added multiple options for animals' images

.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 3.12 (PROJEKT)" 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 3.12 (PROJEKT)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

agent.py

@@ -5,7 +5,19 @@ 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
@@ -68,6 +80,7 @@ class Agent:
 
 def feed_animal(self, animals, goal,const):
     goal_x, goal_y = goal
+    guess = 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:
@@ -76,6 +89,7 @@ def feed_animal(self, animals, goal,const):
                 else:
                     activity_time = False
                 guests = random.randint(1, 15)
+                guess.classify(animal.image)
                 decision = feed_decision(animal.adult, activity_time, animal.ill, const.season, guests, animal._feed, self._dryfood, self._wetfood)
                 if decision != [1]:
                     if decision == [2]:

classification.py

@@ -0,0 +1,48 @@
+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)
+        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()]
+
+# Define the classes
+classes = [
+    "bat",
+    "bear",
+    "elephant",
+    "giraffe",
+    "owl",
+    "parrot",
+    "penguin"
+]
+
+

constants.py

@@ -17,6 +17,10 @@ class Constants:
 
         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)