multi_object_tracker.py 文件源码

def euclidean(bb_test_, bb_gt_):
    """
    Computes similarity using euclidean distance between
    two bboxes in the form [x, y, z, s, r, yaw]
    using  1/ (1 + euclidean_dist)

    """
    x1, y1, z1, s1, r1, yaw1 = get_bbox(bb_test_)
    x2, y2, z2, s2, r2, yaw2 = get_bbox(bb_gt_)

    # o = (np.sum(squared_diff(i,j) for (i,j) in [(x1, x2), (y1, y2), (yaw1, yaw2)]))
    # this is not jit compatible. resort to using for loop:

    output = 0.
    for (i, j) in [(x1, x2), (y1, y2), (z1, z2), (yaw1, yaw2), (s1, s2), (r1, r2)]:
        output += squared_diff(i, j)
    output = 1./(1. + (output ** (1 / 2.)))
    # print('distance {}'.format(o))
    return(output)