169 lines
5.7 KiB
Python
169 lines
5.7 KiB
Python
import pygame, sys
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from traktor import Traktor
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import dijkstra as di
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import matplotlib.pyplot as plt
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from tensorflow.keras.preprocessing import image
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from tensorflow.keras.applications import MobileNetV2
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from tensorflow.keras.applications.mobilenet import preprocess_input, decode_predictions
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import numpy as np
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import os, random
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#u mnie blad z biblioteka
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os.environ['KMP_DUPLICATE_LIB_OK']='True'
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class Game(object):
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def __init__(self):
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#lokalizacje odpowiednich punktow na planszy
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lokalizacje = [[0,0],
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[100, 0],
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[200, 0],
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[300, 0],
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[400, 0],
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[400, 100],
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[300, 100],
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[200, 100],
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[100, 100],
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[0, 100],
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[0, 200],
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[100, 200],
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[200, 200],
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[300, 200],
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[400, 200],
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[400, 300],
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[300, 300],
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[200, 300],
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[100, 300],
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[0, 300],
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[0, 400],
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[100, 400],
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[200, 400],
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[300, 400],
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[400, 400]]
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#ładowanie tablicy ze zdjeciami
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imgs = []
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img_dir = './imgs'
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for _ in lokalizacje:
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imgs.append(img_dir + '/' + random.choice(os.listdir(img_dir)))
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#model do rozpoznawania
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model = MobileNetV2(input_shape=None, alpha=1.0, include_top=True, weights="imagenet", input_tensor=None, pooling=None, classes=1000)
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#inicjalizacja
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pygame.init()
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self.pole = pygame.display.set_mode((501,501))
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self.player = Traktor(self)
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sterowanie = int(input("Podaj czy chcesz sam sterowac traktorem - 1, czy chcesz podawac punkty - 0: "))
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if sterowanie == 1:
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sterowanie = True
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else:
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sterowanie = False
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while True:
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run_classifier = False
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#obsługa zdarzń
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for event in pygame.event.get():
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if event.type == pygame.QUIT:
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pygame.quit()
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sys.exit(0)
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#sterowanie traktorem
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if sterowanie:
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if event.type == pygame.KEYDOWN :
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if event.key == pygame.K_UP:
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self.player.y -= 100
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if self.player.y < 0:
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self.player.y = 0
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if event.key == pygame.K_DOWN:
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self.player.y += 100
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if self.player.y > 400:
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self.player.y = 400
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if event.key == pygame.K_RIGHT:
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self.player.x += 100
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if self.player.x > 400:
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self.player.x = 400
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if event.key == pygame.K_LEFT:
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self.player.x -= 100
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if self.player.x < 0:
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self.player.x = 0
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#okreslanie w ktorym punkcie jesteśmy
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wsp = [self.player.x, self.player.y]
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self.player.punkt = lokalizacje.index(wsp)
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run_classifier = True
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else:
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#po spacji wpisujemy punkty
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if event.type == pygame.KEYDOWN :
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if event.key == pygame.K_SPACE:
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pt = 0
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punkt1 = int(input("Podaj pierwszy punkt (liczba od 0 do 24): "))
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punkt2 = int(input("Podaj drugi punkt (liczba od 0 do 24): "))
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dist = di.dijkstra(self.player.punkt)
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if dist[punkt1] < dist[punkt2]:
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print("Wybralem pierwszy punkt")
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pt = punkt1
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else:
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print("Wybralem drugi punkt")
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pt = punkt2
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self.player.x = lokalizacje[pt][0]
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self.player.y = lokalizacje[pt][1]
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self.player.punkt = pt
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run_classifier = True
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pygame.display.update()
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#rysowanie
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self.pole.fill((0,0,0))
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self.krata()
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self.rysowanie()
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pygame.display.flip()
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if run_classifier:
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#wybieranie zdjecia
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pt = self.player.punkt
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img_path = imgs[pt]
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img = image.load_img(img_path, target_size=(224, 224))
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x = image.img_to_array(img)
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x = np.expand_dims(x, axis=0)
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x = preprocess_input(x)
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#detektor roslin, 5 rezultatow
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plt.imshow(img)
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preds = model.predict(x)
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decoded = decode_predictions(preds, top=1)
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print(decoded)
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plt.show()
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def krata(self):
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#wymiary
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w= 500
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rows = 5
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sizeBtw = w // rows
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x = 0
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y = 0
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for r in range(rows):
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x = x + sizeBtw
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y = y + sizeBtw
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#rysownie karaty
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pygame.draw.line(self.pole, (255,255,255), (x,0), (x,w))
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pygame.draw.line(self.pole, (255,255,255), (0,y), (w,y))
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def rysowanie(self):
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#rysowanie agenta
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self.player.rysowanie()
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if __name__=="__main__":
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Game() |