v = VAEController()
v.load("logs/vae-256.pkl")
#model = keras.models.load_model(path)
model = SAC.load(path)
camera = CSICamera(width=112, height=112)
camera.running = True
print("Imported Camera! Ready to Start!")
while True:
print("Starting to read image")
image = camera.value.copy()
print("Image Read!")
#image = cv2.resize(image, (224//2, 224//2))
print(type(image))
tmp = v.encode_from_raw_image(image)
print("Got image")
input_array[0,:] = tmp
print("Predicing from Model")
action, _ = model.predict(input_array, deterministic = True)
t = (action[1] + 1) / 2
# Convert from [0, 1] to [min, max]
action[1] = (1 - t) * 0.45 + 0.6 * t
print(action)
steer = float(action[0])
throttle = float(action[1])
print("Frame: {}\t Steer: {}\t Throttle:{}".format(i, steer, throttle))
i+=1
car.throttle = throttle
car.steering = steer
parser.add_argument('--n-samples', help='Max number of samples', type=int, default=20)
parser.add_argument('--seed', help='Random generator seed', type=int, default=0)
args = parser.parse_args()
set_global_seeds(args.seed)
if not args.folder.endswith('/'):
args.folder += '/'
vae = VAEController()
vae.load(args.vae_path)
images = [im for im in os.listdir(args.folder) if im.endswith('.png')]
images = np.array(images)
n_samples = len(images)
for i in range(args.n_samples):
# Load test image
image_idx = np.random.randint(n_samples)
image_path = args.folder + images[image_idx]
image = cv2.imread(image_path)
image = cv2.resize(image, (112, 112))
encoded = vae.encode_from_raw_image(image)
reconstructed_image = vae.decode(encoded)[0]
# Plot reconstruction
cv2.imshow("Original", image)
cv2.imshow("Reconstruction", reconstructed_image)
cv2.waitKey(0)
