utils/cotracker/demo.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import os
import torch
import argparse
import numpy as np

from PIL import Image
from cotracker.utils.visualizer import Visualizer, read_video_from_path_frame,read_video_from_path
from cotracker.predictor import CoTrackerPredictor

# Unfortunately MPS acceleration does not support all the features we require,
# but we may be able to enable it in the future

DEFAULT_DEVICE = (
    # "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
    "cuda"
    if torch.cuda.is_available()
    else "cpu"
)

# if DEFAULT_DEVICE == "mps":
#     os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--video_path",
        default="./saved_videos/imput_video",
        help="path to a video",
    )
    parser.add_argument(
        "--mask_path",
        default="./assets/apple_mask.png",
        help="path to a segmentation mask",
    )
    parser.add_argument(
        "--checkpoint",
        # default="./checkpoints/cotracker.pth",
        default=None,
        help="CoTracker model parameters",
    )
    parser.add_argument("--grid_size", type=int, default=15, help="Regular grid size")
    parser.add_argument(
        "--grid_query_frame",
        type=int,
        default=0,
        help="Compute dense and grid tracks starting from this frame",
    )

    parser.add_argument(
        "--backward_tracking",
        action="store_true",
        help="Compute tracks in both directions, not only forward",
    )

    args = parser.parse_args()

    # load the input video frame by frame
    video = read_video_from_path_frame(args.video_path)
    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]

    if args.checkpoint is not None:
        model = CoTrackerPredictor(checkpoint=args.checkpoint)
    else:
        model = torch.hub.load("facebookresearch/co-tracker", "cotracker2")
    model = model.to(DEFAULT_DEVICE)
    video = video.to(DEFAULT_DEVICE)
    # video = video[:, :20]
    pred_tracks, pred_visibility = model(
        video,
        grid_size=args.grid_size,
        grid_query_frame=args.grid_query_frame,
        backward_tracking=args.backward_tracking,
        # segm_mask=segm_mask
    )
    print("computed")
    
    print(pred_tracks.shape)
    print(pred_visibility.shape)
    # save a video with predicted tracks
    seq_name = args.video_path.split("/")[-1]
    vis = Visualizer(save_dir="./saved_videos", pad_value=120, linewidth=2,tracks_leave_trace=-1)
    vis.visualize(
        video,
        pred_tracks,
        pred_visibility,
        query_frame=0 if args.backward_tracking else args.grid_query_frame,
    )