def test_style_transfer():
content_fname = os.path.join(PROJECT_ROOT, 'input/content/modern.jpg')
style_fname = os.path.join(PROJECT_ROOT, 'input/style/goeritz.jpg')
c_img = cv2.imread(content_fname)
s_img = cv2.imread(style_fname)
true_transfer_img = np.load(
os.path.join(PROJECT_ROOT, 'tests/stylized_imgs_truth.npy'))
model = adain_style_transfer(alpha=1.0)
model.load_weights(os.path.join(PROJECT_ROOT, "pretrained/adain.h5"))
content_imgs = preprocess(c_img, (512, 512))
style_imgs = preprocess(s_img, (512, 512))
stylized_imgs = model.predict([content_imgs, style_imgs])
stylized_img = postprocess(stylized_imgs)
assert np.allclose(stylized_img, true_transfer_img)
image[image < 0] = 0
image *= 255
image = image.astype(np.uint8)
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])
DEFAULT_ENCODER_H5 = os.path.join(MODEL_ROOT, "h5", "mobile_encoder.h5")
DEFAULT_DECODER_H5 = os.path.join(MODEL_ROOT, "h5", "vgg_decoder.h5")
content_fname="../input/content/chicago.jpg"
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)
from linear.encoder import vgg_encoder
from linear.decoder import vgg_decoder
from linear.mat import build_model as mat_model
encoder = vgg_encoder(img_size)
decoder = vgg_decoder(int(img_size)/4)
mat = mat_model(input_shape=[int(img_size/4),
int(img_size/4),
256])
# 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, (img_size,img_size))
s_img_prep = preprocess(s_img, (img_size,img_size))
# 3. encoding
c_features = encoder.predict(c_img_prep)
s_features = encoder.predict(s_img_prep)
# 4. mix
cs_features = mat.predict([c_features, s_features])
# 5. decode
stylized_imgs = decoder.predict(cs_features)
# 6. post process
stylized_img = stylized_imgs[0].astype(np.uint8)