input_im = np.array([inference_data.inputs[i]]).astype(np.float32)
path_lr = inference_data.paths_LR[i]
results = sess.run(save_fetch,
feed_dict={
inputs_raw: input_im,
path_LR: path_lr
})
filesets = save_images(results, FLAGS)
for i, f in enumerate(filesets):
print('evaluate image', f['name'])
# The training mode
elif FLAGS.mode == 'train':
# Load data for training and testing
# ToDo Add online downscaling
data = data_loader(FLAGS)
print('Data count = %d' % (data.image_count))
# Connect to the network
if FLAGS.task == 'SRGAN':
Net = SRGAN(data.inputs, data.targets, FLAGS)
elif FLAGS.task == 'SRResnet':
Net = SRResnet(data.inputs, data.targets, FLAGS)
else:
raise NotImplementedError('Unknown task type')
print('Finish building the network!!!')
# Convert the images output from the network
with tf.name_scope('convert_image'):
# Deprocess the images outputed from the model
# Check the output_dir is given
if FLAGS.output_dir is None:
raise ValueError('The output directory is needed')
# Check the output directory to save the checkpoint
if not os.path.exists(FLAGS.output_dir):
os.mkdir(FLAGS.output_dir)
if not os.path.exists(os.path.join(FLAGS.output_dir, 'buffer')):
os.mkdir(os.path.join(FLAGS.output_dir, 'buffer'))
# pyramid = get_pyramid(targets, pyramid_layers) # a list store the pyramid of target textures image
pyrm_layers = FLAGS.pyrm_layers
tar_name = FLAGS.target_dir.split('/')[-1]
tar_name = tar_name.split('.')[0]
targets0 = data_loader(img_dir=None, FLAGS=FLAGS)
# targets = to_tensor(targets0)
#print(os.listdir(path+'buffer/'))
'''
filleds = os.listdir(os.path.join(FLAGS.output_dir,'buffer'))
n = 2048
m = n//512
filleds = [int((_.split('_')[1]).split('.')[0]) for _ in filleds if str(n) in _]
def ind0(i):
return i//(m//2)*2*m + i%(m//2)*2
flag = [(i in filleds) for i in range(m**2)]
filled_inds = [flag[ind0(i)] and flag[ind0(i)+1] and flag[ind0(i)+m] and flag[ind0(i)+m+1] \
for i in range((m//2)**2)]
unfilled_inds = [i for i in range((m//2)**2) if filled_inds[i]==False]
begin = FLAGS.texture_shape[0]//n - 1
# Check the output directory to save the checkpoint
if not os.path.exists(FLAGS.output_dir):
os.mkdir(FLAGS.output_dir)
if FLAGS.summary_dir is None:
raise ValueError('The summary directory is needed')
# Check the summary directory to save the event
if not os.path.exists(FLAGS.summary_dir):
os.mkdir(FLAGS.summary_dir)
# Load data for training and testing
# ToDo Add online downscaling
targets, initials = data_loader(FLAGS)
# how to initialize the synthesis image
if FLAGS.task_mode == 'texture_synthesis':
pass
#contents = initials # initialization image must be noise
elif FLAGS.task_mode == 'style_transfer':
contents = initials
if FLAGS.initials == 'content image':
initials = contents
elif FLAGS.initials == 'noise':
contents_shape = contents.shape[1:3]
initials = None
elif FLAGS.initials == 'style image':
initials = targets
print_configuration_op(FLAGS)
# Check the output_dir is given
if FLAGS.output_dir is None:
raise ValueError('The output directory is needed')
# Check the output directory to save the checkpoint
if not os.path.exists(FLAGS.output_dir):
os.mkdir(FLAGS.output_dir)
# pyramid = get_pyramid(targets, pyramid_layers) # a list store the pyramid of target textures image
pyrm_layers = FLAGS.pyrm_layers
start = time.time()
tar_name = FLAGS.target_dir.split('/')[-1]
tar_name = tar_name.split('.')[0]
targets0 = data_loader(FLAGS)
for i in range(pyrm_layers - 1, -1, -1):
targets = tf.constant(targets0, dtype=tf.float32)
w0, h0 = [targets.shape[1], targets.shape[2]]
w, h = [w0 // (2**i), h0 // (2**i)]
target = tf.image.resize_bicubic(targets, [w, h])
print('\nCurrent image : ', tar_name)
print('Image size : ', target.shape)
print('Now in pyramid layer %i, total %i layer (from L%i to L0)\n' %
(i, pyrm_layers, pyrm_layers - 1))
if i == pyrm_layers - 1: # if now initial the large scale gen_output
if FLAGS.texture_shape == [-1, -1]:
FLAGS.texture_shape = [w, h]
else:
FLAGS.texture_shape = [ FLAGS.texture_shape[0]//(2**i), \
max_iter = len(inference_data.inputs)
print('Evaluation starts!!')
for i in range(max_iter):
input_im = np.array([inference_data.inputs[i]]).astype(np.float32)
path_lr = inference_data.paths_LR[i]
results = sess.run(save_fetch, feed_dict={inputs_raw: input_im, path_LR: path_lr})
filesets = save_images(results, FLAGS)
for i, f in enumerate(filesets):
print('evaluate image', f['name'])
# The training mode
elif FLAGS.mode == 'train':
# Load data for training and testing
# ToDo Add online downscaling
data = data_loader(FLAGS)
print('Data count = %d' % (data.image_count))
# Connect to the network
if FLAGS.task == 'SRGAN':
Net = SRGAN(data.inputs, data.targets, FLAGS)
elif FLAGS.task =='SRResnet':
Net = SRResnet(data.inputs, data.targets, FLAGS)
else:
raise NotImplementedError('Unknown task type')
print('Finish building the network!!!')
# Convert the images output from the network
with tf.name_scope('convert_image'):
# Deprocess the images outputed from the model
