
+class Animal:
+
+    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_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.activity = activity    # diurnal/nocturnal
+        self.ill = ill
+
+    def draw(self, screen, grid_size):
+        if self.adult:
+            # Jeśli zwierzę jest dorosłe, skaluj obrazek na większy rozmiar
+            new_width = grid_size * 2
+            new_height = grid_size * 2
+            scaled_image = pygame.transform.scale(self.image, (new_width, new_height))
+            screen.blit(scaled_image, (self.x * grid_size, self.y * grid_size))
+        else:
+            # Jeśli zwierzę nie jest dorosłe, skaluj obrazek na rozmiar jednej kratki
+            scaled_image = pygame.transform.scale(self.image, (grid_size, grid_size))
+            screen.blit(scaled_image, (self.x * grid_size, self.y * grid_size))
+
+    def draw_exclamation(self, screen, grid_size, x, y):
+        exclamation_image = pygame.image.load('images/exclamation.png')
+        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,food_image):
+        scale = 0.45
+        food_image = pygame.image.load(food_image)
+
+        if(self.adult):
+            y = y + 1
+            scale = 0.7
+        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)))
+
+    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):
+        pass

Animals/animals.py

@@ -0,0 +1,62 @@
+from elephant import Elephant
+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)
+    giraffe2 = Giraffe(0, 0, adult=True)
+    giraffe3 = Giraffe(0, 0, adult=True)
+    giraffe4 = Giraffe(0, 0)
+    giraffe5 = Giraffe(0, 0)
+    bear1 = Bear(0, 0, adult=True)
+    bear2 = Bear(0, 0, adult=True)
+    bear3 = Bear(0, 0)
+    bear4 = Bear(0, 0)
+    bear5 = Bear(0, 0)
+    penguin1 = Penguin(0, 0)
+    penguin2 = Penguin(0, 0)
+    penguin3 = Penguin(0, 0)
+    penguin4 = Penguin(0, 0)
+    elephant1 = Elephant(0, 0, adult=True)
+    elephant2 = Elephant(0, 0, adult=True)
+    elephant3 = Elephant(0, 0)
+    elephant4 = Elephant(0, 0)
+    elephant5 = Elephant(0, 0)
+    parrot1 = Parrot(0, 0)
+    parrot2 = 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, owl1, owl2, bat1, bat2]
+
+    return Animals
+
+def draw_Animals(Animals, const):
+    for Animal in Animals:
+        Animal.draw(const.screen, const.GRID_SIZE)
+        
+        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)

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

Animals/bear.py

@@ -0,0 +1,27 @@
+from animal import Animal
+import pygame
+from datetime import datetime
+
+class Bear(Animal):
+    def __init__(self, x, y, adult=False):
+        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, image_path, food_image,bear_food,environment, activity, ill, adult)
+        self._starttime = datetime.now()
+
+    def getting_hungry(self, const):
+        
+        checktime = datetime.now()
+        delta = checktime - self._starttime
+        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

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
+        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

Animals/giraffe.py

@@ -0,0 +1,26 @@
+from animal import Animal
+import pygame
+from datetime import datetime
+
+class Giraffe(Animal):
+    def __init__(self, x, y, adult=False):
+        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, image_path, food_image,giraffe_food, environment, activity, ill, adult)
+        self._starttime = datetime.now()
+
+    def getting_hungry(self, const):
+        checktime = datetime.now()
+        delta = checktime - self._starttime
+        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

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

Animals/parrot.py

@@ -0,0 +1,26 @@
+from animal import Animal
+import pygame
+from datetime import datetime
+
+class Parrot(Animal):
+    def __init__(self, x, y, adult=False):
+        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, image_path, food_image, parrot_food, environment, activity, ill, adult)
+        self._starttime = datetime.now()
+
+    def getting_hungry(self, const):
+        checktime = datetime.now()
+        delta = checktime - self._starttime
+        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

Animals/penguin.py

@@ -0,0 +1,26 @@
+from animal import Animal
+import pygame
+from datetime import datetime
+
+class Penguin(Animal):
+    def __init__(self, x, y, adult=False):
+        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, image_path, food_image, penguin_food, environment, activity, ill, 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 not const.IS_NIGHT and self._feed < 10:
+            self._feed += minutes_passed
+            self._feed = min(self._feed, 10)
+        return self._feed

agent.py

@@ -1,34 +1,128 @@
 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, x, y, image_path, grid_size):
-        self.x = x
-        self.y = y
+    def __init__(self, istate, image_path, grid_size):
+        self.istate = istate
+        self.x, self.y, self.direction = istate 
         self.grid_size = grid_size
-        self.image = pygame.image.load(image_path)
+        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, const):
+        # Obróć obrazek zgodnie z kierunkiem
+        if self.direction == 'E':
+            self.image= pygame.image.load('images/agent4.png')
+        elif self.direction == 'S':
+            self.image= pygame.image.load('images/agent1.png')
+        elif self.direction == 'W':
+            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, (const.GRID_SIZE, const.GRID_SIZE))
+        const.screen.blit(self.image, (self.x * self.grid_size, self.y * self.grid_size))
 
-    def draw(self, screen):
-        screen.blit(self.image, (self.x * self.grid_size, self.y * self.grid_size))
+        if const.IS_NIGHT: draw_night(const)
 
-    def move(self, dx, dy):
-        self.x += dx
-        self.y += dy
-
-    def handle_event(self, event, grid_height,grid_width, animals, blocked_fields):
+    def handle_event(self, event, max_x, max_y, animals, obstacles,const):
         if event.type == pygame.KEYDOWN:
-            if event.key == pygame.K_UP and self.y > 0 and (self.x, self.y-1) not in blocked_fields:
-                self.move(0, -1)
-            elif event.key == pygame.K_DOWN and self.y < grid_height - 1 and (self.x, self.y+1) not in blocked_fields:
-                self.move(0, 1)
-            elif event.key == pygame.K_LEFT and self.x > 0 and (self.x-1, self.y) not in blocked_fields:
-                self.move(-1, 0)
-            elif event.key == pygame.K_RIGHT and self.x < grid_width - 1 and (self.x+1, self.y) not in blocked_fields:
-                self.move(1, 0)
+            if event.key == pygame.K_UP:
+                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,const)
+            elif event.key == pygame.K_RIGHT:
+                self.move('Turn Right', max_x, max_y, obstacles, animals,const)
 
+    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':
+                new_y -= 1
+            elif self.direction == 'E':
+                new_x += 1
+            elif self.direction == 'S':
+                new_y += 1
+            elif self.direction == 'W':
+                new_x -= 1
+            
+            # Sprawdź, czy nowe położenie mieści się w granicach kraty i nie jest przeszkodą
+            if is_border(new_x, new_y, max_x, max_y) and not(is_obstacle(new_x, new_y, obstacles)):
+                self.x, self.y = new_x, new_y
+                
+        elif action == 'Turn Left':
+            self.direction = {'N': 'W', 'W': 'S', 'S': 'E', 'E': 'N'}[self.direction]
+
+        elif action == 'Turn Right':
+            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,const)
+        take_food(self)
+
+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 self.x == animal.x and self.y == animal.y:
-                if animal.feed()== 'True':
-                    animal._feed = 0
-                    print(animal.name,"fed with",animal.food)
+            if animal.x == goal_x and animal.y == goal_y:
+                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)

animal.py

@@ -1,47 +0,0 @@
-import pygame
-from abc import ABC, abstractmethod
-
-class Animal:
-    def __init__(self, x, y,name, image, food_image, food, environment, adult=False,):
-        self.x = x - 1
-        self.y = y - 1
-        self.name = name
-        self.image = image
-        self.adult = adult
-        self.food = food
-        self.food_image = food_image
-        self._feed = 0
-        self.environment = environment #hot/cold/medium
-
-    def draw(self, screen, grid_size):
-        self.image = pygame.transform.scale(self.image, (grid_size, grid_size))
-        if self.adult:
-            # If adult, draw like AdultAnimal
-            new_width = grid_size * 2
-            new_height = grid_size * 2
-            scaled_image = pygame.transform.scale(self.image, (new_width, new_height))
-            screen.blit(scaled_image, (self.x * grid_size, self.y * grid_size))
-        else:
-            # If not adult, draw like normal Animal
-            screen.blit(self.image, (self.x * grid_size, self.y * grid_size))
-
-    def draw_exclamation(self, screen, grid_size, x, y):
-        exclamation_image = pygame.image.load('images/exclamation.png')
-        exclamation_image = pygame.transform.scale(exclamation_image, (grid_size,grid_size))
-        screen.blit(exclamation_image, (x*grid_size, y*grid_size - grid_size))
-
-    def draw_food(self, screen, grid_size, x, y):
-        food_image = pygame.image.load(self.food_image)
-        food_image = pygame.transform.scale(food_image, (grid_size,grid_size))
-        screen.blit(food_image, (x*grid_size, y*grid_size + grid_size))
-
-    
-
-
-    @abstractmethod
-    def feed(self):
-        pass
-
-    @abstractmethod
-    def getting_hungry(self):
-        pass

bear.py

@@ -1,32 +0,0 @@
-from animal import Animal
-import pygame
-from datetime import datetime
-
-
-
-class Bear(Animal):
-    def __init__(self, x, y, adult=False):
-        Bear_image = pygame.image.load('images/bear.png')
-        name = 'bear'
-        environment = "cold"
-        bear_food = 'meat'
-        food_image = 'images/meat.png'
-        super().__init__(x, y,name, Bear_image, food_image,bear_food,environment, adult)
-        self._starttime = datetime.now()
-
-         
-
-    def feed(self):
-        self.getting_hungry()
-        if self._feed < 2:
-            return 'False'
-        else:
-            return 'True'
-        
-
-    def getting_hungry(self):
-        checktime = datetime.now()
-        delta = checktime - self._starttime
-        minutes_passed = delta.total_seconds() / 60
-        self._feed += minutes_passed
-        self._starttime = checktime

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"
+]
+
+

constants.py

@@ -0,0 +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 = 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)

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
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+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
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+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
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+True,False,True,spring,3,1.96784,2.18597,34.02966,1
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+True,False,True,autumn,0,8.12282,48.68677,8.38304,2
+False,True,True,spring,12,3.26729,29.61584,1.69993,2
+True,False,True,summer,8,1.99886,31.26437,3.51834,1
+False,False,True,summer,7,7.41314,44.88982,34.46453,2
+True,False,True,summer,0,7.24464,8.85289,34.29828,3
+False,True,False,summer,8,2.38628,21.76861,47.20283,3
+True,True,True,autumn,3,7.12112,12.08359,41.06062,3
+True,False,False,winter,5,6.74504,47.09367,1.97357,2
+False,False,False,summer,8,1.23539,35.47945,7.67276,1
+True,False,False,spring,11,7.91742,34.52557,30.96412,3
+False,True,True,winter,14,5.91181,7.53226,16.37669,3
+False,True,True,spring,13,6.07261,47.43572,15.83885,2
+True,True,True,autumn,2,9.22518,31.25996,28.06488,3
+False,True,True,summer,3,8.47609,0.23934,31.25786,3
+False,True,False,autumn,6,0.97126,13.65648,25.59887,1
+True,False,False,spring,14,9.29029,46.83676,12.58912,2
+False,False,False,winter,14,0.14092,4.6673,20.3859,1
+True,False,True,autumn,2,3.52708,37.61372,32.83573,1
+True,True,True,winter,1,4.37134,43.19138,22.04785,1
+True,False,True,summer,9,1.1614,10.9739,42.3009,1
+False,False,False,winter,9,7.27324,29.74731,47.17759,2
+False,True,True,winter,9,3.17153,35.14715,21.37868,2
+False,True,False,autumn,0,2.37863,20.35733,46.96943,1
+False,False,True,autumn,4,0.70656,8.70201,5.26527,1
+True,True,True,winter,1,8.23562,36.01552,25.03969,2
+True,True,False,winter,3,6.65062,6.75622,24.91086,3
+False,False,False,spring,10,2.30179,19.62758,25.57147,1
+True,True,False,autumn,10,6.60812,6.61336,12.39931,3
+False,False,True,summer,3,8.9948,47.39225,18.11157,2
+True,False,True,autumn,5,6.69302,42.62701,13.01677,2
+False,False,False,spring,14,8.53868,33.42545,2.43572,2
+False,True,True,autumn,10,4.46205,10.37542,39.58137,3
+True,True,False,spring,14,5.34262,15.45545,21.48404,3
+True,False,False,winter,9,7.02885,4.88308,27.56619,3
+False,False,False,autumn,13,3.55279,0.17091,5.43831,1
+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

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))
+
+
+
+

draw.py

@@ -0,0 +1,22 @@
+import pygame
+
+def draw_goal(const, goal):
+    x, y = goal
+    rect = (x * const.GRID_SIZE, y * const.GRID_SIZE, const.GRID_SIZE, const.GRID_SIZE)
+    pygame.draw.rect(const.screen, const.RED, rect)
+    pygame.display.flip()
+    pygame.time.delay(2000)
+
+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)
+
+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))

enclosure.py

@@ -1,79 +1,74 @@
 import pygame
 
 class Enclosure:
-    def __init__(self, x1, y1, x2, y2, gate, type, imageH, imageV, imageGate):
+    def __init__(self, x1, y1, x2, y2, gate1, gate2, type, imageH, imageV, imageGate):
         self.x1 = x1 - 1
         self.y1 = y1 - 1
-        #(x1,y1) - wierzchołek przekątnej
+        # (x1,y1) - wierzchołek przekątnej
         self.x2 = x2 - 1
         self.y2 = y2 - 1
-        #(x2,y2) - 2 wierzchołek przekątnej
-        self.gate = gate
+        # (x2,y2) - 2 wierzchołek przekątnej
+        self.gate1 = gate1
+        self.gate2 = gate2
         self.type = type
         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))
-        gate_x, gate_y = self.gate
-        gate_x-=1
-        gate_y-=1
-        rect = pygame.Rect(gate_x * grid_size, gate_y * grid_size, grid_size, grid_size)
-        pygame.draw.rect(screen, (0, 0, 0), rect)  # Fill the area with
-        screen.blit(self.imageGate, (gate_x * grid_size, gate_y * grid_size))
+        gate_x1, gate_y1 = self.gate1
+        gate_x2, gate_y2 = self.gate2
+        gate_x1 -= 1
+        gate_y1 -= 1
+        gate_x2 -= 1
+        gate_y2 -= 1
+        rect1 = pygame.Rect(gate_x1 * grid_size, gate_y1 * grid_size, grid_size, grid_size)
+        rect2 = pygame.Rect(gate_x2 * grid_size, gate_y2 * grid_size, grid_size, grid_size)
+        screen.blit(self.imageGate, (gate_x1 * grid_size, gate_y1 * grid_size))
+        screen.blit(self.imageGate, (gate_x2 * grid_size, gate_y2 * grid_size))
 
-    def gateopen(self, blocked):
-        gate_x, gate_y = self.gate
-        gate_x -= 1
-        gate_y -= 1
-        if (gate_x, gate_y) in blocked:
-                blocked.remove((gate_x, gate_y))
-
-
-
-    def draw(self,screen, grid_size , blocked_fields):
+    def draw(self, screen, grid_size):
         self.imageH = pygame.transform.scale(self.imageH, (grid_size, grid_size))
         self.imageV = pygame.transform.scale(self.imageV, (grid_size, grid_size))
+        gate_x1, gate_y1 = self.gate1
+        gate_x2, gate_y2 = self.gate2
+        gate_x1 -= 1
+        gate_y1 -= 1
+        gate_x2 -= 1
+        gate_y2 -= 1
         if self.x1 < self.x2:
-            for i in range(self.x1, self.x2+1):
-                screen.blit(self.imageH, (i * grid_size, self.y1 * grid_size))
-                blocked_fields.add((i, self.y1))
-                screen.blit(self.imageH, (i * grid_size, self.y2 * grid_size))
-                blocked_fields.add((i, self.y2))
-            if self.y1 < self.y2:
-                for j in range(self.y1, self.y2+1):
-                    screen.blit(self.imageH, (self.x1 * grid_size, j * grid_size))
-                    blocked_fields.add((self.x1, j))
-                    screen.blit(self.imageH, (self.x2 * grid_size, j * grid_size))
-                    blocked_fields.add((self.x2, j))
-            if self.y1 > self.y2:
-                for j in range(self.y2, self.y1+1):
-                    screen.blit(self.imageH, (self.x1 * grid_size, j * grid_size))
-                    blocked_fields.add((self.x1, j))
-                    screen.blit(self.imageH, (self.x2 * grid_size, j * grid_size))
-                    blocked_fields.add((self.x2, j))
-        if self.x1 > self.x2:
-            for i in range(self.x2, self.x1+1):
-                screen.blit(self.imageH, (i * grid_size, self.y1 * grid_size))
-                blocked_fields.add((i, self.y1))
-                screen.blit(self.imageH, (i * grid_size, self.y2 * grid_size))
-                blocked_fields.add((i, self.y2))
-            if self.y1 < self.y2:
-                for j in range(self.y1, self.y2+1):
-                    screen.blit(self.imageH, (self.x1 * grid_size, j * grid_size))
-                    blocked_fields.add((self.x1, j))
-                    screen.blit(self.imageH, (self.x2 * grid_size, j * grid_size))
-                    blocked_fields.add((self.x2, j))
-            if self.y1 > self.y2:
-                for j in range(self.y2, self.y1+1):
-                    screen.blit(self.imageH, (self.x1 * grid_size, j * grid_size))
-                    blocked_fields.add((self.x1, j))
-                    screen.blit(self.imageH, (self.x2 * grid_size, j * grid_size))
-                    blocked_fields.add((self.x2, j))
+            for i in range(self.x1, self.x2 + 1):
+                if (i, self.y1) != (gate_x1, gate_y1) and (i, self.y1) != (gate_x2, gate_y2):
+                    screen.blit(self.imageH, (i * grid_size, self.y1 * grid_size))
+                if (i, self.y2) != (gate_x1, gate_y1) and (i, self.y2) != (gate_x2, gate_y2):
+                    screen.blit(self.imageH, (i * grid_size, self.y2 * grid_size))
+            for j in range(self.y1, self.y2 + 1):
+                if (self.x1, j) != (gate_x1, gate_y1) and (self.x1, j) != (gate_x2, gate_y2):
+                    screen.blit(self.imageV, (self.x1 * grid_size, j * grid_size))
+                if (self.x2, j) != (gate_x1, gate_y1) and (self.x2, j) != (gate_x2, gate_y2):
+                    screen.blit(self.imageV, (self.x2 * grid_size, j * grid_size))
 
+def create_enclosures():
+    fenceH = pygame.image.load('images/fenceHor.png')
+    fenceV = pygame.image.load('images/fenceVer.png')
+    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(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]
 
+    return Enclosures
 
+def draw_enclosures(Enclosures, const):
+    for enclosure in Enclosures:
+        enclosure.draw(const.screen, const.GRID_SIZE)
 
+def draw_gates(Enclosures, const):
+    for enclosure in Enclosures:
+        enclosure.gatebuild(const.screen, const.GRID_SIZE)

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 

giraffe.py

@@ -1,32 +0,0 @@
-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'
-        environment = "hot"
-        food_image = 'images/leaves.png'
-        giraffe_food = 'leaves'
-        super().__init__(x, y,name, Giraffe_image, food_image,giraffe_food, environment, 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):
-        checktime = datetime.now()
-        delta = checktime - self._starttime
-        minutes_passed = delta.total_seconds() / 60
-        self._feed += minutes_passed
-        self._starttime = checktime

main.py

@@ -1,117 +1,158 @@
+import random
 import pygame
 import sys
-from elephant import Elephant
-from giraffe import Giraffe
-from penguin import Penguin
-from parrot import Parrot
-from bear import Bear
+
+sys.path.append('./Animals')
+from animals import create_animals, draw_Animals
 from agent import Agent
-from enclosure import Enclosure
+from enclosure import create_enclosures, draw_enclosures, draw_gates
 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, draw_house
+from season import draw_background
+from night import change_time
+from genetics import genetic_algorithm
 
-BLACK = (0, 0, 0)
-
-GRID_SIZE = 50
-GRID_WIDTH = 30
-GRID_HEIGHT = 15
-
-pygame.init()
-
-WINDOW_SIZE = (GRID_WIDTH * GRID_SIZE, GRID_HEIGHT * GRID_SIZE)
-screen = pygame.display.set_mode(WINDOW_SIZE)
+const = Constants()
+init_pygame(const)
 pygame.display.set_caption("Mini Zoo")
 
-
-
-background_image = pygame.image.load('images/tło.jpg')
-background_image = pygame.transform.scale(background_image, WINDOW_SIZE)
-fenceH = pygame.image.load('images/fenceHor.png')
-fenceV = pygame.image.load('images/fenceVer.png')
-gate = pygame.image.load('images/gate.png')
-
-
-fences = set()
+obstacles = set()
 animals_position = set()
+terrain_obstacles_position = set()
 
-
-
-an1 = Parrot(16, 2)
-an2 = Penguin(8, 6)
-an3 = Bear(14, 9)
-old_an2 = Giraffe(18,4, adult=True)
-old_an1 = Elephant(4, 7, adult=True)
-an4 = Elephant(4,3)
-
-Animals = [an1, an2, an3, an4, old_an1, old_an2]
-
-en1 = Enclosure(1,5,9,11,(9,6),"medium", fenceH, fenceV, gate)
-en2 = Enclosure(29,3, 13,1,(16,3), 'medium', fenceH, fenceV, gate)
-en3 = Enclosure(11,5, 16,11, (12,5),'cold', fenceH, fenceV, gate)
-en4 = Enclosure(19,11, 30,5, (25,5),'hot', fenceH, fenceV, gate)
-en5 = Enclosure(4,13, 28,15, (16,13),'cold', fenceH, fenceV, gate)
-
-
-Enclosures = [en1, en2, en3, en4, en5]
-
-
-def draw_grid():
-    for y in range(0, GRID_HEIGHT * GRID_SIZE, GRID_SIZE):
-        for x in range(0, GRID_WIDTH * GRID_SIZE, GRID_SIZE):
-            rect = pygame.Rect(x, y, GRID_SIZE, GRID_SIZE)
-            pygame.draw.rect(screen, BLACK, rect, 1)
-
-def draw_enclosures():
-    for enclosure in Enclosures:
-        enclosure.draw(screen, GRID_SIZE, fences)
-
-def draw_gates():
-    for enclosure in Enclosures:
-        enclosure.gatebuild(screen, GRID_SIZE)
-
-def opengates():
-    for enclosure in Enclosures:
-        enclosure.gateopen(fences)
-
-def draw_Animals():
-    for Animal in Animals:
-        Animal.draw(screen, GRID_SIZE)
-        if Animal.feed() == 'True':
-           Animal.draw_exclamation(screen, GRID_SIZE, Animal.x, Animal.y)
-        else: 
-            Animal.draw_food(screen,GRID_SIZE,Animal.x,Animal.y)
+Animals = create_animals()
+Enclosures = create_enclosures()
+Terrain_Obstacles = create_obstacles()
 
 def spawn_all_animals():
     for Animal in Animals:
-        spawner1 = Spawner(Animal, Enclosures)
-        spawner1.spawn_animal(fences, animals_position)
+        spawner1 = Spawner(Animal)
+        spawner1.spawn_animal(obstacles, animals_position, Enclosures)
 
+def spawn_obstacles():
+    for terrain_obstacle in Terrain_Obstacles:
+        spawner2 = Spawner(terrain_obstacle)
+        spawner2.spawn_terrain_obstacles(obstacles, animals_position, terrain_obstacles_position, const.GRID_WIDTH, const.GRID_HEIGHT)
 
+def generate_obstacles():
+    for en in Enclosures:
+        # Pobierz współrzędne bramy
+        gate_x, gate_y = en.gate1
+        gate_x -= 1
+        gate_y -= 1
+
+        gate_x2, gate_y2 = en.gate2
+        gate_x2 -= 1
+        gate_y2 -= 1
+        
+        # Dodaj lewy brzeg prostokąta
+        for y in range(en.y1, en.y2 + 1):
+            if (en.x1, y) != (gate_x, gate_y) and (en.x1, y) != (gate_x2, gate_y2):
+                obstacles.add((en.x1, y))
+
+        # Dodaj prawy brzeg prostokąta
+        for y in range(en.y1, en.y2 + 1):
+            if (en.x2, y) != (gate_x, gate_y) and (en.x2, y) != (gate_x2, gate_y2):
+                obstacles.add((en.x2, y))
+
+        # Dodaj górny brzeg prostokąta
+        for x in range(en.x1+1, en.x2):
+            if (x, en.y1) != (gate_x, gate_y) and (x, en.y1) != (gate_x2, gate_y2):
+                obstacles.add((x, en.y1))
+
+        # Dodaj dolny brzeg prostokąta
+        for x in range(en.x1+1, en.x2):
+            if (x, en.y2) != (gate_x, gate_y) and (x, en.y2) != (gate_x2, gate_y2):
+                obstacles.add((x, en.y2))
+
+    return obstacles
 
 def main():
-    agent = Agent(0, 0, 'images/avatar.png', GRID_SIZE)
+    initial_state = (0, 0, 'S')
+    agent = Agent(initial_state, 'images/agent1.png', const.GRID_SIZE)
+    
+    obstacles = generate_obstacles()
+    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, GRID_HEIGHT, GRID_WIDTH, Animals, fences)
+            agent.handle_event(event, const.GRID_WIDTH, const.GRID_HEIGHT, Animals, obstacles,const)
 
+        change_time(const)
+        draw_background(const)
+        draw_enclosures(Enclosures, const)
+        draw_gates(Enclosures, const)
+        draw_house(const)
   
-
-        screen.blit(background_image,(0,0))
-        draw_grid()
-        draw_enclosures()
-        draw_gates()
         if not spawned:
             spawn_all_animals()
+            spawn_obstacles()
+            cost_map = generate_cost_map(Animals, Terrain_Obstacles)
+            for animal in Animals:
+                # animal._feed = 0
+                animal._feed = random.randint(0, 10)
             spawned = True
-        draw_Animals()
-        opengates()
-        agent.draw(screen)
+        
+        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)
         pygame.display.flip()
         clock.tick(10)
 
+        if actions:
+            action = actions.pop(0)
+            agent.move(action, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, Animals, goal,const)
+            pygame.time.wait(200)
+        else:
+            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()

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')

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

parrot.py

@@ -1,32 +0,0 @@
-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'
-        environment = "medium"
-        food_image = 'images/grains.png'
-        parrot_food = 'grains'
-        super().__init__(x, y,name, Parrot_image, food_image,parrot_food, environment, 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):
-        checktime = datetime.now()
-        delta = checktime - self._starttime
-        minutes_passed = delta.total_seconds() / 60
-        self._feed += minutes_passed
-        self._starttime = checktime

penguin.py

@@ -1,32 +0,0 @@
-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'
-        environment = "cold"
-        food_image = 'images/fish.png'
-        penguin_food = 'fish'
-        super().__init__(x, y,name, Penguin_image, food_image,penguin_food,environment, adult)
-        self._starttime = datetime.now()
-
-         
-
-    def feed(self):
-        self.getting_hungry()
-        if self._feed < 2:
-            return 'False'
-        else:
-            return 'True'
-        
-
-    def getting_hungry(self):
-        checktime = datetime.now()
-        delta = checktime - self._starttime
-        minutes_passed = delta.total_seconds() / 60
-        self._feed += minutes_passed
-        self._starttime = checktime

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))

spawner.py

@@ -1,44 +1,55 @@
 import random
 
-
 class Spawner:
-    def __init__(self, animal, enclosures):
-        self.animal = animal
-        self.enclosures = enclosures
+    def __init__(self, entity):
+        self.entity = entity
+
+    def spawn_animal(self, blocked, taken, enclosures):
+        self.enclosures = [enclosure for enclosure in enclosures if enclosure.type == self.entity.environment]
+        # 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
 
-    def spawn_animal(self, blocked, taken):
-        possibilities = self.enclosures
-        fitting = []
-        for option in possibilities:
-            if option.type == self.animal.environment:
-                fitting.append(option)
-        enclosure = random.choice(fitting)
         while True:
-            if enclosure.x1 < enclosure.x2:
-                self.animal.x = random.randint(enclosure.x1, enclosure.x2)
-                if enclosure.y1 < enclosure.y2:
-                    self.animal.y = random.randint(enclosure.y1, enclosure.y2)
-                if enclosure.y1 > enclosure.y2:
-                    self.animal.y = random.randint(enclosure.y2, enclosure.y1)
-            if enclosure.x1 > enclosure.x2:
-                self.animal.x = random.randint(enclosure.x2, enclosure.x1)
-                if enclosure.y1 < enclosure.y2:
-                    self.animal.y = random.randint(enclosure.y1, enclosure.y2)
-                if enclosure.y1 > enclosure.y2:
-                    self.animal.y = random.randint(enclosure.y2, enclosure.y1)
-            if self.check(blocked, taken):
+            if self.entity.adult:
+                self.entity.x = random.randint(enclosure.x1+1, enclosure.x2-2)
+                self.entity.y = random.randint(enclosure.y1+1, enclosure.y2-2)
+            else:
+                self.entity.x = random.randint(enclosure.x1+1, enclosure.x2)
+                self.entity.y = random.randint(enclosure.y1+1, enclosure.y2)
+
+            if self.check(blocked | {(8,5),(3,10),(15,2),(26,2),(11,4),(15,7),(22,4),(24,10),(11,12),(19,12)}, taken):
+                break
+
+    def spawn_terrain_obstacles(self, blocked1, blocked2, taken, grid_width, grid_height):
+        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)
+            y = self.entity.y
+            x = self.entity.x
+            if (x, y) not in blocked1 and (x, y) not in blocked2 and (x, y) not in taken:
+                taken.add((self.entity.x, self.entity.y))
                 break
 
     def check(self, blocked, taken):
-        x = self.animal.x
-        y = self.animal.y
+        x = self.entity.x
+        y = self.entity.y
+
         if (x,y) in blocked or (x,y) in taken:
             return False
-        taken.add((x,y))
+
+        if self.entity.adult:
+            
+            adult_fields = [(x, y), (x+1,y), (x,y+1), (x+1,y+1)] # Duże zwierze zajmuje 4 pola
+
+            if any(field in taken for field in adult_fields): # Jeśli stawiane zwierze jest dorosłe i jakiekolwiek pole jest zajęte, to nie można postawić zwierzęcia
+                return False
+            
+            for field in adult_fields: # Dodaj wszystkie pola zajęte przez duże zwierzę
+                taken.add(field)
+        else:
+            taken.add((x,y))
+
         return True
-
-
-
-
-
-

state_space_search.py

@@ -0,0 +1,128 @@
+from queue import PriorityQueue
+
+DEFAULT_COST_VALUE = 1
+
+def is_border(x, y, max_x, max_y):
+    return 0 <= x < max_x and 0 <= y < max_y
+
+def is_obstacle(x, y, obstacles):
+    return (x, y) in obstacles
+
+def succ(current_state, max_x, max_y, obstacles):
+    successors = []
+    x, y, direction = current_state
+
+    # Akcja: Do przodu
+    direction_x, direction_y = {'N': (0, -1), 'E': (1, 0), 'S': (0, 1), 'W': (-1, 0)}[direction] # Słownik przesunięć w zależności od kierunku
+    new_x, new_y = x + direction_x, y + direction_y
+
+    if is_border(new_x, new_y, max_x, max_y) and not(is_obstacle(new_x, new_y, obstacles)):
+        successors.append(((new_x, new_y, direction), 'Go Forward'))
+
+    # Akcja: Obrót w lewo
+    left_turns = {'N': 'W', 'W': 'S', 'S': 'E', 'E': 'N'} # Słownik kierunków po obrocie w lewo
+    successors.append(((x, y, left_turns[direction]), 'Turn Left')) 
+
+    # Akcja: Obrót w prawo
+    right_turns = {'N': 'E', 'E': 'S', 'S': 'W', 'W': 'N'} # Słownik kierunków po obrocie w prawo
+    successors.append(((x, y, right_turns[direction]), 'Turn Right'))
+
+    return successors
+
+def graphsearch(istate, goal, max_x, max_y, obstacles, cost_map):
+    fringe = PriorityQueue()
+    explored = set()
+
+    fringe.put((0, (istate, None , None)))
+
+    while not fringe.empty():
+        _, node = fringe.get()
+        state, _, _ = node
+
+        if goaltest(state, goal):
+            return build_action_sequence(node), current_cost(node, cost_map)
+
+        explored.add(state)
+
+        successors = succ(state, max_x, max_y, obstacles)
+
+        for new_state, action in successors:
+            new_node = (new_state, node, action)
+
+            p_new_state = current_cost(node, cost_map) + heuristic(state, goal)
+
+            if not is_state_in_queue(new_state, fringe) and new_state not in explored:
+                fringe.put((p_new_state, new_node))
+
+            elif is_state_in_queue(new_state, fringe):
+                for i, (p_existing_state, (existing_state, _, _)) in enumerate(fringe.queue):
+                    if existing_state == new_state and p_existing_state > p_new_state:
+                        fringe.queue[i] = (p_new_state, new_node)
+                    else:
+                        break
+
+    return False, float('inf')
+
+def is_state_in_queue(state, queue):
+    for _, (s, _, _) in queue.queue:
+        if s == state:
+            return True
+    return False    
+
+def build_action_sequence(node):
+    actions = []
+    while node[1] is not None:  # Dopóki nie dojdziemy do korzenia
+        _, parent, action = node
+        actions.append(action)
+        node = parent
+    actions.reverse()
+    return actions
+
+def goaltest(state, goal):
+    x, y, _ = state
+    goal_x, goal_y = goal
+    return (x,y) == (goal_x, goal_y)
+
+def current_cost(node, cost_map):
+    cost = 0
+    while node[1] is not None:  # Dopóki nie dojdziemy do korzenia
+        _, parent, action = node
+        # Dodaj koszt pola z mapy kosztów tylko jeśli akcja to "Forward"
+        if action == 'Go Forward':
+            state, _, _ = node
+            cost += cost_map.get(state[:2], DEFAULT_COST_VALUE)  # Pobiera koszt przejścia przez dane pole, a jeśli koszt nie jest zdefiniowany to bierze wartość domyślną
+
+        if action == 'Turn Right' or action == 'Turn Left':
+            cost += DEFAULT_COST_VALUE
+            
+        node = parent  # Przejdź do rodzica
+    return cost
+
+def heuristic(state, goal):
+    x, y, _ = state
+    goal_x, goal_y = goal
+    return abs(x - goal_x) + abs(y - goal_y)  # Odległość Manhattana do celu
+
+def generate_cost_map(Animals, Terrain_Obstacles, cost_map={}):
+    adult_animal_cost = 15  # Default : 15
+    baby_animal_cost = 10   # Default : 10
+    puddle_cost = 50        # Default : 50
+    bush_cost = 20          # Default : 20
+
+    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, animal.y)] = adult_animal_cost
+        else:
+            cost_map[(animal.x, animal.y)] = baby_animal_cost
+
+    for terrain_obstacle in Terrain_Obstacles:
+        if terrain_obstacle.type == 'puddle':
+            cost_map[(terrain_obstacle.x , terrain_obstacle.y )] = puddle_cost
+        else:
+            cost_map[(terrain_obstacle.x , terrain_obstacle.y )] = bush_cost
+
+    return cost_map
+

terrain_obstacle.py

@@ -0,0 +1,37 @@
+import pygame
+
+class Terrain_Obstacle:
+    def __init__(self, x, y, type , image):
+        self.x = x - 1
+        self.y = y - 1
+        self.type = type
+        self.image = image
+
+    def draw(self, screen, grid_size):
+            scaled_image = pygame.transform.scale(self.image, (grid_size, grid_size))
+            screen.blit(scaled_image, (self.x * grid_size, self.y * grid_size))
+
+def create_obstacles():
+    puddle_image = pygame.image.load('images/puddle.png')
+    bush_image = pygame.image.load('images/bush.png')
+
+    puddle1 = Terrain_Obstacle(0,0,'puddle', puddle_image)
+    puddle2 = Terrain_Obstacle(0,0,'puddle', puddle_image)
+    puddle3 = Terrain_Obstacle(0,0,'puddle', puddle_image)
+    puddle4 = Terrain_Obstacle(0,0,'puddle', puddle_image)
+    puddle5 = Terrain_Obstacle(0,0,'puddle', puddle_image)
+    puddle6 = Terrain_Obstacle(0,0,'puddle', puddle_image)
+    puddle7 = Terrain_Obstacle(0,0,'puddle', puddle_image)
+    bush1 = Terrain_Obstacle(0,0,'bush', bush_image)
+    bush2 = Terrain_Obstacle(0,0,'bush', bush_image)
+    bush3 = Terrain_Obstacle(0,0,'bush', bush_image)
+    bush4 = Terrain_Obstacle(0,0,'bush', bush_image)
+    bush5 = Terrain_Obstacle(0,0,'bush', bush_image)
+    
+    Terrain_Obstacles = [puddle1, puddle2, puddle3, puddle4,  puddle5, puddle6, puddle7, bush1, bush2, bush3, bush4, bush5]
+
+    return Terrain_Obstacles
+
+def draw_Terrain_Obstacles(Terrain_Obstacles, const):
+    for terrain_obstacle in Terrain_Obstacles:
+        terrain_obstacle.draw(const.screen, const.GRID_SIZE)