Python segmentation.random_walker函数代码示例

def test_2d_bf():
    lx = 70
    ly = 100
    data, labels = make_2d_syntheticdata(lx, ly)
    labels_bf = random_walker(data, labels, beta=90, mode="bf")
    assert (labels_bf[25:45, 40:60] == 2).all()
    full_prob_bf = random_walker(data, labels, beta=90, mode="bf", return_full_prob=True)
    assert (full_prob_bf[1, 25:45, 40:60] >= full_prob_bf[0, 25:45, 40:60]).all()
    return data, labels_bf, full_prob_bf

def test_2d_cg_mg():
    lx = 70
    ly = 100
    data, labels = make_2d_syntheticdata(lx, ly)
    labels_cg_mg = random_walker(data, labels, beta=90, mode="cg_mg")
    assert (labels_cg_mg[25:45, 40:60] == 2).all()
    full_prob = random_walker(data, labels, beta=90, mode="cg_mg", return_full_prob=True)
    assert (full_prob[1, 25:45, 40:60] >= full_prob[0, 25:45, 40:60]).all()
    return data, labels_cg_mg

def test_length2_spacing():
    # If this passes without raising an exception (warnings OK), the new
    #   spacing code is working properly.
    np.random.seed(42)
    img = np.ones((10, 10)) + 0.2 * np.random.normal(size=(10, 10))
    labels = np.zeros((10, 10), dtype=np.uint8)
    labels[2, 4] = 1
    labels[6, 8] = 4
    random_walker(img, labels, spacing=(1., 2.))

def test_multispectral_2d():
    lx, ly = 70, 100
    data, labels = make_2d_syntheticdata(lx, ly)
    data2 = data.copy()
    data.shape += (1,)
    data = data.repeat(2, axis=2)   # Result should be identical
    multi_labels = random_walker(data, labels, mode='cg', multichannel=True)
    single_labels = random_walker(data2, labels, mode='cg')
    assert (multi_labels.reshape(labels.shape)[25:45, 40:60] == 2).all()
    return data, multi_labels, single_labels, labels

def test_multispectral_2d():
    lx, ly = 70, 100
    data, labels = make_2d_syntheticdata(lx, ly)
    data = data[..., np.newaxis].repeat(2, axis=-1)  # Expect identical output
    multi_labels = random_walker(data, labels, mode='cg', multichannel=True)
    assert data[..., 0].shape == labels.shape
    single_labels = random_walker(data[..., 0], labels, mode='cg')
    assert (multi_labels.reshape(labels.shape)[25:45, 40:60] == 2).all()
    assert data[..., 0].shape == labels.shape
    return data, multi_labels, single_labels, labels

def test_multispectral_3d():
    n = 30
    lx, ly, lz = n, n, n
    data, labels = make_3d_syntheticdata(lx, ly, lz)
    data.shape += (1,)
    data = data.repeat(2, axis=3)   # Result should be identical
    multi_labels = random_walker(data, labels, mode='cg', multichannel=True)
    single_labels = random_walker(data[..., 0], labels, mode='cg')
    assert (multi_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all()
    assert (single_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all()
    return data, multi_labels, single_labels, labels

def test_multispectral_3d():
    n = 30
    lx, ly, lz = n, n, n
    data, labels = make_3d_syntheticdata(lx, ly, lz)
    data = data[..., np.newaxis].repeat(2, axis=-1)  # Expect identical output
    multi_labels = random_walker(data, labels, mode='cg', multichannel=True)
    assert data[..., 0].shape == labels.shape
    single_labels = random_walker(data[..., 0], labels, mode='cg')
    assert (multi_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all()
    assert (single_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all()
    assert data[..., 0].shape == labels.shape
    return data, multi_labels, single_labels, labels

def test_spacing_1():
    n = 30
    lx, ly, lz = n, n, n
    data, _ = make_3d_syntheticdata(lx, ly, lz)

    # Rescale `data` along Y axis
    # `resize` is not yet 3D capable, so this must be done by looping in 2D.
    data_aniso = np.zeros((n, n * 2, n))
    for i, yz in enumerate(data):
        data_aniso[i, :, :] = resize(yz, (n * 2, n),
                                     mode='constant',
                                     anti_aliasing=False)

    # Generate new labels
    small_l = int(lx // 5)
    labels_aniso = np.zeros_like(data_aniso)
    labels_aniso[lx // 5, ly // 5, lz // 5] = 1
    labels_aniso[lx // 2 + small_l // 4,
                 ly - small_l // 2,
                 lz // 2 - small_l // 4] = 2

    # Test with `spacing` kwarg
    # First, anisotropic along Y
    with expected_warnings(['"cg" mode' + '|' + SCIPY_RANK_WARNING,
                            NUMPY_MATRIX_WARNING]):
        labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg',
                                     spacing=(1., 2., 1.))
    assert (labels_aniso[13:17, 26:34, 13:17] == 2).all()

    # Rescale `data` along X axis
    # `resize` is not yet 3D capable, so this must be done by looping in 2D.
    data_aniso = np.zeros((n, n * 2, n))
    for i in range(data.shape[1]):
        data_aniso[i, :, :] = resize(data[:, 1, :], (n * 2, n),
                                     mode='constant',
                                     anti_aliasing=False)

    # Generate new labels
    small_l = int(lx // 5)
    labels_aniso2 = np.zeros_like(data_aniso)
    labels_aniso2[lx // 5, ly // 5, lz // 5] = 1
    labels_aniso2[lx - small_l // 2,
                  ly // 2 + small_l // 4,
                  lz // 2 - small_l // 4] = 2

    # Anisotropic along X
    with expected_warnings(['"cg" mode' + '|' + SCIPY_RANK_WARNING,
                            NUMPY_MATRIX_WARNING]):
        labels_aniso2 = random_walker(data_aniso,
                                      labels_aniso2,
                                      mode='cg', spacing=(2., 1., 1.))
    assert (labels_aniso2[26:34, 13:17, 13:17] == 2).all()

def test_2d_cg():
    lx = 70
    ly = 100
    data, labels = make_2d_syntheticdata(lx, ly)
    labels_cg = random_walker(data, labels, beta=90, mode='cg')
    assert (labels_cg[25:45, 40:60] == 2).all()
    assert data.shape == labels.shape
    full_prob = random_walker(data, labels, beta=90, mode='cg',
                              return_full_prob=True)
    assert (full_prob[1, 25:45, 40:60] >=
            full_prob[0, 25:45, 40:60]).all()
    assert data.shape == labels.shape
    return data, labels_cg

def test_trivial_cases():
    # When all voxels are labeled
    img = np.ones((10, 10))
    labels = np.ones((10, 10))
    pass_through = random_walker(img, labels)
    np.testing.assert_array_equal(pass_through, labels)

    # When all voxels are labeled AND return_full_prob is True
    labels[:, :5] = 3
    expected = np.concatenate(((labels == 1)[..., np.newaxis],
                               (labels == 3)[..., np.newaxis]), axis=2)
    test = random_walker(img, labels, return_full_prob=True)
    np.testing.assert_array_equal(test, expected)

def test_multispectral_2d():
    lx, ly = 70, 100
    data, labels = make_2d_syntheticdata(lx, ly)
    data = data[..., np.newaxis].repeat(2, axis=-1)  # Expect identical output
    with expected_warnings(['"cg" mode' + '|' + SCIPY_RANK_WARNING,
                            NUMPY_MATRIX_WARNING]):
        multi_labels = random_walker(data, labels, mode='cg',
                                     multichannel=True)
    assert data[..., 0].shape == labels.shape
    with expected_warnings(['"cg" mode' + '|' + SCIPY_RANK_WARNING,
                            NUMPY_MATRIX_WARNING]):
        single_labels = random_walker(data[..., 0], labels, mode='cg')
    assert (multi_labels.reshape(labels.shape)[25:45, 40:60] == 2).all()
    assert data[..., 0].shape == labels.shape
    return data, multi_labels, single_labels, labels

def test_2d_cg():
    lx = 70
    ly = 100
    data, labels = make_2d_syntheticdata(lx, ly)
    with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]):
        labels_cg = random_walker(data, labels, beta=90, mode='cg')
    assert (labels_cg[25:45, 40:60] == 2).all()
    assert data.shape == labels.shape
    with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]):
        full_prob = random_walker(data, labels, beta=90, mode='cg',
                                  return_full_prob=True)
    assert (full_prob[1, 25:45, 40:60] >=
            full_prob[0, 25:45, 40:60]).all()
    assert data.shape == labels.shape
    return data, labels_cg

def test_3d():
    n = 30
    lx, ly, lz = n, n, n
    data, labels = make_3d_syntheticdata(lx, ly, lz)
    labels = random_walker(data, labels, mode='cg')
    assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all()
    return data, labels

def test_2d_cg_mg():
    lx = 70
    ly = 100
    data, labels = make_2d_syntheticdata(lx, ly)
    labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg')
    assert (labels_cg_mg[25:45, 40:60] == 2).all()
    return data, labels_cg_mg

def cluster_by_diffusion(data):
    markers = np.zeros(data.shape, dtype=np.uint)
    markers[data < -0.00] = 1
    markers[data > 0.03] = 2
    labels2 = random_walker(data, markers, beta=10, mode='bf')

    return labels2