with open(model_json, "r") as json_file:
loaded_model_json = json_file.read()
times = []
s = time.time()
cycle_gan_generator = model_from_json(loaded_model_json)
# load weights
cycle_gan_generator.load_weights(model_h5)
tot = time.time() - s
times.append(tot)
print("\nLoaded data and model")
# testing loop
for img_path in test_paths:
# prepare data
img_name = ntpath.basename(img_path).split('.')[0]
im = read_and_resize(img_path, (256, 256))
im = preprocess(im)
im = np.expand_dims(im, axis=0) # (1,256,256,3)
# generate enhanced image
s = time.time()
gen = cycle_gan_generator.predict(im)
gen = deprocess(gen) # Rescale to 0-1
tot = time.time() - s
times.append(tot)
# save samples
misc.imsave(samples_dir + img_name + '_real.png', im[0])
misc.imsave(samples_dir + img_name + '_gen.png', gen[0])
# some statistics
num_test = len(test_paths)
if (num_test == 0):
model_json = checkpoint_dir + model_name_by_epoch + ".json"
with open(model_json, "r") as json_file:
loaded_model_json = json_file.read()
funie_gan_generator = model_from_json(loaded_model_json)
funie_gan_generator.load_weights(model_h5)
print("\nLoaded data and model")
times = []
s = time.time()
for root, dirs, files in os.walk(test_paths):
for img_path in files:
if not img_path.lower().endswith('.jpg'):
continue
img_name = ntpath.basename(img_path).split('.')[0]
im, shape = read_and_resize(os.path.join(root, img_path), (256, 256))
im = preprocess(im)
s = time.time()
gen = funie_gan_generator.predict(im)
gen = deprocess(gen, shape)
tot = time.time() - s
times.append(tot)
misc.imsave(os.path.join(root, img_name + '_gen.png'), gen[0])
# some statistics
num_test = len(test_paths)
if (num_test == 0):
print("\nFound no images for test")
else:
print("\nTotal images: {0}".format(num_test))