# Mikel Broström 🔥 Yolo Tracking 🧾 AGPL-3.0 license

import numpy as np


def intersection_batch(bboxes1, bboxes2):
    bboxes2 = np.expand_dims(bboxes2, 0)
    bboxes1 = np.expand_dims(bboxes1, 1)

    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])
    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])
    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])
    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])
    w = np.maximum(0.0, xx2 - xx1)
    h = np.maximum(0.0, yy2 - yy1)
    intersections = w * h
    return intersections


def box_area(bbox):
    area = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
    return area


def iou_batch(bboxes1, bboxes2):
    """
    From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]
    """
    bboxes2 = np.expand_dims(bboxes2, 0)
    bboxes1 = np.expand_dims(bboxes1, 1)

    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])
    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])
    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])
    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])
    w = np.maximum(0.0, xx2 - xx1)
    h = np.maximum(0.0, yy2 - yy1)
    wh = w * h
    o = wh / (
        (bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])
        + (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1])
        - wh
    )
    return o


def cal_score_dif_batch(bboxes1, bboxes2):
    """
    From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]
    """
    bboxes2 = np.expand_dims(bboxes2, 0)
    bboxes1 = np.expand_dims(bboxes1, 1)

    score2 = bboxes2[..., 4]
    score1 = bboxes1[..., 4]

    return abs(score2 - score1)


def cal_score_dif_batch_two_score(bboxes1, bboxes2):
    """
    From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]
    """
    bboxes2 = np.expand_dims(bboxes2, 0)
    bboxes1 = np.expand_dims(bboxes1, 1)

    score2 = bboxes2[..., 5]
    score1 = bboxes1[..., 4]

    return abs(score2 - score1)


def hmiou(bboxes1, bboxes2):
    """
    Height_Modulated_IoU
    """
    bboxes2 = np.expand_dims(bboxes2, 0)
    bboxes1 = np.expand_dims(bboxes1, 1)

    yy11 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])
    yy12 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])

    yy21 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])
    yy22 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])
    o = (yy12 - yy11) / (yy22 - yy21)

    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])
    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])
    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])
    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])
    w = np.maximum(0.0, xx2 - xx1)
    h = np.maximum(0.0, yy2 - yy1)
    wh = w * h
    o *= wh / (
        (bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])
        + (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1])
        - wh
    )
    return o


def giou_batch(bboxes1, bboxes2):
    """
    :param bbox_p: predict of bbox(N,4)(x1,y1,x2,y2)
    :param bbox_g: groundtruth of bbox(N,4)(x1,y1,x2,y2)
    :return:
    """
    # for details should go to https://arxiv.org/pdf/1902.09630.pdf
    # ensure predict's bbox form
    bboxes2 = np.expand_dims(bboxes2, 0)
    bboxes1 = np.expand_dims(bboxes1, 1)

    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])
    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])
    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])
    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])
    w = np.maximum(0.0, xx2 - xx1)
    h = np.maximum(0.0, yy2 - yy1)
    wh = w * h
    iou = wh / ((bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1]) +
                (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) - wh)

    xxc1 = np.minimum(bboxes1[..., 0], bboxes2[..., 0])
    yyc1 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])
    xxc2 = np.maximum(bboxes1[..., 2], bboxes2[..., 2])
    yyc2 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])
    wc = xxc2 - xxc1
    hc = yyc2 - yyc1
    assert (wc > 0).all() and (hc > 0).all()
    area_enclose = wc * hc
    giou = iou - (area_enclose - wh) / area_enclose
    giou = (giou + 1.0) / 2.0  # resize from (-1,1) to (0,1)
    return giou


def giou_batch_true(bboxes1, bboxes2):
    """
    :param bbox_p: predict of bbox(N,4)(x1,y1,x2,y2)
    :param bbox_g: groundtruth of bbox(N,4)(x1,y1,x2,y2)
    :return:
    """
    # for details should go to https://arxiv.org/pdf/1902.09630.pdf
    # ensure predict's bbox form
    bboxes2 = np.expand_dims(bboxes2, 0)
    bboxes1 = np.expand_dims(bboxes1, 1)

    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])
    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])
    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])
    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])
    w = np.maximum(0.0, xx2 - xx1)
    h = np.maximum(0.0, yy2 - yy1)
    wh = w * h
    union = ((bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1]) +
             (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) - wh)
    iou = wh / union

    xxc1 = np.minimum(bboxes1[..., 0], bboxes2[..., 0])
    yyc1 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])
    xxc2 = np.maximum(bboxes1[..., 2], bboxes2[..., 2])
    yyc2 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])
    wc = xxc2 - xxc1
    hc = yyc2 - yyc1
    assert (wc > 0).all() and (hc > 0).all()
    area_enclose = wc * hc
    giou = iou - (area_enclose - union) / area_enclose
    giou = (giou + 1.0) / 2.0  # resize from (-1,1) to (0,1)
    return giou


def diou_batch(bboxes1, bboxes2):
    """
    :param bbox_p: predict of bbox(N,4)(x1,y1,x2,y2)
    :param bbox_g: groundtruth of bbox(N,4)(x1,y1,x2,y2)
    :return:
    """
    # for details should go to https://arxiv.org/pdf/1902.09630.pdf
    # ensure predict's bbox form
    bboxes2 = np.expand_dims(bboxes2, 0)
    bboxes1 = np.expand_dims(bboxes1, 1)

    # calculate the intersection box
    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])
    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])
    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])
    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])
    w = np.maximum(0.0, xx2 - xx1)
    h = np.maximum(0.0, yy2 - yy1)
    wh = w * h
    iou = wh / (
        (bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])
        + (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1])
        - wh
    )

    centerx1 = (bboxes1[..., 0] + bboxes1[..., 2]) / 2.0
    centery1 = (bboxes1[..., 1] + bboxes1[..., 3]) / 2.0
    centerx2 = (bboxes2[..., 0] + bboxes2[..., 2]) / 2.0
    centery2 = (bboxes2[..., 1] + bboxes2[..., 3]) / 2.0

    inner_diag = (centerx1 - centerx2) ** 2 + (centery1 - centery2) ** 2

    xxc1 = np.minimum(bboxes1[..., 0], bboxes2[..., 0])
    yyc1 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])
    xxc2 = np.maximum(bboxes1[..., 2], bboxes2[..., 2])
    yyc2 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])

    outer_diag = (xxc2 - xxc1) ** 2 + (yyc2 - yyc1) ** 2
    diou = iou - inner_diag / outer_diag

    return (diou + 1) / 2.0  # resize from (-1,1) to (0,1)


def ciou_batch(bboxes1, bboxes2):
    """
    :param bbox_p: predict of bbox(N,4)(x1,y1,x2,y2)
    :param bbox_g: groundtruth of bbox(N,4)(x1,y1,x2,y2)
    :return:
    """
    # for details should go to https://arxiv.org/pdf/1902.09630.pdf
    # ensure predict's bbox form
    bboxes2 = np.expand_dims(bboxes2, 0)
    bboxes1 = np.expand_dims(bboxes1, 1)

    # calculate the intersection box
    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])
    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])
    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])
    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])
    w = np.maximum(0.0, xx2 - xx1)
    h = np.maximum(0.0, yy2 - yy1)
    wh = w * h
    iou = wh / (
        (bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])
        + (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1])
        - wh
    )

    centerx1 = (bboxes1[..., 0] + bboxes1[..., 2]) / 2.0
    centery1 = (bboxes1[..., 1] + bboxes1[..., 3]) / 2.0
    centerx2 = (bboxes2[..., 0] + bboxes2[..., 2]) / 2.0
    centery2 = (bboxes2[..., 1] + bboxes2[..., 3]) / 2.0

    inner_diag = (centerx1 - centerx2) ** 2 + (centery1 - centery2) ** 2

    xxc1 = np.minimum(bboxes1[..., 0], bboxes2[..., 0])
    yyc1 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])
    xxc2 = np.maximum(bboxes1[..., 2], bboxes2[..., 2])
    yyc2 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])

    outer_diag = (xxc2 - xxc1) ** 2 + (yyc2 - yyc1) ** 2

    w1 = bboxes1[..., 2] - bboxes1[..., 0]
    h1 = bboxes1[..., 3] - bboxes1[..., 1]
    w2 = bboxes2[..., 2] - bboxes2[..., 0]
    h2 = bboxes2[..., 3] - bboxes2[..., 1]

    # prevent dividing over zero. add one pixel shift
    h2 = h2 + 1.0
    h1 = h1 + 1.0
    arctan = np.arctan(w2 / h2) - np.arctan(w1 / h1)
    v = (4 / (np.pi**2)) * (arctan**2)
    S = 1 - iou
    alpha = v / (S + v)
    ciou = iou - inner_diag / outer_diag - alpha * v

    return (ciou + 1) / 2.0  # resize from (-1,1) to (0,1)


def ct_dist(bboxes1, bboxes2):
    """
    Measure the center distance between two sets of bounding boxes,
    this is a coarse implementation, we don't recommend using it only
    for association, which can be unstable and sensitive to frame rate
    and object speed.
    """
    bboxes2 = np.expand_dims(bboxes2, 0)
    bboxes1 = np.expand_dims(bboxes1, 1)

    centerx1 = (bboxes1[..., 0] + bboxes1[..., 2]) / 2.0
    centery1 = (bboxes1[..., 1] + bboxes1[..., 3]) / 2.0
    centerx2 = (bboxes2[..., 0] + bboxes2[..., 2]) / 2.0
    centery2 = (bboxes2[..., 1] + bboxes2[..., 3]) / 2.0

    ct_dist2 = (centerx1 - centerx2) ** 2 + (centery1 - centery2) ** 2

    ct_dist = np.sqrt(ct_dist2)

    # The linear rescaling is a naive version and needs more study
    ct_dist = ct_dist / ct_dist.max()
    return ct_dist.max() - ct_dist  # resize to (0,1)


def speed_direction_batch(dets, tracks):
    """
    batch formulation of function 'speed_direction', compute normalized speed from batch bboxes
    @param dets:
    @param tracks:
    @return: normalized speed in batch
    """
    tracks = tracks[..., np.newaxis]
    CX1, CY1 = (dets[:, 0] + dets[:, 2]) / 2.0, (dets[:, 1] + dets[:, 3]) / 2.0
    CX2, CY2 = (tracks[:, 0] + tracks[:, 2]) / 2.0, (tracks[:, 1] + tracks[:, 3]) / 2.0
    dx = CX1 - CX2
    dy = CY1 - CY2
    norm = np.sqrt(dx**2 + dy**2) + 1e-6
    dx = dx / norm
    dy = dy / norm
    return dy, dx  # size: num_track x num_det


def linear_assignment(cost_matrix, thresh=0.0):
    try:  # [hgx0411] goes here!
        import lap

        if thresh != 0:
            _, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh)
        else:
            _, x, y = lap.lapjv(cost_matrix, extend_cost=True)
        return np.array([[y[i], i] for i in x if i >= 0])
    except ImportError:
        from scipy.optimize import linear_sum_assignment

        x, y = linear_sum_assignment(cost_matrix)
        return np.array(list(zip(x, y)))


def cost_vel(Y, X, trackers, velocities, detections, previous_obs, vdc_weight):
    # Y, X = speed_direction_batch(detections, previous_obs)
    inertia_Y, inertia_X = velocities[:, 0], velocities[:, 1]
    inertia_Y = np.repeat(inertia_Y[:, np.newaxis], Y.shape[1], axis=1)
    inertia_X = np.repeat(inertia_X[:, np.newaxis], X.shape[1], axis=1)
    diff_angle_cos = inertia_X * X + inertia_Y * Y
    diff_angle_cos = np.clip(diff_angle_cos, a_min=-1, a_max=1)
    diff_angle = np.arccos(diff_angle_cos)
    diff_angle = (np.pi / 2.0 - np.abs(diff_angle)) / np.pi

    valid_mask = np.ones(previous_obs.shape[0])
    valid_mask[np.where(previous_obs[:, 4] < 0)] = 0

    # iou_matrix = iou_batch(detections, trackers)
    scores = np.repeat(detections[:, -1][:, np.newaxis], trackers.shape[0], axis=1)
    # iou_matrix = iou_matrix * scores # a trick sometiems works, we don't encourage this
    valid_mask = np.repeat(valid_mask[:, np.newaxis], X.shape[1], axis=1)

    angle_diff_cost = (valid_mask * diff_angle) * vdc_weight
    angle_diff_cost = angle_diff_cost.T
    angle_diff_cost = angle_diff_cost * scores
    return angle_diff_cost


def speed_direction_batch_lt(dets, tracks):
    tracks = tracks[..., np.newaxis]
    CX1, CY1 = dets[:, 0], dets[:, 1]
    CX2, CY2 = tracks[:, 0], tracks[:, 1]
    dx = CX1 - CX2
    dy = CY1 - CY2
    norm = np.sqrt(dx**2 + dy**2) + 1e-6
    dx = dx / norm
    dy = dy / norm
    return dy, dx  # size: num_track x num_det


def speed_direction_batch_rt(dets, tracks):
    tracks = tracks[..., np.newaxis]
    CX1, CY1 = dets[:, 0], dets[:, 3]
    CX2, CY2 = tracks[:, 0], tracks[:, 3]
    dx = CX1 - CX2
    dy = CY1 - CY2
    norm = np.sqrt(dx**2 + dy**2) + 1e-6
    dx = dx / norm
    dy = dy / norm
    return dy, dx  # size: num_track x num_det


def speed_direction_batch_lb(dets, tracks):
    tracks = tracks[..., np.newaxis]
    CX1, CY1 = dets[:, 2], dets[:, 1]
    CX2, CY2 = tracks[:, 2], tracks[:, 1]
    dx = CX1 - CX2
    dy = CY1 - CY2
    norm = np.sqrt(dx**2 + dy**2) + 1e-6
    dx = dx / norm
    dy = dy / norm
    return dy, dx  # size: num_track x num_det


def speed_direction_batch_rb(dets, tracks):
    tracks = tracks[..., np.newaxis]
    CX1, CY1 = dets[:, 2], dets[:, 3]
    CX2, CY2 = tracks[:, 2], tracks[:, 3]
    dx = CX1 - CX2
    dy = CY1 - CY2
    norm = np.sqrt(dx**2 + dy**2) + 1e-6
    dx = dx / norm
    dy = dy / norm
    return dy, dx  # size: num_track x num_det


def associate_4_points(
    detections,
    trackers,
    iou_threshold,
    lt,
    rt,
    lb,
    rb,
    previous_obs,
    vdc_weight,
    iou_type=None,
    args=None,
):
    if len(trackers) == 0:
        return (
            np.empty((0, 2), dtype=int),
            np.arange(len(detections)),
            np.empty((0, 5), dtype=int),
        )

    Y1, X1 = speed_direction_batch_lt(detections, previous_obs)
    Y2, X2 = speed_direction_batch_rt(detections, previous_obs)
    Y3, X3 = speed_direction_batch_lb(detections, previous_obs)
    Y4, X4 = speed_direction_batch_rb(detections, previous_obs)
    YC, XC = speed_direction_batch(detections, previous_obs)
    cost_lt = cost_vel(Y1, X1, trackers, lt, detections, previous_obs, vdc_weight)
    cost_rt = cost_vel(Y2, X2, trackers, rt, detections, previous_obs, vdc_weight)
    cost_lb = cost_vel(Y3, X3, trackers, lb, detections, previous_obs, vdc_weight)
    cost_rb = cost_vel(Y4, X4, trackers, rb, detections, previous_obs, vdc_weight)

    iou_matrix = iou_type(detections, trackers)
    angle_diff_cost = cost_lt + cost_rt + cost_lb + cost_rb

    if min(iou_matrix.shape) > 0:
        a = (iou_matrix > iou_threshold).astype(np.int32)
        if a.sum(1).max() == 1 and a.sum(0).max() == 1:
            matched_indices = np.stack(np.where(a), axis=1)
        else:
            matched_indices = linear_assignment(-(iou_matrix + angle_diff_cost))
    else:
        matched_indices = np.empty(shape=(0, 2))

    unmatched_detections = []
    for d, det in enumerate(detections):
        if d not in matched_indices[:, 0]:
            unmatched_detections.append(d)
    unmatched_trackers = []
    for t, trk in enumerate(trackers):
        if t not in matched_indices[:, 1]:
            unmatched_trackers.append(t)

    # filter out matched with low IOU
    matches = []
    for m in matched_indices:
        if iou_matrix[m[0], m[1]] < iou_threshold:
            unmatched_detections.append(m[0])
            unmatched_trackers.append(m[1])
        else:
            matches.append(m.reshape(1, 2))
    if len(matches) == 0:
        matches = np.empty((0, 2), dtype=int)
    else:
        matches = np.concatenate(matches, axis=0)

    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)


def associate_4_points_with_score(
    detections,
    trackers,
    iou_threshold,
    lt,
    rt,
    lb,
    rb,
    previous_obs,
    vdc_weight,
    iou_type=None,
    args=None,
):
    if len(trackers) == 0:
        return (
            np.empty((0, 2), dtype=int),
            np.arange(len(detections)),
            np.empty((0, 5), dtype=int),
        )

    Y1, X1 = speed_direction_batch_lt(detections, previous_obs)
    Y2, X2 = speed_direction_batch_rt(detections, previous_obs)
    Y3, X3 = speed_direction_batch_lb(detections, previous_obs)
    Y4, X4 = speed_direction_batch_rb(detections, previous_obs)
    cost_lt = cost_vel(Y1, X1, trackers, lt, detections, previous_obs, vdc_weight)
    cost_rt = cost_vel(Y2, X2, trackers, rt, detections, previous_obs, vdc_weight)
    cost_lb = cost_vel(Y3, X3, trackers, lb, detections, previous_obs, vdc_weight)
    cost_rb = cost_vel(Y4, X4, trackers, rb, detections, previous_obs, vdc_weight)
    iou_matrix = iou_type(detections, trackers)
    score_dif = cal_score_dif_batch(detections, trackers)

    angle_diff_cost = cost_lt + cost_rt + cost_lb + cost_rb

    # TCM
    angle_diff_cost -= score_dif * args.TCM_first_step_weight

    if min(iou_matrix.shape) > 0:
        a = (iou_matrix > iou_threshold).astype(np.int32)
        if a.sum(1).max() == 1 and a.sum(0).max() == 1:
            matched_indices = np.stack(np.where(a), axis=1)
        else:
            matched_indices = linear_assignment(-(iou_matrix + angle_diff_cost))
    else:
        matched_indices = np.empty(shape=(0, 2))

    unmatched_detections = []
    for d, det in enumerate(detections):
        if d not in matched_indices[:, 0]:
            unmatched_detections.append(d)
    unmatched_trackers = []
    for t, trk in enumerate(trackers):
        if t not in matched_indices[:, 1]:
            unmatched_trackers.append(t)

    # filter out matched with low IOU
    matches = []
    for m in matched_indices:
        if iou_matrix[m[0], m[1]] < iou_threshold:
            unmatched_detections.append(m[0])
            unmatched_trackers.append(m[1])
        else:
            matches.append(m.reshape(1, 2))
    if len(matches) == 0:
        matches = np.empty((0, 2), dtype=int)
    else:
        matches = np.concatenate(matches, axis=0)

    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)


def associate_4_points_with_score_with_reid(
    detections,
    trackers,
    iou_threshold,
    lt,
    rt,
    lb,
    rb,
    previous_obs,
    vdc_weight,
    TCM_first_step_weight,
    iou_type=None,
    emb_cost=None,
    weights=(1.0, 0),
    thresh=0.8,
    long_emb_dists=None,
    with_longterm_reid=False,
    longterm_reid_weight=0.0,
    with_longterm_reid_correction=False,
    longterm_reid_correction_thresh=0.0,
    dataset="dancetrack",
):

    if len(trackers) == 0:
        return (
            np.empty((0, 2), dtype=int),
            np.arange(len(detections)),
            np.empty((0, 5), dtype=int),
        )

    Y1, X1 = speed_direction_batch_lt(detections, previous_obs)
    Y2, X2 = speed_direction_batch_rt(detections, previous_obs)
    Y3, X3 = speed_direction_batch_lb(detections, previous_obs)
    Y4, X4 = speed_direction_batch_rb(detections, previous_obs)
    cost_lt = cost_vel(Y1, X1, trackers, lt, detections, previous_obs, vdc_weight)
    cost_rt = cost_vel(Y2, X2, trackers, rt, detections, previous_obs, vdc_weight)
    cost_lb = cost_vel(Y3, X3, trackers, lb, detections, previous_obs, vdc_weight)
    cost_rb = cost_vel(Y4, X4, trackers, rb, detections, previous_obs, vdc_weight)
    iou_matrix = iou_type(detections, trackers)
    score_dif = cal_score_dif_batch(detections, trackers)

    angle_diff_cost = cost_lt + cost_rt + cost_lb + cost_rb

    # TCM
    angle_diff_cost -= score_dif * TCM_first_step_weight

    if min(iou_matrix.shape) > 0:
        if emb_cost is None:
            a = (iou_matrix > iou_threshold).astype(np.int32)
            if a.sum(1).max() == 1 and a.sum(0).max() == 1:
                matched_indices = np.stack(np.where(a), axis=1)
            else:
                matched_indices = linear_assignment(-(iou_matrix + angle_diff_cost))
        else:
            if not with_longterm_reid:
                matched_indices = linear_assignment(
                    weights[0] * (-(iou_matrix + angle_diff_cost))
                    + weights[1] * emb_cost
                )  # , thresh=thresh
            else:  # long-term reid feats
                matched_indices = linear_assignment(
                    weights[0] * (-(iou_matrix + angle_diff_cost))
                    + weights[1] * emb_cost
                    + longterm_reid_weight * long_emb_dists
                )  # , thresh=thresh

        if matched_indices.size == 0:
            matched_indices = np.empty(shape=(0, 2))
    else:
        matched_indices = np.empty(shape=(0, 2))

    unmatched_detections = []
    for d, det in enumerate(detections):
        if d not in matched_indices[:, 0]:
            unmatched_detections.append(d)
    unmatched_trackers = []
    for t, trk in enumerate(trackers):
        if t not in matched_indices[:, 1]:
            unmatched_trackers.append(t)

    # filter out matched with low IOU (and long-term ReID feats)
    matches = []
    # iou_matrix_thre = iou_matrix if dataset == "dancetrack" else iou_matrix - score_dif
    iou_matrix_thre = iou_matrix - score_dif
    if with_longterm_reid_correction:
        for m in matched_indices:
            if (emb_cost[m[0], m[1]] > longterm_reid_correction_thresh) and (
                iou_matrix_thre[m[0], m[1]] < iou_threshold
            ):
                print("correction:", emb_cost[m[0], m[1]])
                unmatched_detections.append(m[0])
                unmatched_trackers.append(m[1])
            else:
                matches.append(m.reshape(1, 2))
    else:
        for m in matched_indices:
            if iou_matrix_thre[m[0], m[1]] < iou_threshold:
                unmatched_detections.append(m[0])
                unmatched_trackers.append(m[1])
            else:
                matches.append(m.reshape(1, 2))

    if len(matches) == 0:
        matches = np.empty((0, 2), dtype=int)
    else:
        matches = np.concatenate(matches, axis=0)

    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)


def associate_kitti(
    detections, trackers, det_cates, iou_threshold, velocities, previous_obs, vdc_weight
):
    if len(trackers) == 0:
        return (
            np.empty((0, 2), dtype=int),
            np.arange(len(detections)),
            np.empty((0, 5), dtype=int),
        )

    """
        Cost from the velocity direction consistency
    """
    Y, X = speed_direction_batch(detections, previous_obs)
    inertia_Y, inertia_X = velocities[:, 0], velocities[:, 1]
    inertia_Y = np.repeat(inertia_Y[:, np.newaxis], Y.shape[1], axis=1)
    inertia_X = np.repeat(inertia_X[:, np.newaxis], X.shape[1], axis=1)
    diff_angle_cos = inertia_X * X + inertia_Y * Y
    diff_angle_cos = np.clip(diff_angle_cos, a_min=-1, a_max=1)
    diff_angle = np.arccos(diff_angle_cos)
    diff_angle = (np.pi / 2.0 - np.abs(diff_angle)) / np.pi

    valid_mask = np.ones(previous_obs.shape[0])
    valid_mask[np.where(previous_obs[:, 4] < 0)] = 0
    valid_mask = np.repeat(valid_mask[:, np.newaxis], X.shape[1], axis=1)

    scores = np.repeat(detections[:, -1][:, np.newaxis], trackers.shape[0], axis=1)
    angle_diff_cost = (valid_mask * diff_angle) * vdc_weight
    angle_diff_cost = angle_diff_cost.T
    angle_diff_cost = angle_diff_cost * scores

    """
        Cost from IoU
    """
    iou_matrix = iou_batch(detections, trackers)

    """
        With multiple categories, generate the cost for catgory mismatch
    """
    num_dets = detections.shape[0]
    num_trk = trackers.shape[0]
    cate_matrix = np.zeros((num_dets, num_trk))
    for i in range(num_dets):
        for j in range(num_trk):
            if det_cates[i] != trackers[j, 4]:
                cate_matrix[i][j] = -1e6

    cost_matrix = -iou_matrix - angle_diff_cost - cate_matrix

    if min(iou_matrix.shape) > 0:
        a = (iou_matrix > iou_threshold).astype(np.int32)
        if a.sum(1).max() == 1 and a.sum(0).max() == 1:
            matched_indices = np.stack(np.where(a), axis=1)
        else:
            matched_indices = linear_assignment(cost_matrix)
    else:
        matched_indices = np.empty(shape=(0, 2))

    unmatched_detections = []
    for d, det in enumerate(detections):
        if d not in matched_indices[:, 0]:
            unmatched_detections.append(d)
    unmatched_trackers = []
    for t, trk in enumerate(trackers):
        if t not in matched_indices[:, 1]:
            unmatched_trackers.append(t)

    # filter out matched with low IOU
    matches = []
    for m in matched_indices:
        if iou_matrix[m[0], m[1]] < iou_threshold:
            unmatched_detections.append(m[0])
            unmatched_trackers.append(m[1])
        else:
            matches.append(m.reshape(1, 2))
    if len(matches) == 0:
        matches = np.empty((0, 2), dtype=int)
    else:
        matches = np.concatenate(matches, axis=0)

    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)


# compute embedding distance and gating, borrowed and modified from FairMOT
from scipy.spatial.distance import cdist


def embedding_distance(tracks_feat, detections_feat, metric="cosine"):
    """
    :param tracks: list[KalmanBoxTracker]
    :param detections: list[KalmanBoxTracker]
    :param metric:
    :return: cost_matrix np.ndarray
    """

    cost_matrix = np.zeros((len(tracks_feat), len(detections_feat)), dtype=np.float64)
    if cost_matrix.size == 0:
        return cost_matrix
    # det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float64)    # [detection_num, emd_dim]
    # #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.float64)    # [track_num, emd_dim]
    # Nomalized features, metric: cosine, [track_num, detection_num]
    cost_matrix = np.maximum(0.0, cdist(tracks_feat, detections_feat, metric))
    return cost_matrix
