def embedding_distance(tracks, detections, metric='cosine'):
    """
    :param tracks: list[STrack]
    :param detections: list[BaseTrack]
    :param metric:
    :return: cost_matrix np.ndarray
    """

    cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
    if cost_matrix.size == 0:
        return cost_matrix
    det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
    #for i, track in enumerate(tracks):
        #cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric))
    track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float)
    cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric))  # Nomalized features
    return cost_matrix

    :param tracks: list[STrack]
    :param detections: list[BaseTrack]
    :param metric:
    :return: cost_matrix np.ndarray
    
trackers/byte_tracker/matching.py