diff --git a/train/dataset/crop_image.py b/train/dataset/crop_image.py
index 7ab3958..d58ea00 100644
--- a/train/dataset/crop_image.py
+++ b/train/dataset/crop_image.py
@@ -5,6 +5,7 @@
 import json
 import cv2
 import time
+from tqdm import tqdm
 
 parse = argparse.ArgumentParser(description='Generate training data (cropped) for DCFNet_pytorch')
 parse.add_argument('-v', '--visual', dest='visual', action='store_true', help='whether visualise crop')
@@ -13,7 +14,7 @@
 
 args = parse.parse_args()
 
-print args
+print (args)
 
 
 def crop_hwc(image, bbox, out_sz, padding=(0, 0, 0)):
@@ -47,12 +48,14 @@ def cxy_wh_2_bbox(cxy, wh):
 
 count = 0
 begin_time = time.time()
-for snap in snaps:
+for snap in tqdm(snaps):
     frames = snap['frame']
     n_frames = len(frames)
-    for f, frame in enumerate(frames):
+    for f, frame in enumerate(tqdm(frames,leave=False)):
         img_path = join(snap['base_path'], frame['img_path'])
         im = cv2.imread(img_path)
+        #print (img_path) 
+        #print(im.shape)
         avg_chans = np.mean(im, axis=(0, 1))
         bbox = frame['obj']['bbox']
 
@@ -67,17 +70,17 @@ def cxy_wh_2_bbox(cxy, wh):
         lmdb['down_index'][count] = f
         lmdb['up_index'][count] = n_frames - f
         count += 1
-        if count % 100 == 0:
-            elapsed = time.time() - begin_time
-            print("Processed {} images in {:.2f} seconds. "
-                  "{:.2f} images/second.".format(count, elapsed, count / elapsed))
+        #if count % 100 == 0:
+        #    elapsed = time.time() - begin_time
+        #    print("Processed {} images in {:.2f} seconds. "
+        #          "{:.2f} images/second.".format(count, elapsed, count / elapsed))
 
 template_id = np.where(lmdb['up_index'] > 1)[0]  # NEVER use the last frame as template! I do not like bidirectional.
 rand_split = np.random.choice(len(template_id), len(template_id))
 lmdb['train_set'] = template_id[rand_split[:(len(template_id)-num_val)]]
 lmdb['val_set'] = template_id[rand_split[(len(template_id)-num_val):]]
-print len(lmdb['train_set'])
-print len(lmdb['val_set'])
+print ("train set: %i"%len(lmdb['train_set']))
+print ("val set: %i"%len(lmdb['val_set']))
 
 # to list for json
 lmdb['train_set'] = lmdb['train_set'].tolist()