AL-2020/main.py

import pygame
import functions
import sys
import time
import decision_tree
import data

from agent import Agent
from settings import Settings
from board import create_board, draw_board
from random import randint, choice
from mcda import choseProducts

# Inicjalizacja programu i utworzenie obiektu ekrany
def run():
    pygame.init()
    settings = Settings()
    screen = pygame.display.set_mode((settings.screen_width, settings.screen_height))
    pygame.display.set_caption("Inteligentny wózek widłowy")
    agent = Agent(screen, 50, 50, "Down")
    board = create_board(screen)
    my_tree = decision_tree.build_tree(data.learning_data)

    produsctsFromSupply = choseProducts(5)

    # for row in board:
    #     for field in row:
    #         print(field.cost_of_travel)

    path = []
    next_step = None
    # Rozpoczęcie głównej pętli programu
    while True:
        # functions.check_events(agent, board)
        # functions.update_screen(board, screen, agent)
        #
        for event in pygame.event.get():
            if event.type == pygame.QUIT:
                sys.exit()
            elif event.type == pygame.KEYDOWN:
                if event.key == pygame.K_RIGHT:
                    agent.turn_right()
                elif event.key == pygame.K_LEFT:
                    agent.turn_left()
                elif event.key == pygame.K_UP:
                    agent.move_forward(board)
                    print(agent.x, agent.y)
                elif event.key == pygame.K_SPACE:
                    board[9][0].item = choice(data.learning_data)
                    print("Wybrano: " + board[9][0].item[-1])
                    board[9][0].item[-1] = 'Something'
                    field = board[9][0]
                    if not field.is_shelf:
                        path = functions.a_star(board[agent.y][agent.x], field, board)
                        path.pop(len(path) - 1)
                        next_step = path.pop(len(path) - 1)

        if next_step is not None:
            time.sleep(0.5)

            if functions.check_turn(agent, next_step):
                agent.move_forward(board)
                if len(path) != 0:
                    next_step = path.pop()
                else:
                    next_step = None
                    # print(next_step, path)
                    for row in board:
                        for field in row:
                            if not field.is_shelf:
                                field.image = pygame.image.load('img/Field.png')

            else:
                functions.change_turn(agent, next_step)

        if board[agent.y][agent.x].item:
            prediction = decision_tree.print_leaf(decision_tree.classify(board[agent.y][agent.x].item, my_tree))
            print("Agent uważa, że przedmiot to: " + prediction[0])
            board[agent.y][agent.x].item = []

        draw_board(board)
        agent.blitme()

        pygame.display.flip()


run()