deepsort中的代价矩阵是什么时候计算的

gated_metric是_match的一个内嵌函数，在什么时候调用呢？
linear_assignment.matching_cascade->min_cost_matching->min_cost_matching(distance_metric,…)
最后发现是在min_cost_matching函数下调用的：cost_matrix = distance_metric(
tracks, detections, track_indices, detection_indices)

    def _match(self, detections):

        def **gated_metric**(tracks, dets, track_indices, detection_indices):
            features = np.array([dets[i].feature for i in detection_indices])
            targets = np.array([tracks[i].track_id for i in track_indices])
            cost_matrix = self.metric.distance(features, targets)
            cost_matrix = linear_assignment.gate_cost_matrix(
                self.kf, cost_matrix, tracks, dets, track_indices,
                detection_indices)

            return cost_matrix

        # Split track set into confirmed and unconfirmed tracks.
        /confirm/ied_tracks = [
            i for i, t in enumerate(self.tracks) if t.is_/confirm/ied()]
        un/confirm/ied_tracks = [
            i for i, t in enumerate(self.tracks) if not t.is_/confirm/ied()]

        # Associate confirmed tracks using appearance features.
        matches_a, unmatched_tracks_a, unmatched_detections = 
            linear_assignment.**matching_cascade**(
                gated_metric, self.metric.matching_threshold, self.max_age,
                self.tracks, detections, /confirm/ied_tracks)

        # Associate remaining tracks together with unconfirmed tracks using IOU.
        iou_track_candidates = un/confirm/ied_tracks + [
            k for k in unmatched_tracks_a if
            self.tracks[k].time_since_update == 1]
        unmatched_tracks_a = [
            k for k in unmatched_tracks_a if
            self.tracks[k].time_since_update != 1]
        matches_b, unmatched_tracks_b, unmatched_detections = 
            linear_assignment.min_cost_matching(
                iou_matching.iou_cost, self.max_iou_distance, self.tracks,
                detections, iou_track_candidates, unmatched_detections)

        matches = matches_a + matches_b
        unmatched_tracks = list(set(unmatched_tracks_a + unmatched_tracks_b))
        return matches, unmatched_tracks, unmatched_detections

def matching_cascade(
        distance_metric, max_distance, cascade_depth, tracks, detections,
        track_indices=None, detection_indices=None):
    if track_indices is None:
        track_indices = list(range(len(tracks)))
    if detection_indices is None:
        detection_indices = list(range(len(detections)))

    unmatched_detections = detection_indices
    matches = []
    for level in range(cascade_depth):
        if len(unmatched_detections) == 0:  # No detections left
            break

        track_indices_l = [
            k for k in track_indices
            if tracks[k].time_since_update == 1 + level
        ]
        if len(track_indices_l) == 0:  # Nothing to match at this level
            continue

        matches_l, _, unmatched_detections = 
            **min_cost_matching**(
                distance_metric, max_distance, tracks, detections,
                track_indices_l, unmatched_detections)
        matches += matches_l
    unmatched_tracks = list(set(track_indices) - set(k for k, _ in matches))
    return matches, unmatched_tracks, unmatched_detections

def min_cost_matching(
        distance_metric, max_distance, tracks, detections, track_indices=None,
        detection_indices=None):
    if track_indices is None:
        track_indices = np.arange(len(tracks))
    if detection_indices is None:
        detection_indices = np.arange(len(detections))

    if len(detection_indices) == 0 or len(track_indices) == 0:
        return [], track_indices, detection_indices  # Nothing to match.

    cost_matrix = **distance_metric**(
        tracks, detections, track_indices, detection_indices)
    cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5

    row_indices, col_indices = linear_assignment(cost_matrix)

    matches, unmatched_tracks, unmatched_detections = [], [], []
    for col, detection_idx in enumerate(detection_indices):
        if col not in col_indices:
            unmatched_detections.append(detection_idx)
    for row, track_idx in enumerate(track_indices):
        if row not in row_indices:
            unmatched_tracks.append(track_idx)
    for row, col in zip(row_indices, col_indices):
        track_idx = track_indices[row]
        detection_idx = detection_indices[col]
        if cost_matrix[row, col] > max_distance:
            unmatched_tracks.append(track_idx)
            unmatched_detections.append(detection_idx)
        else:
            matches.append((track_idx, detection_idx))
    return matches, unmatched_tracks, unmatched_detections