def __init__(self, init_x, init_y, index_nav):
super().__init__()
self.x = init_x
self.y = init_y
self.status = random.randint(0, 2)
self.IMAGES = [
loader.load_image("green_light.png"),
loader.load_image("yellow_light.png"),
loader.load_image("red_light.png")
]
self.image = self.IMAGES[self.status]
self.remaining_time = self.cal_remain_time()
self.index_nav = index_nav
self.rect = self.image.get_rect()
self.rect.center = self.x, self.y
def generate(self, source, destination = None, samples = 20, grid_width=480, grid_height=240):
# Load the input and output of the graph
input = self.graph.get_tensor_by_name("en_input:0" )
output = self.graph.get_tensor_by_name("decoder/main_out:0")
# For generating a single image for a source image
if os.path.isfile(source):
image = loader.load_image(source, self.image_size, self.image_size)
batch = np.asarray([image for _ in range(self.batch_size)])
preds = self.session.run(output, feed_dict = {input: batch})
batch[0] = utils.add_border(batch[0],color = [1.0,0.0,0.0])
grid = np.concatenate((batch[0], preds[0]), axis=1)
grid = (grid * 255.0).astype(np.uint8)
# For generating a grid of images from a directory of images
elif os.path.isdir(source):
data = loader.DataSet(images_dir = source,
hard_load = False,
width = self.image_size,
height = self.image_size)
batch,_= data.next_batch(self.batch_size)
preds = self.session.run(output, feed_dict={input: batch})
for i in range(samples):
batch[i] = utils.add_border(batch[i],color = [1.0,0.0,0.0])
grid = self.construct_image_grid(batch,preds,samples,grid_width,grid_height)
else:
print(source,"must be an image pathname or a directory of images")
sys.exit()
if destination:
cv2.imwrite(destination, grid)
else:
cv2.imshow("images", grid)
cv2.waitKey()
def __init__(self, init_x, init_y):
super().__init__()
self.image = loader.load_image("map.png")
self.rect = self.image.get_rect()
self.x = init_x
self.y = init_y
self.get_map_nav()
self.get_edges()
self.shortest_paths = self.find_shortest_paths()
self.get_light_pos()
def __init__(self, init_x, init_y, index_nav):
super().__init__()
self.x = init_x
self.y = init_y
self.image_ori = loader.load_image('stone.png')
self.image = self.image_ori
self.rect = self.image.get_rect()
self.rect.center = self.x, self.y
self.index_nav = index_nav
self.status = config.HIDDEN
self.time_appearance = 0
def __init__(self, image, label=""):
"""
Initialize the detector with an image an a label
:type image: numpy.ndarray
:param image: Image to be processed
:type label: str
:param label: Optional label for the image
"""
self.image = loader.load_image(image)
self.label = label
def __init__(self, image, label=""):
"""
Initialize the detector with an image an a label
:type image: numpy.ndarray
:param image: Image to be processed
:type label: str
:param label: Optional label for the image
"""
self.image = loader.load_image(image)
self.label = label
def predict(self, image_path, label=4, max_labels=10):
image = loader.load_image(image_path, self.image_size, self.image_size)
x_batch = np.asarray([image for _ in range(self.batch_size)])
labels = np.zeros(max_labels)
labels[label] = 1.0
y_batch = np.asarray([labels for _ in range(self.batch_size)])
output = self.graph.get_tensor_by_name("decoder/main_out:0")
input = self.graph.get_tensor_by_name("en_input:0")
label = self.graph.get_tensor_by_name("labels:0")
preds = self.session.run(output,
feed_dict={
input: x_batch,
label: y_batch
})
return x_batch[0], preds[0]
def __init__(self):
super().__init__()
self.fuzzy_monitor = fuzzy_controller.FuzzyController()
self.path = []
self.image = loader.load_image("car.png", False)
self.image_ori = self.image
self.color = config.BLACK
self.x = MAP_NAVS[0][0] - config.PARA
self.y = MAP_NAVS[0][1] + config.PARA
self.dir = 90.0
self.speed = 0.3
self.deviation = 0.0
self.angle_deviation = 0.0
self.acceleration = 0
self.lights = []
self.desired_dir = 90.0
self.cur_nav = 0
self.turn = config.STRAIGHT
self.central_point = (0, 0)
self.rect = self.image.get_rect(center=(self.x, self.y))
self.image, self.rect = rot_center(self.image_ori, self.rect, self.dir)
from utils.options import parser
from utils.loader import load_model, load_image, tensor2im
from PIL import Image
if __name__ == "__main__":
args = parser.parse_args()
model = load_model(args.model)
model.setup()
image = load_image(args.image, args.model)
model.set_input(image)
model.test()
fake = model.get_generated()
fake = tensor2im(fake)
image_pil = Image.fromarray(fake)
image_pil.save(f'{args.save_to}/{args.model}.jpg')
def __init__(self, image):
self.image = loader.load_image(image)