def preprocess_one_shot(self, filename_queue):
img0, img1, img2 = self.read_and_decode(filename_queue)
img0 = img0 / 255.
img1 = img1 / 255.
img2 = img2 / 255.
if self.is_normalize_img:
img0 = mvn(img0)
img1 = mvn(img1)
img2 = mvn(img2)
return img0, img1, img2
from utils import mvn
#--------------------------------------------------------------------------
config = config_dict('./config/config.ini')
restore_model = config['test']['restore_model']
b_img0 = tf.placeholder(tf.float32, shape=(1, None, None, 3))
b_img1 = tf.placeholder(tf.float32, shape=(1, None, None, 3))
b_img2 = tf.placeholder(tf.float32, shape=(1, None, None, 3))
#b_img0 = tf.placeholder(tf.float32, shape=(1, 384, 512, 3))
#b_img1 = tf.placeholder(tf.float32, shape=(1, 384, 512, 3))
#b_img2 = tf.placeholder(tf.float32, shape=(1, 384, 512, 3))
b_img0 = mvn(b_img0)
b_img1 = mvn(b_img1)
b_img2 = mvn(b_img2)
img_shape = tf.shape(b_img0)
h = img_shape[1]
w = img_shape[2]
new_h = tf.where(tf.equal(tf.mod(h, 64), 0), h,
(tf.to_int32(tf.floor(h / 64) + 1)) * 64)
new_w = tf.where(tf.equal(tf.mod(w, 64), 0), w,
(tf.to_int32(tf.floor(w / 64) + 1)) * 64)
batch_img0 = tf.image.resize_images(b_img0, [new_h, new_w],
method=1,
align_corners=True)