In [47]:

    

# 논문 Learning a Deep Convolutional Network for Image Super-Resolution
# 의 알고리즘대로 구현
# 2017. 07. 06

import tensorflow as tf
from PIL import Image
import numpy as np

#이미지, 상수들
learning_rate=1e-4
W1=640
H1=360
W2=1280
H2=720
path="../06/"
pref1="360p/"
pref2="720p/"
suff1="_360.jpg"
suff2="_720.jpg"
train_num=500#1000
file_num=2#6#30
#batch_num=1000


#가중치 초기화 함수
def weight_variable(shape, name):
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial, name=name)
#절편 초기화 함수
def bias_variable(shape, name):
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial, name=name)
#2D 컨벌루션 실행
def conv2d(x, W, B):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')+B

def getimage(idx):
    img_1=Image.open(path+pref1+str(idx)+suff1)

    img_1_720 = img_1.resize((1280, 720), Image.BICUBIC)
    array_1_720=np.array(img_1_720)[:, :]
    array_1_720=array_1_720.astype(np.float32)

    img_2=Image.open(path+pref2+str(idx)+suff2)
    array_2=np.array(img_2)[:, :, 0:3]
    array_2=array_2.astype(np.float32)
    return array_1_720, array_2

def l_relu(x, alpha=0.):
    return tf.nn.relu(x)-alpha*tf.nn.relu(-x)

def asImage(tensor):
    result = tensor[0].astype(np.uint8)
    return Image.fromarray(result, 'RGB')

def showres(index, steps):
    test360, test720 = getimage(index)
    A=sess.run(y_result, feed_dict={x_image:[test360], y_image:[test720]})
    result = A.astype(np.uint8)
    #Image.fromarray(array360, 'RGB').save('results/img360.jpg')
    #Image.fromarray(array720, 'RGB').save('results/img720.jpg')
    asImage(result).save('results/result_06_'+str(steps)+'.jpg')


x_image = tf.placeholder(np.float32, shape=[None, H2, W2, 3])
y_image = tf.placeholder(np.float32, shape=[None, H2, W2, 3])

W1 = weight_variable([11, 11, 3, 15], name = 'W1')
B1 = bias_variable([15], name = 'B1')
W2 = weight_variable([1, 1, 15, 12], name = 'W2')
B2 = bias_variable([12], name='B2')
W3 = weight_variable([5, 5, 12, 3], name = 'W3')
B3 = bias_variable([3], name = 'B3')

F1 = l_relu(conv2d(x_image, W1, B1), alpha = 0.3)
F2 = l_relu(conv2d(F1, W2, B2), alpha = 0.3)
y_result = tf.nn.relu(x_image+l_relu(conv2d(F2, W3, B3), alpha = 0.3))

cost = tf.reduce_mean(tf.square(y_image-y_result))
train_step = tf.train.AdamOptimizer(learning_rate).minimize(cost)
saver = tf.train.Saver()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
#saver.restore(sess, "01/models.ckpt")

for steps in range(train_num):
    for index in range(1, file_num):
        ##array360은 360p인 이미지를 확대해서 720p으로 만들어놓은것.
        array360, array720 = getimage(index)
        #asImage([array720]).show()
        sess.run(train_step, feed_dict={x_image:[array360], y_image:[array720]})
    print (str(steps).zfill(3), sess.run(cost, feed_dict={x_image:[array360], y_image:[array720]}))
    if(steps%5==0):
        showres(index, steps)
print ("끝났다")


    

    




000 461.317
001 402.663
002 358.518
003 324.705
004 298.203
005 277.277
006 260.574
007 247.069
008 236.092
009 227.216
010 219.992
011 214.166
012 209.401
013 205.592
014 202.586
015 200.149
016 198.07
017 196.189
018 194.352
019 192.482
020 190.496
021 188.346
022 186.018
023 183.556
024 181.033
025 178.521
026 176.078
027 173.731






    




---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-47-b9f80ccdce9d> in <module>()
     89         array360, array720 = getimage(index)
     90         #asImage([array720]).show()
---> 91         sess.run(train_step, feed_dict={x_image:[array360], y_image:[array720]})
     92     print (str(steps).zfill(3), sess.run(cost, feed_dict={x_image:[array360], y_image:[array720]}))
     93     if(steps%5==0):

/home/alpha/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    776     try:
    777       result = self._run(None, fetches, feed_dict, options_ptr,
--> 778                          run_metadata_ptr)
    779       if run_metadata:
    780         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/home/alpha/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    980     if final_fetches or final_targets:
    981       results = self._do_run(handle, final_targets, final_fetches,
--> 982                              feed_dict_string, options, run_metadata)
    983     else:
    984       results = []

/home/alpha/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1030     if handle is None:
   1031       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1032                            target_list, options, run_metadata)
   1033     else:
   1034       return self._do_call(_prun_fn, self._session, handle, feed_dict,

/home/alpha/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1037   def _do_call(self, fn, *args):
   1038     try:
-> 1039       return fn(*args)
   1040     except errors.OpError as e:
   1041       message = compat.as_text(e.message)

/home/alpha/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1019         return tf_session.TF_Run(session, options,
   1020                                  feed_dict, fetch_list, target_list,
-> 1021                                  status, run_metadata)
   1022 
   1023     def _prun_fn(session, handle, feed_dict, fetch_list):

KeyboardInterrupt: 

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