예제 #1
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def test_compare_autolevels_16bit():
    # compare autolevel(16-bit) and percentile autolevel(16-bit) with p0=0.0
    # and p1=1.0 should returns the same arrays

    image = data.camera().astype(np.uint16) * 4

    selem = disk(20)
    loc_autolevel = rank.autolevel(image, selem=selem)
    loc_perc_autolevel = rank.autolevel_percentile(image, selem=selem,
                                                   p0=.0, p1=1.)

    assert_array_equal(loc_autolevel, loc_perc_autolevel)
예제 #2
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def test_compare_autolevels():
    # compare autolevel and percentile autolevel with p0=0.0 and p1=1.0
    # should returns the same arrays

    image = util.img_as_ubyte(data.camera())

    selem = disk(20)
    loc_autolevel = rank.autolevel(image, selem=selem)
    loc_perc_autolevel = rank.autolevel_percentile(image, selem=selem,
                                                   p0=.0, p1=1.)

    assert_array_equal(loc_autolevel, loc_perc_autolevel)
예제 #3
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def test_compare_autolevels_16bit():
    # compare autolevel(16-bit) and percentile autolevel(16-bit) with p0=0.0
    # and p1=1.0 should returns the same arrays

    image = data.camera().astype(np.uint16) * 4

    selem = disk(20)
    loc_autolevel = rank.autolevel(image, selem=selem)
    loc_perc_autolevel = rank.autolevel_percentile(image, selem=selem,
                                                   p0=.0, p1=1.)

    assert_array_equal(loc_autolevel, loc_perc_autolevel)
예제 #4
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def test_compare_autolevels():
    # compare autolevel and percentile autolevel with p0=0.0 and p1=1.0
    # should returns the same arrays

    image = util.img_as_ubyte(data.camera())

    selem = disk(20)
    loc_autolevel = rank.autolevel(image, selem=selem)
    loc_perc_autolevel = rank.autolevel_percentile(image, selem=selem,
                                                   p0=.0, p1=1.)

    assert_array_equal(loc_autolevel, loc_perc_autolevel)
예제 #5
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the left part of the sky. This is due to a local maximum which is very high
comparing to the rest of the neighborhood. One can moderate this using the
percentile version of the auto-level filter which uses given percentiles (one
inferior, one superior) in place of local minimum and maximum. The example
below illustrates how the percentile parameters influence the local auto-level
result.

"""

from skimage.filter.rank import autolevel_percentile

image = data.camera()

selem = disk(20)
loc_autolevel = autolevel(image, selem=selem)
loc_perc_autolevel0 = autolevel_percentile(image, selem=selem, p0=.00, p1=1.0)
loc_perc_autolevel1 = autolevel_percentile(image, selem=selem, p0=.01, p1=.99)
loc_perc_autolevel2 = autolevel_percentile(image, selem=selem, p0=.05, p1=.95)
loc_perc_autolevel3 = autolevel_percentile(image, selem=selem, p0=.1, p1=.9)

fig, axes = plt.subplots(nrows=3, figsize=(7, 8))
ax0, ax1, ax2 = axes
plt.gray()

ax0.imshow(np.hstack((image, loc_autolevel)))
ax0.set_title('Original / auto-level')

ax1.imshow(np.hstack((loc_perc_autolevel0, loc_perc_autolevel1)),
           vmin=0,
           vmax=255)
ax1.set_title('Percentile auto-level 0%,1%')
예제 #6
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the left part of the sky. This is due to a local maximum which is very high
comparing to the rest of the neighborhood. One can moderate this using the
percentile version of the auto-level filter which uses given percentiles (one
inferior, one superior) in place of local minimum and maximum. The example
below illustrates how the percentile parameters influence the local auto-level
result.

"""

from skimage.filter.rank import autolevel_percentile

image = data.camera()

selem = disk(20)
loc_autolevel = autolevel(image, selem=selem)
loc_perc_autolevel0 = autolevel_percentile(image, selem=selem, p0=.00, p1=1.0)
loc_perc_autolevel1 = autolevel_percentile(image, selem=selem, p0=.01, p1=.99)
loc_perc_autolevel2 = autolevel_percentile(image, selem=selem, p0=.05, p1=.95)
loc_perc_autolevel3 = autolevel_percentile(image, selem=selem, p0=.1, p1=.9)

fig, axes = plt.subplots(nrows=3, figsize=(7, 8))
ax0, ax1, ax2 = axes
plt.gray()

ax0.imshow(np.hstack((image, loc_autolevel)))
ax0.set_title('Original / auto-level')

ax1.imshow(
    np.hstack((loc_perc_autolevel0, loc_perc_autolevel1)), vmin=0, vmax=255)
ax1.set_title('Percentile auto-level 0%,1%')
ax2.imshow(