def _multiple_images(input_image, which_styles, output_dir):
"""Stylizes an image into a set of styles and writes them to disk."""
with tf.Graph().as_default(), tf.Session() as sess:
stylized_images = model.transform(
tf.concat([input_image for _ in range(len(which_styles))], 0),
normalizer_params={
'labels': tf.constant(which_styles),
'num_categories': FLAGS.num_styles,
'center': True,
'scale': True})
_load_checkpoint(sess, FLAGS.checkpoint)
stylized_images = stylized_images.eval()
for which, stylized_image in zip(which_styles, stylized_images):
image_utils.save_np_image(
stylized_image[None, ...],
'{}/{}_{}.png'.format(output_dir, FLAGS.output_basename, which))
def _multiple_styles(input_image, which_styles, output_dir):
"""Stylizes image into a linear combination of styles and writes to disk."""
with tf.Graph().as_default(), tf.Session() as sess:
mixture = _style_mixture(which_styles, FLAGS.num_styles)
stylized_images = model.transform(
input_image,
normalizer_fn=ops.weighted_instance_norm,
normalizer_params={
'weights': tf.constant(mixture),
'num_categories': FLAGS.num_styles,
'center': True,
'scale': True})
_load_checkpoint(sess, FLAGS.checkpoint)
stylized_image = stylized_images.eval()
image_utils.save_np_image(
stylized_image,
os.path.join(output_dir, '%s_%s.png' % (
FLAGS.output_basename, _describe_style(which_styles))))
def main(unused_argv=None):
tf.logging.set_verbosity(tf.logging.INFO)
if not tf.gfile.Exists(FLAGS.output_dir):
tf.gfile.MkDir(FLAGS.output_dir)
with tf.Graph().as_default(), tf.Session() as sess:
# Defines place holder for the style image.
style_img_ph = tf.placeholder(tf.float32, shape=[None, None, 3])
if FLAGS.style_square_crop:
style_img_preprocessed = image_utils.center_crop_resize_image(
style_img_ph, FLAGS.style_image_size)
else:
style_img_preprocessed = image_utils.resize_image(
style_img_ph, FLAGS.style_image_size)
# Defines place holder for the content image.
content_img_ph = tf.placeholder(tf.float32, shape=[None, None, 3])
if FLAGS.content_square_crop:
content_img_preprocessed = image_utils.center_crop_resize_image(
content_img_ph, FLAGS.image_size)
else:
content_img_preprocessed = image_utils.resize_image(
content_img_ph, FLAGS.image_size)
# Defines the model.
stylized_images, _, _, bottleneck_feat = build_model.build_model(
content_img_preprocessed,
style_img_preprocessed,
trainable=False,
is_training=False,
inception_end_point='Mixed_6e',
style_prediction_bottleneck=100,
adds_losses=False)
if tf.gfile.IsDirectory(FLAGS.checkpoint):
checkpoint = tf.train.latest_checkpoint(FLAGS.checkpoint)
else:
checkpoint = FLAGS.checkpoint
tf.logging.info(
'loading latest checkpoint file: {}'.format(checkpoint))
init_fn = slim.assign_from_checkpoint_fn(
checkpoint, slim.get_variables_to_restore())
sess.run([tf.local_variables_initializer()])
init_fn(sess)
# Gets the list of the input style images.
style_img_list = tf.gfile.Glob(FLAGS.style_images_paths)
if len(style_img_list) > FLAGS.maximum_styles_to_evaluate:
np.random.seed(1234)
style_img_list = np.random.permutation(style_img_list)
style_img_list = style_img_list[:FLAGS.maximum_styles_to_evaluate]
# Gets list of input content images.
content_img_list = tf.gfile.Glob(FLAGS.content_images_paths)
for content_i, content_img_path in enumerate(content_img_list):
content_img_np = image_utils.load_np_image_uint8(
content_img_path)[:, :, :3]
content_img_name = os.path.basename(content_img_path)[:-4]
# Saves preprocessed content image.
inp_img_croped_resized_np = sess.run(
content_img_preprocessed,
feed_dict={content_img_ph: content_img_np})
image_utils.save_np_image(
inp_img_croped_resized_np,
os.path.join(FLAGS.output_dir, '%s.jpg' % (content_img_name)))
# Computes bottleneck features of the style prediction network for the
# identity transform.
identity_params = sess.run(
bottleneck_feat, feed_dict={style_img_ph: content_img_np})
for style_i, style_img_path in enumerate(style_img_list):
if style_i > FLAGS.maximum_styles_to_evaluate:
break
style_img_name = os.path.basename(style_img_path)[:-4]
style_image_np = image_utils.load_np_image_uint8(
style_img_path)[:, :, :3]
if style_i % 10 == 0:
tf.logging.info(
'Stylizing (%d) %s with (%d) %s' %
(content_i, content_img_name, style_i, style_img_name))
# Saves preprocessed style image.
style_img_croped_resized_np = sess.run(
style_img_preprocessed,
feed_dict={style_img_ph: style_image_np})
image_utils.save_np_image(
style_img_croped_resized_np,
os.path.join(FLAGS.output_dir,
'%s.jpg' % (style_img_name)))
# Computes bottleneck features of the style prediction network for the
# given style image.
style_params = sess.run(
bottleneck_feat, feed_dict={style_img_ph: style_image_np})
interpolation_weights = ast.literal_eval(
FLAGS.interpolation_weights)
# Interpolates between the parameters of the identity transform and
# style parameters of the given style image.
for interp_i, wi in enumerate(interpolation_weights):
stylized_image_res = sess.run(
stylized_images,
feed_dict={
bottleneck_feat:
identity_params * (1 - wi) + style_params * wi,
content_img_ph:
content_img_np
})
# Saves stylized image.
image_utils.save_np_image(
stylized_image_res,
os.path.join(
FLAGS.output_dir, '%s_stylized_%s_%d.jpg' %
(content_img_name, style_img_name, interp_i)))
def run(content_images_paths,style_images_paths,style_square_crop=False,style_image_size=400,content_square_crop=False,
image_size=400,checkpoint='arbitrary_style_transfer/model.ckpt',maximum_styles_to_evaluate=1024,
interpolation_weights='[1.0]'):
f = open('docs/accessKeys.csv', 'r')
reader = csv.reader(f,delimiter=',')
i = 1
keys = []
for row in reader:
if i == 2:
keys = row
i = i + 1
f.close()
s3 = boto3.client('s3',
aws_access_key_id=keys[0],
aws_secret_access_key=keys[1])
start = time.time()
front = content_images_paths.split('.')[0]
# bucket name, dir of file in s3 bucket, local name
s3.download_file(config.bucketname, 'target1/'+content_images_paths, content_images_paths)
convertimage(content_images_paths, False)
content = 'c_'+front+'.jpg'
s3.download_file(config.bucketname, 'style1/'+style_images_paths, style_images_paths)
convertimage(style_images_paths, True)
style = 's_'+front+'.jpg'
# start evaluate
tf.logging.set_verbosity(tf.logging.INFO)
# if not tf.gfile.Exists(output_dir):
# tf.gfile.MkDir(output_dir)
with tf.Graph().as_default(), tf.Session() as sess:
# Defines place holder for the style image.
style_img_ph = tf.placeholder(tf.float32, shape=[None, None, 3])
if style_square_crop:
style_img_preprocessed = image_utils.center_crop_resize_image(
style_img_ph, style_image_size)
else:
style_img_preprocessed = image_utils.resize_image(style_img_ph,
style_image_size)
# Defines place holder for the content image.
content_img_ph = tf.placeholder(tf.float32, shape=[None, None, 3])
if content_square_crop:
content_img_preprocessed = image_utils.center_crop_resize_image(
content_img_ph, image_size)
else:
content_img_preprocessed = image_utils.resize_image(
content_img_ph, image_size)
# Defines the model.
stylized_images, _, _, bottleneck_feat = build_model.build_model(
content_img_preprocessed,
style_img_preprocessed,
trainable=False,
is_training=False,
inception_end_point='Mixed_6e',
style_prediction_bottleneck=100,
adds_losses=False)
if tf.gfile.IsDirectory(checkpoint):
checkpoint = tf.train.latest_checkpoint(checkpoint)
else:
checkpoint = checkpoint
tf.logging.info('loading latest checkpoint file: {}'.format(checkpoint))
init_fn = slim.assign_from_checkpoint_fn(checkpoint,
slim.get_variables_to_restore())
sess.run([tf.local_variables_initializer()])
init_fn(sess)
# Gets the list of the input style images.
style_img_list = tf.gfile.Glob(style)
if len(style_img_list) > maximum_styles_to_evaluate:
np.random.seed(1234)
style_img_list = np.random.permutation(style_img_list)
style_img_list = style_img_list[:maximum_styles_to_evaluate]
# Gets list of input content images.
content_img_list = tf.gfile.Glob(content)
for content_i, content_img_path in enumerate(content_img_list):
content_img_np = image_utils.load_np_image_uint8(content_img_path)[:, :, :
3]
content_img_name = os.path.basename(content_img_path)[:-4]
# Saves preprocessed content image.
inp_img_croped_resized_np = sess.run(
content_img_preprocessed, feed_dict={
content_img_ph: content_img_np
})
# image_utils.save_np_image(inp_img_croped_resized_np,
# os.path.join(output_dir,
# '%s.jpg' % (content_img_name)))
# Computes bottleneck features of the style prediction network for the
# identity transform.
identity_params = sess.run(
bottleneck_feat, feed_dict={style_img_ph: content_img_np})
for style_i, style_img_path in enumerate(style_img_list):
if style_i > maximum_styles_to_evaluate:
break
style_img_name = os.path.basename(style_img_path)[:-4]
style_image_np = image_utils.load_np_image_uint8(style_img_path)[:, :, :
3]
if style_i % 10 == 0:
tf.logging.info('Stylizing (%d) %s with (%d) %s' %
(content_i, content_img_name, style_i,
style_img_name))
# Saves preprocessed style image.
style_img_croped_resized_np = sess.run(
style_img_preprocessed, feed_dict={
style_img_ph: style_image_np
})
# image_utils.save_np_image(style_img_croped_resized_np,
# os.path.join(output_dir,
# '%s.jpg' % (style_img_name)))
# Computes bottleneck features of the style prediction network for the
# given style image.
style_params = sess.run(
bottleneck_feat, feed_dict={style_img_ph: style_image_np})
interpolation_weights = ast.literal_eval(interpolation_weights)
# Interpolates between the parameters of the identity transform and
# style parameters of the given style image.
for interp_i, wi in enumerate(interpolation_weights):
stylized_image_res = sess.run(
stylized_images,
feed_dict={
bottleneck_feat:
identity_params * (1 - wi) + style_params * wi,
content_img_ph:
content_img_np
})
# Saves stylized image.
image_utils.save_np_image(
stylized_image_res,
front+'.jpg')
upload_file(front+'.jpg','styletransferimage','output1/'+front+'.jpg')
print("finished upload")
os.remove('c_'+front+'.jpg')
os.remove('s_'+front+'.jpg')
os.remove(front+'.jpg')
status = False
retryCounter = 2
while retryCounter > 0:
try:
channel = grpc.insecure_channel('54.164.44.43:50051')
stub = uid_management_pb2_grpc.UidManagementStub(channel)
status = stub.TransferCompleted(uid_management_pb2.Id(id=front))
retryCounter = 0
except:
retryCounter -= 1