AI/GeneticAlgorithm.py

@@ -1,3 +1,5 @@
+import random
+
 import keyboard as keyboard
 
 import field as F
@@ -9,20 +11,18 @@ from src import mapschema as maps
 def genetic_algorithm_setup(field):
     population_units = ["", "w", "p", "s"]
 
-    # TODO REPREZENTACJA OSOBNIKA - MACIERZ ROZKłADU PLONÓW
+    # new_population to be
     population_text = []
-    population_text_single = []
-
-    population_size = 10
 
     # Populate the population_text array
-    for k in range(population_size):
-        population_text_single = []
     for row in range(D.GSIZE):
-            population_text_single.append([])
+        population_text.append([])
         for column in range(D.GSIZE):
-                population_text_single[row].append(random.choice(population_units))
+            population_text[row].append(random.choice(population_units))
-        population_text.append(population_text_single)
+
+    # printer
+    for _ in population_text:
+        print(population_text)
 
     """
     Genetic algorithm parameters:
@@ -31,110 +31,75 @@ def genetic_algorithm_setup(field):
     """
 
     # units per population in generation
-    best_outputs = []
+    sol_per_pop = 8
-    num_generations = 100
+    num_parents_mating = 4
-    num_parents = 4
+
+    population_values = []
+    fitness_row = []
+
+    # population Fitness
+    for i in range(0, D.GSIZE):
+        for j in range(D.GSIZE):
+            fitness_row.append(local_fitness(field, i, j, population_text))
+        population_values.append(fitness_row)
+
+    best_outputs = []
+    num_generations = 10
 
-    # iterative var
     generation = 0
-    stop = 0
+
-    # TODO WARUNEK STOPU
+    while generation < num_generations:
-    while generation < num_generations and stop < 3:
         if keyboard.is_pressed('space'):
             generation += 1
 
             print("Generation : ", generation)
             # Measuring the fitness of each chromosome in the population.
 
-            # population Fitness
+            fitness = cal_pop_fitness(population_values)
-            fitness = []
-
-            for i in range(0, population_size):
-                fitness.append((i, population_fitness(population_text[i], field, population_size)))
-
             print("Fitness")
             print(fitness)
 
-            best = sorted(fitness, key=lambda tup: tup[1], reverse=True)[0:num_parents]
+            # best_outputs.append(best_Output(new_population))
-
-            # Leaderboard only
-            best_outputs.append(best[0][1])
-
             # The best result in the current iteration.
-            print("Best result : ", best[0])
+            # print("Best result : ", best_Output(new_population))
 
-            # TODO METODA WYBORU OSOBNIKA - RANKING
             # Selecting the best parents in the population for mating.
-            parents = [population_text[i[0]] for i in best]
+            parents = select_mating_pool(new_population, fitness,
-            parents_copy = copy.deepcopy(parents)
+                                         num_parents_mating)
             print("Parents")
-            for i in range(0, len(parents)):
+            print(parents)
-                print('\n'.join([''.join(['{:4}'.format(item) for item in row])
-                                 for row in parents[i]]))
-                print("")
 
             # Generating next generation using crossover.
-            offspring_x = random.randint(1, D.GSIZE - 2)
+            offspring_crossover = crossover(parents, offspring_size=(pop_size[0] - parents.shape[0], num_weights))
-            offspring_y = random.randint(1, D.GSIZE - 2)
-
-            # TODO OPERATOR KRZYŻOWANIA
-            offspring_crossover = crossover(parents)
             print("Crossover")
-            for i in range(0, len(offspring_crossover)):
+            print(offspring_crossover)
-                print('\n'.join([''.join(['{:4}'.format(item) for item in row])
-                                 for row in offspring_crossover[i]]))
-                print("")
 
-            # TODO OPERATOR MUTACJI
+            # Adding some variations to the offspring using mutation.
-            offspring_mutation = mutation(population_units, offspring_crossover, population_size - num_parents,
+            offspring_mutation = mutation(offspring_crossover, num_mutations=2)
-                                          num_mutations=10)
             print("Mutation")
-            for i in range(0, len(offspring_mutation)):
+            print(offspring_mutation)
-                print('\n'.join([''.join(['{:4}'.format(item) for item in row])
-                                 for row in offspring_mutation[i]]))
-                print("")
 
-            population_text_copy = copy.deepcopy(population_text)
+            # Creating the new population based on the parents and offspring.
-            unused_indexes = [i for i in range(0, population_size) if i not in [j[0] for j in best]]
+            new_population[0:parents.shape[0], :] = parents
-            # Creating next generation
+            new_population[parents.shape[0]:, :] = offspring_mutation
-            population_text = []
-            for k in parents_copy:
-                population_text.append(k)
-            for k in range(0, len(offspring_mutation)):
-                population_text.append(offspring_mutation[k])
-            while len(population_text) < population_size:
-                x = random.choice(unused_indexes)
-                population_text.append(population_text_copy[x])
-                unused_indexes.remove(x)
 
-            # TODO WARUNEK STOPU
+    # Getting the best solution after iterating finishing all generations.
-            stop = 0
+    # At first, the fitness is calculated for each solution in the final generation.
-            if generation > 10:
+    fitness = cal_pop_fitness(new_population)
-                if best_outputs[-1] / best_outputs[-2] < 1.001:
+    # Then return the index of that solution corresponding to the best fitness.
-                    stop += 1
+    best_match_idx = numpy.where(fitness == numpy.max(fitness))
-                if best_outputs[-1] / best_outputs[-3] < 1.001:
-                    stop += 1
-                if best_outputs[-2] / best_outputs[-3] < 1.001:
-                    stop += 1
 
-    # final Fitness
+    print("Best solution : ", new_population[best_match_idx, :])
-    fitness = []
+    print("Best solution fitness : ", fitness[best_match_idx])
-    for i in range(0, population_size):
-        fitness.append((i, population_fitness(population_text[i], field, population_size)))
 
-    print("Final Fitness")
+    import matplotlib.pyplot
-    print(fitness)
 
-    best = sorted(fitness, key=lambda tup: tup[1])[0:num_parents]
+    matplotlib.pyplot.plot(best_outputs)
+    matplotlib.pyplot.xlabel("Iteration")
+    matplotlib.pyplot.ylabel("Fitness")
+    matplotlib.pyplot.show()
 
-    print("Best solution : ", )
+    # return best iteration of field
-    for i in range(0, D.GSIZE):
-        print(population_text[best[0][0]][i])
-    print("Best solution fitness : ", best[0][1])
-
-    pretty_printer(best_outputs)
-
-    # TODO REALLY return best iteration of field
     return 0
 
 

AI/NN_accuracy.py

@@ -1,47 +1,63 @@
-import torch
-import torchvision
-import torchvision.transforms as transforms
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-import numpy as np
-from matplotlib.pyplot import imshow
-import os
 import PIL
-import numpy as np
+import torchvision.transforms as transforms
-from matplotlib.pyplot import imshow
+
-import neural_network
+from AI import neural_network
-from matplotlib.pyplot import imshow
 
 
-# wcześniej grader.py
+# wcześniej grinder.py
 # Get accuracy for neural_network model 'network_model.pth'
 def NN_accuracy():
     # Create the model
-    net = neural_network.Net()
+    model = neural_network.Net()
 
     # Load state_dict
-    neural_network.load_network_from_structure(net)
+    neural_network.load_network_from_structure(model)
+
+    # Create the preprocessing transformation here
+    transform = transforms.Compose([neural_network.Negative(), transforms.ToTensor()])
+
+    # load your image(s)
+    img = PIL.Image.open('../src/test/0_100.jpg')
+    img2 = PIL.Image.open('../src/test/1_100.jpg')
+    img3 = PIL.Image.open('../src/test/4_100.jpg')
+    img4 = PIL.Image.open('../src/test/5_100.jpg')
+
+    # Transform
+    input = transform(img)
+    input2 = transform(img2)
+    input3 = transform(img3)
+    input4 = transform(img4)
+
+    # unsqueeze batch dimension, in case you are dealing with a single image
+    input = input.unsqueeze(0)
+    input2 = input2.unsqueeze(0)
+    input3 = input3.unsqueeze(0)
+    input4 = input4.unsqueeze(0)
 
     # Set model to eval
-    net.eval()
+    model.eval()
 
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    # Get prediction
+    output = model(input)
+    output2 = model(input2)
+    output3 = model(input3)
+    output4 = model(input4)
 
-    folderlist = os.listdir(os.path.dirname(__file__) + "\\test")
+    print(output)
+    index = output.cpu().data.numpy().argmax()
+    print(index)
 
-    tested = 0
+    print(output2)
-    correct = 0
+    index = output2.cpu().data.numpy().argmax()
+    print(index)
 
-    for folder in folderlist:
+    print(output3)
-        for file in os.listdir(os.path.dirname(__file__) + "\\test\\" + folder):
+    index = output3.cpu().data.numpy().argmax()
-            if neural_network.result_from_network(net, os.path.dirname(__file__) + "\\test\\" + folder + "\\" + file) == folder:
+    print(index)
-                correct += 1
-                tested += 1
-            else:
-                tested += 1
 
-    print(correct/tested)
+    print(output4)
+    index = output4.cpu().data.numpy().argmax()
+    print(index)
 
 
 if __name__ == "__main__":

AI/ga_methods.py

@@ -1,8 +1,3 @@
-import copy
-import random
-
-import matplotlib
-import matplotlib.pyplot
 import numpy
 
 import src.dimensions as D
@@ -10,94 +5,88 @@ import src.dimensions as D
 
 # Genetic Algorithm methods
 
-def local_fitness(field, x, y, plants_case):
+def local_fitness(field, x, y, plants):
     soil_value = 0
     if field[x][y].field_type == "soil":
         soil_value = 1
     else:
         soil_value = 0.5
 
-    if plants_case[x][y] == "":
+    if plants[x][y] == "":
         plant_value = 0
-    elif plants_case[x][y] == "w":
+    elif plants[x][y] == "w":
         plant_value = 1
-    elif plants_case[x][y] == "p":
+    elif plants[x][y] == "p":
         plant_value = 2
-    elif plants_case[x][y] == "s":
+    elif plants[x][y] == "s":
         plant_value = 3
-    else:
-        plant_value = 1
 
     neighbour_bonus = 1
-
     if x - 1 >= 0:
-        if plants_case[x][y] == plants_case[x - 1][y]:
+        if plants[x][y] == plants[x - 1][y]:
             neighbour_bonus += 1
     if x + 1 < D.GSIZE:
-        if plants_case[x][y] == plants_case[x + 1][y]:
+        if plants[x][y] == plants[x + 1][y]:
             neighbour_bonus += 1
     if y - 1 >= 0:
-        if plants_case[x][y] == plants_case[x][y - 1]:
+        if plants[x][y] == plants[x][y - 1]:
             neighbour_bonus += 1
     if y + 1 < D.GSIZE:
-        if plants_case[x][y] == plants_case[x][y + 1]:
+        if plants[x][y] == plants[x][y + 1]:
             neighbour_bonus += 1
 
+    # TODO * multiculture_bonus
     local_fitness_value = (soil_value + plant_value) * (0.5 * neighbour_bonus + 1)
     return local_fitness_value
 
 
-def population_fitness(population_text_local, field, population_size):
+def cal_pop_fitness(pop):
     # Calculating the fitness value of each solution in the current population.
     # The fitness function calulates the sum of products between each input and its corresponding weight.
-    fitness = []
+    fitness = sum(map(sum, pop))
-
-    for k in range(population_size):
-        population_values_single = []
-        population_values_single_row = []
-        fitness_row = []
-
-        for i in range(0, D.GSIZE):
-            for j in range(0, D.GSIZE):
-                population_values_single_row.append(local_fitness(field, i, j, population_text_local))
-            population_values_single.append(population_values_single_row)
-
-        for i in range(D.GSIZE):
-            fitness_row.append(sum(population_values_single[i]))
-        fitness = sum(fitness_row)
     return fitness
 
 
-def crossover(local_parents):
+def select_mating_pool(pop, fitness, num_parents):
-    ret = []
+    # Selecting the best individuals in the current generation as parents for producing the offspring of the next generation.
-    for i in range(0, len(local_parents)):
+    parents = numpy.empty((num_parents, pop.shape[1]))
-        child = copy.deepcopy(local_parents[i])
+    for parent_num in range(num_parents):
-        # Vertical randomization
+        max_fitness_idx = numpy.where(fitness == numpy.max(fitness))
-        width = random.randint(1, D.GSIZE // len(local_parents))  # width of stripes
+        max_fitness_idx = max_fitness_idx[0][0]
-        indexes_parents = numpy.random.permutation(range(0, len(local_parents)))  # sorting of stripes
+        parents[parent_num, :] = pop[max_fitness_idx, :]
-        beginning = random.randint(0, len(local_parents[0]) - width * len(
+        fitness[max_fitness_idx] = -99999999999
-            local_parents))  # point we start putting the stripes from
+    return parents
-        for x in indexes_parents:
-            child[beginning:beginning + width] = local_parents[x][beginning:beginning + width]
-            beginning += width
-        ret.append(child)
-    return ret
 
 
-def mutation(population_units, offspring_crossover, num_mutants, num_mutations=10):
+def crossover(parents, offspring_size):
-    for case in range(0, len(offspring_crossover)):
+    offspring = numpy.empty(offspring_size)
-        for mutation in range(0, num_mutations):
+    # The point at which crossover takes place between two parents. Usually, it is at the center.
-            mutation_x = random.randint(0, D.GSIZE - 1)
+    crossover_point = numpy.uint8(offspring_size[1] / 2)
-            mutation_y = random.randint(0, D.GSIZE - 1)
-            mutation_value = random.choice(population_units)
-            offspring_crossover[case][mutation_x][mutation_y] = mutation_value
-            num_mutants -= 1
 
+    for k in range(offspring_size[0]):
+        # Index of the first parent to mate.
+        parent1_idx = k % parents.shape[0]
+        # Index of the second parent to mate.
+        parent2_idx = (k + 1) % parents.shape[0]
+        # The new offspring will have its first half of its genes taken from the first parent.
+        offspring[k, 0:crossover_point] = parents[parent1_idx, 0:crossover_point]
+        # The new offspring will have its second half of its genes taken from the second parent.
+        offspring[k, crossover_point:] = parents[parent2_idx, crossover_point:]
+    return offspring
+
+
+def mutation(offspring_crossover, num_mutations=1):
+    mutations_counter = numpy.uint8(offspring_crossover.shape[1] / num_mutations)
+    # Mutation changes a number of genes as defined by the num_mutations argument. The changes are random.
+    for idx in range(offspring_crossover.shape[0]):
+        gene_idx = mutations_counter - 1
+        for mutation_num in range(num_mutations):
+            # The random value to be added to the gene.
+            random_value = numpy.random.uniform(-1.0, 1.0, 1)
+            offspring_crossover[idx, gene_idx] = offspring_crossover[idx, gene_idx] + random_value
+            gene_idx = gene_idx + mutations_counter
     return offspring_crossover
 
 
-def pretty_printer(best_outputs):
+def best_Output(new_population):
-    matplotlib.pyplot.plot(best_outputs)
+    return numpy.max(numpy.sum(new_population * equation_inputs, axis=1))
-    matplotlib.pyplot.xlabel("Iteration")
-    matplotlib.pyplot.ylabel("Fitness")
-    matplotlib.pyplot.show()

AI/neural_network.py

@@ -1,20 +1,18 @@
-import torch
-import torchvision
-import torchvision.transforms as transforms
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-import numpy as np
-from matplotlib.pyplot import imshow
-import os
 import PIL
 import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision
+import torchvision.transforms as transforms
 from matplotlib.pyplot import imshow
 
+
 def to_negative(img):
     img = PIL.ImageOps.invert(img)
     return img
 
+
 class Negative(object):
     def __init__(self):
         pass
@@ -22,46 +20,41 @@ class Negative(object):
     def __call__(self, img):
         return to_negative(img)
 
-transform = transforms.Compose([Negative(), transforms.ToTensor()])
-train_set = torchvision.datasets.ImageFolder(root='train', transform=transform)
-classes = ("pepper", "potato", "strawberry", "tomato")
 
-BATCH_SIZE = 4
+def plotdigit(image):
+    img = np.reshape(image, (-1, 100))
+    imshow(img, cmap='Greys')
+
+
+transform = transforms.Compose([Negative(), transforms.ToTensor()])
+train_set = torchvision.datasets.ImageFolder(root='../src/train', transform=transform)
+classes = ("apple", "potato")
+
+BATCH_SIZE = 2
 train_loader = torch.utils.data.DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True, num_workers=0)
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
+
 class Net(nn.Module):
     def __init__(self):
-        super().__init__()
+        super(Net, self).__init__()
-        self.network = nn.Sequential(
+        self.flatten = nn.Flatten()
-            nn.Conv2d(3, 32, kernel_size=3, padding=1), #3 channels to 32 channels
+        self.linear_relu_stack = nn.Sequential(
+            nn.Linear(3 * 100 * 100, 512),
             nn.ReLU(),
-            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
+            nn.Linear(512, 512),
             nn.ReLU(),
-            nn.MaxPool2d(2, 2), # output: 64 channels x 50 x 50 image size - decrease
+            nn.Linear(512, 2),
+            nn.ReLU()
+        )
+        self.linear_relu_stack = self.linear_relu_stack.to(device)
 
-            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
+    def forward(self, x):
-            nn.ReLU(),
+        x = self.flatten(x).to(device)
-            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1), #increase power of model 
+        logits = self.linear_relu_stack(x).to(device)
-            nn.ReLU(),
+        return logits
-            nn.MaxPool2d(2, 2), # output: 128 x 25 x 25
 
-            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
-            nn.ReLU(),
-            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
-            nn.ReLU(),
-            nn.MaxPool2d(5, 5), # output: 256 x 5 x 5
-
-            nn.Flatten(), #a single vector 256*5*5,
-            nn.Linear(256*5*5, 1024),
-            nn.ReLU(),
-            nn.Linear(1024, 512),
-            nn.ReLU(),
-            nn.Linear(512, 4))
-        
-    def forward(self, xb):
-        return self.network(xb)
 
 def training_network():
     net = Net()
@@ -70,7 +63,7 @@ def training_network():
     criterion = nn.CrossEntropyLoss()
     optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
 
-    for epoch in range(10):
+    for epoch in range(4):
         running_loss = 0.0
         for i, data in enumerate(train_loader, 0):
             inputs, labels = data[0].to(device), data[1].to(device)
@@ -81,7 +74,7 @@ def training_network():
             optimizer.step()
 
             running_loss += loss.item()
-            if i % 200 == 199:
+            if i % 2000 == 1999:
                 print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss))
                 running_loss = 0.0
 
@@ -91,8 +84,8 @@ def training_network():
 
 def result_from_network(net, loaded_image):
     image = PIL.Image.open(loaded_image)
-    pil_to_tensor = transforms.Compose([Negative(), transforms.ToTensor()])(image.convert("RGB")).unsqueeze_(0)
+    pil_to_tensor = transforms.ToTensor()(image.convert("RGB")).unsqueeze_(0)
-    outputs = net(pil_to_tensor)
+    outputs = net(pil_to_tensor.to(device))
 
     return classes[torch.max(outputs, 1)[1]]
 
@@ -106,6 +99,7 @@ def load_network_from_structure(network):
     network.load_state_dict(torch.load('network_model.pth'))
 
 
+# Create network_model.pth
 if __name__ == "__main__":
     print(torch.cuda.is_available())
     training_network()

field.py

@@ -1,7 +1,9 @@
 import pygame
+
 from src.colors import *
 from src.dimensions import *
 
+
 class Field(pygame.sprite.Sprite):
     def __init__(self, row, column, field_type):
         super(Field, self).__init__()
@@ -24,62 +26,22 @@ class Field(pygame.sprite.Sprite):
         self.position = [row, column]
         self.hydration = 0
         self.planted = 0
-        self.fertility = 0
+        self.fertility = 1
         self.tractor_there = False
 
     def hydrate(self):
         if self.field_type == "soil" and self.hydration <= 5:
             self.hydration += 1
-                if self.fertility == 1:
+
-                    if self.hydration == 0:
+            # color field to it's hydration value
-                        self.surf.fill(REDDISH0)
+            self.surf.fill(eval('BROWN' + str(self.hydration)))
-                        self.fertility = 0
-                    if self.hydration == 1:
-                        self.surf.fill(REDDISH1)
-                    if self.hydration == 2:
-                        self.surf.fill(REDDISH2)
-                    if self.hydration == 3:
-                        self.surf.fill(REDDISH3)
-                    if self.hydration == 4 or self.hydration == 5:
-                        self.surf.fill(REDDISH4)
-                else:
-                    if self.hydration == 0:
-                        self.surf.fill(BROWN0)
-                    if self.hydration == 1:
-                        self.surf.fill(BROWN1)
-                    if self.hydration == 2:
-                        self.surf.fill(BROWN2)
-                    if self.hydration == 3:
-                        self.surf.fill(BROWN3)
-                    if self.hydration == 4 or self.hydration == 5:
-                        self.surf.fill(BROWN4)
 
     def dehydrate(self):
         if self.field_type == "soil" and self.hydration > 0:
             self.hydration -= 1
-                if self.fertility == 1:
+
-                    if self.hydration == 0:
+            # color field to it's hydration value
-                        self.surf.fill(REDDISH0)
+            self.surf.fill(eval('BROWN' + str(self.hydration)))
-                        self.fertility = 0
-                    if self.hydration == 1:
-                        self.surf.fill(REDDISH1)
-                    if self.hydration == 2:
-                        self.surf.fill(REDDISH2)
-                    if self.hydration == 3:
-                        self.surf.fill(REDDISH3)
-                    if self.hydration == 4 or self.hydration == 5:
-                        self.surf.fill(REDDISH4)
-                else:
-                    if self.hydration == 0:
-                        self.surf.fill(BROWN0)
-                    if self.hydration == 1:
-                        self.surf.fill(BROWN1)
-                    if self.hydration == 2:
-                        self.surf.fill(BROWN2)
-                    if self.hydration == 3:
-                        self.surf.fill(BROWN3)
-                    if self.hydration == 4 or self.hydration == 5:
-                        self.surf.fill(BROWN4)
 
     def free(self):
         self.planted = 0

main.py

@@ -10,15 +10,12 @@ from pygame.locals import (
     QUIT
 )
 
-
 # Import other files from project
 import field as F
 import node as N
 import plant as P
 import src.colors as C
 import src.dimensions as D
-import AI.GeneticAlgorithm as ga
-import AI.neural_network as nn
 import tractor as T
 from src import mapschema as maps
 
@@ -47,16 +44,6 @@ if __name__ == "__main__":
             field[row].append(fieldbit)
 
     # genetic_algorithm_setup(field)
-    num_of_plants = 0
-    plant_pops = []
-    best_plant_pop = []
-
-    goal_gen = 100
-    best_plant_pop, plant_pops, num_of_plants, fitness = ga.genetic_algorithm_setup(field, plant_pops, goal_gen)
-
-    net = nn.Net()
-    nn.load_network_from_structure(net)
-    net.eval()
 
     # Create Tractor object
     tractor = T.Tractor(field, [0, 0])
@@ -71,11 +58,9 @@ if __name__ == "__main__":
     for row in range(D.GSIZE):
         plants.append([])
         for column in range(D.GSIZE):
-            if best_plant_pop[column][row] != "":
+            if mapschema[column][row] != 0:
-                plantbit = P.Plant(field[row][column], best_plant_pop[column][row])
+                plantbit = P.Plant(field[row][column], mapschema[column][row])
                 plants[row].append(plantbit)
-            else:
-                plants[row].append(0)
 
     # Create list for tractor instructions
     path = []
@@ -92,6 +77,7 @@ if __name__ == "__main__":
     # Main loop
     while RUNNING:
 
+        # Look at every event in the queue
         for event in pygame.event.get():
             # Did the user hit a key?
             if event.type == KEYDOWN:
@@ -119,11 +105,19 @@ if __name__ == "__main__":
                 tractor.rotate_right()
             elif path[0] == "hydrate":
                 tractor.hydrate(field)
-            elif path[0] == "fertilize":
+
-                if plants[tractor.position[1]][tractor.position[0]]:
-                    tractor.fertilize(field, plants, nn.result_from_network(net, plants[tractor.position[0]][tractor.position[1]].testimage))
             path.pop(0)
 
+        # Get all keys pressed at a time CURRENTLY UNUSED
+        pressed_keys = pygame.key.get_pressed()
+
+        # control tractor with pressed keys CURRENTLY UNUSED
+        if pressed_keys[K_UP]:
+            tractor.move()
+        elif pressed_keys[K_LEFT]:
+            tractor.rotate_left()
+        elif pressed_keys[K_RIGHT]:
+            tractor.rotate_right()
 
         # Set the screen background
         screen.fill(C.DBROWN)
@@ -139,7 +133,6 @@ if __name__ == "__main__":
         # Plants grow with every 10th tick, then they are drawn
         for row in plants:
             for plant in row:
-                if plant != 0:
                 plant.tick()
                 plant.grow()
                 screen.blit(plant.surf, plant.rect)
@@ -157,4 +150,4 @@ if __name__ == "__main__":
         pygame.display.flip()
 
         # Ensure program maintains a stable framerate
-        clock.tick(35)
+        clock.tick(8)

node.py

@@ -137,12 +137,10 @@ class Node:
             closedList.append(currentNode)
 
             if currentNode.field[currentNode.position[0]][currentNode.position[1]].planted and \
-                currentNode.field[currentNode.position[0]][currentNode.position[1]].field_type == "soil" and \
                     currentNode.field[currentNode.position[0]][currentNode.position[1]].hydration < 2:
                 path = []
                 for _ in range(currentNode.field[currentNode.position[0]][currentNode.position[1]].hydration, 4):
                     path.append("hydrate")
-                path.append("fertilize")
                 current = currentNode
                 while current is not None:
                     path.append(current.action)
@@ -176,57 +174,3 @@ class Node:
                     continue
 
                 heapq.heappush(openList, child)
-
-    def findPathToPlantSpot(self, goals):
-        startNode = Node(self.field, self.position, self.rotation)
-
-        openList = []
-        closedList = []
-
-        startNode.parent = None
-
-        heapq.heappush(openList, startNode)
-
-        while len(openList) > 0:
-            currentNode = heapq.heappop(openList)
-
-            closedList.append(currentNode)
-
-            if not currentNode.field[currentNode.position[0]][currentNode.position[1]].planted and \
-                    goals[currentNode.position[0]][currentNode.position[1]] != "":
-                path = []
-                path.append("plant")
-                current = currentNode
-                while current is not None:
-                    path.append(current.action)
-                    current = current.parent
-                return path[::-1]
-
-            children = succesor(currentNode)
-
-            perm = 0
-            for child in children:
-                for closedChild in closedList:
-                    if child.position == closedChild.position and child.rotation == closedChild.rotation and child.action == closedChild.action:
-                        perm = 1
-                        break
-                if perm == 1:
-                    perm = 0
-                    continue
-                child.parent = currentNode
-                child.startCost = currentNode.startCost + child.field[child.position[0]][child.position[1]].moveCost
-                child.heuristic = abs(startNode.position[0] - child.position[0]) + abs(
-                    startNode.position[1] - child.position[1])
-                child.totalCost = child.startCost + child.heuristic
-
-                for openNode in openList:
-                    if child.position == openNode.position and child.rotation == openNode.rotation and child.action == openNode.action and child.startCost >= openNode.startCost:
-                        perm = 1
-                        break
-
-                if perm == 1:
-                    perm = 0
-                    continue
-
-                heapq.heappush(openList, child)
-        

plant.py

@@ -1,32 +1,29 @@
-import os
-import random
-
 from AI.decision_tree import *
 from src.dimensions import *
 from src.sprites import *
-from src.colors import *
 
-path = os.path.dirname(__file__) + "\\src\\test\\"
 
 class Plant(pygame.sprite.Sprite):
     def __init__(self, field, species):
+        super(Plant, self).__init__()
         self.species = species
-        if self.species == "tomato":
+        if self.species == "wheat":
+            self.growth_speed = 1.5
+            self.humidity_needed = 2
             self.img0 = wheat_img_0
             self.img1 = wheat_img_1
             self.img2 = wheat_img_2
             self.img3 = wheat_img_3
         elif self.species == "potato":
+            self.growth_speed = 1
+            self.humidity_needed = 1
             self.img0 = potato_img_0
             self.img1 = potato_img_1
             self.img2 = potato_img_2
             self.img3 = potato_img_3
         elif self.species == "strawberry":
-            self.img0 = strawberry_img_0
+            self.growth_speed = 0.8
-            self.img1 = strawberry_img_1
+            self.humidity_needed = 1
-            self.img2 = strawberry_img_2
-            self.img3 = strawberry_img_3
-        elif self.species == "pepper":
             self.img0 = strawberry_img_0
             self.img1 = strawberry_img_1
             self.img2 = strawberry_img_2
@@ -41,94 +38,9 @@ class Plant(pygame.sprite.Sprite):
         field.planted = True
         self.tickscount = 0
         self.ticks = 0
-        self.path = path + self.species + "\\"
-        self.testimage = self.path + random.choice(os.listdir(self.path))
 
     def dtree(self):
-        if self.field.hydration == 4:
+        decision_tree(self)
-            if self.is_healthy == 1:
-                if self.field.tractor_there == 0:
-                    if self.ticks == 0:
-                        return 0
-                    elif self.ticks == 1:
-                        return 1
-                elif self.field.tractor_there == 1:
-                    return 0
-            elif self.is_healthy == 0:
-                return 0
-        elif self.field.hydration == 2:
-            if self.species == "pepper":
-                if self.ticks == 1:
-                    if self.is_healthy == 1:
-                        return 1
-                    elif self.is_healthy == 0:
-                        return 0
-                elif self.ticks == 0:
-                    return 0
-            elif self.species == "potato":
-                return 0
-            elif self.species == "tomato":
-                return 0
-            elif self.species == "strawberry":
-                return 0
-        elif self.field.hydration == 1:
-            if self.species == "potato":
-                return 0
-            elif self.species == "strawberry":
-                if self.ticks == 1:
-                    return -1
-                elif self.ticks == 0:
-                    return 0
-            elif self.species == "tomato":
-                return 0
-            elif self.species == "pepper":
-                if self.is_healthy == 0:
-                    return 0
-                elif self.is_healthy == 1:
-                    if self.field.tractor_there == 0:
-                        if self.ticks == 0:
-                            return 0
-                        elif self.ticks == 1:
-                            return 1
-                    elif self.field.tractor_there == 1:
-                        return 0
-        elif self.field.hydration == 3:
-            if self.ticks == 1:
-                if self.field.tractor_there == 0:
-                    if self.is_healthy == 1:
-                        if self.species == "potato":
-                            if self.field.fertility == 1:
-                                return 1
-                            elif self.field.fertility == 0:
-                                return 0
-                        elif self.species == "strawberry":
-                            return 1
-                        elif self.species == "pepper":
-                            return 1
-                        elif self.species == "tomato":
-                            return 1
-                    elif self.is_healthy == 0:
-                        return 0
-                elif self.field.tractor_there == 1:
-                    return 0
-            elif self.ticks == 0:
-                return 0
-        elif self.field.hydration == 5:
-            if self.field.tractor_there == 1:
-                return 0
-            elif self.field.tractor_there == 0:
-                if self.is_healthy == 0:
-                    return 0
-                elif self.is_healthy == 1:
-                    if self.ticks == 1:
-                        return 1
-                    elif self.ticks == 0:
-                        return 0
-        elif self.field.hydration == 0:
-            if self.ticks == 0:
-                return 0
-            elif self.ticks == 1:
-                return -1
 
     def update(self):
         if self.growth == 0:
@@ -151,7 +63,6 @@ class Plant(pygame.sprite.Sprite):
             self.growth = 4
         if self.growth < 0:
             self.growth = 0
-
         self.update()
 
     def tick(self):
@@ -159,6 +70,3 @@ class Plant(pygame.sprite.Sprite):
         if self.tickscount >= 25:
             self.tickscount = 0
             self.ticks = 1
-
-    def remove(self):
-        self.field.planted = False

src/colors.py

@@ -7,14 +7,6 @@ BROWN2 = (140, 110, 55)
 BROWN3 = (110, 85, 40)
 BROWN4 = (80, 60, 20)
 BROWN5 = (80, 60, 20)
-
-REDDISH0 = (230, 150, 90)
-REDDISH1 = (210, 130, 70)
-REDDISH2 = (190, 110, 55)
-REDDISH3 = (160, 85, 40)
-REDDISH4 = (130, 60, 20)
-REDDISH5 = (130, 60, 20)
-
 DBROWN = (65, 50, 20)
 LBROWN = (108, 97, 62)
 BLUE = (18, 93, 156)

src/dimensions.py

@@ -2,12 +2,12 @@
 GSIZE = 10
 
 # This sets the WIDTH and HEIGHT of each grid location
-WIDTH = 80
+WIDTH = 35
-HEIGHT = 80
+HEIGHT = 35
 
 # This sets the margin between each cell
 MARGIN = 5
 
 # Window size
 SCREEN_WIDTH = GSIZE * (WIDTH + MARGIN) + MARGIN
-SCREEN_HEIGHT = GSIZE * (HEIGHT + MARGIN) + MARGIN + 100
+SCREEN_HEIGHT = GSIZE * (HEIGHT + MARGIN) + MARGIN

tractor.py

@@ -1,6 +1,7 @@
+from pygame.locals import (K_c)
+
 from src.dimensions import *
 from src.sprites import *
-from plant import *
 
 
 class Tractor(pygame.sprite.Sprite):
@@ -72,13 +73,10 @@ class Tractor(pygame.sprite.Sprite):
         field[self.position[0]][self.position[1]].hydrate()
 
     def cut(self, field, pressed_keys):
+        if pressed_keys[K_c]:
             field[self.position[0]][self.position[1]].free()
 
-    def plant(self, plant_map, plants):
+    def plant(self, field, plant, pressed_keys):
-        print(plant_map[self.position[0]][self.position[1]])
+        if field.planted == 0:
-        plant = Plant(self.field[self.position[0]][self.position[1]], plant_map[self.position[0]][self.position[1]])
+            field.planted = plant
-        plants.append(plant)
+            plant.field = field
-
-    def fertilize(self, field, plants, type):
-        if plants[self.position[0]][self.position[1]].species == type:
-            field[self.position[0]][self.position[1]].fertility = 1