scikit-image

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import numpy as np
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import pytest
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from numpy import sqrt, ceil
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from numpy.testing import assert_almost_equal
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from skimage import data
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from skimage import img_as_float
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from skimage.feature import daisy
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def test_daisy_color_image_unsupported_error():
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    img = np.zeros((20, 20, 3))
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    with pytest.raises(ValueError):
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        daisy(img)
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def test_daisy_desc_dims():
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    img = img_as_float(data.astronaut()[:128, :128].mean(axis=2))
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    rings = 2
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    histograms = 4
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    orientations = 3
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    descs = daisy(img, rings=rings, histograms=histograms, orientations=orientations)
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    assert descs.shape[2] == (rings * histograms + 1) * orientations
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    rings = 4
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    histograms = 5
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    orientations = 13
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    descs = daisy(img, rings=rings, histograms=histograms, orientations=orientations)
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    assert descs.shape[2] == (rings * histograms + 1) * orientations
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def test_descs_shape():
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    img = img_as_float(data.astronaut()[:256, :256].mean(axis=2))
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    radius = 20
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    step = 8
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    descs = daisy(img, radius=radius, step=step)
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    assert descs.shape[0] == ceil((img.shape[0] - radius * 2) / float(step))
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    assert descs.shape[1] == ceil((img.shape[1] - radius * 2) / float(step))
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    img = img[:-1, :-2]
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    radius = 5
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    step = 3
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    descs = daisy(img, radius=radius, step=step)
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    assert descs.shape[0] == ceil((img.shape[0] - radius * 2) / float(step))
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    assert descs.shape[1] == ceil((img.shape[1] - radius * 2) / float(step))
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@pytest.mark.parametrize('dtype', [np.float32, np.float64])
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def test_daisy_sigmas_and_radii(dtype):
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    img = data.astronaut()[:64, :64].mean(axis=2).astype(dtype, copy=False)
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    sigmas = [1, 2, 3]
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    radii = [1, 2]
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    descs = daisy(img, sigmas=sigmas, ring_radii=radii)
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    assert descs.dtype == img.dtype
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def test_daisy_incompatible_sigmas_and_radii():
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    img = img_as_float(data.astronaut()[:64, :64].mean(axis=2))
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    sigmas = [1, 2]
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    radii = [1, 2]
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    with pytest.raises(ValueError):
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        daisy(img, sigmas=sigmas, ring_radii=radii)
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def test_daisy_normalization():
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    img = img_as_float(data.astronaut()[:64, :64].mean(axis=2))
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    descs = daisy(img, normalization='l1')
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    for i in range(descs.shape[0]):
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        for j in range(descs.shape[1]):
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            assert_almost_equal(np.sum(descs[i, j, :]), 1)
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    descs_ = daisy(img)
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    assert_almost_equal(descs, descs_)
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    descs = daisy(img, normalization='l2')
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    for i in range(descs.shape[0]):
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        for j in range(descs.shape[1]):
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            assert_almost_equal(sqrt(np.sum(descs[i, j, :] ** 2)), 1)
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    orientations = 8
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    descs = daisy(img, orientations=orientations, normalization='daisy')
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    desc_dims = descs.shape[2]
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    for i in range(descs.shape[0]):
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        for j in range(descs.shape[1]):
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            for k in range(0, desc_dims, orientations):
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                assert_almost_equal(
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                    sqrt(np.sum(descs[i, j, k : k + orientations] ** 2)), 1
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                )
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    img = np.zeros((50, 50))
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    descs = daisy(img, normalization='off')
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    for i in range(descs.shape[0]):
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        for j in range(descs.shape[1]):
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            assert_almost_equal(np.sum(descs[i, j, :]), 0)
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    with pytest.raises(ValueError):
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        daisy(img, normalization='does_not_exist')
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def test_daisy_visualization():
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    img = img_as_float(data.astronaut()[:32, :32].mean(axis=2))
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    descs, descs_img = daisy(img, visualize=True)
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    assert descs_img.shape == (32, 32, 3)
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