def main(args):
style = args.style
#img_width = img_height = args.image_size
output_file = args.input[-14:-4]
print("Ouputfilestores in", output_file)
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)
print(output_file)
imsave('%s_output.png' % output_file, y)
return y
def pred(self, image, media_filter, blend_alpha, original_color, style,
output_file):
aspect_ratio, x = preprocess_reflect_video(image, size_multiple=4)
t1 = time.time()
y = self.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)
#imshow(y)
return y
def main(args):
texture = args.texture
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
#processing for texture model
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.compile(
Adam(),
dummy_loss) # Dummy loss since we are learning from regularizes
#load texture model
model.load_weights(texture, 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_texture.png' % output_file, y)
imshow(y)
#processing for second style transform
aspect_ratio2, x2 = preprocess_reflect_layer2(y, size_multiple=4)
img_width2 = img_height2 = x2.shape[1]
net2 = nets.image_transform_net(img_width2, img_height2)
model2 = nets.loss_net(net2.output, net2.input, img_width2, img_height2,
"", 0, 0)
model2.compile(Adam(), dummy_loss)
#load style model
model2.load_weights(style, by_name=False)
y2 = net2.predict(x2)[0]
y2 = crop_image(y2, aspect_ratio)
print("process: %s" % (time.time() - t1))
ox2 = crop_image(x2[0], aspect_ratio2)
y2 = median_filter_all_colours(y2, media_filter)
if blend_alpha > 0:
y2 = blend(ox2, y2, blend_alpha)
if original_color > 0:
y2 = original_colors(ox2, y2, original_color)
#save and display the transformed image
imsave('%s_output.png' % output_file, y2)
imshow(y2)
