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ultralytics 8.3.38 SAM 2 video inference (#14851)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
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Laughing 2024-11-26 19:38:23 +08:00 • committed by GitHub
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ultralytics/trackers/utils/matching.py
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@ -27,10 +27,9 @@ def linear_assignment(cost_matrix: np.ndarray, thresh: float, use_lap: bool = Tr
use_lap (bool): Use lap.lapjv for the assignment. If False, scipy.optimize.linear_sum_assignment is used.
Returns:
(tuple): A tuple containing:
- matched_indices (np.ndarray): Array of matched indices of shape (K, 2), where K is the number of matches.
- unmatched_a (np.ndarray): Array of unmatched indices from the first set, with shape (L,).
- unmatched_b (np.ndarray): Array of unmatched indices from the second set, with shape (M,).
matched_indices (np.ndarray): Array of matched indices of shape (K, 2), where K is the number of matches.
unmatched_a (np.ndarray): Array of unmatched indices from the first set, with shape (L,).
unmatched_b (np.ndarray): Array of unmatched indices from the second set, with shape (M,).
Examples:
>>> cost_matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])