add: astar in agent.py

app.py

@@ -9,7 +9,6 @@ from classes.agent import Agent
 from collections import deque
 import threading
 import time
-from astar import AStar
 
 pygame.init()
 window = pygame.display.set_mode((prefs.WIDTH, prefs.HEIGHT))
@@ -22,6 +21,16 @@ def initBoard():
         row = []
         for j in range(prefs.GRID_SIZE):
             cell = Cell(i, j, 1)
+            # Wybierz kolor dla płytki na podstawie jej położenia
+            if i == 0 or i == prefs.GRID_SIZE - 1 or j == 0 or j == prefs.GRID_SIZE - 1:
+                color = (100, 20, 20)  
+            elif i == 1 or i == prefs.GRID_SIZE - 2 or j == 1 or j == prefs.GRID_SIZE - 2:
+                color = (20, 100, 20)
+            elif i == 2 or i == prefs.GRID_SIZE - 3 or j == 2 or j == prefs.GRID_SIZE - 3:
+                color = (20, 20, 100)  
+            else:
+                color = (150, 200, 200) 
+            cell.color = color
             row.append(cell)
         cells.append(row)
 
@@ -50,7 +59,9 @@ def initBoard():
 def draw_grid(window, cells, agent):
     for i in range(prefs.GRID_SIZE):
         for j in range(prefs.GRID_SIZE):
-            cells[i][j].update(window)
+            cell = cells[i][j]
+            color = cell.color
+            pygame.draw.rect(window, cell.color, (i*prefs.CELL_SIZE, j*prefs.CELL_SIZE, prefs.CELL_SIZE, prefs.CELL_SIZE))
             if(cells[i][j].interactableItem):
                 cells[i][j].interactableItem.update(window)
             cells[i][j].blit_text(cells[i][j].waga, i*50+6, j*52+6, 12,window)
@@ -115,8 +126,14 @@ while running:
         watek.start()
 
     if keys[K_g]:
-        path = AStar(cells).astar((agent.current_cell.X, agent.current_cell.Y), (target_x, target_y))
+        path, cost = agent.astar((target_x, target_y), start_cost=0)
         print("Shortest path:", path)
+        print("Total cost:", cost)
+        watek = threading.Thread(target=watekDlaSciezkiAgenta)
+        watek.daemon = True
+        watek.start()
+
+        
 
     if pygame.key.get_pressed()[pygame.K_e]:
         if agent.current_cell.interactableItem and pygame.time.get_ticks() - agent.last_interact_time > 500:

astar.py

@@ -1,49 +0,0 @@
-from collections import deque
-import heapq
-
-class AStar:
-    def __init__(self, cells):
-        self.cells = cells
-
-    def heuristic(self, current, target):
-        # Euclidean distance heuristic
-        dx = abs(current[0] - target[0])
-        dy = abs(current[1] - target[1])
-        return dx + dy
-
-    def get_neighbors(self, cell):
-        neighbors = []
-        x, y = cell[0], cell[1]
-        if x > 0 and not self.cells[x - 1][y].blocking_movement:
-            neighbors.append((x - 1, y))
-        if x < len(self.cells) - 1 and not self.cells[x + 1][y].blocking_movement:
-            neighbors.append((x + 1, y))
-        if y > 0 and not self.cells[x][y - 1].blocking_movement:
-            neighbors.append((x, y - 1))
-        if y < len(self.cells[x]) - 1 and not self.cells[x][y + 1].blocking_movement:
-            neighbors.append((x, y + 1))
-        return neighbors
-
-    def astar(self, start, target):
-        open_list = [(0, start)]
-        came_from = {}
-        g_score = {start: 0}
-
-        while open_list:
-            _, current = heapq.heappop(open_list)
-            if current == target:
-                path = []
-                while current in came_from:
-                    path.append(current)
-                    current = came_from[current]
-                return path[::-1]
-
-            for neighbor in self.get_neighbors(current):
-                tentative_g_score = g_score[current] + 1
-                if tentative_g_score < g_score.get(neighbor, float('inf')):
-                    came_from[neighbor] = current
-                    g_score[neighbor] = tentative_g_score
-                    f_score = tentative_g_score + self.heuristic(neighbor, target)
-                    heapq.heappush(open_list, (f_score, neighbor))
-
-        return []

classes/agent.py

@@ -1,6 +1,9 @@
 import pygame
 from collections import deque
+from classes.cell import Cell
 import prefs
+import heapq
+
 class Agent:
     def __init__(self, x, y, cells, baseScore=0):
         self.sprite = pygame.image.load("sprites/BartenderNew64.png").convert_alpha()
@@ -258,6 +261,71 @@ class Agent:
     
         return []
     
+    #Algorytm astar
+    def get_cost(self, cell):
+        x, y = cell[0], cell[1]
+        if x == 0 or x == len(self.cells) - 1 or y == 0 or y == len(self.cells[0]) - 1:
+            return 15  # Koszt dla pól na krawędziach
+        elif x == 1 or x == len(self.cells) - 2 or y == 1 or y == len(self.cells[0]) - 2:
+            return 10  # Koszt dla pól drugiego rzędu i przedostatniego oraz drugiej kolumny i przedostatniej
+        elif x == 2 or x == len(self.cells) - 3 or y == 2 or y == len(self.cells[0]) - 3:
+            return 5   # Koszt dla pól trzeciego rzędu i trzeciego od końca oraz trzeciej kolumny i trzeciej od końca
+        else:
+            return 1
+    
+    def heuristic(self, current, target):
+        # Manhattan distance heuristic
+        dx = abs(current[0] - target[0])
+        dy = abs(current[1] - target[1])
+        return dx + dy
+
+    def priority(self, state, target):
+    # Oblicza priorytet dla danego stanu
+        g_score = self.g_score[state]
+        h_score = self.heuristic(state, target)
+        return g_score + h_score
+
+    def astar(self, target, start_cost=0):
+        if not isinstance(target, tuple) or len(target) != 2:
+            raise ValueError("Target must be a tuple of two elements (x, y).")
+
+        open_list = [(start_cost, (self.current_cell.X, self.current_cell.Y, self.directionPOM))]
+        came_from = {}
+        g_score = {(self.current_cell.X, self.current_cell.Y, self.directionPOM): start_cost}
+
+        while open_list:
+            _, current = heapq.heappop(open_list)
+            if isinstance(current, int):
+                raise ValueError("Current must be a tuple of three elements (x, y, direction).")
+
+            x, y, _ = current  # Unpack the current tuple
+            if (x, y) == target:  # Check if the current cell's coordinates match the target
+                path = []
+                while current in came_from:
+                    path.append((current[0], current[1]))  # Append only coordinates (x, y) to the path
+                    current = came_from[current]
+                path = path[::-1]  # Reverse the path
+                cost = g_score[(x, y, self.directionPOM)]  # Retrieve the cost from the g_score dictionary
+                return path, cost
+
+            for neighbor in self.get_neighbors(self.cells[x][y], self.cells):
+                neighbor_coords = (neighbor.X, neighbor.Y, self.directionPOM)  # Convert neighbor cell to tuple
+                tentative_g_score = g_score[current] + self.get_cost(neighbor_coords)
+                if tentative_g_score < g_score.get(neighbor_coords, float('inf')):
+                    came_from[neighbor_coords] = current
+                    g_score[neighbor_coords] = tentative_g_score
+                    f_score = tentative_g_score + self.heuristic(neighbor_coords, target)
+                    heapq.heappush(open_list, (f_score, neighbor_coords))
+        
+
+        return [], float('inf')  # If no path found, return an empty path and infinite cost
+
+
+
+
+
+
+
    
 
 

prefs.py

@@ -3,7 +3,8 @@ HEIGHT = WIDTH
 GRID_SIZE = 12
 CELL_SIZE = WIDTH // GRID_SIZE
 SPAWN_POINT = (5, 5)
-COLORS = [(130, 200, 200)]
+COLORS = [(100, 20, 20), (20, 100, 20), (20, 20, 100),(150, 200, 200)]
+