72 lines
2.1 KiB
Python
72 lines
2.1 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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import os
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import torch
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import argparse
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import numpy as np
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from torchvision.io import read_video
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from PIL import Image
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from cotracker.utils.visualizer import Visualizer
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from cotracker.predictor import CoTrackerPredictor
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--video_path",
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default="./assets/apple.mp4",
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help="path to a video",
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)
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parser.add_argument(
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"--mask_path",
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default="./assets/apple_mask.png",
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help="path to a segmentation mask",
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)
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parser.add_argument(
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"--checkpoint",
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default="./checkpoints/cotracker_stride_4_wind_8.pth",
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help="cotracker model",
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)
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parser.add_argument("--grid_size", type=int, default=0, help="Regular grid size")
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parser.add_argument(
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"--grid_query_frame",
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type=int,
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default=0,
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help="Compute dense and grid tracks starting from this frame ",
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)
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parser.add_argument(
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"--backward_tracking",
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action="store_true",
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help="Compute tracks in both directions, not only forward",
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)
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args = parser.parse_args()
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# load the input video frame by frame
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video = read_video(args.video_path)
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video = video[0].permute(0, 3, 1, 2)[None].float()
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segm_mask = np.array(Image.open(os.path.join(args.mask_path)))
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segm_mask = torch.from_numpy(segm_mask)[None, None]
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model = CoTrackerPredictor(checkpoint=args.checkpoint)
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pred_tracks, pred_visibility = model(
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video,
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grid_size=args.grid_size,
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grid_query_frame=args.grid_query_frame,
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backward_tracking=args.backward_tracking,
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# segm_mask=segm_mask
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)
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print("computed")
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# save a video with predicted tracks
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seq_name = args.video_path.split("/")[-1]
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vis = Visualizer(save_dir="./saved_videos", pad_value=120, linewidth=3)
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vis.visualize(video, pred_tracks, query_frame=args.grid_query_frame)
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