Esempio n. 1
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def test_triangle_float_images():
    text = data.text()
    int_bins = text.max() - text.min() + 1
    # Set nbins to match the uint case and threshold as float.
    assert(round(threshold_triangle(
        text.astype(np.float), nbins=int_bins)) == 104)
    # Check that rescaling image to floats in unit interval is equivalent.
    assert(round(threshold_triangle(text / 255., nbins=int_bins) * 255) == 104)
    # Repeat for inverted image.
    assert(round(threshold_triangle(
        np.invert(text).astype(np.float), nbins=int_bins)) == 151)
    assert (round(threshold_triangle(
        np.invert(text) / 255., nbins=int_bins) * 255) == 151)
def test_triangle_float_images():
    text = data.text()
    int_bins = text.max() - text.min() + 1
    # Set nbins to match the uint case and threshold as float.
    assert(round(threshold_triangle(
        text.astype(np.float), nbins=int_bins)) == 104)
    # Check that rescaling image to floats in unit interval is equivalent.
    assert(round(threshold_triangle(text / 255., nbins=int_bins) * 255) == 104)
    # Repeat for inverted image.
    assert(round(threshold_triangle(
        np.invert(text).astype(np.float), nbins=int_bins)) == 151)
    assert (round(threshold_triangle(
        np.invert(text) / 255., nbins=int_bins) * 255) == 151)
Esempio n. 3
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def test_triangle_flip():
    # Depending on the skewness, the algorithm flips the histogram.
    # We check that the flip doesn't affect too much the result.
    img = data.camera()
    inv_img = np.invert(img)
    t = threshold_triangle(inv_img)
    t_inv_img = inv_img > t
    t_inv_inv_img = np.invert(t_inv_img)

    t = threshold_triangle(img)
    t_img = img > t

    # Check that most of the pixels are identical
    # See numpy #7685 for a future np.testing API
    unequal_pos = np.where(t_img.ravel() != t_inv_inv_img.ravel())
    assert(len(unequal_pos[0]) / t_img.size < 1e-2)
Esempio n. 4
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def test_triangle_flip():
    # Depending on the skewness, the algorithm flips the histogram.
    # We check that the flip doesn't affect too much the result.
    img = data.camera()
    inv_img = np.invert(img)
    t = threshold_triangle(inv_img)
    t_inv_img = inv_img > t
    t_inv_inv_img = np.invert(t_inv_img)

    t = threshold_triangle(img)
    t_img = img > t

    # Check that most of the pixels are identical
    # See numpy #7685 for a future np.testing API
    unequal_pos = np.where(t_img.ravel() != t_inv_inv_img.ravel())
    assert (len(unequal_pos[0]) / t_img.size < 1e-2)
Esempio n. 5
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def test_triangle_uint_images():
    assert(threshold_triangle(np.invert(data.text())) == 151)
    assert(threshold_triangle(data.text()) == 104)
    assert(threshold_triangle(data.coins()) == 80)
    assert(threshold_triangle(np.invert(data.coins())) == 175)
Esempio n. 6
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def test_triangle_uint_images():
    assert (threshold_triangle(np.invert(data.text())) == 151)
    assert (threshold_triangle(data.text()) == 104)
    assert (threshold_triangle(data.coins()) == 80)
    assert (threshold_triangle(np.invert(data.coins())) == 175)
Esempio n. 7
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def test_triangle_images():
    assert threshold_triangle(np.invert(data.text())) == 151
    assert threshold_triangle(data.text()) == 104
    assert threshold_triangle(data.coins()) == 80
    assert threshold_triangle(np.invert(data.coins())) == 175