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