dodanie sieci neuronowej i inne zmiany

.idea/.gitignore

@@ -0,0 +1,3 @@
+# Default ignored files
+/shelf/
+/workspace.xml

.idea/Gra-SI_nn.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/Gra-SI_nn.iml" filepath="$PROJECT_DIR$/.idea/Gra-SI_nn.iml" />
+    </modules>
+  </component>
+</project>

.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>

agent.py

@@ -20,13 +20,13 @@ class Agent(pygame.sprite.Sprite):
         self.y_change = 0
 
         #self.AGENT_IMG = pygame.image.load("./zdjecia/"+self.AGENT_IMAGES[self.direction]+".png")
-        self.AGENT_IMG_RIGHT = pygame.image.load("./zdjecia/gandalf-prawo.png")
+        self.AGENT_IMG_RIGHT = pygame.image.load("./pozostale_zdjecia/gandalf-prawo.png")
         self.AGENT_RIGHT = pygame.transform.scale(self.AGENT_IMG_RIGHT,(64,64))
-        self.AGENT_IMG_DOWN = pygame.image.load("./zdjecia/gandalf-dol.png")
+        self.AGENT_IMG_DOWN = pygame.image.load("./pozostale_zdjecia/gandalf-dol.png")
         self.AGENT_DOWN = pygame.transform.scale(self.AGENT_IMG_DOWN,(64,64))
-        self.AGENT_IMG_LEFT = pygame.image.load("./zdjecia/gandalf-lewo.png")
+        self.AGENT_IMG_LEFT = pygame.image.load("./pozostale_zdjecia/gandalf-lewo.png")
         self.AGENT_LEFT = pygame.transform.scale(self.AGENT_IMG_LEFT,(64,64))
-        self.AGENT_IMG_UP = pygame.image.load("./zdjecia/gandalf-gora.png")
+        self.AGENT_IMG_UP = pygame.image.load("./pozostale_zdjecia/gandalf-gora.png")
         self.AGENT_UP = pygame.transform.scale(self.AGENT_IMG_UP,(64,64))
 
         self.AGENT = self.AGENT_RIGHT

main.py

@@ -6,6 +6,7 @@ from mobs import *
 #from unknown_mob import *  #unknown mob
 from bfs import *
 from heapq import *
+from nn import *
 
 class Game:
 
@@ -16,10 +17,10 @@ class Game:
         self.running = True
         self.clock = pygame.time.Clock()
           
-        self.BACKGROUND_IMG= pygame.image.load("./zdjecia/podloze.jpg")
+        self.BACKGROUND_IMG= pygame.image.load("./pozostale_zdjecia/podloze.jpg")
         self.BACKGROUND = pygame.transform.scale(self.BACKGROUND_IMG,(64,64))
 
-        self.LVL_ICON_PNG = pygame.image.load("./zdjecia/lvl_icon.png")
+        self.LVL_ICON_PNG = pygame.image.load("./pozostale_zdjecia/lvl_icon.png")
         self.LVL_ICON = pygame.transform.scale(self.LVL_ICON_PNG,(24,24))
 
         pygame.display.set_caption('Gra-SI')
@@ -212,7 +213,6 @@ print("path_true:",path_true)
 
 
         
-        
 g = Game()
 g.new()
 while g.running:

map_add_ons.py

@@ -14,7 +14,7 @@ class Rocks(pygame.sprite.Sprite):
         self.width = TILE_SIZE
         self.height = TILE_SIZE
 
-        self.ROCKS_PNG = pygame.image.load("./zdjecia/rock.png")
+        self.ROCKS_PNG = pygame.image.load("./pozostale_zdjecia/rock.png")
         self.ROCKS = pygame.transform.scale(self.ROCKS_PNG,(64,64))
 
         self.image = pygame.Surface([self.width, self.height])
@@ -39,7 +39,7 @@ class Grass(pygame.sprite.Sprite):
         self.width = TILE_SIZE
         self.height = TILE_SIZE
 
-        self.GRASS_PNG = pygame.image.load("./zdjecia/grass.png")
+        self.GRASS_PNG = pygame.image.load("./pozostale_zdjecia/grass.png")
         self.GRASS = pygame.transform.scale(self.GRASS_PNG,(64,64))
 
         self.image = pygame.Surface([self.width, self.height])
@@ -67,7 +67,7 @@ class Health_flower(pygame.sprite.Sprite):
         self.width = TILE_SIZE
         self.height = TILE_SIZE
 
-        self.FLOWER_IMG = pygame.image.load("./zdjecia/flower.png")
+        self.FLOWER_IMG = pygame.image.load("./pozostale_zdjecia/flower.png")
         self.FLOWER = pygame.transform.scale(self.FLOWER_IMG,(64,64))  
 
         self.image = pygame.Surface([self.width, self.height])
@@ -92,7 +92,7 @@ class Little_Rocks(pygame.sprite.Sprite):
         self.width = TILE_SIZE
         self.height = TILE_SIZE
 
-        self.LITTLE_ROCKS_PNG = pygame.image.load("./zdjecia/little_rocks.png")
+        self.LITTLE_ROCKS_PNG = pygame.image.load("./pozostale_zdjecia/little_rocks.png")
         self.LITTLE_ROCKS = pygame.transform.scale(self.LITTLE_ROCKS_PNG,(64,64))
 
         self.image = pygame.Surface([self.width, self.height])

mobs.py

@@ -14,7 +14,7 @@ class Archer_ork(pygame.sprite.Sprite):
         self.width = TILE_SIZE
         self.height = TILE_SIZE
 
-        self.ARCHER_ORK_IMG = pygame.image.load("./zdjecia/ork_lucznik.png")
+        self.ARCHER_ORK_IMG = pygame.image.load("./pozostale_zdjecia/ork_lucznik.png")
         self.ARCHER_ORK = pygame.transform.scale(self.ARCHER_ORK_IMG,(64,64))  
 
         self.image = pygame.Surface([self.width, self.height])
@@ -41,7 +41,7 @@ class Infantry_ork(pygame.sprite.Sprite):
         self.width = TILE_SIZE
         self.height = TILE_SIZE
 
-        self.INFANTRY_ORK_IMG = pygame.image.load("./zdjecia/ork-piechota.png")
+        self.INFANTRY_ORK_IMG = pygame.image.load("./pozostale_zdjecia/ork-piechota.png")
         self.INFANTRY_ORK = pygame.transform.scale(self.INFANTRY_ORK_IMG,(64,64)) 
 
         self.image = pygame.Surface([self.width, self.height])
@@ -98,7 +98,7 @@ class Sauron(pygame.sprite.Sprite):
         self.width = TILE_SIZE
         self.height = TILE_SIZE
 
-        self.SAURON_IMG = pygame.image.load("./zdjecia/sauron.png")
+        self.SAURON_IMG = pygame.image.load("./pozostale_zdjecia/sauron.png")
         self.SAURON = pygame.transform.scale(self.SAURON_IMG,(64,64))
 
         self.image = pygame.Surface([self.width, self.height])

nn.py

@@ -0,0 +1,122 @@
+import os
+import glob
+import PIL
+from PIL import Image
+import tensorflow as tf
+import pickle
+from tensorflow import keras
+from keras import layers
+from keras.models import Sequential
+import pathlib
+
+
+class NeuralN:
+#    @staticmethod
+ #   def predict(image):
+        data_dir = pathlib.Path('zdjecia')
+        saved_model_path = pathlib.Path('trained_model.h5')
+        class_names_path = pathlib.Path("class_names.pkl")
+        image_count = sum(len(files) for _, _, files in os.walk(data_dir))
+
+        print(image_count)
+
+        # ORK_ARCHER = list(glob.glob('C:\\mobs_photos\\ORK_ARCHER'))
+        # im = PIL.Image.open(ORK_ARCHER[0])
+        # im.show()
+        if os.path.exists(saved_model_path):
+            model = tf.keras.models.load_model(saved_model_path)
+            print("Saved model loaded")
+            with open(class_names_path, 'rb') as f:
+                class_names = pickle.load(f)
+                print("Class names loaded.")
+
+        else:
+            train_ds = tf.keras.utils.image_dataset_from_directory(
+                data_dir,
+                validation_split=0.2,
+                subset="training",
+                seed=123,
+                image_size=(180, 180),
+                batch_size=32)
+
+            val_ds = tf.keras.utils.image_dataset_from_directory(
+                data_dir,
+                validation_split=0.2,
+                subset="validation",
+                seed=123,
+                image_size=(180, 180),
+                batch_size=32)
+
+            # test_ds = tf.keras.utils.image_dataset_from_directory(
+            #    data_dir,
+            #    seed=123,
+            #    image_size=(180, 180),
+            #    batch_size=32)
+
+            class_names = train_ds.class_names
+            print(class_names)
+
+            num_classes = len(class_names)
+
+            model = Sequential([
+                layers.Rescaling(1. / 255, input_shape=(180, 180, 3)),
+                layers.Conv2D(16, 3, padding='same', activation='relu'),
+                layers.MaxPooling2D(),
+                layers.Conv2D(32, 3, padding='same', activation='relu'),
+                layers.MaxPooling2D(),
+                layers.Conv2D(64, 3, padding='same', activation='relu'),
+                layers.MaxPooling2D(),
+                layers.Flatten(),
+                layers.Dense(128, activation='relu'),
+                layers.Dense(num_classes)
+            ])
+
+            model.compile(optimizer='adam',
+                          loss=tf.keras.losses.SparseCategoricalCrossentropy(
+                              from_logits=True),
+                          metrics=['accuracy'])
+            model.summary()
+
+            epochs = 1
+            history = model.fit(
+                train_ds,
+                validation_data=val_ds,
+                epochs=epochs
+            )
+            model.save("trained_model.h5")
+            print("Model trained and saved.")
+            with open(class_names_path, 'wb') as f:
+                pickle.dump(train_ds.class_names, f)
+                print("Class names saved.")
+            # loaded_model = tf.keras.models.load_model("trained_model.h5")
+        probability_model = tf.keras.Sequential([model,
+                                                 tf.keras.layers.Softmax()])
+
+        #image_path = image
+        image_path = pathlib.Path('path_to_image')
+        image = Image.open(image_path)
+
+        # Preprocess the image
+        image = image.resize((180, 180))  # Resize to match the input size of the model
+        image_array = tf.keras.preprocessing.image.img_to_array(image)
+        image_array = image_array / 255.0  # Normalize pixel values
+
+        # Add an extra dimension to the image array
+        image_array = tf.expand_dims(image_array, 0)
+        # Make the prediction
+        predictions = probability_model.predict(image_array)
+
+        # Convert the predictions to class labels
+        predicted_label = class_names[predictions[0].argmax()]
+        #actions = {
+         #   'ORK_MELEE': 'fight',
+          #  'ORK_ARCHER': 'change_dir',
+           # 'SAURON': 'change_dir'
+        #}
+
+        # Get the action for the predicted character
+        #action = actions.get(predicted_label, 'unknown')
+
+        # Print the predicted label
+        print(predicted_label)
+        # return predicted_label, action

unknown_mob.py

@@ -14,7 +14,7 @@ class Unknown_mob(pygame.sprite.Sprite):
         self.width = TILE_SIZE
         self.height = TILE_SIZE
 
-        self.UNKNOWN_MOB_IMG = pygame.image.load("./zdjecia/dragon.jpg")
+        self.UNKNOWN_MOB_IMG = pygame.image.load("./pozostale_zdjecia/dragon.jpg")
         self.UKNOWN_MOB = pygame.transform.scale(self.UNKNOWN_MOB_IMG,(64,64))  
 
         self.image = pygame.Surface([self.width, self.height])