diff --git a/demo1.py b/demo1.py
index 503984e..5a0c7c5 100644
--- a/demo1.py
+++ b/demo1.py
@@ -3,14 +3,25 @@ import torch
 
 from base64 import b64encode
 from cotracker.utils.visualizer import Visualizer, read_video_from_path
+import numpy as np
+from PIL import Image
+import time
 
+device = torch.device('cuda:3' if torch.cuda.is_available() else 'cpu')
 
+start_time = time.time()
+print(f'Using device: {device}')
+print(f'start loading video')
 video = read_video_from_path('./assets/F1_shorts.mp4')
+print(f'video shape: {video.shape}')
+# video = torch.from_numpy(video).permute(0, 3, 1, 2)[None].float().to(device)
 video = torch.from_numpy(video).permute(0, 3, 1, 2)[None].float()
+end_time = time.time()
+print(f'video shape after permute: {video.shape}')
+print("Load video Time taken: {:.2f} seconds".format(end_time - start_time))
 
 from cotracker.predictor import CoTrackerPredictor
 
-device = torch.device('cuda:2' if torch.cuda.is_available() else 'cpu')
 
 model = CoTrackerPredictor(
     checkpoint=os.path.join(
@@ -27,25 +38,45 @@ grid_query_frame=20
 
 import torch.nn.functional as F
 # video_interp = F.interpolate(video[0], [200, 360], mode="bilinear")[None].to(device)
+interp_size = (720, 1280)
+video_interp = F.interpolate(video[0], [interp_size[0], interp_size[1]], mode="bilinear")[None].to(device)
+print(f'video_interp shape: {video_interp.shape}')
 
-import time
 start_time = time.time()
 # pred_tracks, pred_visibility = model(video_interp, 
-pred_tracks, pred_visibility = model(video, 
-                                     grid_query_frame=grid_query_frame, backward_tracking=True)
+input_mask='./assets/F1_mask.png'
+segm_mask = Image.open(input_mask)
+interp_size = (interp_size[1], interp_size[0])
+segm_mask = segm_mask.resize(interp_size, Image.BILINEAR)
+segm_mask = np.array(Image.open(input_mask))
+segm_mask = torch.tensor(segm_mask).to(device)
+# pred_tracks, pred_visibility = model(video, 
+pred_tracks, pred_visibility = model(video_interp, 
+                                     grid_query_frame=grid_query_frame, backward_tracking=True,
+                                     segm_mask=segm_mask )
 end_time = time.time() 
 
 print("Time taken: {:.2f} seconds".format(end_time - start_time))
 
+start_time = time.time()
+print(f'start visualizing')
 vis = Visualizer(
     save_dir='./videos',
     pad_value=20,
     linewidth=1,
     mode='optical_flow'
 )
+print(f'vis initialized')
+end_time = time.time()
+print("Time taken: {:.2f} seconds".format(end_time - start_time))
+start_time = time.time()
+print(f'start visualize')
 vis.visualize(
-    # video=video_interp,
-    video=video,
+    video=video_interp,
+    # video=video,
     tracks=pred_tracks,
     visibility=pred_visibility,
-    filename='dense');
\ No newline at end of file
+    filename='dense2');
+print(f'done')
+end_time = time.time()
+print("Time taken: {:.2f} seconds".format(end_time - start_time))
\ No newline at end of file