From 1a88fee6a7bab0a25f2e4e7a0857adc70376ea51 Mon Sep 17 00:00:00 2001
From: "jules@lens" <julescarbon@gmail.com>
Date: Sun, 18 Nov 2018 21:21:11 +0100
Subject: move thing up

---
 test.py | 49 ++++++++++++++++++++++++-------------------------
 1 file changed, 24 insertions(+), 25 deletions(-)

diff --git a/test.py b/test.py
index 3347063..d1b74d2 100644
--- a/test.py
+++ b/test.py
@@ -36,6 +36,30 @@ if not(args.T == 'L' or args.T =='G'):
     print('Invalid input type: {} (Must be L(Low-resolution) or G(Ground-truth))'.format(args.T))
     exit(1)
 
+def process_dir(v):
+    scene_name = v.split('/')[-1]
+    os.mkdir('./results/{}L/L/{}/'.format(args.L, scene_name))
+    
+    dir_frames = glob.glob(v + '/*.png')
+    dir_frames.sort()
+
+    frames = []
+    for f in dir_frames:
+        frames.append(LoadImage(f))
+    frames = np.asarray(frames)
+    frames_padded = np.lib.pad(frames, pad_width=((T_in//2,T_in//2),(0,0),(0,0),(0,0)), mode='constant')
+    
+    for i in range(frames.shape[0]):
+        print('Scene {}: Frame {}/{} processing'.format(scene_name, i+1, frames.shape[0]))
+        in_L = frames_padded[i:i+T_in]  # select T_in frames
+        in_L = in_L[np.newaxis,:,:,:,:]
+        
+        out_H = sess.run(GH, feed_dict={L: in_L, is_train: False})
+        out_H = np.clip(out_H, 0, 1)
+
+        Image.fromarray(np.around(out_H[0,0]*255).astype(np.uint8)).save('./results/{}L/L/{}/Frame{:03d}.png'.format(args.L, scene_name, i+1))
+        
+    
 
 def G(x, is_train):  
     # shape of x: [B,T_in,H,W,C]
@@ -137,28 +161,3 @@ with tf.Session(config=config) as sess:
             dir_inputs = glob.glob('./inputs/L/*')
             for v in dir_inputs:
                 process_dir(v)
-
-def process_dir(v):
-    scene_name = v.split('/')[-1]
-    os.mkdir('./results/{}L/L/{}/'.format(args.L, scene_name))
-    
-    dir_frames = glob.glob(v + '/*.png')
-    dir_frames.sort()
-
-    frames = []
-    for f in dir_frames:
-        frames.append(LoadImage(f))
-    frames = np.asarray(frames)
-    frames_padded = np.lib.pad(frames, pad_width=((T_in//2,T_in//2),(0,0),(0,0),(0,0)), mode='constant')
-    
-    for i in range(frames.shape[0]):
-        print('Scene {}: Frame {}/{} processing'.format(scene_name, i+1, frames.shape[0]))
-        in_L = frames_padded[i:i+T_in]  # select T_in frames
-        in_L = in_L[np.newaxis,:,:,:,:]
-        
-        out_H = sess.run(GH, feed_dict={L: in_L, is_train: False})
-        out_H = np.clip(out_H, 0, 1)
-
-        Image.fromarray(np.around(out_H[0,0]*255).astype(np.uint8)).save('./results/{}L/L/{}/Frame{:03d}.png'.format(args.L, scene_name, i+1))
-        
-    
-- 
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