def make_patches(hr_image, lr_image, scale, resize):
hr_image = tf.stack(flip([hr_image]))
lr_image = tf.stack(flip([lr_image]))
hr_image = util.crop_by_pixel(hr_image, 12)
lr_image = util.crop_by_pixel(lr_image, 12 / scale)
hr_patches = util.image_to_patches(hr_image)
if resize:
lr_image = util.resize_func(lr_image, tf.shape(hr_image)[1:3])
lr_patches = util.image_to_patches(lr_image)
else:
lr_patches = util.image_to_patches(lr_image, scale)
return hr_patches, lr_patches
with open(FLAGS.hr_flist) as f:
hr_filename_list = f.read().splitlines()
with open(FLAGS.lr_flist) as f:
lr_filename_list = f.read().splitlines()
filename_queue = tf.train.string_input_producer(lr_filename_list,
num_epochs=2,
shuffle=False)
reader = tf.WholeFileReader()
_, image_file = reader.read(filename_queue)
lr_image = tf.image.decode_image(image_file, channels=3)
lr_image = tf.image.convert_image_dtype(lr_image, tf.float32)
lr_image = tf.expand_dims(lr_image, 0)
lr_image_shape = tf.shape(lr_image)[1:3]
hr_image_shape = lr_image_shape * FLAGS.scale
if (data.resize):
lr_image = util.resize_func(lr_image, hr_image_shape)
lr_image = tf.reshape(lr_image,
[1, hr_image_shape[0], hr_image_shape[1], 3])
else:
lr_image = tf.reshape(lr_image,
[1, lr_image_shape[0], lr_image_shape[1], 3])
lr_image_padded = util.pad_boundary(lr_image)
hr_image = model.build_model(lr_image_padded - 0.5,
FLAGS.scale,
training=False,
reuse=False)
hr_image = util.crop_center(hr_image, hr_image_shape)
if (data.residual):
if (data.resize):
hr_image += lr_image
else:
def make_residual(hr_image, lr_image):
hr_image = tf.expand_dims(hr_image, 0)
lr_image = tf.expand_dims(lr_image, 0)
hr_image_shape = tf.shape(hr_image)[1:3]
res_image = hr_image - util.resize_func(lr_image, hr_image_shape)
return tf.reshape(res_image, [hr_image_shape[0], hr_image_shape[1], 3])