style_fname="../input/style/asheville.jpg"
alpha = 1.0
if __name__ == '__main__':
# 1. contents / style images
c_img = cv2.imread(content_fname)[:,:,::-1]
s_img = cv2.imread(style_fname)[:,:,::-1]
# 2. load input imgs
c_img_prep = preprocess(c_img, (256,256))
s_img_prep = preprocess(s_img, (256,256))
# 3. encoding
from adain.encoder import mobile_encoder
from adain.decoder import combine_and_decode_model
encoder = mobile_encoder()
decoder = combine_and_decode_model()
encoder.load_weights(DEFAULT_ENCODER_H5)
decoder.load_weights(DEFAULT_DECODER_H5)
c_features = encoder.predict(c_img_prep)
s_features = encoder.predict(s_img_prep)
stylized_imgs = decoder.predict([c_features, s_features])
stylized_img = stylized_imgs[0].astype(np.uint8)
# 5. plot
plot([c_img, s_img, stylized_img])
return image
if __name__ == '__main__':
encoder_input = 416
decoder_input = int(encoder_input/8)
# 1. contents / style images
c_img = cv2.imread(content_fname)[:,:,::-1]
s_img = cv2.imread(style_fname)[:,:,::-1]
# 2. load input imgs
c_img_prep = preprocess(c_img, (encoder_input,encoder_input))
s_img_prep = preprocess(s_img, (encoder_input,encoder_input))
# 3. encoding
encoder = mobile_encoder(input_size=encoder_input)
mobile_decoder = build_mobile_combine_decoder(decoder_input)
# mobile_decoder.load_weights("adain/models/h5/mobile_decoder.h5", by_name=True)
mobile_decoder.load_weights("mobile_decoder.h5", by_name=True)
c_features = encoder.predict(c_img_prep)
s_features = encoder.predict(s_img_prep)
stylized_imgs = mobile_decoder.predict([c_features, s_features])
print(stylized_imgs.max(), stylized_imgs.min())
img = postprocess(stylized_imgs[0])
# 5. plot
plot([c_img, s_img, img])