diff --git a/demo.py b/demo.py
index ed7b517..43d76bb 100644
--- a/demo.py
+++ b/demo.py
@@ -25,7 +25,7 @@
 
 import torchvision.transforms as transforms
 import torchvision.datasets as dset
-from scipy.misc import imread
+# from scipy.misc import imread
 from roi_data_layer.roidb import combined_roidb
 from roi_data_layer.roibatchLoader import roibatchLoader
 from model.utils.config import cfg, cfg_from_file, cfg_from_list, get_output_dir
@@ -260,14 +260,9 @@ def _get_image_blob(im):
         # Load the demo image
         else:
           im_file = os.path.join(args.image_dir, imglist[num_images])
-          im_in = np.array(imread(im_file))
-          # resize
-          # im_in = np.array(Image.fromarray(im_in).resize((640, 360)))
-        if len(im_in.shape) == 2:
-          im_in = im_in[:,:,np.newaxis]
-          im_in = np.concatenate((im_in,im_in,im_in), axis=2)
-        # rgb -> bgr
-        im = im_in[:,:,::-1]
+          im_in = cv2.imread(im_file)
+        # bgr
+        im = im_in
 
         blobs, im_scales = _get_image_blob(im)
         assert len(im_scales) == 1, "Only single-image batch implemented"
diff --git a/lib/roi_data_layer/minibatch.py b/lib/roi_data_layer/minibatch.py
index 9a0bf02..7aa8c42 100755
--- a/lib/roi_data_layer/minibatch.py
+++ b/lib/roi_data_layer/minibatch.py
@@ -10,9 +10,10 @@
 from __future__ import division
 from __future__ import print_function
 
+import cv2
 import numpy as np
 import numpy.random as npr
-from scipy.misc import imread
+# from scipy.misc import imread
 from model.utils.config import cfg
 from model.utils.blob import prep_im_for_blob, im_list_to_blob
 import pdb
@@ -70,15 +71,16 @@ def _get_image_blob(roidb, scale_inds):
   processed_ims = []
   im_scales = []
   for i in range(num_images):
-    #im = cv2.imread(roidb[i]['image'])
-    im = imread(roidb[i]['image'])
+    im = cv2.imread(roidb[i]['image'])
+    # im = imread(roidb[i]['image'])
 
-    if len(im.shape) == 2:
-      im = im[:,:,np.newaxis]
-      im = np.concatenate((im,im,im), axis=2)
+    # if len(im.shape) == 2:
+    #   im = im[:,:,np.newaxis]
+    #   im = np.concatenate((im,im,im), axis=2)
     # flip the channel, since the original one using cv2
     # rgb -> bgr
-    im = im[:,:,::-1]
+    # im = im[:,:,::-1]
+
 
     if roidb[i]['flipped']:
       im = im[:, ::-1, :]