def __init__(self, SCR_WIDTH=640, SCR_HEIGHT=480):
pygame.sprite.Sprite.__init__(self)
self.SCR_WIDTH = SCR_WIDTH
self.SCR_HEIGHT = SCR_HEIGHT
resource_path = cfg.resource_path
img_ship_1 = ImageLoader.load(resource_path, 'ship.png', 2.0)
img_ship_2 = ImageLoader.load(resource_path, 'ship2.png', 2.0)
self.images = [img_ship_1, img_ship_2]
self.image = self.images[0]
self.imgShot = ImageLoader.load(resource_path, 'player_shot.png', 1.0)
self.rect = self.image.get_rect()
self.rect.centerx = self.SCR_WIDTH / 2
self.rect.bottom = self.SCR_HEIGHT
self.bullets = []
self.shot_delay = 0
self.score = 0
self.lives = Player.MAX_LIVES
self.fire_sound = pygame.mixer.Sound(
os.path.join(resource_path, 'laser1.wav'))
self.fire_sound.set_volume(.01)
self.safe_time = Player.SAFE_TIME # New player starts "safe"
self.left, self.right, self.fire, self.safe = False, False, False, False
def __init__(self, SCR_WIDTH, SCR_HEIGHT): self.SCR_SIZE = SCR_WIDTH, SCR_HEIGHT self.NUM_ENEMIES = 6 self.EFFECT_DURATION = 10 self.NUM_STARS = 30 self.SAFE_TIME = 90 self.player = Player(SCR_WIDTH, SCR_HEIGHT) self.enemies = [] for e in range(self.NUM_ENEMIES): self.enemies.append(Enemy(self.SCR_SIZE[0], self.SCR_SIZE[1])) self.resource_path = cfg.resource_path self.effect_images = self.load_images() self.sounds = self.load_sounds() self.hud_font = pygame.font.Font(os.path.join(self.resource_path, 'joystix monospace.ttf'), 24) self.msg_font = pygame.font.Font(os.path.join(self.resource_path, 'joystix monospace.ttf'), 42) self.img_shipIcon = ImageLoader.load(self.resource_path, 'ship.png', 1.2) self.effects = [] self.enemy_level_step = 20 self.paused = False
train_dataset = train_dataset.shuffle(BUFFER_SIZE)
train_dataset = train_dataset.batch(BATCH_SIZE)
test_dataset = tf.data.Dataset.list_files(PATH + 'test/*.jpg')
test_dataset = test_dataset.map(il.load_image_test)
test_dataset = test_dataset.batch(BATCH_SIZE)
# 3. Build the models, losses and optimizers
# 3-1. Build the G model
# Modified U-Net
generator = Generator().generate()
display_on = False
if display_on:
inp, re = il.load(PATH + 'train/100.jpg')
gen_output = generator(inp[tf.newaxis, ...], training=False)
plt.imshow(gen_output[0, ...])
ret = input()
# 3-2. Build teh D model
discriminator = Discriminator().generate()
if display_on:
disc_out = discriminator([inp[tf.newaxis, ...], gen_output],
training=False)
plt.imshow(disc_out[0, ..., -1], vmin=-20, vmax=20, cmap='RdBu_r')
plt.colorbar()
ret = input()
# prepare the losses
def load_images(self): img_boom_1 = ImageLoader.load(self.resource_path, 'boom.png', 2.0) img_boom_2 = ImageLoader.load(self.resource_path, 'boom2.png', 2.0) img_boom_3 = ImageLoader.load(self.resource_path, 'boom3.png', 2.0) return [img_boom_1, img_boom_2, img_boom_3]