def test_random_sizes():
# make sure the size is not a problem
niter = 10
elem = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]], dtype=np.uint8)
for m, n in np.random.random_integers(1, 100, size=(10, 2)):
mask = np.ones((m, n), dtype=np.uint8)
image8 = np.ones((m, n), dtype=np.uint8)
out8 = np.empty_like(image8)
rank.mean(image=image8,
selem=elem,
mask=mask,
out=out8,
shift_x=0,
shift_y=0)
assert_array_equal(image8.shape, out8.shape)
rank.mean(image=image8,
selem=elem,
mask=mask,
out=out8,
shift_x=+1,
shift_y=+1)
assert_array_equal(image8.shape, out8.shape)
image16 = np.ones((m, n), dtype=np.uint16)
out16 = np.empty_like(image8, dtype=np.uint16)
rank.mean(image=image16,
selem=elem,
mask=mask,
out=out16,
shift_x=0,
shift_y=0)
assert_array_equal(image16.shape, out16.shape)
rank.mean(image=image16,
selem=elem,
mask=mask,
out=out16,
shift_x=+1,
shift_y=+1)
assert_array_equal(image16.shape, out16.shape)
rank.percentile_mean(image=image16,
mask=mask,
out=out16,
selem=elem,
shift_x=0,
shift_y=0,
p0=.1,
p1=.9)
assert_array_equal(image16.shape, out16.shape)
rank.percentile_mean(image=image16,
mask=mask,
out=out16,
selem=elem,
shift_x=+1,
shift_y=+1,
p0=.1,
p1=.9)
assert_array_equal(image16.shape, out16.shape)
def test_random_sizes():
# make sure the size is not a problem
niter = 10
elem = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]], dtype=np.uint8)
for m, n in np.random.random_integers(1, 100, size=(10, 2)):
mask = np.ones((m, n), dtype=np.uint8)
image8 = np.ones((m, n), dtype=np.uint8)
out8 = np.empty_like(image8)
rank.mean(image=image8, selem=elem, mask=mask, out=out8,
shift_x=0, shift_y=0)
assert_array_equal(image8.shape, out8.shape)
rank.mean(image=image8, selem=elem, mask=mask, out=out8,
shift_x=+1, shift_y=+1)
assert_array_equal(image8.shape, out8.shape)
image16 = np.ones((m, n), dtype=np.uint16)
out16 = np.empty_like(image8, dtype=np.uint16)
rank.mean(image=image16, selem=elem, mask=mask, out=out16,
shift_x=0, shift_y=0)
assert_array_equal(image16.shape, out16.shape)
rank.mean(image=image16, selem=elem, mask=mask, out=out16,
shift_x=+1, shift_y=+1)
assert_array_equal(image16.shape, out16.shape)
rank.percentile_mean(image=image16, mask=mask, out=out16,
selem=elem, shift_x=0, shift_y=0, p0=.1, p1=.9)
assert_array_equal(image16.shape, out16.shape)
rank.percentile_mean(image=image16, mask=mask, out=out16,
selem=elem, shift_x=+1, shift_y=+1, p0=.1, p1=.9)
assert_array_equal(image16.shape, out16.shape)
def test_selem_dtypes():
image = np.zeros((5, 5), dtype=np.uint8)
out = np.zeros_like(image)
mask = np.ones_like(image, dtype=np.uint8)
image[2, 2] = 255
image[2, 3] = 128
image[1, 2] = 16
for dtype in (np.uint8, np.uint16, np.int32, np.int64, np.float32,
np.float64):
elem = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]], dtype=dtype)
rank.mean(image=image,
selem=elem,
out=out,
mask=mask,
shift_x=0,
shift_y=0)
assert_array_equal(image, out)
rank.percentile_mean(image=image,
selem=elem,
out=out,
mask=mask,
shift_x=0,
shift_y=0)
assert_array_equal(image, out)
def test_selem_dtypes():
image = np.zeros((5, 5), dtype=np.uint8)
out = np.zeros_like(image)
mask = np.ones_like(image, dtype=np.uint8)
image[2, 2] = 255
image[2, 3] = 128
image[1, 2] = 16
for dtype in (np.uint8, np.uint16, np.int32, np.int64,
np.float32, np.float64):
elem = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]], dtype=dtype)
rank.mean(image=image, selem=elem, out=out, mask=mask,
shift_x=0, shift_y=0)
assert_array_equal(image, out)
rank.percentile_mean(image=image, selem=elem, out=out, mask=mask,
shift_x=0, shift_y=0)
assert_array_equal(image, out)
def test_bitdepth():
# test the different bit depth for rank16
elem = np.ones((3, 3), dtype=np.uint8)
out = np.empty((100, 100), dtype=np.uint16)
mask = np.ones((100, 100), dtype=np.uint8)
for i in range(5):
image = np.ones((100, 100), dtype=np.uint16) * 255 * 2 ** i
r = rank.percentile_mean(image=image, selem=elem, mask=mask,
out=out, shift_x=0, shift_y=0, p0=.1, p1=.9)
def test_bitdepth():
# test the different bit depth for rank16
elem = np.ones((3, 3), dtype=np.uint8)
out = np.empty((100, 100), dtype=np.uint16)
mask = np.ones((100, 100), dtype=np.uint8)
for i in range(5):
image = np.ones((100, 100), dtype=np.uint16) * 255 * 2**i
r = rank.percentile_mean(image=image,
selem=elem,
mask=mask,
out=out,
shift_x=0,
shift_y=0,
p0=.1,
p1=.9)
complete image (background and details). Bilateral mean exhibits a high
filtering rate for continuous area (i.e. background) while higher image
frequencies remain untouched.
"""
import numpy as np
import matplotlib.pyplot as plt
from skimage import data
from skimage.morphology import disk
import skimage.filter.rank as rank
a16 = (data.coins()).astype(np.uint16) * 16
selem = disk(20)
f1 = rank.percentile_mean(a16, selem=selem, p0=.1, p1=.9)
f2 = rank.bilateral_mean(a16, selem=selem, s0=500, s1=500)
f3 = rank.mean(a16, selem=selem)
# display results
fig, axes = plt.subplots(nrows=3, figsize=(15, 10))
ax0, ax1, ax2 = axes
ax0.imshow(np.hstack((a16, f1)))
ax0.set_title('percentile mean')
ax1.imshow(np.hstack((a16, f2)))
ax1.set_title('bilateral mean')
ax2.imshow(np.hstack((a16, f3)))
ax2.set_title('local mean')
plt.show()
