scikit-image

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import numpy as np
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from skimage.measure import block_reduce
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from skimage._shared import testing
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from skimage._shared.testing import assert_equal
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def test_block_reduce_sum():
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    image1 = np.arange(4 * 6).reshape(4, 6)
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    out1 = block_reduce(image1, (2, 3))
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    expected1 = np.array([[24, 42], [96, 114]])
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    assert_equal(expected1, out1)
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    image2 = np.arange(5 * 8).reshape(5, 8)
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    out2 = block_reduce(image2, (3, 3))
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    expected2 = np.array([[81, 108, 87], [174, 192, 138]])
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    assert_equal(expected2, out2)
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def test_block_reduce_mean():
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    image1 = np.arange(4 * 6).reshape(4, 6)
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    out1 = block_reduce(image1, (2, 3), func=np.mean)
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    expected1 = np.array([[4.0, 7.0], [16.0, 19.0]])
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    assert_equal(expected1, out1)
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    image2 = np.arange(5 * 8).reshape(5, 8)
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    out2 = block_reduce(image2, (4, 5), func=np.mean)
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    expected2 = np.array([[14.0, 10.8], [8.5, 5.7]])
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    assert_equal(expected2, out2)
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def test_block_reduce_median():
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    image1 = np.arange(4 * 6).reshape(4, 6)
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    out1 = block_reduce(image1, (2, 3), func=np.median)
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    expected1 = np.array([[4.0, 7.0], [16.0, 19.0]])
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    assert_equal(expected1, out1)
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    image2 = np.arange(5 * 8).reshape(5, 8)
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    out2 = block_reduce(image2, (4, 5), func=np.median)
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    expected2 = np.array([[14.0, 6.5], [0.0, 0.0]])
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    assert_equal(expected2, out2)
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    image3 = np.array([[1, 5, 5, 5], [5, 5, 5, 1000]])
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    out3 = block_reduce(image3, (2, 4), func=np.median)
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    assert_equal(5, out3)
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def test_block_reduce_min():
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    image1 = np.arange(4 * 6).reshape(4, 6)
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    out1 = block_reduce(image1, (2, 3), func=np.min)
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    expected1 = np.array([[0, 3], [12, 15]])
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    assert_equal(expected1, out1)
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    image2 = np.arange(5 * 8).reshape(5, 8)
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    out2 = block_reduce(image2, (4, 5), func=np.min)
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    expected2 = np.array([[0, 0], [0, 0]])
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    assert_equal(expected2, out2)
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def test_block_reduce_max():
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    image1 = np.arange(4 * 6).reshape(4, 6)
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    out1 = block_reduce(image1, (2, 3), func=np.max)
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    expected1 = np.array([[8, 11], [20, 23]])
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    assert_equal(expected1, out1)
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    image2 = np.arange(5 * 8).reshape(5, 8)
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    out2 = block_reduce(image2, (4, 5), func=np.max)
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    expected2 = np.array([[28, 31], [36, 39]])
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    assert_equal(expected2, out2)
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def test_invalid_block_size():
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    image = np.arange(4 * 6).reshape(4, 6)
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    with testing.raises(ValueError):
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        block_reduce(image, [1, 2, 3])
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    with testing.raises(ValueError):
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        block_reduce(image, [1, 0.5])
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def test_default_block_size():
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    image = np.arange(4 * 6).reshape(4, 6)
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    out = block_reduce(image, func=np.min)
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    expected = np.array([[0, 2, 4], [12, 14, 16]])
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    assert_equal(expected, out)
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def test_scalar_block_size():
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    image = np.arange(6 * 6).reshape(6, 6)
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    out = block_reduce(image, 3, func=np.min)
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    expected1 = np.array([[0, 3], [18, 21]])
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    assert_equal(expected1, out)
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    expected2 = block_reduce(image, (3, 3), func=np.min)
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    assert_equal(expected2, out)
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def test_func_kwargs_same_dtype():
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    image = np.array(
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        [
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            [97, 123, 173, 227],
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            [217, 241, 221, 214],
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            [211, 11, 170, 53],
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            [214, 205, 101, 57],
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        ],
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        dtype=np.uint8,
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    )
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    out = block_reduce(image, (2, 2), func=np.mean, func_kwargs={'dtype': np.uint8})
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    expected = np.array([[41, 16], [32, 31]], dtype=np.uint8)
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    assert_equal(out, expected)
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    assert out.dtype == expected.dtype
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def test_func_kwargs_different_dtype():
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    image = np.array(
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        [
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            [0.45745366, 0.67479345, 0.20949775, 0.3147348],
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            [0.7209286, 0.88915504, 0.66153409, 0.07919526],
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            [0.04640037, 0.54008495, 0.34664343, 0.56152301],
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            [0.58085003, 0.80144708, 0.87844473, 0.29811511],
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        ],
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        dtype=np.float64,
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    )
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    out = block_reduce(image, (2, 2), func=np.mean, func_kwargs={'dtype': np.float16})
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    expected = np.array([[0.6855, 0.3164], [0.4922, 0.521]], dtype=np.float16)
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    assert_equal(out, expected)
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    assert out.dtype == expected.dtype
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