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_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)
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)
.. image:: PLOT2RST.current_figure
another way to maximize the number of greylevels used for an image is to apply
a local autoleveling, i.e. here a pixel greylevel is proportionally remapped
between local minimum and local maximum.
The following example shows how local autolevel enhances the camara man picture.
"""
from skimage.filter.rank import autolevel
ima = data.camera()
selem = disk(10)
auto = autolevel(ima.astype(np.uint16), disk(20))
# display results
fig = plt.figure(figsize=[10, 7])
plt.subplot(1, 2, 1)
plt.imshow(ima, cmap=plt.cm.gray)
plt.xlabel('original')
plt.subplot(1, 2, 2)
plt.imshow(auto, cmap=plt.cm.gray)
plt.xlabel('local autolevel')
"""
.. image:: PLOT2RST.current_figure
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
.. image:: PLOT2RST.current_figure
another way to maximize the number of greylevels used for an image is to apply
a local autoleveling, i.e. here a pixel greylevel is proportionally remapped
between local minimum and local maximum.
The following example shows how local autolevel enhances the camara man picture.
"""
from skimage.filter.rank import autolevel
ima = data.camera()
selem = disk(10)
auto = autolevel(ima.astype(np.uint16), disk(20))
# display results
fig = plt.figure(figsize=[10, 7])
plt.subplot(1, 2, 1)
plt.imshow(ima, cmap=plt.cm.gray)
plt.xlabel('original')
plt.subplot(1, 2, 2)
plt.imshow(auto, cmap=plt.cm.gray)
plt.xlabel('local autolevel')
"""
.. image:: PLOT2RST.current_figure
This filter is very sensitive to local outlayers, see the little white spot in
def autolevel(image, disk_size):
return rank.autolevel(image, disk(disk_size))
glob_hist = np.histogram(glob, bins=np.arange(0, 256))
loc_hist = np.histogram(loc, bins=np.arange(0, 256))
fig, ax = plt.subplots(4, 2, figsize=(10, 10))
fig.suptitle('George O. Barros - Alguns Filtros (scikit-image)')
ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8 = ax.ravel()
ax1.imshow(noisy_image, interpolation='nearest', cmap=plt.cm.gray)
ax1.set_title('imagens')
ax1.axis('off')
ax2.plot(hist[1][:-1], hist[0], lw=2)
ax2.set_title('Histogram')
ax3.imshow(glob, interpolation='nearest', cmap=plt.cm.gray)
ax3.axis('off')
ax4.plot(glob_hist[1][:-1], glob_hist[0], lw=2)
ax5.imshow(loc, interpolation='nearest', cmap=plt.cm.gray)
ax5.axis('off')
ax6.plot(loc_hist[1][:-1], loc_hist[0], lw=2)
auto = autolevel(noisy_image.astype(np.uint16), disk(20))
ax7.imshow(auto, cmap=plt.cm.gray)
ax7.axis('off')
histAutoLevel= np.histogram(auto, bins=np.arange(0, 256))
ax8.plot(histAutoLevel[1][:-1], histAutoLevel[0], lw=2)
plt.show()
def autolevel(image, disk_size):
return rank.autolevel(image, disk(disk_size))
