Fix resize arg to data-manager

themightyoarfish · Mar 3, 2018 · 2e23c4d · 2e23c4d
1 parent 8b38f32
commit 2e23c4d
Show file tree

Hide file tree

Showing 3 changed files with 18 additions and 9 deletions.
diff --git a/main.py b/main.py
@@ -33,16 +33,25 @@ def main():
             help='Length of the sequences used for training the RNN.')
     parser.add_argument('-r', '--use-dropout', action='store_true', default=False,
             help='Use dropout (during training)')
+    parser.add_argument('-w', '--width', type=int, required=False, default=0,
+            help='Resize images to long edge')
     args = parser.parse_args()
     if args.use_dropout:
         print('Use dropout')
 
-    dm = DataManager(
-                dataset_path=args.dataset,
-                batch_size=args.batch_size,
-                sequence_length=args.sequence_length,
-                debug=True,
-                resize_to_width=640)
+    if args.width == 0:
+        dm = DataManager(
+                    dataset_path=args.dataset,
+                    batch_size=args.batch_size,
+                    sequence_length=args.sequence_length,
+                    debug=True)
+    else:
+        dm = DataManager(
+                    dataset_path=args.dataset,
+                    batch_size=args.batch_size,
+                    sequence_length=args.sequence_length,
+                    debug=True,
+                    resize_to_width=args.width)
 
     image_shape = dm.getImageShape()
 

diff --git a/preprocess_data.py b/preprocess_data.py
@@ -8,7 +8,7 @@
 
 
 def main():
-    parser = ArgumentParser('Preprocess robot data vor DeepVO')
+    parser = ArgumentParser('Preprocess robot data vor DeepVO, This process is destructive.')
     parser.add_argument('-d', '--data', type=str, required=True,
                         help='Path to dataset (a folder with "images" and "poses" subfolders.)')
     parser.add_argument('-f', '--to-float', required=False, default=False,

diff --git a/utils.py b/utils.py
@@ -287,13 +287,13 @@ def loadImage(self, id):
     def saveImage(self, id, img):
         np.save(self.image_file_template % id, img)
 
-    def loadImages(self, ids, resize_imgs=True):
+    def loadImages(self, ids):
         num_images = len(ids)
         images     = np.empty([num_images, self.H, self.W, self.C], dtype=self.dtype)
         for i in range(0, num_images):
             # right colors:
             img = self.loadImage(ids[i])
-            if resize_imgs:
+            if img.shape != (self.H, self.W, self.C):
                 images[i] = resize(img, output_shape=(self.H, self.W))
             else:
                 images[i] = img