Update README.md

README.md

@@ -20,17 +20,19 @@ CoTracker can track:
 
 ## How to use
 Here is how to use this model in the **offline mode**:
+```pip install imageio[ffmpeg]```, then:
 ```python
 import torch
 # Download the video
 url = 'https://github.com/facebookresearch/co-tracker/blob/main/assets/apple.mp4'
-# pip install imageio[ffmpeg]
+
 import imageio.v3 as iio
 frames = iio.imread(url, plugin="FFMPEG")  # plugin="pyav"
-video = torch.tensor(frames).permute(0, 3, 1, 2)[None].float().to(device)  # B T C H W
 
-grid_size = 10
 device = 'cuda'
+grid_size = 10
+video = torch.tensor(frames).permute(0, 3, 1, 2)[None].float().to(device)  # B T C H W
+
 # Run Offline CoTracker:
 cotracker = torch.hub.load("facebookresearch/co-tracker", "cotracker2").to(device)
 pred_tracks, pred_visibility = cotracker(video, grid_size=grid_size) # B T N 2,  B T N 1
@@ -38,14 +40,16 @@ pred_tracks, pred_visibility = cotracker(video, grid_size=grid_size) # B T N 2,
 and in the **online mode**:
 ```python
 cotracker = torch.hub.load("facebookresearch/co-tracker", "cotracker2_online").to(device)
+
 # Run Online CoTracker, the same model with a different API:
+# Initialize online processing
+cotracker(video_chunk=video, is_first_step=True, grid_size=grid_size)  
+
+# Process the video
 for ind in range(0, video.shape[1] - cotracker.step, cotracker.step):
     pred_tracks, pred_visibility = cotracker(
-        video_chunk=video[:, ind : ind + cotracker.step * 2],
-        is_first_step=(ind == 0),
-        grid_size=grid_size
-    ) # B T N 2,  B T N 1
-
+        video_chunk=video[:, ind : ind + cotracker.step * 2]
+    )  # B T N 2,  B T N 1
 ```
 Online processing is more memory-efficient and allows for the processing of longer videos or videos in real-time.