work on what_is_it func;

algorithms/a_star.py

@@ -4,11 +4,10 @@ import heapq
 from dataclasses import dataclass, field
 from typing import Tuple, Optional, List
 
-from algorithms.genetic.const import MAP_ALIASES
 from common.constants import ROWS, COLUMNS, LEFT, RIGHT, UP, DOWN
 from common.helpers import directions
 
-EMPTY_FIELDS = [MAP_ALIASES.get("SAND"), MAP_ALIASES.get("GRASS"), ' ']
+EMPTY_FIELDS = ['s', 'g', ' ']
 
 TURN_LEFT = 'TURN_LEFT'
 TURN_RIGHT = 'TURN_RIGHT'

algorithms/genetic/common.py

@@ -1,142 +0,0 @@
-from dataclasses import dataclass
-
-import numpy as np
-
-from const import *
-from typing import List, Dict, Tuple
-
-import numpy.typing as npt
-
-
-@dataclass
-class Position:
-    row: int
-    col: int
-
-
-@dataclass
-class Area:
-    position: Position
-    width: int
-    height: int
-
-
-AREAS_TO_CROSS = [
-    # up above left knights spawn
-    Area(position=Position(row=0, col=0),
-         width=KNIGHTS_SPAWN_WIDTH,
-         height=LEFT_KNIGHTS_SPAWN_FIRST_ROW),
-
-    # down below left knights spawn
-    Area(position=Position(row=LEFT_KNIGHTS_SPAWN_FIRST_ROW + KNIGHTS_SPAWN_HEIGHT, col=0),
-         width=KNIGHTS_SPAWN_WIDTH,
-         height=ROWS - LEFT_KNIGHTS_SPAWN_FIRST_ROW - KNIGHTS_SPAWN_HEIGHT),
-
-    # between left knights spawn and castle
-    Area(position=Position(row=0, col=KNIGHTS_SPAWN_WIDTH),
-         width=CASTLE_SPAWN_FIRST_COL - KNIGHTS_SPAWN_WIDTH,
-         height=ROWS),
-
-    # up above castle
-    Area(position=Position(row=0, col=CASTLE_SPAWN_FIRST_COL),
-         width=2,
-         height=CASTLE_SPAWN_FIRST_ROW),
-
-    # down below castle
-    Area(position=Position(row=CASTLE_SPAWN_FIRST_ROW + 2, col=CASTLE_SPAWN_FIRST_COL),
-         width=2,
-         height=ROWS - CASTLE_SPAWN_FIRST_ROW - 2),
-
-    # between castle and right knights spawn
-    Area(position=Position(row=0, col=CASTLE_SPAWN_FIRST_COL + 2),
-         width=RIGHT_KNIGHTS_SPAWN_FIRST_COL - CASTLE_SPAWN_FIRST_COL - 2,
-         height=ROWS),
-
-    # up above right knights spawn
-    Area(position=Position(row=0, col=RIGHT_KNIGHTS_SPAWN_FIRST_COL),
-         width=KNIGHTS_SPAWN_WIDTH,
-         height=RIGHT_KNIGHTS_SPAWN_FIRST_ROW),
-
-    # down below right knights spawn
-    Area(position=Position(row=RIGHT_KNIGHTS_SPAWN_FIRST_ROW + KNIGHTS_SPAWN_HEIGHT, col=RIGHT_KNIGHTS_SPAWN_FIRST_COL),
-         width=KNIGHTS_SPAWN_WIDTH,
-         height=ROWS - RIGHT_KNIGHTS_SPAWN_FIRST_ROW - KNIGHTS_SPAWN_HEIGHT),
-]
-
-
-def dfs(grid: npt.NDArray, visited: Dict[Tuple[int, int], bool], position: Position, rows: int, cols: int) -> None:
-    visited[(position.row, position.col)] = True
-
-    row_vector = [0, 0, 1, -1]
-    col_vector = [-1, 1, 0, 0]
-
-    neighbours = []
-    for i in range(4):
-        rr = position.row + row_vector[i]
-        cc = position.col + col_vector[i]
-        if rr < 0 or rr >= ROWS:
-            continue
-        elif cc < 0 or cc >= COLUMNS:
-            continue
-        else:
-            p = Position(rr, cc)
-            if (p.row, p.col) in visited:
-                neighbours.append(p)
-
-    for neighbour in neighbours:
-        if not visited[(neighbour.row, neighbour.col)]:
-            dfs(grid, visited, neighbour, rows, cols)
-
-
-def get_islands(grid: npt.NDArray, positions: List[Position], rows: int = ROWS, cols: int = COLUMNS) -> List[Position]:
-    """it returns list of all islands roots"""
-    visited = {}
-
-    for position in positions:
-        visited[(position.row, position.col)] = False
-
-    islands = 0
-    roots = []
-    for position in positions:
-        if not visited[(position.row, position.col)]:
-            dfs(grid, visited, position, rows, cols)
-            roots.append(position)
-            islands += 1
-
-    return roots
-
-
-def find_neighbours(grid: npt.NDArray, col: int, row: int) -> List[Position]:
-    dr = [-1, 1, 0, 0]
-    dc = [0, 0, -1, 1]
-
-    neighbours = []
-
-    for i in range(4):
-        rr = row + dr[i]
-        cc = col + dc[i]
-
-        if 0 <= rr < ROWS and 0 <= cc < COLUMNS and grid[rr][cc] == MAP_ALIASES.get('GRASS'):
-            neighbours.append(Position(row=rr, col=cc))
-
-    return neighbours
-
-
-def get_tiles_positions(grid: npt.NDArray):
-    sands = []
-    trees = []
-    waters = []
-    monsters = []
-
-    for row_num in range(len(grid)):
-        for col_num in range(len(grid[row_num])):
-            if grid[row_num][col_num] == MAP_ALIASES.get('WATER'):
-                waters.append(Position(row=row_num, col=col_num))
-            elif grid[row_num][col_num] == MAP_ALIASES.get('TREE'):
-                trees.append(Position(row=row_num, col=col_num))
-            elif grid[row_num][col_num] == MAP_ALIASES.get('SAND'):
-                sands.append(Position(row=row_num, col=col_num))
-            elif grid[row_num][col_num] == MAP_ALIASES.get('MONSTER'):
-                monsters.append(Position(row=row_num, col=col_num))
-
-    return sands, trees, waters, monsters

algorithms/genetic/const.py

@@ -1,29 +0,0 @@
-# map config
-KNIGHTS_PER_TEAM_COUNT = 4
-SAND_COUNT = 21
-WATER_COUNT = 21
-TREE_COUNT = 37
-MONSTERS_COUNT = 2
-CASTLES_COUNT = 1
-ROWS = 19
-COLUMNS = 24
-KNIGHTS_SPAWN_WIDTH = 4
-KNIGHTS_SPAWN_HEIGHT = 7
-LEFT_KNIGHTS_SPAWN_FIRST_ROW = 6
-LEFT_KNIGHTS_SPAWN_FIRST_COL = 0
-RIGHT_KNIGHTS_SPAWN_FIRST_ROW = 6
-RIGHT_KNIGHTS_SPAWN_FIRST_COL = 20
-CASTLE_SPAWN_FIRST_ROW = 7
-CASTLE_SPAWN_FIRST_COL = 11
-
-# map aliases
-MAP_ALIASES = {
-    "GRASS": 0,
-    "SAND": 1,
-    "WATER": 2,
-    "TREE": 3,
-    "MONSTER": 4,
-    "CASTLE": 5,
-    "KNIGHT_RED": 6,
-    "KNIGHT_BLUE": 7,
-}

algorithms/genetic/genome.py

@@ -1,166 +0,0 @@
-import math
-import random
-from copy import deepcopy
-from random import randrange
-from typing import List
-
-import numpy as np
-import numpy.typing as npt
-
-from common import Position, get_islands, AREAS_TO_CROSS, find_neighbours, get_tiles_positions
-from const import *
-
-
-class Genome:
-    grid: npt.NDArray
-    knights_red: List[Position]
-    knights_blue: List[Position]
-    waters: List[Position]
-    trees: List[Position]
-    sands: List[Position]
-    monsters: List[Position]
-    fitness: int
-    sand_islands: List[Position]
-    tree_islands: List[Position]
-    water_islands: List[Position]
-
-    def __init__(self):
-        self.grid = np.zeros((ROWS, COLUMNS), dtype=int)
-        self.fitness = 0
-
-        self.knights_red = spawn_objects_in_given_area(
-            grid=self.grid,
-            object_alias=MAP_ALIASES.get("KNIGHT_RED"),
-            objects_count=KNIGHTS_PER_TEAM_COUNT,
-            spawn_position_start=Position(row=LEFT_KNIGHTS_SPAWN_FIRST_ROW, col=LEFT_KNIGHTS_SPAWN_FIRST_COL),
-            width=KNIGHTS_SPAWN_WIDTH,
-            height=KNIGHTS_SPAWN_HEIGHT
-        )
-
-        self.knights_blue = spawn_objects_in_given_area(
-            grid=self.grid,
-            object_alias=MAP_ALIASES.get("KNIGHT_BLUE"),
-            objects_count=KNIGHTS_PER_TEAM_COUNT,
-            spawn_position_start=Position(row=RIGHT_KNIGHTS_SPAWN_FIRST_ROW, col=RIGHT_KNIGHTS_SPAWN_FIRST_COL),
-            width=KNIGHTS_SPAWN_WIDTH,
-            height=KNIGHTS_SPAWN_HEIGHT
-        )
-
-        spawn_objects_in_given_area(
-            grid=self.grid,
-            object_alias=MAP_ALIASES.get("CASTLE"),
-            objects_count=4,
-            spawn_position_start=Position(row=CASTLE_SPAWN_FIRST_ROW, col=CASTLE_SPAWN_FIRST_COL),
-            width=2,
-            height=2
-        )
-
-        self.waters = spawn_objects_in_given_area(grid=self.grid, object_alias=MAP_ALIASES.get("WATER"),
-                                                  objects_count=WATER_COUNT)
-        self.trees = spawn_objects_in_given_area(grid=self.grid, object_alias=MAP_ALIASES.get("TREE"),
-                                                 objects_count=TREE_COUNT)
-        self.sands = spawn_objects_in_given_area(grid=self.grid, object_alias=MAP_ALIASES.get("SAND"),
-                                                 objects_count=SAND_COUNT)
-        self.monsters = spawn_objects_in_given_area(grid=self.grid, object_alias=MAP_ALIASES.get("MONSTER"),
-                                                    objects_count=MONSTERS_COUNT)
-
-        self.sand_islands = get_islands(self.grid, self.sands)
-        self.tree_islands = get_islands(self.grid, self.trees)
-        self.water_islands = get_islands(self.grid, self.waters)
-
-    def update_map(self):
-        self.sands, self.trees, self.waters, self.monsters = get_tiles_positions(self.grid)
-
-        self.sand_islands = get_islands(self.grid, self.sands)
-        self.tree_islands = get_islands(self.grid, self.trees)
-        self.water_islands = get_islands(self.grid, self.waters)
-
-    def calc_fitness(self):
-        score = SAND_COUNT + TREE_COUNT + WATER_COUNT
-        score = score - len(self.sand_islands) - len(self.tree_islands) - len(self.water_islands)
-
-        sands, trees, waters, monsters = get_tiles_positions(self.grid)
-
-        if len(monsters) != MONSTERS_COUNT:
-            self.fitness = 0
-            return
-
-        if len(sands) < SAND_COUNT or len(trees) < TREE_COUNT or len(waters) < WATER_COUNT:
-            self.fitness = 5
-            return
-
-        self.fitness = score
-
-    def crossover(self, partner):
-        # replace a randomly selected part of the grid with partner's part
-        child = Genome()
-        child.grid = deepcopy(self.grid)
-        area_to_cross = random.choice(AREAS_TO_CROSS)
-
-        for row in range(area_to_cross.position.row, area_to_cross.position.row + area_to_cross.height):
-            for col in range(area_to_cross.position.col, area_to_cross.position.col + area_to_cross.width):
-                child.grid[row][col] = partner.grid[row][col]
-
-        child.update_map()
-
-        return child
-
-    def mutate(self, mutation_rate: float):
-        # remove 1 item from a random island and add a neighbor to another island
-        if random.random() < mutation_rate:
-
-            # select islands of the same, random type
-            islands_of_same_type = random.choice([self.sand_islands, self.tree_islands, self.water_islands])
-            random_index = random.randint(0, len(islands_of_same_type) - 1)
-            island = islands_of_same_type[random_index]
-            next_island = islands_of_same_type[(random_index + 1) % len(islands_of_same_type)]
-
-            free_tiles_nearby = find_neighbours(self.grid, next_island.col, next_island.row)
-
-            tile_type = self.grid[island.row][island.col]
-            self.grid[island.row][island.col] = MAP_ALIASES.get('GRASS')
-
-            # todo: if there are no free tiles around then randomize another next_island
-            if len(free_tiles_nearby) > 0:
-                random_free_tile = random.choice(free_tiles_nearby)
-                island.row = random_free_tile.row
-                island.col = random_free_tile.col
-                self.grid[island.row][island.col] = tile_type
-
-                self.update_map()
-
-
-def is_empty(grid: npt.NDArray, position: Position) -> bool:
-    return grid[position.row, position.col] in [MAP_ALIASES.get("GRASS"), MAP_ALIASES.get("SAND")]
-
-
-def is_invalid_area(spawn_position_start, height, width) -> bool:
-    return spawn_position_start.row + height - 1 < 0 or \
-           spawn_position_start.row + height - 1 >= ROWS or \
-           spawn_position_start.col + width - 1 < 0 or \
-           spawn_position_start.col + width - 1 >= COLUMNS
-
-
-def spawn_objects_in_given_area(grid: npt.NDArray,
-                                object_alias: str,
-                                objects_count: int = 1,
-                                spawn_position_start: Position = Position(row=0, col=0),
-                                width: int = COLUMNS,
-                                height: int = ROWS) -> List[Position]:
-    if is_invalid_area(spawn_position_start, height, width):
-        raise ValueError("Invalid spawn area")
-
-    objects_remaining = int(objects_count)
-    positions = []
-
-    while objects_remaining > 0:
-        row = randrange(spawn_position_start.row, spawn_position_start.row + height)
-        col = randrange(spawn_position_start.col, spawn_position_start.col + width)
-        position = Position(row=row, col=col)
-
-        if is_empty(grid=grid, position=position):
-            grid[position.row, position.col] = object_alias
-            positions.append(position)
-            objects_remaining -= 1
-
-    return positions

algorithms/genetic/map_generator.py

@@ -1,26 +0,0 @@
-from algorithms.genetic.genome import Genome
-from algorithms.genetic.map_importer_exporter import export_map
-from population import Population
-
-
-def main() -> None:
-    population_size = 500
-    mutation_rate = 0.3
-
-    population = Population(mutation_rate, population_size, 55)
-
-    while not population.evaluate():
-        # create next generation
-        population.generate()
-
-        # calc fitness
-        population.calc_fitness()
-
-    print(population.best_genome.grid)
-    print("Fitness of the best: ", population.best_genome.fitness)
-
-    export_map(population.best_genome.grid)
-
-
-if __name__ == '__main__':
-    main()

algorithms/genetic/map_importer_exporter.py

@@ -1,42 +0,0 @@
-import json
-import random
-import string
-from datetime import datetime
-from pathlib import Path
-import numpy
-import numpy.typing as npt
-from os import listdir
-from os.path import isfile, join
-
-
-# Save map to file
-def export_map(grid: npt.NDArray):
-    json_data = {"map": grid.tolist()}
-
-    now = datetime.now()
-    file_name = "map_" + now.strftime("%Y_%m_%d_%H_%M_%S") + ".json"
-    path = Path("../../resources/maps/")
-    file_to_open = path / file_name
-
-    with open(file_to_open, "w+") as write_file:
-        json.dump(json_data, write_file)
-    print("Saved map to file " + file_name)
-
-
-def import_random_map() -> object:
-    path = "resources/maps"
-    files = [f for f in listdir(path) if isfile(join(path, f))]
-    random_map_name = random.choice(files)
-    return import_map(random_map_name)
-
-
-# Read map from file
-def import_map(file_name: string) -> object:
-    file_to_open = "resources/maps/" + file_name
-    with open(file_to_open, "r") as read_file:
-        print("Reading map from file " + file_name)
-        decoded_json = json.load(read_file)
-
-        decoded_grid = numpy.asarray(decoded_json["map"])
-        print(decoded_grid)
-    return decoded_grid.tolist()

algorithms/genetic/population.py

@@ -1,81 +0,0 @@
-import random
-from typing import List
-
-import numpy as np
-import numpy.typing as npt
-
-from genome import Genome
-
-
-class Population:
-    population: List[Genome] = []  # array to hold the current population
-    mating_pool: List[Genome] = []  # array which we will use for our "mating pool"
-    generations: int = 0  # number of generations
-    finished: bool = False  # are we finished evolving?
-    mutation_rate: float
-    perfect_score: int
-    best_genome: Genome
-
-    def __init__(self, mutation_rate, population_size, perfect_score=20):
-        self.mutation_rate = mutation_rate
-        self.perfect_score = perfect_score
-
-        for i in range(0, population_size):
-            new_genome = Genome()
-            new_genome.calc_fitness()
-            self.population.append(new_genome)
-
-    # create a new generation
-    def generate(self):
-        max_fitness = 0
-        for genome in self.population:
-            if genome.fitness > max_fitness:
-                max_fitness = genome.fitness
-
-        print("Max fitness of generation " + str(self.generations) + " = " + str(max_fitness))
-
-        # refill the population with children from the mating pool
-        new_population = []
-        for genome in self.population:
-            partner_a = self.accept_reject(max_fitness)
-            partner_b = self.accept_reject(max_fitness)
-            child = partner_a.crossover(partner_b)
-            child.mutate(self.mutation_rate)
-            new_population.append(child)
-
-        self.population = new_population
-        self.generations += 1
-
-    # select random with correct probability from population
-    def accept_reject(self, max_fitness: int):
-        safe_flag = 0
-
-        while safe_flag < 10000:
-            partner = random.choice(self.population)
-            r = random.randint(0, max_fitness)
-
-            if r < partner.fitness:
-                return partner
-
-            safe_flag += 1
-
-    # compute the current "most fit" member of the population
-    def evaluate(self):
-        record = 0
-        best_index = 0
-
-        for index in range(len(self.population)):
-            genome = self.population[index]
-            if genome.fitness > record:
-                record = genome.fitness
-                best_index = index
-
-        self.best_genome = self.population[best_index]
-        if record >= self.perfect_score:
-            self.finished = True
-
-        return self.finished
-
-    def calc_fitness(self):
-        for genome in self.population:
-            genome.calc_fitness()

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

@@ -1,11 +1,9 @@
 import torch
 import pytorch_lightning as pl
 import torch.nn as nn
-from torch.optim import SGD, Adam, lr_scheduler
+from torch.optim import Adam
 import torch.nn.functional as F
-from torch.utils.data import DataLoader
+from common.constants import BATCH_SIZE, LEARNING_RATE
-from watersandtreegrass import WaterSandTreeGrass
-from common.constants import DEVICE, BATCH_SIZE, NUM_EPOCHS, LEARNING_RATE, SETUP_PHOTOS, ID_TO_CLASS
 
 
 class NeuralNetwork(pl.LightningModule):
@@ -16,11 +14,9 @@ class NeuralNetwork(pl.LightningModule):
         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.fc1 = nn.Linear(48*18*18, 4)
         self.relu3 = nn.ReLU()
-        self.fc2 = nn.Linear(800, 400)
+        self.fc2 = nn.Linear(500, num_classes)
-        self.relu4 = nn.ReLU()
-        self.fc3 = nn.Linear(400, 4)
         self.logSoftmax = nn.LogSoftmax(dim=1)
 
         self.batch_size = batch_size
@@ -34,10 +30,6 @@ class NeuralNetwork(pl.LightningModule):
         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)
         return x
 

algorithms/neural_network/neural_network_interface.py

@@ -1,17 +1,53 @@
 import torch
 import common.helpers
+from algorithms.neural_network.neural_network import NeuralNetwork
+from algorithms.neural_network.watersandtreegrass import WaterSandTreeGrass
 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
-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('D:/DEV/UAM/WMICraft/algorithms/neural_network/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):
+# CNN = NeuralNetwork()
-    model = NeuralNetwork.load_from_checkpoint('./lightning_logs/version_23/checkpoints/epoch=3-step=324.ckpt')
+# common.helpers.createCSV()
-    num_correct = 0
-    num_samples = 0
-    model = model.to(DEVICE)
-    model.eval()
 
-    with torch.no_grad():
+#trainer = pl.Trainer(accelerator='gpu', devices=1, callbacks=[EarlyStopping('val_loss')], max_epochs=NUM_EPOCHS)
-        for photo, label in tset:
+# trainer = pl.Trainer(accelerator='cpu', devices=1, auto_lr_find=True, max_epochs=NUM_EPOCHS)
-            photo = photo.to(DEVICE)
+#
-            label = label.to(DEVICE)
+# trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=SETUP_PHOTOS)
-
+# testset = WaterSandTreeGrass('./data/test_csv_file.csv', transform=SETUP_PHOTOS)
-            scores = model(photo)
+# train_loader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)
-            predictions = scores.argmax(dim=1)
+# test_loader = DataLoader(testset, batch_size=BATCH_SIZE)
-            num_correct += (predictions == label).sum()
+#
-            num_samples += predictions.size(0)
+# trainer.fit(CNN, train_loader, test_loader)
-
-        print(f'Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}%')
-
-
-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_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)
-
-
-#print(what_is_it('../../resources/textures/grass2.png', True))
-
-#check_accuracy_data()
-
 #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
@@ -77,15 +77,16 @@ BAR_HEIGHT_MULTIPLIER = 0.1
 
 
 #NEURAL_NETWORK
-LEARNING_RATE = 0.000630957344480193
+LEARNING_RATE = 0.00478630092322638
 BATCH_SIZE = 64
-NUM_EPOCHS = 9
+NUM_EPOCHS = 20
 
 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.ToPILImage(),
     transforms.ToTensor(),
     transforms.Resize((36, 36)),
     transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])

common/helpers.py

@@ -1,8 +1,6 @@
 from typing import Tuple, List
 
 import pygame
-
-from algorithms.genetic.const import MAP_ALIASES
 from common.constants import GRID_CELL_PADDING, GRID_CELL_SIZE, COLUMNS, ROWS, CLASSES, CLASS_TO_ID
 import csv
 import os
@@ -101,7 +99,7 @@ def castle_neighbors(map, castle_bottom_right_row, castle_bottom_right_col):
     return neighbors
 
 
-def find_neighbours(grid: List[List[int]], col: int, row: int) -> List[Tuple[int, int]]:
+def find_neighbours(grid: List[List[str]], col: int, row: int) -> List[Tuple[int, int]]:
     dr = [-1, 1, 0, 0]
     dc = [0, 0, -1, 1]
 
@@ -113,7 +111,7 @@ def find_neighbours(grid: List[List[int]], col: int, row: int) -> List[Tuple[int
 
         if rr < 0 or cc < 0: continue
         if rr >= ROWS or cc >= COLUMNS: continue
-        if grid[rr][cc] not in [MAP_ALIASES.get("GRASS"), MAP_ALIASES.get("SAND"), '.']: continue
+        if grid[rr][cc] not in ['g', 's', '.']: continue
         neighbours.append((rr, cc))
 
     return neighbours

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)
@@ -34,7 +34,7 @@ class DecisionTree:
         self.model = DecisionTreeClassifier(criterion='entropy')
         self.model.fit(self.train_set.values, self.goals)
 
-    def predict_move(self, grid: List[List[int]], current_knight: Knight, castle: Castle, monsters: List[Monster],
+    def predict_move(self, grid: List[List[str]], current_knight: Knight, castle: Castle, monsters: List[Monster],
                      opponents: List[Knight]) -> \
             List[Tuple[int, int]]:
         distance_to_castle = manhattan_distance(current_knight.position, castle.position)
@@ -42,15 +42,14 @@ class DecisionTree:
         monsters_parsed = []
         for monster in monsters:
             monsters_parsed.append((manhattan_distance(current_knight.position, monster.position), parse_hp(
-                monster.health_bar.current_hp)))
+                monster.current_hp)))
 
         opponents_parsed = []
         for opponent in opponents:
             opponents_parsed.append(
-                (manhattan_distance(current_knight.position, opponent.position),
+                (manhattan_distance(current_knight.position, opponent.position), parse_hp(opponent.health_bar.current_hp)))
-                 parse_hp(opponent.health_bar.current_hp)))
 
-        prediction = self.get_prediction(tower_dist=distance_to_castle, tower_hp=castle.health_bar.current_hp,
+        prediction = self.get_prediction(tower_dist=distance_to_castle, tower_hp=castle.current_hp,
                                          mob1_dist=monsters_parsed[0][0], mob1_hp=monsters_parsed[0][1],
                                          mob2_dist=monsters_parsed[1][0], mob2_hp=monsters_parsed[1][1],
                                          opp1_dist=opponents_parsed[0][0], opp1_hp=opponents_parsed[0][1],
@@ -58,7 +57,7 @@ class DecisionTree:
                                          opp3_dist=opponents_parsed[2][0], opp3_hp=opponents_parsed[2][1],
                                          opp4_dist=opponents_parsed[3][0], opp4_hp=opponents_parsed[3][1],
                                          agent_hp=current_knight.health_bar.current_hp)
-        print(f'Prediction = {prediction}')
+        print(prediction)
         if prediction == 'tower':  # castle...
             return castle_neighbors(grid, castle_bottom_right_row=castle.position[0],
                                     castle_bottom_right_col=castle.position[1])

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
@@ -62,8 +61,8 @@ class Game:
                     if event.key == pygame.K_n:
                         print_numbers_flag = not print_numbers_flag
 
-                if event.type == NEXT_TURN:  # is called every 'TURN_INTERVAL' milliseconds
+                # if event.type == NEXT_TURN:  # is called every 'TURN_INTERVAL' milliseconds
-                    level.handle_turn()
+                #     level.handle_turn()
 
             stats.update()
             logs.draw()

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

@@ -3,11 +3,11 @@ import random
 import pygame
 
 from algorithms.a_star import a_star, State, TURN_RIGHT, TURN_LEFT, FORWARD
-from algorithms.genetic.const import MAP_ALIASES
+from algorithms.neural_network.neural_network_interface import what_is_it
-from algorithms.genetic.map_importer_exporter import import_random_map
 from common.constants import *
 from learning.decision_tree import DecisionTree
 from logic.knights_queue import KnightsQueue
+from logic.spawner import Spawner
 from models.castle import Castle
 from models.knight import Knight
 from models.monster import Monster
@@ -22,7 +22,7 @@ class Level:
         # sprite group setup
         self.sprites = pygame.sprite.LayeredUpdates()
 
-        self.map = []
+        self.map = [['g' for _ in range(COLUMNS)] for y in range(ROWS)]
 
         self.list_knights_blue = []
         self.list_knights_red = []
@@ -31,20 +31,29 @@ 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()
+        print("Create map")
-        self.setup_base_tiles()
+        print(what_is_it('D:/DEV/UAM/WMICraft/resources/textures/t2.jpg'))
-        self.setup_objects()
+        # self.generate_map()
-        self.knights_queue = KnightsQueue(self.list_knights_blue, self.list_knights_red)
+        # self.setup_base_tiles()
+        # self.setup_objects()
+        # self.knights_queue = KnightsQueue(self.list_knights_blue, self.list_knights_red)
+
+    def generate_map(self):
+        spawner = Spawner(self.map)
+        spawner.spawn_where_possible(['w' for _ in range(NBR_OF_WATER)])
+        spawner.spawn_where_possible(['t' for _ in range(NBR_OF_TREES)])
+        spawner.spawn_where_possible(['s' for _ in range(NBR_OF_SANDS)])
+
+        spawner.spawn_in_area(['k_b' for _ in range(4)], LEFT_KNIGHTS_SPAWN_FIRST_ROW, LEFT_KNIGHTS_SPAWN_FIRST_COL,
+                              KNIGHTS_SPAWN_WIDTH, KNIGHTS_SPAWN_HEIGHT)
+        spawner.spawn_in_area(['k_r' for _ in range(4)], RIGHT_KNIGHTS_SPAWN_FIRST_ROW, RIGHT_KNIGHTS_SPAWN_FIRST_COL,
+                              KNIGHTS_SPAWN_WIDTH, KNIGHTS_SPAWN_HEIGHT)
+
+        spawner.spawn_in_area(['c'], CASTLE_SPAWN_FIRST_ROW, CASTLE_SPAWN_FIRST_COL, CASTLE_SPAWN_WIDTH,
+                              CASTLE_SPAWN_HEIGHT, 2)
+
+        spawner.spawn_where_possible(['m' for _ in range(NBR_OF_MONSTERS)])
 
     def setup_base_tiles(self):
         textures = []
@@ -57,15 +66,15 @@ class Level:
             for col_index, col in enumerate(row):
 
                 # add base tiles, e.g. water, tree, grass
-                if col == MAP_ALIASES.get('WATER'):
+                if col == "w":
                     texture_index = 5
                     texture_surface = textures[texture_index][1]
                     Tile((col_index, row_index), texture_surface, self.sprites, 'w')
-                elif col == MAP_ALIASES.get('TREE'):
+                elif col == "t":
                     texture_index = 6
                     texture_surface = textures[texture_index][1]
                     Tile((col_index, row_index), texture_surface, self.sprites, 't')
-                elif col == MAP_ALIASES.get('SAND'):
+                elif col == "s":
                     texture_index = 4
                     texture_surface = textures[texture_index][1]
                     Tile((col_index, row_index), texture_surface, self.sprites)
@@ -82,148 +91,37 @@ class Level:
             for col_index, col in enumerate(row):
 
                 # add objects, e.g. knights, monsters, castle
-                if col == MAP_ALIASES.get('KNIGHT_BLUE'):
+                if col == "k_b":
                     knight = Knight(self.screen, (col_index, row_index), self.sprites, "blue")
                     self.map[row_index][col_index] = knight
                     self.list_knights_blue.append(knight)
-                elif col == MAP_ALIASES.get('KNIGHT_RED'):
+                elif col == "k_r":
                     knight = Knight(self.screen, (col_index, row_index), self.sprites, "red")
                     self.map[row_index][col_index] = knight
                     self.list_knights_red.append(knight)
-                elif col == MAP_ALIASES.get('MONSTER'):
+                elif col == "m":
                     monster = Monster(self.screen, (col_index, row_index), self.sprites)
                     self.map[row_index][col_index] = monster
                     self.list_monsters.append(monster)
-                elif col == MAP_ALIASES.get('CASTLE'):
+                elif col == "c":
                     castle_count += 1
                     if castle_count == 4:
                         castle = Castle(self.screen, (col_index, row_index), self.sprites)
                         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):
+        print("next turn")
         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,
-                                                    opponents=self.list_knights_blue
+                                                    opponents=self.list_knights_red
-                                                    if current_knight.team_alias() == 'k_r' else self.list_knights_red,
+                                                    if current_knight.team_alias == 'k_r' else self.list_knights_blue,
                                                     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 +134,6 @@ 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()
@@ -257,7 +142,7 @@ class Level:
             current_knight.rotate_right()
         elif next_action == FORWARD:
             current_knight.step_forward()
-            self.map[knight_pos_y][knight_pos_x] = MAP_ALIASES.get("GRASS")
+            self.map[knight_pos_y][knight_pos_x] = 'g'
 
             # update knight on map
             if current_knight.direction.name == UP:
@@ -281,6 +166,3 @@ 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):
+def load_knight_textures():
-    if team == "blue":
+    random_index = random.randint(1, 4)
-        random_index = 3
-    else:
-        random_index = 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.max_hp = random.randint(7, 12)
-        self.attack = random.randint(2, 4)
+        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.max_hp = random.randrange(15, 25)
-        self.health_bar = HealthBar(screen, self.rect, current_hp=self.max_hp, max_hp=self.max_hp,
+        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

resources/maps/map_2022_06_02_13_27_18.json

@@ -1 +0,0 @@
-{"map": [[0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1], [0, 0, 0, 0, 3, 3, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 3], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 3], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 4, 0, 0, 0, 2, 0, 3], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2], [0, 0, 3, 3, 3, 0, 0, 0, 3, 3, 3, 3, 0, 2, 2, 2, 0, 0, 0, 0, 0, 7, 2, 0], [0, 0, 0, 6, 0, 0, 0, 2, 2, 2, 0, 5, 5, 0, 2, 0, 0, 2, 2, 2, 2, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 5, 5, 0, 2, 0, 0, 0, 0, 2, 2, 1, 1, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 3, 1, 7], [6, 0, 0, 6, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 1, 1, 0], [6, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 3, 3, 0, 0, 0, 0, 3, 3, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 0, 1, 1, 7, 0], [0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 3, 1, 1, 1, 0], [0, 3, 3, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 1, 0, 0], [0, 3, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 4, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}

resources/maps/map_2022_06_06_14_55_49.json

@@ -1 +0,0 @@
-{"map": [[0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 3, 0, 0, 0, 2, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 2, 2, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 6, 3, 3, 0, 0, 0, 0, 0, 3, 0, 4, 0, 3, 0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 7, 7, 0, 0], [6, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 3, 7, 0, 0, 0], [0, 0, 6, 6, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0], [0, 2, 2, 2, 2, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 2, 0, 0, 0, 0, 0, 4, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}

resources/maps/map_2022_06_06_14_58_11.json

@@ -1 +0,0 @@
-{"map": [[0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 3], [0, 0, 0, 0, 2, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 3, 0], [0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 2, 0], [0, 0, 3, 3, 3, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 2, 0], [0, 0, 3, 0, 3, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 3, 3, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 6, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0], [0, 6, 0, 0, 0, 0, 3, 0, 0, 0, 0, 5, 5, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 5, 5, 1, 2, 0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 4, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 3, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 4], [6, 0, 0, 6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 1, 1, 0, 7, 0, 7, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 3, 3, 2, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 0, 0], [0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}

ui/screens/credits.py

@@ -1,31 +1,6 @@
-import pygame
-
 from ui.screens.screen import Screen
-from common.colors import BLACK
+
-from common.helpers import draw_text
 
 class Credits(Screen):
-
     def __init__(self, screen, clock):
         super().__init__('credits', screen, clock)
-
-    def display_screen(self):
-        running = True
-        while running:
-            self.screen.fill((252, 164, 12))
-            draw_text('Twórcy :', BLACK, self.screen, 520, 150)
-            draw_text('Angelika Iskra', BLACK, self.screen, 520, 250)
-            draw_text('Dawid Korzępa', BLACK, self.screen, 520, 300)
-            draw_text('Juliusz Sadowski', BLACK, self.screen, 520, 350)
-            draw_text('Aleksandra Muczyńska', BLACK, self.screen, 520, 400)
-            draw_text('Jerzy Tomaszewski', BLACK, self.screen, 520, 450)
-            draw_text('Mateusz Konofał', BLACK, self.screen, 520, 500)
-            for event in pygame.event.get():
-                if event.type == pygame.QUIT:
-                    running = False
-                if event.type == pygame.KEYDOWN:
-                    if event.key == pygame.K_ESCAPE:
-                        running = False
-            pygame.display.update()
-            self.clock.tick(60)
-

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: 10', 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)
+        draw_text('PUNKTY: 10', 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