rozpoznany sauron = zmiana na inny cel

bfs.py

@@ -1,6 +1,6 @@
 import pygame
 from config import *
-class Bfs:
+class Bfs():
 
     def __init__(self,game):
 
@@ -12,12 +12,12 @@ class Bfs:
         self.enemy_cells = []
     def heuristic(a,b):
         return abs(a[0]-b[0])+abs(a[1]-b[1])
-    def bfs(self):
+    def bfs(self,goal_cell):
         print("x: ", self.game.agent.x, "y: ", self.game.agent.y)
         cost_road=0
         self.open_queue.append(self.get_cell_number(self.game.agent.x,self.game.agent.y))
         # tutaj dodaje się cel agenta
-        goal_cell = self.get_cell_number(self.game.flower.x,self.game.flower.y)
+        #goal_cell = self.get_cell_number(self.game.flower.x,self.game.flower.y)
 
         path = []
         processing = True

main.py

@@ -26,6 +26,7 @@ class Game:
         pygame.display.set_caption('Gra-SI')
 
         self.bfs = Bfs(self)
+        self.nn = NeuralN()
 
     def new(self): # tworzy się nowa sesja grania
         self.all_sprites = pygame.sprite.LayeredUpdates()
@@ -87,7 +88,32 @@ class Game:
             if event.type == pygame.MOUSEBUTTONDOWN:
                 mouse_presses = pygame.mouse.get_pressed()
                 if mouse_presses[0]:
-                    self.bfs.bfs()
+                    #self.bfs.bfs(self.bfs.get_cell_number(self.flower.x,self.flower.y))
+                    x = self.sauron.x
+                    y = self.sauron.y
+                    mob_image = self.sauron.SAURON_IMG
+                    while True:
+                        prediction = self.prediction_road(x,y,mob_image)
+                        if prediction == "SAURON":
+                            x = self.infantry_ork.x
+                            y = self.infantry_ork.y
+                            mob_image = self.infantry_ork.INFANTRY_ORK_IMG
+                            prediction = self.prediction_road(x,y,mob_image)
+                        else:
+                            self.bfs.bfs(self.bfs.get_cell_number(x,y)) 
+                           
+
+    def prediction_road(self,x,y,mob_image):
+        mob_goal = (self.bfs.get_cell_number(x,y))
+        if self.bfs.get_up_cell(mob_goal) == None:
+            goal = self.bfs.get_down_cell(mob_goal)
+        else:
+            goal = self.bfs.get_up_cell(mob_goal)
+            self.bfs.bfs(goal)
+            prediction = self.nn.predict(mob_image)
+        return prediction
+
+
 
     def map(self): # tworzenie mapy
         self.clock.tick(FRAMERATE)
@@ -213,6 +239,8 @@ print("path_true:",path_true)
 
 
 
+
+        
 g = Game()
 g.new()
 while g.running:

nn.py

@@ -12,7 +12,7 @@ import pathlib
 
 class NeuralN:
 #    @staticmethod
- #   def predict(image):
+    def predict(self,image):
         data_dir = pathlib.Path('zdjecia')
         saved_model_path = pathlib.Path('trained_model.h5')
         class_names_path = pathlib.Path("class_names.pkl")
@@ -93,7 +93,7 @@ class NeuralN:
                                                  tf.keras.layers.Softmax()])
 
         #image_path = image
-        image_path = pathlib.Path('path_to_image')
+        image_path = pathlib.Path('zdjecia\ORK_ARCHER\ork_archer (942).jpg')
         image = Image.open(image_path)
 
         # Preprocess the image
@@ -119,4 +119,4 @@ class NeuralN:
 
         # Print the predicted label
         print(predicted_label)
-        # return predicted_label, action
+        return predicted_label#, action