fixed agent rotation

collect

@@ -24,11 +24,11 @@ edge [fontname="helvetica"] ;
 6 -> 10 ;
 11 [label="garbage_weight <= 0.612\ngini = 0.094\nsamples = 61\nvalue = [3, 58]\nclass = no-collect"] ;
 10 -> 11 ;
-12 [label="distance <= 10.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = collect"] ;
+12 [label="space_occupied <= 0.382\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = collect"] ;
 11 -> 12 ;
-13 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
+13 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = no-collect"] ;
 12 -> 13 ;
-14 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = no-collect"] ;
+14 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
 12 -> 14 ;
 15 [label="garbage_type <= 2.5\ngini = 0.065\nsamples = 59\nvalue = [2, 57]\nclass = no-collect"] ;
 11 -> 15 ;
@@ -36,7 +36,7 @@ edge [fontname="helvetica"] ;
 15 -> 16 ;
 17 [label="garbage_weight <= 15.925\ngini = 0.26\nsamples = 13\nvalue = [2, 11]\nclass = no-collect"] ;
 15 -> 17 ;
-18 [label="odour_intensity <= 5.724\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = collect"] ;
+18 [label="fuel <= 13561.0\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = collect"] ;
 17 -> 18 ;
 19 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = collect"] ;
 18 -> 19 ;
@@ -50,15 +50,15 @@ edge [fontname="helvetica"] ;
 5 -> 23 ;
 24 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = no-collect"] ;
 23 -> 24 ;
-25 [label="odour_intensity <= 8.841\ngini = 0.219\nsamples = 8\nvalue = [7, 1]\nclass = collect"] ;
+25 [label="days_since_last_collection <= 22.0\ngini = 0.219\nsamples = 8\nvalue = [7, 1]\nclass = collect"] ;
 23 -> 25 ;
 26 [label="gini = 0.0\nsamples = 6\nvalue = [6, 0]\nclass = collect"] ;
 25 -> 26 ;
-27 [label="space_occupied <= 0.936\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = collect"] ;
+27 [label="odour_intensity <= 8.841\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = collect"] ;
 25 -> 27 ;
-28 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = no-collect"] ;
+28 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
 27 -> 28 ;
-29 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
+29 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = no-collect"] ;
 27 -> 29 ;
 30 [label="odour_intensity <= 7.156\ngini = 0.292\nsamples = 107\nvalue = [88, 19]\nclass = collect"] ;
 0 -> 30 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
@@ -88,18 +88,14 @@ edge [fontname="helvetica"] ;
 40 -> 42 ;
 43 [label="gini = 0.0\nsamples = 8\nvalue = [0, 8]\nclass = no-collect"] ;
 42 -> 43 ;
-44 [label="days_since_last_collection <= 20.0\ngini = 0.48\nsamples = 10\nvalue = [4, 6]\nclass = no-collect"] ;
+44 [label="distance <= 24.0\ngini = 0.48\nsamples = 10\nvalue = [4, 6]\nclass = no-collect"] ;
 42 -> 44 ;
 45 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = collect"] ;
 44 -> 45 ;
-46 [label="paid_on_time <= 0.5\ngini = 0.375\nsamples = 8\nvalue = [2, 6]\nclass = no-collect"] ;
+46 [label="space_occupied <= 0.243\ngini = 0.375\nsamples = 8\nvalue = [2, 6]\nclass = no-collect"] ;
 44 -> 46 ;
-47 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
+47 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = collect"] ;
 46 -> 47 ;
-48 [label="space_occupied <= 0.243\ngini = 0.245\nsamples = 7\nvalue = [1, 6]\nclass = no-collect"] ;
+48 [label="gini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = no-collect"] ;
 46 -> 48 ;
-49 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
-48 -> 49 ;
-50 [label="gini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = no-collect"] ;
-48 -> 50 ;
 }

garbage_truck.py

@@ -1,11 +1,16 @@
 from heuristicfn import heuristicfn
 
+
 FIELDWIDTH = 50
 TURN_FUEL_COST = 10
 MOVE_FUEL_COST = 200
 MAX_FUEL = 20000
 MAX_SPACE = 5
-MAX_WEIGHT = 200
+MAX_WEIGHT = 400
+MAX_WEIGHT_GLASS = 100
+MAX_WEIGHT_MIXED = 100
+MAX_WEIGHT_PAPER = 100
+MAX_WEIGHT_PLASTIC = 100
 
 
 class GarbageTruck:
@@ -18,6 +23,10 @@ class GarbageTruck:
         self.fuel = MAX_FUEL
         self.free_space = MAX_SPACE
         self.weight_capacity = MAX_WEIGHT
+        self.weight_capacity_glass = MAX_WEIGHT_GLASS
+        self.weight_capacity_mixed = MAX_WEIGHT_MIXED
+        self.weight_capacity_paper = MAX_WEIGHT_PAPER
+        self.weight_capacity_plastic = MAX_WEIGHT_PLASTIC
         self.rect = rect
         self.orientation = orientation
         self.request_list = request_list #lista domów do odwiedzenia
@@ -45,6 +54,8 @@ class GarbageTruck:
     def next_destination(self):
         
         for i in range(len(self.request_list)):
+            if(self.request_list==[]):
+                break
             request = self.request_list[i]
 
             #nie ma miejsca w zbiorniku lub za ciężkie śmieci
@@ -55,33 +66,44 @@ class GarbageTruck:
             if heuristicfn(request.x_pos, request.y_pos, self.dump_x, self.dump_y) // 50 * 200 > self.fuel:
                 continue
             
-
+            self.request_list.pop(i)
-
+            self.free_space -= request.volume
-            distance = heuristicfn(self.rect.x, self.rect.y, request.x_pos, request.y_pos) // 50
+            self.weight_capacity -= request.weight
-
+            return request.x_pos, request.y_pos   
-            r = [
-                self.fuel, 
-                distance,
-                request.volume,
-                request.last_collection,
-                request.is_paid,
-                request.odour_intensity,
-                request.weight,
-                request.type
-            ]
-            if self.clf.predict([r]) == True:
-                self.request_list.pop(i)
-                self.free_space -= request.volume
-                self.weight_capacity -= request.weight
-                return request.x_pos, request.y_pos   
         return self.dump_x, self.dump_y
             
 
 
-    def collect(self):
+    def collect(self, garbage_type):
         if self.rect.x == self.dump_x and self.rect.y == self.dump_y:
             self.fuel = MAX_FUEL
             self.free_space = MAX_SPACE
             self.weight_capacity = MAX_WEIGHT
-        print(f'agent at ({self.rect.x}, {self.rect.y}); fuel: {self.fuel}; free space: {self.free_space}; weight capacity: {self.weight_capacity}')
+            self.weight_capacity_plastic = MAX_WEIGHT_PLASTIC
+            self.weight_capacity_mixed = MAX_WEIGHT_MIXED
+            self.weight_capacity_glass = MAX_WEIGHT_GLASS
+            self.weight_capacity_paper = MAX_WEIGHT_PAPER
+        if self.request_list==[]:
+            return 1
+        else:
+             request = self.request_list[0]
+        if garbage_type == "glass":
+            if request.weight > self.weight_capacity_glass:
+                return 1
+            self.weight_capacity_glass -= request.weight
+        elif garbage_type == "mixed":
+            if request.weight > self.weight_capacity_mixed:
+                return 1
+            self.weight_capacity_mixed -= request.weight
+        elif garbage_type == "paper":
+            if request.weight > self.weight_capacity_paper:
+                return 1
+            self.weight_capacity_paper -= request.weight
+        elif garbage_type == "plastic":
+            if request.weight > self.weight_capacity_plastic:
+                return 1
+            self.weight_capacity_plastic -= request.weight
+
+        print(f'agent at ({self.rect.x}, {self.rect.y}); fuel: {self.fuel}; free space: {self.free_space}; weight capacity: {self.weight_capacity}, glass_capacity: {self.weight_capacity_glass}, mixed_capacity: {self.weight_capacity_mixed}, paper_capacity: {self.weight_capacity_paper}, plastic_capacity: {self.weight_capacity_plastic}')
+        return 0
         pass

genetic.py

@@ -0,0 +1,162 @@
+import pygame
+from treelearn import treelearn
+import loadmodel
+from astar import astar
+from state import State
+import time
+from garbage_truck import GarbageTruck
+from heuristicfn import heuristicfn
+from map import randomize_map
+from heuristicfn import heuristicfn
+import pygame as pg
+import random
+from request import Request
+
+def determine_fitness(requests_list):
+    distances = []
+    for i in range(len(requests_list)+1): #from: request_list[i].x_pos and .y_pos
+        temp = []
+        for j in range(len(requests_list)+1):
+            if j<i:
+                temp.append('-')
+            elif j==i:
+                temp.append(0)
+            elif j>i:
+                if i==0:
+                    dist = heuristicfn(0, 0, requests_list[j-1].x_pos, requests_list[j-1].y_pos)
+                    temp.append(dist)
+                else:
+                    dist = heuristicfn(requests_list[i-1].x_pos, requests_list[i-1].y_pos, requests_list[j-1].x_pos, requests_list[j-1].y_pos)
+                    temp.append(dist)
+        distances.append(temp)
+    return(distances)
+
+
+def perform_permutation(obj_list, perm_list):
+    result = [None] * len(obj_list)
+
+    for i, index in enumerate(perm_list):
+        result[int(index)-1] = obj_list[i-1]
+    return result
+
+def apply_genetic(request_list):
+    print("Genetic algorithm started")
+
+    distances = determine_fitness(request_list)
+    population_size = 12
+    num_generations = 8
+    mutation_rate = 0.3
+    NUM = len(distances)
+
+    def initialize_population():
+        population = []
+        for _ in range(population_size):
+            chromosome = ['0']
+            while True:
+                if len(chromosome) == NUM:
+                    chromosome.append('0')
+                    break
+
+                temp = random.randint(1, NUM-1)
+                temp_str = str(temp)
+                if temp_str not in chromosome:
+                    chromosome.append(temp_str)
+            population.append(chromosome)
+        return population
+    
+    def calculate_route_length(route):
+        length = 0
+        for i in range(len(route)-1):
+            p = int(route[i])
+            q = int(route[i + 1])
+            length += distances[int(min(p,q))][int(max(p,q))]
+        return length
+
+    def calculate_fitness(population):
+        fitness_scores = []
+        for chromosome in population:
+            fitness_scores.append(1 / calculate_route_length(chromosome))
+        return fitness_scores
+
+    def parents_selection(population, fitness_scores):
+        selected_parents = []
+        for _ in range(len(population)):
+            candidates = random.sample(range(len(population)), 2)
+            fitness1 = fitness_scores[candidates[0]]
+            fitness2 = fitness_scores[candidates[1]]
+            selected_parent = population[candidates[0]] if fitness1 > fitness2 else population[candidates[1]]
+            selected_parents.append(selected_parent)
+        return selected_parents
+
+
+
+    def david_crossover(parent1, parent2):
+        start_index = random.randint(1, len(parent1)-3)
+        end_index = random.randint(start_index+1, len(parent1)-2)
+        parent1_chain = parent1[start_index:end_index+1]
+        parent2_letters = []
+        for trash in parent2[1:-1]:
+            if trash not in parent1_chain:
+                parent2_letters.append(trash)
+        child = [parent2[0]]+parent2_letters[0:start_index] + parent1_chain + parent2_letters[start_index:]+[parent2[-1]]
+        """         print('PARENTS: ')
+        print(parent1)
+        print(parent2)
+        print('CHILDS:')
+        print(child) """
+        return child
+
+    def mutation(chromosome):
+        index1 = random.randint(1, len(chromosome)-2)
+        index2 = random.randint(1, len(chromosome)-2)
+        chromosome[index1], chromosome[index2] = chromosome[index2], chromosome[index1]
+        return chromosome
+
+    def genetic_algorithm():
+        population = initialize_population()
+        for _ in range(num_generations):
+            fitness_scores = calculate_fitness(population)
+            parents = parents_selection(population, fitness_scores)
+            offspring = []
+            for i in range(0, len(parents), 2):
+                parent1 = parents[i]
+                parent2 = parents[i+1]
+                child1 = david_crossover(parent1, parent2)
+                child2 = david_crossover(parent2, parent1)
+                offspring.extend([child1, child2])
+            population = offspring
+            for i in range(len(population)):
+                if random.random() < mutation_rate:
+                    population[i] = mutation(population[i])
+        return population
+
+
+    best_route = None
+    best_length = float('inf')
+    population = genetic_algorithm()
+    for chromosome in population:
+        length = calculate_route_length(chromosome)
+        if length < best_length:
+            best_length = length
+            best_route = chromosome
+
+    print("Permutation chosen: ", best_route)
+    print("Its length:", best_length)
+    permuted_list = perform_permutation(request_list, best_route[1:-1])
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

heuristicfn.py

@@ -1,3 +1,2 @@
 def heuristicfn(startx, starty, goalx, goaly):
     return abs(startx - goalx) + abs(starty - goaly)
-    # return pow(((startx//50)-(starty//50)),2) + pow(((goalx//50)-(goaly//50)),2)

loadmodel.py

@@ -0,0 +1,44 @@
+import torch
+import torchvision
+import torchvision.transforms as transforms
+import PIL.Image as Image
+import os
+
+
+def classify(image_path):
+    model = torch.load('./model_training/garbage_model.pth')
+    mean = [0.6908, 0.6612, 0.6218]
+    std = [0.1947, 0.1926, 0.2086]
+    classes = [
+        "glass",
+        "mixed",
+        "paper",
+        "plastic",
+    ]
+    image_transforms = transforms.Compose([
+        transforms.Resize((128, 128)),
+        transforms.ToTensor(),
+        transforms.Normalize(torch.Tensor(mean), torch.Tensor(std))
+    ])
+
+    model = model.eval()
+    image = Image.open(image_path)
+    image = image_transforms(image).float()
+    image = image.unsqueeze(0)
+
+    output = model(image)
+    _, predicted = torch.max(output.data, 1)
+
+    label = os.path.basename(os.path.dirname(image_path))
+    prediction = classes[predicted.item()]
+    print(f"predicted: {prediction}")
+    if label == prediction:
+        print("predicted correctly.")
+    else:
+        print("predicted incorrectly.")
+    return prediction
+
+
+# classify("./model_training/test.jpg")
+
+

main.py

@@ -1,13 +1,15 @@
 import pygame
 from treelearn import treelearn
-
+import loadmodel
-
 from astar import astar
 from state import State
 import time
 from garbage_truck import GarbageTruck
 from heuristicfn import heuristicfn
 from map import randomize_map
+from tree import apply_tree
+from genetic import apply_genetic
+
 
 pygame.init()
 WIDTH, HEIGHT = 800, 800
@@ -18,14 +20,18 @@ AGENT = pygame.transform.scale(AGENT_IMG, (50, 50))
 FPS = 10
 FIELDCOUNT = 16
 FIELDWIDTH = 50
+BASE_IMG = pygame.image.load("Tiles/Base.jpg")
+BASE = pygame.transform.scale(BASE_IMG, (50, 50))
 
-GRASS_IMG = pygame.image.load("grass.png")
+def draw_window(agent, fields, flip, turn):
-GRASS = pygame.transform.scale(GRASS_IMG, (50, 50))
-def draw_window(agent, fields, flip):
     if flip:
         direction = pygame.transform.flip(AGENT, True, False)
+        if turn:
+            direction = pygame.transform.rotate(AGENT, -90)
     else:
         direction = pygame.transform.flip(AGENT, False, False)
+        if turn:
+            direction = pygame.transform.rotate(AGENT, 90)
     for i in range(16):
         for j in range(16):
             window.blit(fields[i][j], (i * 50, j * 50))
@@ -37,40 +43,65 @@ def main():
     clf = treelearn()
     clock = pygame.time.Clock()
     run = True
-    fields, priority_array, request_list = randomize_map()
+    fields, priority_array, request_list, imgpath_array = randomize_map()
+    apply_tree(request_list)
+    apply_genetic(request_list)
     agent = GarbageTruck(0, 0, pygame.Rect(0, 0, 50, 50), 0, request_list, clf)   # tworzenie pola dla agenta
+    low_space = 0
     while run:
         clock.tick(FPS)
         for event in pygame.event.get():
             if event.type == pygame.QUIT:
                 run = False
-        draw_window(agent, fields, False)   # false = kierunek east (domyslny), true = west
+        draw_window(agent, fields, False, False)   # false = kierunek east (domyslny), true = west
         x, y = agent.next_destination()
         if x == agent.rect.x and y == agent.rect.y:
             print('out of jobs')
             break
-        steps = astar(State(None, None, agent.rect.x, agent.rect.y, agent.orientation, priority_array[agent.rect.x//50][agent.rect.y//50], heuristicfn(agent.rect.x, agent.rect.y, x, y)), x, y, priority_array)
+        if low_space == 1:
+            x, y = 0, 0
+        steps = astar(State(None, None, agent.rect.x, agent.rect.y, agent.orientation,
+                            priority_array[agent.rect.x//50][agent.rect.y//50],
+                            heuristicfn(agent.rect.x, agent.rect.y, x, y)), x, y, priority_array)
         for interm in steps:
             if interm.action == 'LEFT':
                 agent.turn_left()
-                draw_window(agent, fields, True)
+                if agent.orientation == 0:
+                    draw_window(agent, fields, False, False)
+                elif agent.orientation == 2:
+                    draw_window(agent, fields, True, False)
+                elif agent.orientation == 1:
+                    draw_window(agent, fields, True, True)
+                else:
+                    draw_window(agent, fields, False, True)
             elif interm.action == 'RIGHT':
                 agent.turn_right()
-                draw_window(agent, fields, False)
+                if agent.orientation == 0:
+                    draw_window(agent, fields, False, False)
+                elif agent.orientation == 2:
+                    draw_window(agent, fields, True, False)
+                elif agent.orientation == 1:
+                    draw_window(agent, fields, True, True)
+                else:
+                    draw_window(agent, fields, False, True)
             elif interm.action == 'FORWARD':
                 agent.forward()
                 if agent.orientation == 0:
-                    draw_window(agent, fields, False)
+                    draw_window(agent, fields, False, False)
                 elif agent.orientation == 2:
-                    draw_window(agent, fields, True)
+                    draw_window(agent, fields, True, False)
+                elif agent.orientation == 1:
+                    draw_window(agent, fields, True, True)
                 else:
-                    draw_window(agent, fields, False)
+                    draw_window(agent, fields, False, True)
             time.sleep(0.3)
-        agent.collect()
+        if (agent.rect.x // 50 != 0) or (agent.rect.y // 50 != 0):
-        fields[agent.rect.x//50][agent.rect.y//50] = GRASS
+            garbage_type = loadmodel.classify(imgpath_array[agent.rect.x // 50][agent.rect.y // 50])
-        priority_array[agent.rect.x//50][agent.rect.y//50] = 1
+            low_space = agent.collect(garbage_type)
-        time.sleep(0.5)
 
+        fields[agent.rect.x//50][agent.rect.y//50] = BASE
+        priority_array[agent.rect.x//50][agent.rect.y//50] = 100
+        time.sleep(0.5)
 
     pygame.quit()
 

map.py

@@ -1,44 +1,127 @@
-import pygame, random
+import pygame as pg
+import random
 from request import Request
 
-DIRT_IMG = pygame.image.load("dirt.jpg")
-DIRT = pygame.transform.scale(DIRT_IMG, (50, 50))
-GRASS_IMG = pygame.image.load("grass.png")
-GRASS = pygame.transform.scale(GRASS_IMG, (50, 50))
-SAND_IMG = pygame.image.load("sand.jpeg")
-SAND = pygame.transform.scale(SAND_IMG, (50, 50))
-COBBLE_IMG = pygame.image.load("cobble.jpeg")
-COBBLE = pygame.transform.scale(COBBLE_IMG, (50, 50))
 
-def randomize_map():        # tworzenie mapy z losowymi polami
+STRAIGHT_IMG = pg.image.load("Tiles/Straight.jpg")
+STRAIGHT_VERTICAL = pg.transform.scale(STRAIGHT_IMG, (50, 50))
+STRAIGHT_HORIZONTAL = pg.transform.rotate(STRAIGHT_VERTICAL, 270)
+BASE_IMG = pg.image.load("Tiles/Base.jpg")
+BASE = pg.transform.scale(BASE_IMG, (50, 50))
+BEND_IMG = pg.image.load("Tiles/Bend.jpg")
+BEND1 = pg.transform.scale(BEND_IMG, (50, 50))
+BEND2 = pg.transform.rotate(BEND1, 90)
+BEND3 = pg.transform.rotate(pg.transform.flip(pg.transform.rotate(BEND1, 180), True, True), 180)
+BEND4 = pg.transform.rotate(BEND1, -90)
+INTERSECTION_IMG = pg.image.load("Tiles/Intersection.jpg")
+INTERSECTION = pg.transform.scale(INTERSECTION_IMG, (50, 50))
+JUNCTION_IMG = pg.image.load("Tiles/Junction.jpg")
+JUNCTION_SOUTH = pg.transform.scale(JUNCTION_IMG, (50, 50))
+JUNCTION_NORTH = pg.transform.rotate(pg.transform.flip(JUNCTION_SOUTH, True, False), 180)
+JUNCTION_EAST = pg.transform.rotate(JUNCTION_SOUTH, -90)
+JUNCTION_WEST = pg.transform.rotate(JUNCTION_SOUTH, 90)
+END_IMG = pg.image.load("Tiles/End.jpg")
+END1 = pg.transform.flip(pg.transform.rotate(pg.transform.scale(END_IMG, (50, 50)), 180), False, True)
+END2 = pg.transform.rotate(END1, 90)
+DIRT_IMG = pg.image.load("Tiles/dirt.jpg")
+DIRT = pg.transform.scale(DIRT_IMG, (50, 50))
+GRASS_IMG = pg.image.load("Tiles/grass.png")
+GRASS = pg.transform.scale(GRASS_IMG, (50, 50))
+SAND_IMG = pg.image.load("Tiles/sand.jpeg")
+SAND = pg.transform.scale(SAND_IMG, (50, 50))
+COBBLE_IMG = pg.image.load("Tiles/cobble.jpeg")
+COBBLE = pg.transform.scale(COBBLE_IMG, (50, 50))
+
+
+def randomize_map():  # tworzenie mapy z losowymi polami
     request_list = []
     field_array_1 = []
     field_array_2 = []
+    imgpath_array = [[0 for x in range(16)] for x in range(16)]
     field_priority = []
+    map_array = [['b',  'sh', 'sh', 'sh', 'sh', 'jw', 'sh', 'sh', 'sh', 'sh', 'jw', 'sh', 'sh', 'sh', 'b3', 'g'],
+                 ['sv',  'g',  'g',  'g',  'g', 'sv',  'g',  'g',  'g',  'g', 'sv',  'g',  'g',  'g', 'sv', 'g'],
+                 ['sv',  'g',  'g',  'g',  'g', 'sv',  'g',  'g',  'g',  'g', 'sv',  'g', 'gr',  'g', 'sv', 'g'],
+                 ['js', 'sh', 'sh', 'sh', 'sh',  'i', 'sh', 'sh', 'sh', 'sh', 'jn',  'g', 'gr',  'g', 'sv', 'g'],
+                 ['sv',  'g',  'g',  'g',  'g', 'sv',  'g',  'g',  'g',  'g', 'sv',  'g', 'gr',  'g', 'sv', 'g'],
+                 ['sv',  'g', 'gr', 'gr',  'g', 'sv',  'g',  'g',  'g',  'g', 'sv',  'g',  'g',  'g', 'sv', 'g'],
+                 ['sv',  'g', 'gr', 'gr',  'g', 'js', 'sh', 'sh', 'sh', 'sh',  'i', 'sh', 'sh', 'sh', 'jn', 'g'],
+                 ['sv',  'g',  'g',  'g',  'g', 'sv',  'g',  'g',  'g',  'g', 'sv',  'g',  'g',  'g', 'sv', 'g'],
+                 ['b1', 'sh', 'jw', 'sh', 'sh', 'jn',  'g', 'gr', 'gr',  'g', 'sv',  'g', 'gr',  'g', 'sv', 'g'],
+                 ['g',   'g', 'sv',  'g',  'g', 'sv',  'g', 'gr', 'gr',  'g', 'sv',  'g',  'g',  'g', 'sv', 'g'],
+                 ['gr',  'g', 'sv',  'g',  'g', 'sv',  'g', 'gr', 'gr',  'g', 'js', 'sh', 'sh', 'sh', 'jn', 'g'],
+                 ['gr',  'g', 'sv',  'g',  'g', 'sv',  'g',  'g',  'g',  'g', 'sv',  'g',  'g',  'g', 'sv', 'g'],
+                 ['gr',  'g', 'js', 'sh', 'sh',  'i', 'sh', 'sh', 'sh', 'sh', 'jn',  'g', 'gr',  'g', 'sv', 'g'],
+                 ['gr',  'g', 'sv',  'g',  'g', 'sv',  'g', ' g',  'g',  'g', 'sv',  'g', 'gr',  'g', 'sv', 'g'],
+                 ['gr',  'g', 'sv',  'g',  'g', 'sv',  'g',  'g',  'g',  'g', 'sv',  'g',  'g',  'g', 'sv', 'g'],
+                 ['gr',  'g', 'b1', 'sh', 'sh', 'je', 'sh', 'sh', 'sh', 'sh', 'je', 'sh', 'sh', 'sh', 'b4', 'g'],
+                 ]
+
     for i in range(16):
         temp_priority = []
         for j in range(16):
-            if i in (0, 1) and j in (0, 1):
+            if map_array[i][j] == 'b':
-                field_array_2.append(GRASS)
+                field_array_2.append(BASE)
                 temp_priority.append(1)
+            elif map_array[i][j] == 'b3':
+                field_array_2.append(BEND3)
+                temp_priority.append(1)
+            elif map_array[i][j] == 'b4':
+                field_array_2.append(BEND4)
+                temp_priority.append(1)
+            elif map_array[i][j] == 'b1':
+                field_array_2.append(BEND1)
+                temp_priority.append(1)
+            elif map_array[i][j] == 'sh':
+                field_array_2.append(STRAIGHT_VERTICAL)
+                temp_priority.append(1)
+            elif map_array[i][j] == 'sv':
+                field_array_2.append(STRAIGHT_HORIZONTAL)
+                temp_priority.append(1)
+            elif map_array[i][j] == 'i':
+                field_array_2.append(INTERSECTION)
+                temp_priority.append(1)
+            elif map_array[i][j] == 'je':
+                field_array_2.append(JUNCTION_EAST)
+                temp_priority.append(1)
+            elif map_array[i][j] == 'jw':
+                field_array_2.append(JUNCTION_WEST)
+                temp_priority.append(1)
+            elif map_array[i][j] == 'js':
+                field_array_2.append(JUNCTION_SOUTH)
+                temp_priority.append(1)
+            elif map_array[i][j] == 'jn':
+                field_array_2.append(JUNCTION_NORTH)
+                temp_priority.append(1)
+            elif map_array[i][j] == 'gr':
+                field_array_2.append(BASE)
+                temp_priority.append(1000)
             else:
                 prob = random.uniform(0, 100)
-                if 0 <= prob <= 12:
+                if 0 <= prob <= 20:
-                    field_array_2.append(COBBLE)
+                    garbage_type = random.choice(['glass', 'mixed', 'paper', 'plastic'])
+                    garbage_image_number = random.randrange(1, 100)
+                    GARBAGE_IMG = pg.image.load(
+                        f"./model_training/test_dataset/{garbage_type}/{garbage_type} ({str(garbage_image_number)}).jpg")
+                    GARBAGE = pg.transform.scale(GARBAGE_IMG, (50, 50))
+                    field_array_2.append(GARBAGE)
+                    imgpath_array[i][j] = (
+                        f"./model_training/test_dataset/{garbage_type}/{garbage_type} ({str(garbage_image_number)}).jpg")
+
                     temp_priority.append(100)
                     request_list.append(Request(
-                        i*50,j*50, #lokacja
+                        i * 50, j * 50,  # lokacja
-                        random.randint(0,3), #typ śmieci
+                        random.randint(0, 3),  # typ śmieci
-                        random.random(), #objętość śmieci
+                        random.random(),  # objętość śmieci
-                        random.randint(0,30), #ostatni odbiór
+                        random.randint(0, 30),  # ostatni odbiór
-                        random.randint(0,1), #czy opłacone w terminie
+                        random.randint(0, 1),  # czy opłacone w terminie
-                        random.random() * 10, #intensywność odoru
+                        random.random() * 10,  # intensywność odoru
-                        random.random() * 50 #waga śmieci                        
+                        random.random() * 50  # waga śmieci
                     ))
                 else:
-                    field_array_2.append(GRASS)
+                    field_array_2.append(BASE)
-                    temp_priority.append(1)
+                    temp_priority.append(1000)
         field_array_1.append(field_array_2)
         field_array_2 = []
         field_priority.append(temp_priority)
-    return field_array_1, field_priority, request_list
+    return field_array_1, field_priority, request_list, imgpath_array

model_training/main.py

@@ -0,0 +1,177 @@
+import os
+import torch
+import torchvision
+import torchvision.transforms as transforms
+from torch.utils.data import Dataset, random_split, DataLoader
+from torchvision.transforms import Compose, Lambda, ToTensor, Resize, CenterCrop, Normalize
+import matplotlib.pyplot as plt
+import numpy as np
+import torchvision.models as models
+import torch.nn as nn
+import torch.optim as optim
+
+def main():
+    torch.manual_seed(42)
+    # input_size = 49152
+    # hidden_sizes = [64, 128]
+    # output_size = 10
+
+    classes = os.listdir('./train_dataset')
+    print(classes)
+    mean = [0.6908, 0.6612, 0.6218]
+    std = [0.1947, 0.1926, 0.2086]
+
+    training_dataset_path = './train_dataset'
+    training_transforms = transforms.Compose([Resize((128,128)), ToTensor(), Normalize(torch.Tensor(mean), torch.Tensor(std))])
+    train_dataset = torchvision.datasets.ImageFolder(root=training_dataset_path, transform=training_transforms)
+    train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=32, shuffle=True)
+
+    testing_dataset_path = './test_dataset'
+    testing_transforms = transforms.Compose([Resize((128,128)), ToTensor(), Normalize(torch.Tensor(mean), torch.Tensor(std))])
+    test_dataset = torchvision.datasets.ImageFolder(root=testing_dataset_path, transform=testing_transforms)
+    test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=32, shuffle=False)
+
+    # Mean and Standard Deviation approximations
+    def get_mean_and_std(loader):
+        mean = 0.
+        std = 0.
+        total_images_count = 0
+        for images, _ in loader:
+            image_count_in_a_batch = images.size(0)
+            #print(images.shape)
+            images = images.view(image_count_in_a_batch, images.size(1), -1)
+            #print(images.shape)
+            mean += images.mean(2).sum(0)
+            std += images.std(2).sum(0)
+            total_images_count += image_count_in_a_batch
+        mean /= total_images_count
+        std /= total_images_count
+        return mean, std
+
+    print(get_mean_and_std(train_loader))
+
+    # Show images with applied transformations
+    def show_transformed_images(dataset):
+        loader = torch.utils.data.DataLoader(dataset, batch_size=6, shuffle=True)
+        batch = next(iter(loader))
+        images, labels = batch
+
+        grid = torchvision.utils.make_grid(images, nrow=3)
+        plt.figure(figsize=(11,11))
+        plt.imshow(np.transpose(grid, (1,2,0)))
+        print('labels: ', labels)
+        plt.show()
+
+    show_transformed_images(train_dataset)
+
+    # Neural network training:
+    def set_device():
+        if torch.cuda.is_available():
+            dev = "cuda:0"
+        else:
+            dev = "cpu"
+        return torch.device(dev)
+
+
+    def train_nn(model,train_loader,test_loader,criterion,optimizer,n_epochs):
+        device = set_device()
+        best_acc = 0
+
+        for epoch in range(n_epochs):
+            print("Epoch number %d " % (epoch+1))
+            model.train()
+            running_loss = 0.0
+            running_correct = 0.0
+            total = 0
+
+            for data in train_loader:
+                images, labels = data
+                images = images.to(device)
+                labels = labels.to(device)
+                total += labels.size(0)
+
+                # Back propagation
+                optimizer.zero_grad()
+                outputs = model(images)
+                _, predicted = torch.max(outputs.data, 1)
+                loss = criterion(outputs, labels)
+
+                loss.backward()
+                optimizer.step()
+                running_loss += loss.item()
+                running_correct += (labels==predicted).sum().item()
+
+            epoch_loss = running_loss/len(train_loader)
+            epoch_acc = 100.00 * running_correct / total
+
+            print(" - Training dataset. Got %d out of %d images correctly (%.3f%%). Epoch loss: %.3f" % (running_correct, total, epoch_acc, epoch_loss))
+            test_dataset_acc = evaluate_model_on_test_set(model, test_loader)
+
+            if(test_dataset_acc > best_acc):
+                best_acc = test_dataset_acc
+                save_checkpoint(model, epoch, optimizer, best_acc)
+
+        print("Finished")
+        return model
+
+
+    def evaluate_model_on_test_set(model, test_loader):
+        model.eval()
+        predicted_correctly_on_epoch = 0
+        total = 0
+        device = set_device()
+
+        with torch.no_grad():
+            for data in test_loader:
+                images, labels = data
+                images = images.to(device)
+                labels = labels.to(device)
+                total += labels.size(0)
+
+                outputs = model(images)
+                _, predicted = torch.max(outputs.data, 1)
+                predicted_correctly_on_epoch += (predicted == labels).sum().item()
+
+        epoch_acc = 100.0 * predicted_correctly_on_epoch / total
+        print(" - Testing dataset. Got %d out of %d images correctly (%.3f%%)" % (predicted_correctly_on_epoch, total, epoch_acc))
+
+        return epoch_acc
+
+
+    # Saving the checkpoint:
+    def save_checkpoint(model, epoch, optimizer, best_acc):
+        state = {
+            'epoch': epoch+1,
+            'model': model.state_dict(),
+            'best_accuracy': best_acc,
+            'optimizer': optimizer.state_dict(),
+        }
+        torch.save(state, 'model_best_checkpoint.zip')
+
+
+    resnet18_model = models.resnet18(pretrained=True) #Increase n_epochs if False
+    num_features = resnet18_model.fc.in_features
+    number_of_classes = 4
+    resnet18_model.fc = nn.Linear(num_features, number_of_classes)
+    device = set_device()
+    resnet_18_model = resnet18_model.to(device)
+    loss_fn = nn.CrossEntropyLoss() #criterion
+
+    optimizer = optim.SGD(resnet_18_model.parameters(), lr=0.01, momentum=0.9, weight_decay=0.003)
+    train_nn(resnet_18_model, train_loader, test_loader, loss_fn, optimizer, 5)
+
+
+    # Saving the model:
+    checkpoint = torch.load('model_best_checkpoint.pth.zip')
+
+    resnet18_model = models.resnet18()
+    num_features = resnet18_model.fc.in_features
+    number_of_classes = 4
+    resnet18_model.fc = nn.Linear(num_features, number_of_classes)
+    resnet18_model.load_state_dict(checkpoint['model'])
+
+    torch.save(resnet18_model, 'garbage_model.pth')
+
+
+if __name__ == "__main__":
+    main()