def enhance_contrast(rgb):
"""A preprocessing step that enhances the local contrast of each
color channel"""
s = rgb.shape
d = disk(s[0] / 5)
for i in range(3):
rgb[:, :, i] = percentile_autolevel(rgb[:, :, i], d, p0=.1, p1=.9)
return rgb
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.percentile_autolevel(image, selem=selem,
p0=.0, p1=1.)
assert_array_equal(loc_autolevel, loc_perc_autolevel)
def test_compare_autolevels():
# compare autolevel and percentile autolevel with p0=0.0 and p1=1.0
# should returns the same arrays
image = data.camera()
selem = disk(20)
loc_autolevel = rank.autolevel(image, selem=selem)
loc_perc_autolevel = rank.percentile_autolevel(image, selem=selem,
p0=.0, p1=1.)
assert_array_equal(loc_autolevel, loc_perc_autolevel)
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.percentile_autolevel(image,
selem=selem,
p0=.0,
p1=1.)
assert_array_equal(loc_autolevel, loc_perc_autolevel)
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.percentile_autolevel(image,
selem=selem,
p0=.0,
p1=1.)
assert_array_equal(loc_autolevel, loc_perc_autolevel)
This filter is very sensitive to local outlayers, see the little white spot in
the sky left part. 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 autolevel 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 autolevel result.
"""
from skimage.filter.rank import percentile_autolevel
image = data.camera()
selem = disk(20)
loc_autolevel = autolevel(image, selem=selem)
loc_perc_autolevel0 = percentile_autolevel(image, selem=selem, p0=.00, p1=1.0)
loc_perc_autolevel1 = percentile_autolevel(image, selem=selem, p0=.01, p1=.99)
loc_perc_autolevel2 = percentile_autolevel(image, selem=selem, p0=.05, p1=.95)
loc_perc_autolevel3 = percentile_autolevel(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 / autolevel')
ax1.imshow(np.hstack((loc_perc_autolevel0, loc_perc_autolevel1)),
vmin=0,
vmax=255)
ax1.set_title('percentile autolevel 0%,1%')
This filter is very sensitive to local outlayers, see the little white spot in
the sky left part. 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 autolevel 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 autolevel result.
"""
from skimage.filter.rank import percentile_autolevel
image = data.camera()
selem = disk(20)
loc_autolevel = autolevel(image, selem=selem)
loc_perc_autolevel0 = percentile_autolevel(image, selem=selem, p0=.00, p1=1.0)
loc_perc_autolevel1 = percentile_autolevel(image, selem=selem, p0=.01, p1=.99)
loc_perc_autolevel2 = percentile_autolevel(image, selem=selem, p0=.05, p1=.95)
loc_perc_autolevel3 = percentile_autolevel(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 / autolevel')
ax1.imshow(
np.hstack((loc_perc_autolevel0, loc_perc_autolevel1)), vmin=0, vmax=255)
ax1.set_title('percentile autolevel 0%,1%')
ax2.imshow(