How to use the stardist.matching.matching function in stardist

def test_load_and_predict():
    model_path = path_model3d()
    model = StarDist3D(None, name=model_path.name, basedir=str(model_path.parent))
    img, mask = real_image3d()
    x = normalize(img,1,99.8)
    prob, dist = model.predict(x, n_tiles=(1,2,2))
    assert prob.shape == dist.shape[:3]
    assert model.config.n_rays == dist.shape[-1]
    labels, _ = model.predict_instances(x)
    assert labels.shape == img.shape[:3]
    stats = matching(mask, labels, thresh=0.5)
    assert (stats.fp, stats.tp, stats.fn) == (0, 30, 21)
    return model, labels

def test_load_and_predict():
    model_path = path_model2d()
    model = StarDist2D(None, name=model_path.name, basedir=str(model_path.parent))
    img, mask = real_image2d()
    x = normalize(img,1,99.8)
    prob, dist = model.predict(x, n_tiles=(2,3))
    assert prob.shape == dist.shape[:2]
    assert model.config.n_rays == dist.shape[-1]
    labels, polygons = model.predict_instances(x)
    assert labels.shape == img.shape[:2]
    assert labels.max() == len(polygons['coord'])
    assert len(polygons['coord']) == len(polygons['points']) == len(polygons['prob'])
    stats = matching(mask, labels, thresh=0.5)
    assert (stats.fp, stats.tp, stats.fn) == (1, 48, 17)

def test_acc(img):
    prob = edt_prob(img)
    dist = star_dist(img, n_rays=32, mode="cpp")
    coord = dist_to_coord(dist)
    points = non_maximum_suppression(coord, prob, prob_thresh=0.4)
    img2 = polygons_to_label(coord, prob, points, shape=img.shape)
    m = matching(img, img2)
    acc = m.accuracy
    print("accuracy {acc:.2f}".format(acc=acc))
    assert acc > 0.9