.gitignore

@@ -149,5 +149,4 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-.idea/
-/algorithms/neural_network/data/
+.idea/

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

@@ -10,39 +10,23 @@ from common.constants import DEVICE, BATCH_SIZE, NUM_EPOCHS, LEARNING_RATE, SETU
 
 class NeuralNetwork(pl.LightningModule):
     def __init__(self, numChannels=3, batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE, num_classes=4):
-        super(NeuralNetwork, self).__init__()
-        self.conv1 = nn.Conv2d(numChannels, 24, (3, 3), padding=1)
-        self.relu1 = nn.ReLU()
-        self.maxpool1 = nn.MaxPool2d((2, 2), stride=2)
-        self.conv2 = nn.Conv2d(24, 48, (3, 3), padding=1)
-        self.relu2 = nn.ReLU()
-        self.fc1 = nn.Linear(48*18*18, 800)
-        self.relu3 = nn.ReLU()
-        self.fc2 = nn.Linear(800, 400)
-        self.relu4 = nn.ReLU()
-        self.fc3 = nn.Linear(400, 4)
-        self.logSoftmax = nn.LogSoftmax(dim=1)
-
+        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 = self.conv1(x)
-        x = self.relu1(x)
-        x = self.maxpool1(x)
-        x = self.conv2(x)
-        x = self.relu2(x)
         x = x.reshape(x.shape[0], -1)
-        x = self.fc1(x)
-        x = self.relu3(x)
-        x = self.fc2(x)
-        x = self.relu4(x)
-        x = self.fc3(x)
-        x = self.logSoftmax(x)
+        x = self.layer(x)
         return x
 
     def configure_optimizers(self):
-        optimizer = Adam(self.parameters(), lr=self.learning_rate)
+        optimizer = SGD(self.parameters(), lr=self.learning_rate)
         return optimizer
 
     def training_step(self, batch, batch_idx):

algorithms/neural_network/neural_network_interface.py

@@ -10,8 +10,44 @@ from torch.optim import Adam
 import matplotlib.pyplot as plt
 import pytorch_lightning as pl
 from pytorch_lightning.callbacks import EarlyStopping
-import torchvision.transforms.functional as F
-from PIL import Image
+
+
+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():
@@ -59,13 +95,12 @@ def check_accuracy_tiles():
 
 
 def what_is_it(img_path, show_img=False):
-    image = Image.open(img_path).convert('RGB')
+    image = read_image(img_path, mode=ImageReadMode.RGB)
     if show_img:
-        plt.imshow(image)
+        plt.imshow(plt.imread(img_path))
         plt.show()
-
     image = SETUP_PHOTOS(image).unsqueeze(0)
-    model = NeuralNetwork.load_from_checkpoint('./lightning_logs/version_20/checkpoints/epoch=3-step=324.ckpt')
+    model = NeuralNetwork.load_from_checkpoint('./lightning_logs/version_3/checkpoints/epoch=8-step=810.ckpt')
 
     with torch.no_grad():
         model.eval()
@@ -73,53 +108,18 @@ def what_is_it(img_path, show_img=False):
         return ID_TO_CLASS[idx]
 
 
-def check_accuracy(tset):
-    model = NeuralNetwork.load_from_checkpoint('./lightning_logs/version_23/checkpoints/epoch=3-step=324.ckpt')
-    num_correct = 0
-    num_samples = 0
-    model = model.to(DEVICE)
-    model.eval()
-
-    with torch.no_grad():
-        for photo, label in tset:
-            photo = photo.to(DEVICE)
-            label = label.to(DEVICE)
-
-            scores = model(photo)
-            predictions = scores.argmax(dim=1)
-            num_correct += (predictions == label).sum()
-            num_samples += predictions.size(0)
-
-        print(f'Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}%')
+CNN = NeuralNetwork()
 
 
-def check_accuracy_data():
-    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)
-
-    print("Accuracy of train_set:")
-    check_accuracy(train_loader)
-    print("Accuracy of test_set:")
-    check_accuracy(test_loader)
-
-#CNN = NeuralNetwork()
-#common.helpers.createCSV()
-
-#trainer = pl.Trainer(accelerator='gpu', callbacks=EarlyStopping('val_loss'), devices=1, max_epochs=NUM_EPOCHS)
+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)
+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)
-
-
-#print(what_is_it('../../resources/textures/grass2.png', True))
-
-#check_accuracy_data()
-
-#check_accuracy_tiles()
+check_accuracy_tiles()
+print(what_is_it('../../resources/textures/sand.png', True))

algorithms/neural_network/watersandtreegrass.py

@@ -3,7 +3,6 @@ from torch.utils.data import Dataset
 import pandas as pd
 from torchvision.io import read_image, ImageReadMode
 from common.helpers import createCSV
-from PIL import Image
 
 
 class WaterSandTreeGrass(Dataset):
@@ -16,8 +15,7 @@ class WaterSandTreeGrass(Dataset):
         return len(self.img_labels)
 
     def __getitem__(self, idx):
-        image = Image.open(self.img_labels.iloc[idx, 0]).convert('RGB')
-
+        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:

common/constants.py

@@ -6,7 +6,7 @@ GAME_TITLE = 'WMICraft'
 WINDOW_HEIGHT = 800
 WINDOW_WIDTH = 1360
 FPS_COUNT = 60
-TURN_INTERVAL = 200
+TURN_INTERVAL = 500
 
 GRID_CELL_PADDING = 5
 GRID_CELL_SIZE = 36
@@ -77,17 +77,19 @@ BAR_HEIGHT_MULTIPLIER = 0.1
 
 
 #NEURAL_NETWORK
-LEARNING_RATE = 0.000630957344480193
+LEARNING_RATE = 0.13182567385564073
 BATCH_SIZE = 64
-NUM_EPOCHS = 9
+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.Resize((36, 36)),
     transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
 ])
 

learning/dataset_tree.csv

@@ -299,4 +299,4 @@ tower_dist;mob1_dist;mob2_dist;opp1_dist;opp2_dist;opp3_dist;opp4_dist;agent_hp;
 29;25;30;19;35;38;33;6;68;5;1;0;5;11;6;mob1
 23;43;41;25;27;26;19;7;12;8;3;4;10;11;9;tower
 7;9;18;31;36;21;16;4;23;8;4;9;8;11;5;tower
-35;21;39;36;36;37;33;10;41;9;4;1;0;7;0;mob1
+35;21;39;36;36;37;33;10;41;9;4;1;0;7;0;mob1

learning/decision_tree.py

@@ -26,7 +26,7 @@ def parse_idx_of_opp_or_monster(s: str) -> int:
 
 class DecisionTree:
     def __init__(self) -> None:
-        data_frame = pd.read_csv('learning/dataset_tree_1000.csv', delimiter=';')
+        data_frame = pd.read_csv('learning/dataset_tree.csv', delimiter=';')
         unlabeled_goals = data_frame['goal']
         self.goals_label_encoder = LabelEncoder()
         self.goals = self.goals_label_encoder.fit_transform(unlabeled_goals)

logic/game.py

@@ -45,7 +45,6 @@ class Game:
         # create level
         level.create_map()
         stats = Stats(self.screen, level.list_knights_blue, level.list_knights_red)
-        level.setup_stats(stats)
 
         print_numbers_flag = False
         running = True

logic/knights_queue.py

@@ -10,7 +10,7 @@ class KnightsQueue:
     def dequeue_knight(self):
         if self.both_teams_alive():
             knight = self.queues[self.team_idx_turn].popleft()
-            if knight.health_bar.current_hp <= 0:
+            if knight.max_hp <= 0:
                 return self.dequeue_knight()
             else:
                 self.queues[self.team_idx_turn].append(knight)

logic/level.py

@@ -31,15 +31,6 @@ class Level:
 
         self.knights_queue = None
 
-        self.stats = None
-
-    def setup_stats(self, stats):
-        self.stats = stats
-
-    def add_points(self, team, points_to_add):
-        if self.stats is not None:
-            self.stats.add_points(team, points_to_add)
-
     def create_map(self):
         self.map = import_random_map()
         self.setup_base_tiles()
@@ -101,119 +92,11 @@ class Level:
                         self.map[row_index][col_index] = castle
                         self.list_castles.append(castle)
 
-    #def attack_knight(self, knights_list, positions, current_knight):
-    #    op_pos_1 = current_knight.position[0] - 1, current_knight.position[1]
-    #    positions.append(op_pos_1)
-    #    op_pos_2 = current_knight.position[0], current_knight.position[1] - 1
-    #    positions.append(op_pos_2)
-    #    op_pos_3 = current_knight.position[0] + 1, current_knight.position[1]
-    #    positions.append(op_pos_3)
-    #    op_pos_4 = current_knight.position[0], current_knight.position[1] + 1
-    #    positions.append(op_pos_4)
-    #    for some_knight in knights_list:
-    #        for some_position in positions:
-    #            if (some_knight.position == some_position and some_knight.team != current_knight.team):
-    #                some_knight.health_bar.take_dmg(current_knight.attack)
-    #                if some_knight.health_bar.current_hp == 0:
-    #                    some_knight.kill()
-    #    positions.clear()
-
-    def attack_knight_left(self, knights_list, current_knight):
-        position_left = current_knight.position[0] - 1, current_knight.position[1]
-        for some_knight in knights_list:
-            if (some_knight.position == position_left and some_knight.team != current_knight.team):
-                some_knight.health_bar.take_dmg(current_knight.attack)
-                if some_knight.health_bar.current_hp <= 0:
-                    some_knight.kill()
-                    self.add_points(current_knight.team, 5)
-        for monster in self.list_monsters:
-            if monster.position == position_left:
-                monster.health_bar.take_dmg(current_knight.attack)
-                if monster.health_bar.current_hp <= 0:
-                    monster.kill()
-                    self.add_points(current_knight.team, monster.points)
-                else:
-                    current_knight.health_bar.take_dmg(monster.attack)
-                    if current_knight.health_bar.current_hp <= 0:
-                        current_knight.kill()
-        for castle in self.list_castles:
-            if castle.position == position_left:
-                castle.health_bar.take_dmg(current_knight.attack)
-
-
-    def attack_knight_right(self, knights_list, current_knight):
-        position_right = current_knight.position[0] + 1, current_knight.position[1]
-        for some_knight in knights_list:
-            if (some_knight.position == position_right and some_knight.team != current_knight.team):
-                some_knight.health_bar.take_dmg(current_knight.attack)
-                if some_knight.health_bar.current_hp == 0:
-                    some_knight.kill()
-                    self.add_points(current_knight.team, 5)
-        for monster in self.list_monsters:
-            if monster.position == position_right:
-                monster.health_bar.take_dmg(current_knight.attack)
-                if monster.health_bar.current_hp <= 0:
-                    monster.kill()
-                    self.add_points(current_knight.team, monster.points)
-                else:
-                    current_knight.health_bar.take_dmg(monster.attack)
-                    if current_knight.health_bar.current_hp <= 0:
-                        current_knight.kill()
-        for castle in self.list_castles:
-            if castle.position == position_right:
-                castle.health_bar.take_dmg(current_knight.attack)
-
-    def attack_knight_up(self, knights_list, current_knight):
-        position_up = current_knight.position[0], current_knight.position[1] - 1
-        for some_knight in knights_list:
-            if (some_knight.position == position_up and some_knight.team != current_knight.team):
-                some_knight.health_bar.take_dmg(current_knight.attack)
-                if some_knight.health_bar.current_hp == 0:
-                    some_knight.kill()
-                    self.add_points(current_knight.team, 5)
-        for monster in self.list_monsters:
-            if monster.position == position_up:
-                monster.health_bar.take_dmg(current_knight.attack)
-                if monster.health_bar.current_hp <= 0:
-                    monster.kill()
-                    self.add_points(current_knight.team, monster.points)
-                else:
-                    current_knight.health_bar.take_dmg(monster.attack)
-                    if current_knight.health_bar.current_hp <= 0:
-                        current_knight.kill()
-        for castle in self.list_castles:
-            if castle.position == position_up:
-                castle.health_bar.take_dmg(current_knight.attack)
-
-    def attack_knight_down(self, knights_list, current_knight):
-        position_down = current_knight.position[0], current_knight.position[1] + 1
-        for some_knight in knights_list:
-            if (some_knight.position == position_down and some_knight.team != current_knight.team):
-                some_knight.health_bar.take_dmg(current_knight.attack)
-                if some_knight.health_bar.current_hp == 0:
-                    some_knight.kill()
-                    self.add_points(current_knight.team, 5)
-        for monster in self.list_monsters:
-            if monster.position == position_down:
-                monster.health_bar.take_dmg(current_knight.attack)
-                if monster.health_bar.current_hp <= 0:
-                    monster.kill()
-                    self.add_points(current_knight.team, monster.points)
-                else:
-                    current_knight.health_bar.take_dmg(monster.attack)
-                    if current_knight.health_bar.current_hp <= 0:
-                        current_knight.kill()
-        for castle in self.list_castles:
-            if castle.position == position_down:
-                castle.health_bar.take_dmg(current_knight.attack)
-
     def handle_turn(self):
         current_knight = self.knights_queue.dequeue_knight()
-        knights_list = self.list_knights_red + self.list_knights_blue
         print("next turn " + current_knight.team)
         knight_pos_x = current_knight.position[0]
         knight_pos_y = current_knight.position[1]
-        positions = []
 
         goal_list = self.decision_tree.predict_move(grid=self.map, current_knight=current_knight,
                                                     monsters=self.list_monsters,
@@ -221,9 +104,6 @@ class Level:
                                                     if current_knight.team_alias() == 'k_r' else self.list_knights_red,
                                                     castle=self.list_castles[0])
 
-        if (len(self.list_knights_blue) == 0 or len(self.list_knights_red) == 0):
-            pygame.quit()
-
         if len(goal_list) == 0:
             return
 
@@ -236,19 +116,6 @@ class Level:
             return
 
         next_action = action_list.pop(0)
-
-        #if current_knight.health_bar.current_hp != 0:
-        #self.attack_knight(knights_list, positions, current_knight)
-
-        if current_knight.direction.name == UP:
-            self.attack_knight_up(knights_list, current_knight)
-        elif current_knight.direction.name == DOWN:
-            self.attack_knight_down(knights_list, current_knight)
-        elif current_knight.direction.name == RIGHT:
-            self.attack_knight_right(knights_list, current_knight)
-        elif current_knight.direction.name == LEFT:
-            self.attack_knight_left(knights_list, current_knight)
-
         if next_action == TURN_LEFT:
             self.logs.enqueue_log(f'AI {current_knight.team}: Obrót w lewo.')
             current_knight.rotate_left()
@@ -259,7 +126,7 @@ class Level:
             current_knight.step_forward()
             self.map[knight_pos_y][knight_pos_x] = MAP_ALIASES.get("GRASS")
 
-                # update knight on map
+            # update knight on map
             if current_knight.direction.name == UP:
                 self.logs.enqueue_log(f'AI {current_knight.team}: Ruch do góry.')
                 self.map[knight_pos_y - 1][knight_pos_x] = current_knight.team_alias()
@@ -281,6 +148,3 @@ class Level:
         # update and draw the game
         self.sprites.draw(self.screen)
         self.sprites.update()
-
-
-

models/castle.py

@@ -16,7 +16,8 @@ class Castle(pygame.sprite.Sprite):
         position_in_px = (parse_cord(position[0]), parse_cord(position[1]))
         self.rect = self.image.get_rect(center=position_in_px)
         self.max_hp = 80
-        self.health_bar = HealthBar(screen, self.rect, current_hp=self.max_hp, max_hp=self.max_hp, calculate_xy=True, calculate_size=True)
+        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

@@ -7,11 +7,8 @@ from common.helpers import parse_cord
 from logic.health_bar import HealthBar
 
 
-def load_knight_textures(team):
-    if team == "blue":
-        random_index = 3
-    else:
-        random_index = 4
+def load_knight_textures():
+    random_index = random.randint(1, 4)
     states = [
         pygame.image.load(f'resources/textures/knight_{random_index}_up.png').convert_alpha(),  # up = 0
         pygame.image.load(f'resources/textures/knight_{random_index}_right.png').convert_alpha(),  # right = 1
@@ -27,7 +24,7 @@ class Knight(pygame.sprite.Sprite):
         super().__init__(group)
 
         self.direction = Direction.DOWN
-        self.states = load_knight_textures(team)
+        self.states = load_knight_textures()
 
         self.image = self.states[self.direction.value]
         self.position = position
@@ -36,11 +33,11 @@ class Knight(pygame.sprite.Sprite):
         self.rect = self.image.get_rect(topleft=position_in_px)
 
         self.team = team
-        self.max_hp = random.randint(9, 13)
-        self.attack = random.randint(2, 4)
+        self.max_hp = random.randint(7, 12)
+        self.attack = random.randint(4, 7)
         self.defense = random.randint(1, 4)
         self.points = 1
-        self.health_bar = HealthBar(screen, self.rect, current_hp=self.max_hp, max_hp=self.max_hp, calculate_xy=True, calculate_size=True)
+        self.health_bar = HealthBar(screen, self.rect, current_hp=random.randint(1, self.max_hp), max_hp=self.max_hp, calculate_xy=True, calculate_size=True)
 
     def rotate_left(self):
         self.direction = self.direction.left()

models/monster.py

@@ -22,13 +22,14 @@ class Monster(pygame.sprite.Sprite):
         position_in_px = (parse_cord(position[0]), parse_cord(position[1]))
         self.rect = self.image.get_rect(topleft=position_in_px)
         self.position = position
-        self.max_hp = random.randrange(15, 20)
-        self.health_bar = HealthBar(screen, self.rect, current_hp=self.max_hp, max_hp=self.max_hp,
+        self.max_hp = random.randrange(15, 25)
+        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)
-        self.attack = random.randrange(4, 6)
+        self.attack = random.randrange(2, 10)
         if self.image == monster_images[0]:
             self.max_hp = 20
-            self.attack = 6
+            self.attack = 9
             self.points = 10
         elif self.image == monster_images[1]:
             self.max_hp = 15

ui/stats.py

@@ -23,8 +23,6 @@ class Stats:
                                          pygame.Rect(self.x + 210, self.y + 210, 100, 15),
                                          current_hp=sum([knight.get_current_hp() for knight in self.list_knights_red]),
                                          max_hp=sum([knight.get_max_hp() for knight in self.list_knights_red]))
-        self.blue_team_points = 0
-        self.red_team_points = 0
 
     def update(self):
 
@@ -52,16 +50,12 @@ class Stats:
 
         # texts
         draw_text('Rycerze: ' + str(len(self.list_knights_blue)), FONT_DARK, self.screen, self.x + 35, self.y + 240, 18)  # blue
+        draw_text('Fortece: ' + str(len(self.list_knights_red)), FONT_DARK, self.screen, self.x + 35, self.y + 270, 18)  # red
 
-        draw_text('Rycerze: ' + str(len(self.list_knights_red)), FONT_DARK, self.screen, self.x + 215, self.y + 240, 18)
+        draw_text('Rycerze: 4', FONT_DARK, self.screen, self.x + 215, self.y + 240, 18)
+        draw_text('Fortece: 0', FONT_DARK, self.screen, self.x + 215, self.y + 270, 18)
 
         # points
         pygame.draw.rect(self.screen, ORANGE, pygame.Rect(self.x, self.y + 390, 340, 3))
-        draw_text('PUNKTY: ' + str(self.blue_team_points), FONT_DARK, self.screen, self.x + 35, self.y + 408, 18, True)
-        draw_text('PUNKTY: ' + str(self.red_team_points), FONT_DARK, self.screen, self.x + 215, self.y + 408, 18, True)
-
-    def add_points(self, team, points):
-        if team == "blue":
-            self.blue_team_points += points
-        else:
-            self.red_team_points += points
+        draw_text('PUNKTY: 10', FONT_DARK, self.screen, self.x + 35, self.y + 408, 18, True)
+        draw_text('PUNKTY: 10', FONT_DARK, self.screen, self.x + 215, self.y + 408, 18, True)