tractor moves in loop now

source/NN/neural_network.py

@@ -7,6 +7,7 @@ import matplotlib.pyplot as plt
 from NN.model import *
 from PIL import Image
 import pygame
+from area.constants import GREY
 
 device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
 
@@ -84,16 +85,22 @@ def load_image(image_path):
     testImage = testImage.unsqueeze(0)
     return testImage
 
+#display the image for prediction next to the field
 def display_image(screen, image_path, position):
     image = pygame.image.load(image_path)
     image = pygame.transform.scale(image, (250, 250))
     screen.blit(image, position)
 
+#display result of the guessed image (text under the image)
 def display_result(screen, position, predicted_class):
     font = pygame.font.Font(None, 30)
     displayed_text = font.render("The predicted image is: "+str(predicted_class), 1, (255,255,255))
     screen.blit(displayed_text, position)
 
+def clear_text_area(win, x, y, width, height, color=GREY):
+    pygame.draw.rect(win, color, (x, y, width, height))
+    pygame.display.update()
+
 def guess_image(model, image_tensor):
     with torch.no_grad():
         testOutput = model(image_tensor)

source/area/tractor.py

@@ -156,6 +156,6 @@ def do_actions(tractor, WIN, move_list):
             tractor.rotate_to_left()
         tractor.draw_tractor(WIN)
         pygame.display.update()
-        time.sleep(1)
+        time.sleep(0.5)
 
 

source/main.py

@@ -12,7 +12,7 @@ from ground import Dirt
 from plant import Plant
 from bfs import graphsearch, Istate, succ
 from astar import a_star
-from NN.neural_network import load_model, load_image, guess_image, display_image, display_result
+from NN.neural_network import load_model, load_image, guess_image, display_image, display_result, clear_text_area
 from PIL import Image
 from genetic import genetic_algorithm
 
@@ -30,39 +30,15 @@ def main():
     window = drawWindow(WIN)
     pygame.display.update()
 
-#getting coordinates of our "goal tile":
-    tile_index = get_tile_index()
-    tile_x, tile_y = get_tile_coordinates(tile_index)
-    if tile_x is not None and tile_y is not None:
-        print(f"Coordinates of tile {tile_index} are: ({tile_x}, {tile_y})")
-    else: print("Such tile does not exist")
-
-    #mark the goal tile:
-    tiles[tile_index].image = "resources/images/sampling_goal.png"
-    image = pygame.image.load(tiles[tile_index].image).convert()
-    image = pygame.transform.scale(image, (TILE_SIZE, TILE_SIZE))
-    WIN.blit(image, (tiles[tile_index].x, tiles[tile_index].y))
-    pygame.display.flip()
 
 
-#graphsearch activation:
-    istate = Istate(170, 100, 2)  #initial state
-
-    goaltest = []                 
-    goaltest.append(tile_x)       #final state (consists of x and y because direction doesnt matter)
-    goaltest.append(tile_y)
 
+#Tractor initialization:
     tractor = Tractor(0*TILE_SIZE, 0*TILE_SIZE, 2, None, None)
     tractor.rect.x += fieldX  
-    tractor.rect.y += fieldY 
-    tractor.tractor_start = ((istate.get_x(), istate.get_y()))
-    #tractor.tractor_start = ((istate.get_x(), istate.get_y(), istate.get_direction))
-    tractor.tractor_end = ((goaltest[0], goaltest[1]))
-
-    #moves = (graphsearch(istate, succ, goaltest, tractor))
-    moves = (a_star(istate, succ, goaltest, tractor))
-    print(moves)
-
+    tractor.rect.y += fieldY
+    tractor.tractor_start = ((170, 100))
+    istate = Istate(170, 100, 2)  #initial state
 
 
 #main loop:    
@@ -72,7 +48,35 @@ def main():
                 run = False       
         time.sleep(1)
 
-        # movement based on route-planning (test):
+        #getting coordinates of our "goal tile":
+        tile_index = get_tile_index()
+        tile_x, tile_y = get_tile_coordinates(tile_index)
+        if tile_x is not None and tile_y is not None:
+            print(f"Coordinates of tile {tile_index} are: ({tile_x}, {tile_y})")
+        else: print("Such tile does not exist")
+
+        #mark the goal tile:
+        tiles[tile_index].image = "resources/images/sampling_goal.png"
+        image = pygame.image.load(tiles[tile_index].image).convert()
+        image = pygame.transform.scale(image, (TILE_SIZE, TILE_SIZE))
+        WIN.blit(image, (tiles[tile_index].x, tiles[tile_index].y))
+        pygame.display.flip()
+
+
+        tractor.tractor_end = ((tile_x, tile_y))
+        goaltest = []    #final state (consists of x and y because direction doesnt matter)   
+        goaltest.append(tile_x)
+        goaltest.append(tile_y)
+        goaltest[0] = tile_x
+        goaltest[1]=tile_y        
+       
+        #moves = (graphsearch(istate, succ, goaltest, tractor))     #<-------BFS
+        moves = (a_star(istate, succ, goaltest, tractor))
+        print(moves)
+
+
+
+        # movement based on route-planning:
 
         tractor.draw_tractor(WIN)
         time.sleep(1)
@@ -89,6 +93,7 @@ def main():
         image_tensor = load_image(image_path)
         prediction = guess_image(load_model(), image_tensor)
         
+        clear_text_area(WIN, WIDTH - 50, 600, 400, 50)
         display_result(WIN, (WIDTH - 50 , 600), prediction)      #display text under the photo
         pygame.display.update()
         print(f"The predicted image is: {prediction}")
@@ -155,7 +160,10 @@ def main():
         #work on field:
         if predykcje == 'work':
             tractor.work_on_field(goalTile, d1, p1)
-        time.sleep(50)
+        
+        #update the initial state for the next target:
+        istate = Istate(tile_x, tile_y, tractor.direction)
+        time.sleep(5)
         print("\n")