114 lines
3.6 KiB
Python
114 lines
3.6 KiB
Python
import numpy as np
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from skimage._shared.testing import assert_array_almost_equal, assert_equal
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from skimage import color, data
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from skimage.filters import threshold_local, gaussian
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from skimage.util.apply_parallel import apply_parallel
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import pytest
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da = pytest.importorskip('dask.array')
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def test_apply_parallel():
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# data
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a = np.arange(144).reshape(12, 12).astype(float)
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# apply the filter
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expected1 = threshold_local(a, 3)
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result1 = apply_parallel(threshold_local, a, chunks=(6, 6), depth=5,
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extra_arguments=(3,),
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extra_keywords={'mode': 'reflect'})
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assert_array_almost_equal(result1, expected1)
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def wrapped_gauss(arr):
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return gaussian(arr, 1, mode='reflect')
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expected2 = gaussian(a, 1, mode='reflect')
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result2 = apply_parallel(wrapped_gauss, a, chunks=(6, 6), depth=5)
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assert_array_almost_equal(result2, expected2)
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expected3 = gaussian(a, 1, mode='reflect')
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result3 = apply_parallel(
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wrapped_gauss, da.from_array(a, chunks=(6, 6)), depth=5, compute=True
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)
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assert isinstance(result3, np.ndarray)
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assert_array_almost_equal(result3, expected3)
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def test_apply_parallel_lazy():
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# data
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a = np.arange(144).reshape(12, 12).astype(float)
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d = da.from_array(a, chunks=(6, 6))
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# apply the filter
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expected1 = threshold_local(a, 3)
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result1 = apply_parallel(threshold_local, a, chunks=(6, 6), depth=5,
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extra_arguments=(3,),
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extra_keywords={'mode': 'reflect'},
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compute=False)
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# apply the filter on a Dask Array
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result2 = apply_parallel(threshold_local, d, depth=5,
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extra_arguments=(3,),
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extra_keywords={'mode': 'reflect'})
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assert isinstance(result1, da.Array)
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assert_array_almost_equal(result1.compute(), expected1)
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assert isinstance(result2, da.Array)
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assert_array_almost_equal(result2.compute(), expected1)
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def test_no_chunks():
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a = np.ones(1 * 4 * 8 * 9).reshape(1, 4, 8, 9)
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def add_42(arr):
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return arr + 42
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expected = add_42(a)
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result = apply_parallel(add_42, a)
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assert_array_almost_equal(result, expected)
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def test_apply_parallel_wrap():
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def wrapped(arr):
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return gaussian(arr, 1, mode='wrap')
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a = np.arange(144).reshape(12, 12).astype(float)
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expected = gaussian(a, 1, mode='wrap')
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result = apply_parallel(wrapped, a, chunks=(6, 6), depth=5, mode='wrap')
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assert_array_almost_equal(result, expected)
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def test_apply_parallel_nearest():
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def wrapped(arr):
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return gaussian(arr, 1, mode='nearest')
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a = np.arange(144).reshape(12, 12).astype(float)
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expected = gaussian(a, 1, mode='nearest')
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result = apply_parallel(wrapped, a, chunks=(6, 6), depth={0: 5, 1: 5},
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mode='nearest')
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assert_array_almost_equal(result, expected)
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@pytest.mark.parametrize('dtype', (np.float32, np.float64))
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@pytest.mark.parametrize('chunks', (None, (128, 128, 3)))
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@pytest.mark.parametrize('depth', (0, 8, (8, 8, 0)))
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def test_apply_parallel_rgb(depth, chunks, dtype):
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cat = data.chelsea().astype(dtype) / 255.
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func = color.rgb2ycbcr
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cat_ycbcr_expected = func(cat)
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cat_ycbcr = apply_parallel(func, cat, chunks=chunks, depth=depth,
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dtype=dtype, multichannel=True)
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assert_equal(cat_ycbcr.dtype, cat.dtype)
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assert_array_almost_equal(cat_ycbcr_expected, cat_ycbcr)
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