improved decision tree implementation

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

@@ -1,5 +1,6 @@
+from NN.neural_network import clear_text_area
 from crop_protection_product import CropProtectionProduct
-from area.constants import TILE_SIZE, DIRECTION_EAST, DIRECTION_SOUTH, DIRECTION_WEST, DIRECTION_NORTH
+from area.constants import TILE_SIZE, DIRECTION_EAST, DIRECTION_SOUTH, DIRECTION_WEST, DIRECTION_NORTH, WIDTH
 from area.field import fieldX, fieldY, tiles
 import pygame
 import time
@@ -38,16 +39,19 @@ class Tractor:
             self.image = pygame.image.load('resources/images/tractor_left.png').convert_alpha()
         
 
-    def work_on_field(self, tile, ground, plant1):
+    def work_on_field(self, screen, tile, ground, plant1):
+        results = []
         if plant1 is None:
             tile.randomizeContent()
             # sprobuj zasadzic cos
             print("Tarctor planted something")
+            results.append("Tarctor planted something")
         elif plant1.growth_level == 100:
             tile.plant = None
             ground.nutrients_level -= 40
             ground.water_level -= 40
             print("Tractor collected something")
+            results.append("Tractor collected something")
         else:
             plant1.try_to_grow(50,50) #mozna dostosowac jeszcze
             ground.nutrients_level -= 11
@@ -61,6 +65,7 @@ class Tractor:
             elif plant1.plant_type == self.spinosad.plant_type:
                 t = "Tractor used Spinosad"
             print(t)
+            results.append(t)
             ground.pest = False
         if ground.weed:
             # traktor pozbywa się chwastow
@@ -71,13 +76,21 @@ class Tractor:
             elif plant1.plant_type == self.metazachlor.plant_type:
                 t = "Tractor used Metazachlor"
             print(t)
+            results.append(t)
             ground.weed = False
         if ground.water_level < plant1.water_requirements:
             ground.water_level += 20
             print("Tractor watered the plant")
+            results.append("Tractor watered the plant")
         if ground.nutrients_level < plant1.nutrients_requirements:
             ground.nutrients_level += 20
             print("Tractor added some nutrients")
+            results.append("Tractor added some nutrients")
+
+        clear_text_area(screen, WIDTH-90, 100, 400, 100)
+        for idx, result in enumerate(results):
+            display_work_results(screen, result, (WIDTH-90, 100 + idx * 30))
+        
 
 
 
@@ -156,6 +169,13 @@ 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)
+
+#displays results of the "work_on_field" function next to the field:
+def display_work_results(screen, text, position):
+    font = pygame.font.Font(None, 30)
+    displayed_text = font.render(text, 1, (255,255,255))
+    screen.blit(displayed_text, position)
+    pygame.display.update()
     
 

source/main.py

@@ -5,14 +5,14 @@ import pandas as pd
 import joblib
 from area.constants import WIDTH, HEIGHT, TILE_SIZE
 from area.field import drawWindow
-from area.tractor import Tractor, do_actions
+from area.tractor import Tractor, do_actions, display_work_results
 from area.field import tiles, fieldX, fieldY
 from area.field import get_tile_coordinates, get_tile_index
 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
 
@@ -30,6 +30,24 @@ def main():
     window = drawWindow(WIN)
     pygame.display.update()
 
+
+
+
+#Tractor initialization:
+    tractor = Tractor(0*TILE_SIZE, 0*TILE_SIZE, 2, None, None)
+    tractor.rect.x += fieldX  
+    tractor.rect.y += fieldY
+    tractor.tractor_start = ((170, 100))
+    istate = Istate(170, 100, 2)  #initial state
+
+
+#main loop:    
+    while run:
+        for event in pygame.event.get():
+            if event.type == pygame.QUIT:
+                run = False       
+        time.sleep(1)
+
         #getting coordinates of our "goal tile":
         tile_index = get_tile_index()
         tile_x, tile_y = get_tile_coordinates(tile_index)
@@ -45,34 +63,20 @@ def main():
         pygame.display.flip()
 
 
-#graphsearch activation:
+        tractor.tractor_end = ((tile_x, tile_y))
-    istate = Istate(170, 100, 2)  #initial state
+        goaltest = []    #final state (consists of x and y because direction doesnt matter)   
-
+        goaltest.append(tile_x)
-    goaltest = []                 
-    goaltest.append(tile_x)       #final state (consists of x and y because direction doesnt matter)
         goaltest.append(tile_y)
+        goaltest[0] = tile_x
+        goaltest[1]=tile_y        
        
-    tractor = Tractor(0*TILE_SIZE, 0*TILE_SIZE, 2, None, None)
+        #moves = (graphsearch(istate, succ, goaltest, tractor))     #<-------BFS
-    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)
 
 
 
-#main loop:    
+        # movement based on route-planning:
-    while run:
-        for event in pygame.event.get():
-            if event.type == pygame.QUIT:
-                run = False       
-        time.sleep(1)
-
-        # movement based on route-planning (test):
 
         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}")
@@ -102,6 +107,7 @@ def main():
         #getting the name and type of the recognized plant:
         p1.update_name(prediction)
         
+        
 #decission tree test:
         if d1.pest:
             pe = 1
@@ -131,19 +137,71 @@ def main():
                     t3 = True
                     t4 = False
 
+        weather_n = random.randint(1, 4)
+        if weather_n == 1:
+            h1 = True
+            h2 = False
+            h3 = False
+            h4 = False
+        else:
+            h1 = False
+            if weather_n == 2:
+                h2 = True
+                h3 = False
+                h4 = False
+            else:
+                h2 = False
+                if weather_n == 3:
+                    h3 = True
+                    h4 = False
+                else:
+                    h3 = False
+                    h4 = True   
+
+        season_n = random.randint(1,4)
+        if season_n == 1:
+            s1 = True
+            s2 = False
+            s3 = False
+            s4 = False
+            temp_n = random.randint(0,22)
+        else:
+            s1 = False
+            if season_n == 2:
+                s2 = True
+                s3 = False
+                s4 = False
+                temp_n = random.randint(0,22)
+            else:
+                s2 = False
+                if season_n == 3:
+                    s3 = True
+                    s4 = False
+                    temp_n = random.randint(20,39)
+                else:
+                    s3 = False
+                    s4 = True
+                    temp_n = random.randint(-20, 10)
+        
+        anomaly_n = random.randint(1, 10)
+        if anomaly_n == 1:
+            a1 = True
+        else:
+            a1 = False
         dane = {
-            'anomalies': [True],
+                'anomalies': [a1],
-            'temp': [17],
+                'temp': [temp_n],
                 'water': [d1.water_level],
                 'nutri': [d1.nutrients_level],
                 'pests': [pe],
                 'weeds': [we],
                 'ripeness': [p1.growth_level],
-            'season_autumn': [True], 'season_spring': [False], 'season_summer': [False], 'season_winter': [False],
+                'season_autumn': [s1], 'season_spring': [s2], 'season_summer': [s3], 'season_winter': [s4],
-            'weather_heavyCloudy': [False], 'weather_partCloudy': [False], 'weather_precipitation': [False],
+                'weather_heavyCloudy': [h1], 'weather_partCloudy': [h2], 'weather_precipitation': [h3],
-            'weather_sunny': [True],
+                'weather_sunny': [h4],
                 'type_cereal': [t1], 'type_fruit': [t2], 'type_none': [t3], 'type_vegetable': [t4]
         }
+        
         df = pd.DataFrame(dane)
         df.to_csv('model_data.csv', index=False)
 
@@ -154,8 +212,11 @@ def main():
 
         #work on field:
         if predykcje == 'work':
-            tractor.work_on_field(goalTile, d1, p1)
+            tractor.work_on_field(WIN, goalTile, d1, p1)
-        time.sleep(50)
+    
+        #update the initial state for the next target:
+        istate = Istate(tile_x, tile_y, tractor.direction)
+        time.sleep(2)
         print("\n")