Traktor/source/main.py

import pygame
import time
import random
import pandas as pd
import joblib
from area.constants import WIDTH, HEIGHT, TILE_SIZE
from area.field import drawWindow
from area.tractor import Tractor, do_actions
from area.field import tiles, fieldX, fieldY
from area.field import get_tile_coordinates, get_tile_index
from ground import Dirt
from plant import Plant
from bfs import graphsearch, Istate, succ
from astar import a_star
from NN.neural_network import load_model, load_image, guess_image, display_image, display_result
from PIL import  Image

pygame.init()
WIN_WIDTH = WIDTH + 300
WIN = pygame.display.set_mode((WIN_WIDTH, HEIGHT))
pygame.display.set_caption('Intelligent tractor')


def main():
    run = True
    window = drawWindow(WIN)
    pygame.display.update()

#getting coordinates of our "goal tile":
    tile_index = get_tile_index()
    tile_x, tile_y = get_tile_coordinates(tile_index)
    if tile_x is not None and tile_y is not None:
        print(f"Coordinates of tile {tile_index} are: ({tile_x}, {tile_y})")
    else: print("Such tile does not exist")

    #mark the goal tile:
    tiles[tile_index].image = "resources/images/sampling_goal.png"
    image = pygame.image.load(tiles[tile_index].image).convert()
    image = pygame.transform.scale(image, (TILE_SIZE, TILE_SIZE))
    WIN.blit(image, (tiles[tile_index].x, tiles[tile_index].y))
    pygame.display.flip()


#graphsearch activation:
    istate = Istate(170, 100, 2)  #initial state

    goaltest = []                 
    goaltest.append(tile_x)       #final state (consists of x and y because direction doesnt matter)
    goaltest.append(tile_y)

    tractor = Tractor(0*TILE_SIZE, 0*TILE_SIZE, 2, None, None)
    tractor.rect.x += fieldX  
    tractor.rect.y += fieldY 
    tractor.tractor_start = ((istate.get_x(), istate.get_y()))
    #tractor.tractor_start = ((istate.get_x(), istate.get_y(), istate.get_direction))
    tractor.tractor_end = ((goaltest[0], goaltest[1]))

    #moves = (graphsearch(istate, succ, goaltest, tractor))
    moves = (a_star(istate, succ, goaltest, tractor))
    print(moves)



#main loop:    
    while run:
        for event in pygame.event.get():
            if event.type == pygame.QUIT:
                run = False       
        time.sleep(1)

        # movement based on route-planning (test):

        tractor.draw_tractor(WIN)
        time.sleep(1)
        if moves != False:
            do_actions(tractor, WIN, moves)


        #guessing the image under the tile:
        goalTile = tiles[tile_index]
        image_path = goalTile.photo
        display_image(WIN, goalTile.photo, (WIDTH-20 , 300))   #displays photo next to the field
        pygame.display.update()

        image_tensor = load_image(image_path)
        prediction = guess_image(load_model(), image_tensor)
        
        display_result(WIN, (WIDTH - 50 , 600), prediction)      #display text under the photo
        pygame.display.update()
        print(f"The predicted image is: {prediction}")


        p1 = Plant('wheat', 'cereal', random.randint(1,100), random.randint(1,100), random.randint(1,100))
        goalTile.plant = p1
        d1 = Dirt(random.randint(1, 100), random.randint(1,100))
        d1.pests_and_weeds()
        goalTile.ground=d1
        #getting the name and type of the recognized plant:
        p1.update_name(prediction)

        #decission tree test:
        if d1.pest:
            pe = 1
        else:
            pe = 0
        if d1.weed:
            we = 1
        else:
            we = 0
        if p1.plant_type == 'cereal':
            t1 = True
            t2 = False
            t3 = False
            t4 = False
        else:
            t1 = False
            if p1.plant_type == 'fruit':
                t2 = True
                t3 = False
                t4 = False
            else:
                t2 = False
                if p1.plant_type == 'vegetable':
                    t4 = True
                    t3 = False
                else:
                    t3 = True
                    t4 = False

        dane = {
            'anomalies': [True],
            'temp': [17],
            'water': [d1.water_level],
            'nutri': [d1.nutrients_level],
            'pests': [pe],
            'weeds': [we],
            'ripeness': [p1.growth_level],
            'season_autumn': [True], 'season_spring': [False], 'season_summer': [False], 'season_winter': [False],
            'weather_heavyCloudy': [False], 'weather_partCloudy': [False], 'weather_precipitation': [False],
            'weather_sunny': [True],
            'type_cereal': [t1], 'type_fruit': [t2], 'type_none': [t3], 'type_vegetable': [t4]
        }
        df = pd.DataFrame(dane)
        df.to_csv('model_data.csv', index=False)

        model = joblib.load('model.pkl')
        nowe_dane = pd.read_csv('model_data.csv')
        predykcje = model.predict(nowe_dane)
        print(predykcje)

        #work on field:
        if predykcje == 'work':
            tractor.work_on_field(goalTile, d1, p1)
        time.sleep(50)
        print("\n")


main()