add centering and boundary cropping for data

ychfan · Apr 12, 2017 · 6526f17 · 6526f17
1 parent bbf83f5
commit 6526f17
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Showing 3 changed files with 7 additions and 3 deletions.
diff --git a/data.py b/data.py
@@ -20,6 +20,8 @@ def dataset(hr_flist, lr_flist, scale, resize=resize, residual=residual):
         hr_image = make_residual(hr_image, lr_image)
     hr_patches0, lr_patches0 = make_patches(hr_image, lr_image, scale, resize)
     hr_patches1, lr_patches1 = make_patches(tf.image.rot90(hr_image), tf.image.rot90(lr_image), scale, resize)
+    lr_patches0 -= 0.5
+    lr_patches1 -= 0.5
     return tf.concat([hr_patches0, hr_patches1], 0), tf.concat([lr_patches0, lr_patches1], 0)
 
 def make_residual(hr_image, lr_image):
@@ -31,7 +33,9 @@ def make_residual(hr_image, lr_image):
 
 def make_patches(hr_image, lr_image, scale, resize):
     hr_image = tf.stack(flip([hr_image]))
-    lr_image = tf.stack(flip([lr_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])

diff --git a/predict.py b/predict.py
@@ -36,7 +36,7 @@
     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, FLAGS.scale, training=False, reuse=False)
+    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):

diff --git a/validate.py b/validate.py
@@ -39,7 +39,7 @@
     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)
-    sr_image = model.build_model(lr_image_padded, FLAGS.scale, training=False, reuse=False)
+    sr_image = model.build_model(lr_image_padded - 0.5, FLAGS.scale, training=False, reuse=False)
     sr_image = util.crop_center(sr_image, hr_image_shape)
     if (data.residual):
         if (data.resize):