algorithms/bfs.py

@@ -51,7 +51,7 @@ def graphsearch(initial_state: State, map, goal_list, fringe: List[Node] = None,
     explored_states = set()
     fringe_states = set()
 
-    # root Node
+    # train Node
     fringe.append(Node(initial_state))
     fringe_states.add((initial_state.row, initial_state.column, initial_state.direction))
 
@@ -71,7 +71,7 @@ def graphsearch(initial_state: State, map, goal_list, fringe: List[Node] = None,
             parent = element.parent
 
             while parent is not None:
-                # root's action will be None, don't add it
+                # train's action will be None, don't add it
                 if parent.action is not None:
                     actions_sequence.append(parent.action)
                 parent = parent.parent

algorithms/neural_network/lightning_logs/version_0/hparams.yaml

@@ -0,0 +1 @@
+{}

algorithms/neural_network/lightning_logs/version_1/hparams.yaml

@@ -0,0 +1 @@
+{}

algorithms/neural_network/lightning_logs/version_2/hparams.yaml

@@ -0,0 +1 @@
+{}

algorithms/neural_network/lightning_logs/version_3/hparams.yaml

@@ -0,0 +1 @@
+{}

algorithms/neural_network/neural_network.py

@@ -0,0 +1,48 @@
+import torch
+import pytorch_lightning as pl
+import torch.nn as nn
+from torch.optim import SGD, Adam, lr_scheduler
+import torch.nn.functional as F
+from torch.utils.data import DataLoader
+from watersandtreegrass import WaterSandTreeGrass
+from common.constants import DEVICE, BATCH_SIZE, NUM_EPOCHS, LEARNING_RATE, SETUP_PHOTOS, ID_TO_CLASS
+
+
+class NeuralNetwork(pl.LightningModule):
+    def __init__(self, numChannels=3, batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE, num_classes=4):
+        super().__init__()
+        self.layer = nn.Sequential(
+            nn.Linear(36*36*3, 300),
+            nn.ReLU(),
+            nn.Linear(300, 4),
+            nn.LogSoftmax(dim=-1)
+        )
+        self.batch_size = batch_size
+        self.learning_rate = learning_rate
+
+    def forward(self, x):
+        x = x.reshape(x.shape[0], -1)
+        x = self.layer(x)
+        return x
+
+    def configure_optimizers(self):
+        optimizer = SGD(self.parameters(), lr=self.learning_rate)
+        return optimizer
+
+    def training_step(self, batch, batch_idx):
+        x, y = batch
+        scores = self(x)
+        loss = F.nll_loss(scores, y)
+        return loss
+
+    def validation_step(self, batch, batch_idx):
+        x, y = batch
+        scores = self(x)
+        val_loss = F.nll_loss(scores, y)
+        self.log("val_loss", val_loss, on_step=True, on_epoch=True, sync_dist=True)
+
+    def test_step(self, batch, batch_idx):
+        x, y = batch
+        scores = self(x)
+        test_loss = F.nll_loss(scores, y)
+        self.log("test_loss", test_loss, on_step=True, on_epoch=True, sync_dist=True)

algorithms/neural_network/neural_network_interface.py

@@ -0,0 +1,125 @@
+import torch
+import common.helpers
+from common.constants import DEVICE, BATCH_SIZE, NUM_EPOCHS, LEARNING_RATE, SETUP_PHOTOS, ID_TO_CLASS
+from watersandtreegrass import WaterSandTreeGrass
+from torch.utils.data import DataLoader
+from neural_network import NeuralNetwork
+from torchvision.io import read_image, ImageReadMode
+import torch.nn as nn
+from torch.optim import Adam
+import matplotlib.pyplot as plt
+import pytorch_lightning as pl
+from pytorch_lightning.callbacks import EarlyStopping
+
+
+def train(model):
+    model = model.to(DEVICE)
+    model.train()
+    trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=SETUP_PHOTOS)
+    testset = WaterSandTreeGrass('./data/test_csv_file.csv', transform=SETUP_PHOTOS)
+    train_loader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)
+    test_loader = DataLoader(testset, batch_size=BATCH_SIZE, shuffle=True)
+
+    criterion = nn.CrossEntropyLoss()
+    optimizer = Adam(model.parameters(), lr=LEARNING_RATE)
+
+    for epoch in range(NUM_EPOCHS):
+        for batch_idx, (data, targets) in enumerate(train_loader):
+            data = data.to(device=DEVICE)
+            targets = targets.to(device=DEVICE)
+
+            scores = model(data)
+            loss = criterion(scores, targets)
+
+            optimizer.zero_grad()
+            loss.backward()
+
+            optimizer.step()
+
+            if batch_idx % 4 == 0:
+                print("epoch: %d loss: %.4f" % (epoch, loss.item()))
+
+    print("FINISHED TRAINING!")
+    torch.save(model.state_dict(), "./learnednetwork.pth")
+
+    print("Checking accuracy for the train set.")
+    check_accuracy(train_loader)
+    print("Checking accuracy for the test set.")
+    check_accuracy(test_loader)
+    print("Checking accuracy for the tiles.")
+    check_accuracy_tiles()
+
+
+def check_accuracy_tiles():
+    answer = 0
+    for i in range(100):
+        if what_is_it('../../resources/textures/grass_with_tree.jpg') == 'tree':
+            answer = answer + 1
+    print("Accuracy(%) grass_with_tree.jpg",  answer)
+
+    answer = 0
+    for i in range(100):
+        if what_is_it('../../resources/textures/grass2.png') == 'grass':
+            answer = answer + 1
+    print("Accuracy(%) grass2.png",  answer)
+
+    answer = 0
+    for i in range(100):
+        if what_is_it('../../resources/textures/grass3.png') == 'grass':
+            answer = answer + 1
+    print("Accuracy(%) grass3.png",  answer)
+
+    answer = 0
+    for i in range(100):
+        if what_is_it('../../resources/textures/grass4.png') == 'grass':
+            answer = answer + 1
+    print("Accuracy(%) grass4.png", answer)
+
+    answer = 0
+    for i in range(100):
+        if what_is_it('../../resources/textures/grass1.png') == 'grass':
+            answer = answer + 1
+    print("Accuracy(%) grass1.png", answer)
+
+    answer = 0
+    for i in range(100):
+        if what_is_it('../../resources/textures/water.png') == 'water':
+            answer = answer + 1
+    print("Accuracy(%) water.png", answer)
+
+    answer = 0
+    for i in range(100):
+        if what_is_it('../../resources/textures/sand.png') == 'sand':
+            answer = answer + 1
+    print("Accuracy(%) sand.png", answer)
+
+
+def what_is_it(img_path, show_img=False):
+    image = read_image(img_path, mode=ImageReadMode.RGB)
+    if show_img:
+        plt.imshow(plt.imread(img_path))
+        plt.show()
+    image = SETUP_PHOTOS(image).unsqueeze(0)
+    model = NeuralNetwork.load_from_checkpoint('./lightning_logs/version_3/checkpoints/epoch=8-step=810.ckpt')
+
+    with torch.no_grad():
+        model.eval()
+        idx = int(model(image).argmax(dim=1))
+        return ID_TO_CLASS[idx]
+
+
+CNN = NeuralNetwork()
+
+
+trainer = pl.Trainer(accelerator='gpu', devices=1, auto_scale_batch_size=True, callbacks=[EarlyStopping('val_loss')], max_epochs=NUM_EPOCHS)
+#trainer = pl.Trainer(accelerator='gpu', devices=1, auto_lr_find=True, max_epochs=NUM_EPOCHS)
+
+trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=SETUP_PHOTOS)
+testset = WaterSandTreeGrass('./data/test_csv_file.csv', transform=SETUP_PHOTOS)
+train_loader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)
+test_loader = DataLoader(testset, batch_size=BATCH_SIZE)
+
+#trainer.fit(CNN, train_loader, test_loader)
+#trainer.tune(CNN, train_loader, test_loader)
+check_accuracy_tiles()
+print(what_is_it('../../resources/textures/sand.png', True))

algorithms/neural_network/watersandtreegrass.py

@@ -0,0 +1,25 @@
+import torch
+from torch.utils.data import Dataset
+import pandas as pd
+from torchvision.io import read_image, ImageReadMode
+from common.helpers import createCSV
+
+
+class WaterSandTreeGrass(Dataset):
+    def __init__(self, annotations_file, transform=None):
+        createCSV()
+        self.img_labels = pd.read_csv(annotations_file)
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.img_labels)
+
+    def __getitem__(self, idx):
+        image = read_image(self.img_labels.iloc[idx, 0], mode=ImageReadMode.RGB)
+        label = torch.tensor(int(self.img_labels.iloc[idx, 1]))
+
+        if self.transform:
+            image = self.transform(image)
+
+        return image, label
+

common/constants.py

@@ -1,4 +1,6 @@
 from enum import Enum
+import torchvision.transforms as transforms
+import torch
 
 GAME_TITLE = 'WMICraft'
 WINDOW_HEIGHT = 800
@@ -63,12 +65,34 @@ ACTION = {
     "go": 0,
 }
 
+LEFT = 'LEFT'
+RIGHT = 'RIGHT'
+UP = 'UP'
+DOWN = 'DOWN'
+
 # HEALTH_BAR
 BAR_ANIMATION_SPEED = 1
 BAR_WIDTH_MULTIPLIER = 0.9  # (0;1>
 BAR_HEIGHT_MULTIPLIER = 0.1
 
-LEFT = 'LEFT'
-RIGHT = 'RIGHT'
-UP = 'UP'
-DOWN = 'DOWN'
+
+#NEURAL_NETWORK
+LEARNING_RATE = 0.13182567385564073
+BATCH_SIZE = 64
+NUM_EPOCHS = 50
+
+DEVICE = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+print("Using ", DEVICE)
+CLASSES = ['grass', 'sand', 'tree', 'water']
+
+SETUP_PHOTOS = transforms.Compose([
+    transforms.Resize(36),
+    transforms.CenterCrop(36),
+    transforms.ToPILImage(),
+    transforms.ToTensor(),
+    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
+])
+
+ID_TO_CLASS = {i: j for i, j in enumerate(CLASSES)}
+CLASS_TO_ID = {value: key for key, value in ID_TO_CLASS.items()}
+

common/helpers.py

@@ -1,6 +1,9 @@
 from typing import Tuple, List
 
 import pygame
+from common.constants import GRID_CELL_PADDING, GRID_CELL_SIZE, COLUMNS, ROWS, CLASSES, CLASS_TO_ID
+import csv
+import os
 
 from common.constants import GRID_CELL_PADDING, GRID_CELL_SIZE
 from common.constants import ROWS, COLUMNS, LEFT, RIGHT, UP, DOWN
@@ -24,6 +27,44 @@ def draw_text(text, color, surface, x, y, text_size=30, is_bold=False):
     surface.blit(textobj, textrect)
 
 
+def createCSV():
+    train_data_path = './data/train'
+    test_data_path = './data/test'
+
+    if os.path.exists(train_data_path):
+        train_csvfile = open('./data/train_csv_file.csv', 'w', newline="")
+        writer = csv.writer(train_csvfile)
+        writer.writerow(["filepath", "type"])
+
+        for class_name in CLASSES:
+            class_dir = train_data_path + "/" + class_name
+            for filename in os.listdir(class_dir):
+                f = os.path.join(class_dir, filename)
+                if os.path.isfile(f):
+                    writer.writerow([f, CLASS_TO_ID[class_name]])
+
+        train_csvfile.close()
+
+    else:
+        print("Brak plików do uczenia")
+
+    if os.path.exists(test_data_path):
+        test_csvfile = open('./data/test_csv_file.csv', 'w', newline="")
+        writer = csv.writer(test_csvfile)
+        writer.writerow(["filepath", "type"])
+
+        for class_name in CLASSES:
+            class_dir = test_data_path + "/" + class_name
+            for filename in os.listdir(class_dir):
+                f = os.path.join(class_dir, filename)
+                if os.path.isfile(f):
+                    writer.writerow([f, CLASS_TO_ID[class_name]])
+
+        test_csvfile.close()
+    else:
+        print("Brak plików do testowania")
+
+
 def print_numbers():
     display_surface = pygame.display.get_surface()
     font = pygame.font.SysFont('Arial', 16)

logic/health_bar.py

@@ -46,7 +46,7 @@ class HealthBar:
     def heal(self, amount):
         if self.current_hp + amount < self.max_hp:
             self.current_hp += amount
-        elif self.current_hp + amount > self.max_hp:
+        elif self.current_hp + amount >= self.max_hp:
             self.current_hp = self.max_hp
 
     def show(self):

logic/level.py

@@ -155,19 +155,6 @@ class Level:
                 self.logs.enqueue_log(f'AI {current_knight.team}: Ruch w lewo.')
                 self.map[knight_pos_y][knight_pos_x - 1] = current_knight.team_alias()
 
-    def update_health_bars(self):
-        for knight in self.list_knights_blue:
-            knight.health_bar.update()
-
-        for knight in self.list_knights_red:
-            knight.health_bar.update()
-
-        for monster in self.list_monsters:
-            monster.health_bar.update()
-
-        for castle in self.list_castles:
-            castle.health_bar.update()
-
     def update(self):
         bg_width = (GRID_CELL_PADDING + GRID_CELL_SIZE) * COLUMNS + BORDER_WIDTH
         bg_height = (GRID_CELL_PADDING + GRID_CELL_SIZE) * ROWS + BORDER_WIDTH
@@ -175,4 +162,4 @@ class Level:
 
         # update and draw the game
         self.sprites.draw(self.screen)
-        self.update_health_bars()  # has to be called last
+        self.sprites.update()

models/castle.py

@@ -18,3 +18,6 @@ class Castle(pygame.sprite.Sprite):
         self.max_hp = 80
         self.current_hp = random.randint(1, self.max_hp)
         self.health_bar = HealthBar(screen, self.rect, current_hp=self.current_hp, max_hp=self.max_hp, calculate_xy=True, calculate_size=True)
+
+    def update(self):
+        self.health_bar.update()

models/knight.py

@@ -43,6 +43,9 @@ class Knight(pygame.sprite.Sprite):
         self.direction = self.direction.left()
         self.image = self.states[self.direction.value]
 
+    def update(self):
+        self.health_bar.update()
+
     def rotate_right(self):
         self.direction = self.direction.right()
         self.image = self.states[self.direction.value]

models/monster.py

@@ -43,3 +43,6 @@ class Monster(pygame.sprite.Sprite):
             self.max_hp = 7
             self.attack = 2
             self.points = 2
+
+    def update(self):
+        self.health_bar.update()