diff --git a/codes/data_scripts/extract_subimages.py b/codes/data_scripts/extract_subimages.py
index aeace7aa..dd0cf41f 100644
--- a/codes/data_scripts/extract_subimages.py
+++ b/codes/data_scripts/extract_subimages.py
@@ -13,18 +13,19 @@ import data.util as data_util  # noqa: E402
 
 def main():
     mode = 'single'  # single (one input folder) | pair (extract corresponding GT and LR pairs)
+    split_img = False
     opt = {}
     opt['n_thread'] = 20
     opt['compression_level'] = 3  # 3 is the default value in cv2
     # CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size and longer
     # compression time. If read raw images during training, use 0 for faster IO speed.
     if mode == 'single':
-        opt['input_folder'] = 'D:\\vix_cropped'
-        opt['save_folder'] = 'D:\\vix_tiled'
-        opt['crop_sz'] = 800  # the size of each sub-image
-        opt['step'] = 640  # step of the sliding crop window
-        opt['thres_sz'] = 200  # size threshold
-        extract_signle(opt)
+        opt['input_folder'] = 'Z:\\4k6k\\datasets\\adrianna\\adrianna_vids\\images'
+        opt['save_folder'] = 'Z:\\4k6k\\datasets\\adrianna\\adrianna_vids\\tiled'
+        opt['crop_sz'] = 64  # the size of each sub-image
+        opt['step'] = 48  # step of the sliding crop window
+        opt['thres_sz'] = 20  # size threshold
+        extract_single(opt, split_img)
     elif mode == 'pair':
         GT_folder = '../../datasets/div2k/DIV2K_train_HR'
         LR_folder = '../../datasets/div2k/DIV2K_train_LR_bicubic/X4'
@@ -60,14 +61,14 @@ def main():
         opt['crop_sz'] = crop_sz
         opt['step'] = step
         opt['thres_sz'] = thres_sz
-        extract_signle(opt)
+        extract_single(opt)
         print('process LR...')
         opt['input_folder'] = LR_folder
         opt['save_folder'] = save_LR_folder
         opt['crop_sz'] = crop_sz // scale_ratio
         opt['step'] = step // scale_ratio
         opt['thres_sz'] = thres_sz // scale_ratio
-        extract_signle(opt)
+        extract_single(opt)
         assert len(data_util._get_paths_from_images(save_GT_folder)) == len(
             data_util._get_paths_from_images(
                 save_LR_folder)), 'different length of save_GT_folder and save_LR_folder.'
@@ -75,7 +76,7 @@ def main():
         raise ValueError('Wrong mode.')
 
 
-def extract_signle(opt):
+def extract_single(opt, split_img=False):
     input_folder = opt['input_folder']
     save_folder = opt['save_folder']
     if not osp.exists(save_folder):
@@ -93,13 +94,17 @@ def extract_signle(opt):
 
     pool = Pool(opt['n_thread'])
     for path in img_list:
-        pool.apply_async(worker, args=(path, opt), callback=update)
+        if split_img:
+            pool.apply_async(worker, args=(path, opt, True, False), callback=update)
+            pool.apply_async(worker, args=(path, opt, True, True), callback=update)
+        else:
+            pool.apply_async(worker, args=(path, opt), callback=update)
     pool.close()
     pool.join()
     print('All subprocesses done.')
 
 
-def worker(path, opt):
+def worker(path, opt, split_mode=False, left_img=True):
     crop_sz = opt['crop_sz']
     step = opt['step']
     thres_sz = opt['thres_sz']
@@ -113,6 +118,22 @@ def worker(path, opt):
         h, w, c = img.shape
     else:
         raise ValueError('Wrong image shape - {}'.format(n_channels))
+       
+    # Uncomment to filter any image that doesnt meet a threshold size.
+    #if w < 3000:
+    #    return
+
+    left = 0
+    right = w
+    if split_mode:
+        if left_img:
+            left = 0
+            right = int(w/2)
+        else:
+            left = int(w/2)
+            right = w
+        w = int(w/2)
+    img = img[:, left:right]
 
     h_space = np.arange(0, h - crop_sz + 1, step)
     if h - (h_space[-1] + crop_sz) > thres_sz:
@@ -130,9 +151,11 @@ def worker(path, opt):
             else:
                 crop_img = img[x:x + crop_sz, y:y + crop_sz, :]
             crop_img = np.ascontiguousarray(crop_img)
+            # If this fails, change it and the imwrite below to the write extension.
+            assert img_name.contains(".png")
             cv2.imwrite(
                 osp.join(opt['save_folder'],
-                         img_name.replace('.png', '_s{:03d}.png'.format(index))), crop_img,
+                         img_name.replace('.png', '_l{:05d}_s{:03d}.png'.format(left, index))), crop_img,
                 [cv2.IMWRITE_PNG_COMPRESSION, opt['compression_level']])
     return 'Processing {:s} ...'.format(img_name)