def METHOD_NAME():
    r = numpy.random.RandomState()
    r.seed(52)
    base = numpy.zeros((100, 100), bool)
    base[r.randint(0, 100, size=10), r.randint(0, 100, size=10)] = True
    gt = base.copy()
    gt[r.randint(0, 100, size=5), r.randint(0, 100, size=5)] = True
    test = base.copy()
    test[r.randint(0, 100, size=5), r.randint(0, 100, size=5)] = True
    gt = scipy.ndimage.binary_dilation(gt, numpy.ones((5, 5), bool))
    test = scipy.ndimage.binary_dilation(test, numpy.ones((5, 5), bool))
    gt_labels, _ = scipy.ndimage.label(gt, numpy.ones((3, 3), bool))
    test_labels, _ = scipy.ndimage.label(test, numpy.ones((3, 3), bool))
    workspace, module = make_obj_workspace(
        gt_labels,
        test_labels,
        dict(image=numpy.ones(gt_labels.shape)),
        dict(image=numpy.ones(test_labels.shape)),
    )
    module.run(workspace)
    measurements = workspace.measurements
    mname = "_".join(
        (
            cellprofiler.modules.measureobjectoverlap.C_IMAGE_OVERLAP,
            cellprofiler.modules.measureobjectoverlap.FTR_RAND_INDEX,
            GROUND_TRUTH_OBJ,
            ID_OBJ,
        )
    )
    expected_rand_index = measurements.get_current_image_measurement(mname)
    rand_index = measurements.get_current_image_measurement(mname)
    assert round(abs(rand_index - expected_rand_index), 7) == 0
    mname = "_".join(
        (
            cellprofiler.modules.measureobjectoverlap.C_IMAGE_OVERLAP,
            cellprofiler.modules.measureobjectoverlap.FTR_ADJUSTED_RAND_INDEX,
            GROUND_TRUTH_OBJ,
            ID_OBJ,
        )
    )
    adjusted_rand_index = measurements.get_current_image_measurement(mname)