def styleTransfer(sourcepath, stylepath):
path = 'static/uploads'
base_image_path = os.path.join(path, "source.png")
style_reference_image_path = os.path.join(path, "style.png")
result_prefix = "static/results/result"
global content_weight
global total_variation_weight
global style_weight
total_variation_weight = 1e-6
style_weight = 3e-6
content_weight = 5e-7
width, height = image.load_img(base_image_path).size
global img_nrows
global img_ncols
img_nrows = 400
img_ncols = int(width * img_nrows / height)
model = VGG19(weights="imagenet", include_top=False)
outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])
global feature_extractor
feature_extractor = Model(inputs=model.inputs, outputs=outputs_dict)
global style_layer_names
style_layer_names = [
"block1_conv1",
"block2_conv1",
"block3_conv1",
"block4_conv1",
"block5_conv1",
]
global content_layer_name
content_layer_name = "block5_conv2"
optimizer = SGD(schedules.ExponentialDecay(
initial_learning_rate=100.0, decay_steps=100, decay_rate=0.96))
base_image = preprocess_image(base_image_path)
style_reference_image = preprocess_image(style_reference_image_path)
combination_image = tf.Variable(preprocess_image(base_image_path))
iterations = 100
for i in range(1, iterations + 1):
loss, grads = compute_loss_and_grads(
combination_image, base_image, style_reference_image)
optimizer.apply_gradients([(grads, combination_image)])
print("Iteration %d: loss=%.2f" % (i, (loss)))
print(combination_image)
img = deprocess_image(combination_image.numpy())
fname = result_prefix + ".png"
image.save_img(fname, img)
img_nrows = 400
img_ncols = int(width * img_nrows / height)
optimizer = SGD(
keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate=100.0, decay_steps=100, decay_rate=0.96
)
)
base_image = preprocess_image(base_image_path)
style_reference_image = preprocess_image(style_image_path)
combination_image = tf.Variable(preprocess_image(base_image_path))
iterations = 4000
for i in tqdm(range(1, iterations + 1)):
loss, grads = compute_loss_and_grads(
combination_image, base_image, style_reference_image
)
optimizer.apply_gradients([(grads, combination_image)])
if i % 10 == 0:
print("Iteration get_ipython().run_line_magic("d:", " loss=%.2f\" % (i, loss))")
result_saver(i)
