def feed_transform(self, data_in, paths_out):
checkpoint_dir = os.path.join(self.flags.checkpoint_dir,
self.style_img_name, 'model')
img_shape = utils.imread(data_in[0]).shape
g = tf.Graph()
soft_config = tf.ConfigProto(allow_soft_placement=True)
soft_config.gpu_options.allow_growth = True
with g.as_default(), tf.Session(config=soft_config) as sess:
img_placeholder = tf.placeholder(tf.float32,
shape=[None, *img_shape],
name='img_placeholder')
model = Transfer()
pred = model(img_placeholder)
saver = tf.train.Saver()
if os.path.isdir(checkpoint_dir):
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
raise Exception('No checkpoint found...')
else:
saver.restore(sess, checkpoint_dir)
img = np.asarray([utils.imread(data_in[0])]).astype(np.float32)
_pred = sess.run(pred, feed_dict={img_placeholder: img})
utils.imsave(paths_out[0], _pred[0]) # paths_out and _pred is list
def feed_transform(data_in, paths_out, checkpoint_dir):
img_shape = utils.imread(data_in[0]).shape
g = tf.Graph()
soft_config = tf.ConfigProto(allow_soft_placement=True)
soft_config.gpu_options.allow_growth = True
with g.as_default(), tf.Session(config=soft_config) as sess:
img_placeholder = tf.placeholder(tf.float32,
shape=[None, *img_shape],
name='img_placeholder')
model = Transfer()
pred = model(img_placeholder)
saver = tf.train.Saver()
if os.path.isdir(checkpoint_dir):
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
raise Exception('No checkpoint found...')
else:
saver.restore(sess, checkpoint_dir)
img = np.asarray([utils.imread(data_in[0])]).astype(np.float32)
start_tic = time.time()
_pred = sess.run(pred, feed_dict={img_placeholder: img})
end_toc = time.time()
print('PT: {:.2f} msec.\n'.format((end_toc - start_tic) * 1000))
utils.imsave(paths_out[0], _pred[0]) # paths_out and _pred is list
def feed_transform(style_image, paths_out, checkpoint_dir):
img_shape = style_image.shape
print("manan")
print(img_shape)
g = tf.Graph()
soft_config = tf.ConfigProto(allow_soft_placement=True)
soft_config.gpu_options.allow_growth = True
with g.as_default(), tf.Session(config=soft_config) as sess:
img_placeholder = tf.placeholder(tf.float32,
shape=[None, *img_shape],
name='img_placeholder')
model = Transfer()
pred = model(img_placeholder)
saver = tf.train.Saver()
if os.path.isdir(checkpoint_dir):
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
raise Exception('No checkpoint found...')
else:
saver.restore(sess, checkpoint_dir)
img = np.asarray([style_image]).astype(np.float32)
start_tic = time.time()
_pred = sess.run(pred, feed_dict={img_placeholder: img})
end_toc = time.time()
print('PT: {:.2f} msec.\n'.format((end_toc - start_tic) * 1000))
img = np.clip(_pred[0], 0, 255).astype(np.uint8)
return img
def feed_forward_video(path_in, path_out, checkpoint_dir):
# initialize video cap
video_cap = VideoFileClip(path_in, audio=False)
# initialize writer
video_writer = ffmpeg_writer.FFMPEG_VideoWriter(path_out,
video_cap.size,
video_cap.fps,
codec='libx264',
preset='medium',
bitrate='2000k',
audiofile=path_in,
threads=None,
ffmpeg_params=None)
g = tf.Graph()
soft_config = tf.ConfigProto(allow_soft_placement=True)
soft_config.gpu_options.allow_growth = True
with g.as_default(), tf.Session(config=soft_config) as sess:
batch_shape = (None, video_cap.size[1], video_cap.size[0], 3)
img_placeholder = tf.placeholder(tf.float32,
shape=batch_shape,
name='img_placeholder')
model = Transfer()
pred = model(img_placeholder)
saver = tf.train.Saver()
if os.path.isdir(checkpoint_dir):
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
raise Exception('No checkpoint found...')
else:
saver.restore(sess, checkpoint_dir)
frame_id = 0
for frame in video_cap.iter_frames():
print('frame id: {}'.format(frame_id))
_pred = sess.run(pred,
feed_dict={
img_placeholder:
np.asarray([frame]).astype(np.float32)
})
video_writer.write_frame(np.clip(_pred, 0, 255).astype(np.uint8))
frame_id += 1
video_writer.close()
help = 'Path to save the output.')
return parser
'''
style_src_path = r'E:\python.file\git-Repository\TF--style-transfer-collection\style_src\star.jpg'
content_src_path = r'E:\python.file\git-Repository\TF--style-transfer-collection\content_src\girl.jpg'
style_src = get_img(style_src_path,max_size = 512)
content_src = get_img(content_src_path,max_size = 800)
transfer_op = Transfer(content_src,style_src,content_layers = ['conv2_2','conv3_2'],pool_method = 'avg')
img = transfer_op.transfer()
'''
if __name__ == '__main__':
parser = Argparser()
args = parser.parse_args()
content_img = utils.get_img_PIL(args.content_img,max_size = args.content_shape)
style_img = utils.get_img_PIL(args.style_img,max_size = args.style_shape)
transfer_op = Transfer(content_img = content_img , style_img = style_img ,
loss_para = args.loss_para , pool_method = args.pool_method ,
content_layers = args.content_layers , style_layers = args.style_layers ,
epoches = args.epoches , save_path = args.save_path)
transfer_op.transfer()