In [27]:

    

# 거창하게 GUN이라고 했지만 사실 해상도를 거의 두배씩 두번 올렸다.

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

#이미지, 상수들
learning_rate=1e-4
ratio=16/9
H1=360
W1=int(ratio*H1)
H15=480
W15=int(ratio*H15)
H2=720
W2=int(ratio*H2)
path="../06/"
pref1="360p/"
pref2="720p/"
suff1="_360.jpg"
suff2="_720.jpg"
train_num=500#1000
file_num=1#6#30
#batch_num=1000


    

In [39]:

    

#가중치 초기화 함수
def weight_variable(shape, name):
  initial = tf.truncated_normal(shape, stddev=0.01)
  return tf.Variable(initial, name=name)
#절편 초기화 함수
def bias_variable(shape, name):
  initial = 0.#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)
    array_1=np.array(img_1)[:, :]
    array_1=array_1.astype(np.float32)
    
    img_1_720 = img_1.resize((W2, H2), Image.BILINEAR)
    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,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):
    inputarray ,test144, test720 = getimage(index)
    A=sess.run(y_result, feed_dict={x_image144:[inputarray], x_image:[test144], y_image:[test720]})
    result = A.astype(np.uint8)
    #Image.fromarray(array144, 'RGB').save('results/img144.jpg')
    #Image.fromarray(array720, 'RGB').save('results/img720.jpg')
    asImage(result).save('results/'+str(steps)+'.jpg')


    

In [47]:

    

x_image144 = tf.placeholder(np.float32, shape=[None, H1, W1, 3], name='original_image_360')
x_image = tf.placeholder(np.float32, shape=[None, H2, W2, 3], name='bilinear_magnified_image_720')
y_image = tf.placeholder(np.float32, shape=[None, H2, W2, 3], name='answer_image')

weight1 = weight_variable([20, 20, 3, 30], name = 'weight1')
bias1 = bias_variable([30], name = 'bias1')
weight2 = weight_variable([1, 1, 30, 20], name = 'weight2')
bias2 = bias_variable([20], name='bias2')
weight3 = weight_variable([5, 5, 20, 3], name = 'weight3')
bias3 = bias_variable([3], name = 'bias3')

weight4 = weight_variable([20, 20, 3, 30], name = 'weight4')
bias4 = bias_variable([30], name = 'bias4')
weight5 = weight_variable([1, 1, 30, 20], name = 'weight5')
bias5 = bias_variable([20], name='bias5')
weight6 = weight_variable([5, 5, 20, 3], name = 'weight6')
bias6 = bias_variable([3], name = 'bias6')


    

In [48]:

    

Img1=tf.image.resize_bilinear(x_image144, (H15,W15))
F1 = l_relu(conv2d(Img1, weight1, bias1), alpha = 0.3)
F2 = l_relu(conv2d(F1, weight2, bias2), alpha = 0.3)
F3 = l_relu(conv2d(F2, weight3, bias3), alpha = 0.3)
Img2=tf.image.resize_bilinear(F3, (H2,W2))
F4 = l_relu(conv2d(Img2, weight4, bias4), alpha = 0.3)
F5 = l_relu(conv2d(F4, weight5, bias5), alpha = 0.3)
F6 = l_relu(conv2d(F5, weight6, bias6), alpha = 0.3)
y_result= l_relu(x_image+F6,alpha=0.)


    

In [49]:

    

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")


    

In [50]:

    

for steps in range(train_num):
    for index in range(1, file_num+1):
        ##array144은 144p인 이미지를 확대해서 720p으로 만들어놓은것.
        inputarray, array144, array720 = getimage(index)
        #asImage([array720]).show()
        sess.run(train_step, feed_dict={x_image144:[inputarray], x_image:[array144], y_image:[array720]})
    inputarray, array144, array720 = getimage(index)
    print (str(steps).zfill(3), sess.run(cost, feed_dict={x_image144:[inputarray],x_image:[array144], y_image:[array720]}))
    if(steps%5==0):
        showres(index, steps)
print ("끝났다. 근데 사실 이걸 본적은 한번도 없다.")


    

    




000 67.6674
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004 67.6692
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018 67.6251
019 67.6151
020 67.6025
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026 67.44
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028 67.3241
029 67.2511
030 67.1689
031 67.0757
032 66.9643
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035 66.5079
036 66.3109
037 66.0887
038 65.8404
039 65.5676
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072 61.7033
073 61.6525
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080 61.2767
081 61.2171
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083 61.1002
084 61.041
085 60.9811
086 60.9191
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089 60.7229
090 60.6583
091 60.595
092 60.53
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097 60.1873
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102 59.8377
103 59.7672
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106 59.5635
107 59.4942
108 59.4269
109 59.3615
110 59.2958
111 59.2292
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113 59.097
114 59.0336
115 58.9713
116 58.9098
117 58.8488
118 58.789
119 58.73
120 58.6718
121 58.6139
122 58.5561
123 58.4983
124 58.4402
125 58.3821
126 58.3242
127 58.2654
128 58.2057
129 58.145
130 58.0835
131 58.0212
132 57.9584
133 57.8943
134 57.8301
135 57.7644
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137 57.6302
138 57.5629
139 57.4936
140 57.424
141 57.3539
142 57.2829
143 57.2109
144 57.1382
145 57.0647
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147 56.9144
148 56.837
149 56.7596
150 56.6805
151 56.6003
152 56.5191
153 56.4369






    




---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-50-c49abfac8805> in <module>()
      4         inputarray, array144, array720 = getimage(index)
      5         #asImage([array720]).show()
----> 6         sess.run(train_step, feed_dict={x_image144:[inputarray], x_image:[array144], y_image:[array720]})
      7     inputarray, array144, array720 = getimage(index)
      8     print (str(steps).zfill(3), sess.run(cost, feed_dict={x_image144:[inputarray],x_image:[array144], y_image:[array720]}))

/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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