add colab demo

README.md

@@ -6,6 +6,10 @@
 
 [[`Paper`]()] [[`Project`](https://co-tracker.github.io/)] [[`BibTeX`](#citing-cotracker)]
 
+<a target="_blank" href="https://colab.research.google.com/github/facebookresearch/co-tracker/blob/main/notebooks/demo.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
+
 ![bmx-bumps](./assets/bmx-bumps.gif)
 
 **CoTracker** is a fast transformer-based model that can track any point in a video. It brings to tracking some of the benefits of Optical Flow.
@@ -15,7 +19,7 @@ CoTracker can track:
 - Points sampled on a regular grid on any video frame 
 - Manually selected points
 
-Try these tracking modes for yourself with our [Colab demo](https://github.com/facebookresearch/co-tracker/notebooks/demo.ipynb).
+Try these tracking modes for yourself with our [Colab demo](https://colab.research.google.com/github/facebookresearch/co-tracker/blob/master/notebooks/demo.ipynb).
 
 
 
@@ -43,7 +47,7 @@ cd ..
 
 
 ## Running the Demo:
-Try our [Colab demo](https://github.com/facebookresearch/co-tracker/notebooks/demo.ipynb) or run a local demo with 10*10 points sampled on a grid on the first frame of a video:
+Try our [Colab demo](https://colab.research.google.com/github/facebookresearch/co-tracker/blob/master/notebooks/demo.ipynb) or run a local demo with 10*10 points sampled on a grid on the first frame of a video:
 ```
 python demo.py --grid_size 10
 ```

cotracker/utils/visualizer.py

@@ -18,6 +18,22 @@ from torch.utils.tensorboard import SummaryWriter
 import matplotlib.pyplot as plt
 
 
+def read_video_from_path(path):
+    cap = cv2.VideoCapture(path)
+    if not cap.isOpened():
+        print("Error opening video file")
+    else:
+        frames = []
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if ret == True:
+                frames.append(np.array(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)))
+            else:
+                break
+        cap.release()
+    return np.stack(frames)
+
+
 class Visualizer:
     def __init__(
         self,

demo.py

@@ -5,13 +5,13 @@
 # LICENSE file in the root directory of this source tree.
 
 import os
+import cv2
 import torch
 import argparse
 import numpy as np
 
-from torchvision.io import read_video
 from PIL import Image
-from cotracker.utils.visualizer import Visualizer
+from cotracker.utils.visualizer import Visualizer, read_video_from_path
 from cotracker.predictor import CoTrackerPredictor
 
 
@@ -49,8 +49,8 @@ if __name__ == "__main__":
     args = parser.parse_args()
 
     # load the input video frame by frame
-    video = read_video(args.video_path)
+    video = read_video_from_path(args.video_path)
-    video = video[0].permute(0, 3, 1, 2)[None].float()
+    video = torch.from_numpy(video).permute(0, 3, 1, 2)[None].float()
     segm_mask = np.array(Image.open(os.path.join(args.mask_path)))
     segm_mask = torch.from_numpy(segm_mask)[None, None]