add a demo code

demo1.py

@@ -0,0 +1,47 @@
+import os
+import torch
+
+from base64 import b64encode
+from cotracker.utils.visualizer import Visualizer, read_video_from_path
+
+
+video = read_video_from_path('./assets/F1_shorts.mp4')
+video = torch.from_numpy(video).permute(0, 3, 1, 2)[None].float()
+
+from cotracker.predictor import CoTrackerPredictor
+
+model = CoTrackerPredictor(
+    checkpoint=os.path.join(
+        './checkpoints/cotracker2.pth'
+    )
+)
+
+# pred_tracks, pred_visibility = model(video, grid_size=30)
+
+# vis = Visualizer(save_dir='./videos', pad_value=100)
+# vis.visualize(video=video, tracks=pred_tracks, visibility=pred_visibility, filename='teaser');
+
+grid_query_frame=20
+
+import torch.nn.functional as F
+video_interp = F.interpolate(video[0], [200, 360], mode="bilinear")[None]
+
+import time
+start_time = time.time()
+pred_tracks, pred_visibility = model(video_interp, 
+                                     grid_query_frame=grid_query_frame, backward_tracking=True)
+end_time = time.time() 
+
+print("Time taken: {:.2f} seconds".format(end_time - start_time))
+
+vis = Visualizer(
+    save_dir='./videos',
+    pad_value=20,
+    linewidth=1,
+    mode='optical_flow'
+)
+vis.visualize(
+    video=video,
+    tracks=pred_tracks,
+    visibility=pred_visibility,
+    filename='dense');