def build_SAE_network(config):
nb_layers = 5
autoencoder, encoder, decoder = utilModelREDNet.build_REDNet(
nb_layers, config.window, config.nb_filters, config.kernel,
config.dropout, config.stride, config.every)
autoencoder.compile(optimizer='adam', loss=util.micro_fm, metrics=['mse'])
pkg_models = os.listdir(
os.path.join(os.path.dirname(os.path.abspath(__file__)), 'MODELS'))
if config.modelpath.replace('MODELS/', '') in pkg_models:
config.modelpath = os.path.join(
os.path.dirname(os.path.abspath(__file__)), config.modelpath)
autoencoder.load_weights(config.modelpath)
return autoencoder
def build_SAE_network(config, weights_filename):
nb_layers = 5
autoencoder, encoder, decoder = utilModelREDNet.build_REDNet(
nb_layers, config.window, config.nb_filters, config.kernel,
config.dropout, config.stride, config.every)
autoencoder.compile(optimizer='adam', loss=util.micro_fm, metrics=['mse'])
print(autoencoder.summary())
if config.loadmodel != None:
print('Loading initial weights from', config.loadmodel)
autoencoder.load_weights(config.loadmodel)
elif config.test == True:
print('Loading test weights from', weights_filename)
autoencoder.load_weights(weights_filename)
return autoencoder
help='save the output image')
args = parser.parse_args()
if args.step == -1:
args.step = args.window
if args.demo == False and args.outFilename == None:
util.print_error(
"ERROR: no output mode selected\nPlease choose between -demo or -save options"
)
parser.print_help()
quit()
nb_layers = 5
autoencoder, encoder, decoder = utilModelREDNet.build_REDNet(
nb_layers, args.window, args.nb_filters, args.kernel, args.dropout,
args.stride, args.every)
autoencoder.compile(optimizer='adam', loss=util.micro_fm, metrics=['mse'])
autoencoder.load_weights(args.modelpath)
img = cv2.imread(args.imgpath, False)
img = np.asarray(img)
rows = img.shape[0]
cols = img.shape[1]
if img.shape[0] < args.window or img.shape[1] < args.window: # Scale approach
new_rows = args.window if img.shape[0] < args.window else img.shape[0]
new_cols = args.window if img.shape[1] < args.window else img.shape[1]
img = cv2.resize(img, (new_cols, new_rows), interpolation=cv2.INTER_CUBIC)