def main(args):
weights = os.listdir("./models/fast_style_transfer/original_pretrained/")
styles = [filename.split("_weights")[0] for filename in weights]
if len(styles) == 0:
print(
"You have no original pretrained model, please check the link on github to download them"
)
else:
print("You have {} pretrained models can be used to produce images".
format(len(styles)))
datagen = image.ImageDataGenerator()
image_path = args.path
epoch = 82783
batch_size = 1
img_width = img_height = args.image_size
content_gen = datagen.flow_from_directory(image_path,
shuffle=False,
target_size=(img_width,
img_height),
batch_size=batch_size,
class_mode=None)
model = nets.image_transform_net(img_width, img_height)
start_time = time.time()
for style in styles:
print("\nProcessing {}".format(style))
single_model_start_time = time.time()
model = nets.image_transform_net(img_width, img_height)
model.load_weights(
"./models/fast_style_transfer/original_pretrained/" + style +
'_weights.h5')
for i in range(epoch):
content = content_gen.next()
result = model.predict(content)
image_name = content_gen.filenames[i][2:]
if i % 10 == 0:
print("\repoch: %d" % (i), end="")
save_without_deprocess(result,
img_width,
img_height,
save=True,
name=image_name,
iterate=i,
style=style)
print("\nProcessing {} finish in {}".format(
style,
time.time() - single_model_start_time))
gc.collect()
end_time = time.time() - start_time
print("\nTotal Time cost: ", end_time)
def apply_style(content, style):
print(style)
style = style
input_file = content
media_filter = 3
aspect_ratio, x = preprocess_reflect_image(input_file, size_multiple=4)
img_width = img_height = x.shape[1]
model = image_transform_net(img_width, img_height)
model.compile(
Adam(),
dummy_loss) # Dummy loss since we are learning from regularizes
model.load_weights(STYLE_MODEL_DIR + style + '_weights.h5', by_name=True)
t1 = time.time()
y = model.predict(x)[0]
y = crop_image(y, aspect_ratio)
print("process: %s" % (time.time() - t1))
ox = crop_image(x[0], aspect_ratio)
y = median_filter_all_colours(y, media_filter)
del model
K.clear_session()
gc.collect()
return y
def transfer(base_image,
syle_image_path,
original_color=0,
blend=0,
media_filter=3):
style = split_path(syle_image_path)
input_file = base_image
original_color = original_color
blend_alpha = blend
media_filter = media_filter
#aspect_ratio, x = preprocess_reflect_image(input_file, size_multiple=4)
aspect_ratio, x = preprocess_reflect_image(input_file, size_multiple=4)
#img_width = x.shape[1]
#img_height = x.shape[2]
img_width = img_height = x.shape[1]
model = nets.image_transform_net(img_width, img_height)
#model = nets.loss_net(net.output,net.input,img_width,img_height,"",0,0)
model.compile(
Adam(),
dummy_loss) # Dummy loss since we are learning from regularizes
model.load_weights(file_path.MODELS_PATH +
"/fast_style_transfer/pretrained/" + style +
'_weights.h5',
by_name=True)
print('Model loaded')
t1 = time.time()
y = model.predict(x)[0]
y = crop_image(y, aspect_ratio)
print("process: %s" % (time.time() - t1))
ox = crop_image(x[0], aspect_ratio)
y = median_filter_all_colours(y, media_filter)
if blend_alpha > 0:
y = blend(ox, y, blend_alpha)
if original_color > 0:
y = original_colors(ox, y, original_color)
output = BytesIO()
im = toimage(y)
im.save(output, format='JPEG')
del model
K.clear_session()
gc.collect()
return output.getvalue()
def main(args):
style = args.style
#img_width = img_height = args.image_size
output_file = args.output
input_file = args.input
original_color = args.original_color
blend_alpha = args.blend
media_filter = args.media_filter
aspect_ratio, x = preprocess_reflect_image(input_file, size_multiple=4)
img_width = img_height = x.shape[1]
net = nets.image_transform_net(img_width, img_height)
model = nets.loss_net(net.output, net.input, img_width, img_height, "", 0,
0)
#model.summary()
model.compile(
Adam(),
dummy_loss) # Dummy loss since we are learning from regularizes
model.load_weights("pretrained/" + style + '_weights.h5', by_name=False)
t1 = time.time()
y = net.predict(x)[0]
y = crop_image(y, aspect_ratio)
print("process: %s" % (time.time() - t1))
ox = crop_image(x[0], aspect_ratio)
y = median_filter_all_colours(y, media_filter)
if blend_alpha > 0:
y = blend(ox, y, blend_alpha)
if original_color > 0:
y = original_colors(ox, y, original_color)
imsave('%s_output.png' % output_file, y)
def main(args):
style_weight = args.style_weight
content_weight = args.content_weight
tv_weight = args.tv_weight
style = args.style
img_width = img_height = args.image_size
style_image_path = get_style_img_path(style)
net = nets.image_transform_net(img_width, img_height, tv_weight)
model = nets.loss_net(net.output, net.input, img_width, img_height,
style_image_path, content_weight, style_weight)
model.summary()
nb_epoch = 82785 * 2
train_batchsize = 1
train_image_path = "E:\cocodataset"
learning_rate = 1e-3 #1e-3
optimizer = Adam() # Adam(lr=learning_rate,beta_1=0.99)
model.compile(
optimizer,
dummy_loss) # Dummy loss since we are learning from regularizes
datagen = ImageDataGenerator()
dummy_y = np.zeros(
(train_batchsize, img_width, img_height,
3)) # Dummy output, not used since we use regularizers to train
#model.load_weights(style+'_weights.h5',by_name=False)
skip_to = 0
i = 0
t1 = time.time()
for x in datagen.flow_from_directory(train_image_path,
class_mode=None,
batch_size=train_batchsize,
target_size=(img_width, img_height),
shuffle=False):
if i > nb_epoch:
break
if i < skip_to:
i += train_batchsize
if i % 1000 == 0:
print("skip to: %d" % i)
continue
hist = model.train_on_batch(x, dummy_y)
if i % 50 == 0:
print(hist, (time.time() - t1))
t1 = time.time()
if i % 500 == 0:
print("epoc: ", i)
val_x = net.predict(x)
display_img(i, x[0], style)
display_img(i, val_x[0], style, True)
model.save_weights(style + '_weights.h5')
i += train_batchsize