mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-25 13:30:51 +08:00
Removed test file that has been moved to restoration submodule
This commit is contained in:
emmanuelle
@@ -1,169 +0,0 @@
|
||||
import numpy as np
|
||||
from numpy.testing import run_module_suite, assert_raises, assert_equal
|
||||
|
||||
from skimage import filter, data, color, img_as_float
|
||||
|
||||
|
||||
np.random.seed(1234)
|
||||
|
||||
|
||||
lena = img_as_float(data.lena()[:256, :256])
|
||||
lena_gray = color.rgb2gray(lena)
|
||||
|
||||
|
||||
def test_denoise_tv_chambolle_2d():
|
||||
# lena image
|
||||
img = lena_gray
|
||||
# add noise to lena
|
||||
img += 0.5 * img.std() * np.random.random(img.shape)
|
||||
# clip noise so that it does not exceed allowed range for float images.
|
||||
img = np.clip(img, 0, 1)
|
||||
# denoise
|
||||
denoised_lena = filter.denoise_tv_chambolle(img, weight=60.0)
|
||||
# which dtype?
|
||||
assert denoised_lena.dtype in [np.float, np.float32, np.float64]
|
||||
from scipy import ndimage
|
||||
grad = ndimage.morphological_gradient(img, size=((3, 3)))
|
||||
grad_denoised = ndimage.morphological_gradient(
|
||||
denoised_lena, size=((3, 3)))
|
||||
# test if the total variation has decreased
|
||||
assert grad_denoised.dtype == np.float
|
||||
assert (np.sqrt((grad_denoised**2).sum())
|
||||
< np.sqrt((grad**2).sum()) / 2)
|
||||
|
||||
|
||||
def test_denoise_tv_chambolle_multichannel():
|
||||
denoised0 = filter.denoise_tv_chambolle(lena[..., 0], weight=60.0)
|
||||
denoised = filter.denoise_tv_chambolle(lena, weight=60.0, multichannel=True)
|
||||
assert_equal(denoised[..., 0], denoised0)
|
||||
|
||||
|
||||
def test_denoise_tv_chambolle_float_result_range():
|
||||
# lena image
|
||||
img = lena_gray
|
||||
int_lena = np.multiply(img, 255).astype(np.uint8)
|
||||
assert np.max(int_lena) > 1
|
||||
denoised_int_lena = filter.denoise_tv_chambolle(int_lena, weight=60.0)
|
||||
# test if the value range of output float data is within [0.0:1.0]
|
||||
assert denoised_int_lena.dtype == np.float
|
||||
assert np.max(denoised_int_lena) <= 1.0
|
||||
assert np.min(denoised_int_lena) >= 0.0
|
||||
|
||||
|
||||
def test_denoise_tv_chambolle_3d():
|
||||
"""Apply the TV denoising algorithm on a 3D image representing a sphere."""
|
||||
x, y, z = np.ogrid[0:40, 0:40, 0:40]
|
||||
mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2
|
||||
mask = 100 * mask.astype(np.float)
|
||||
mask += 60
|
||||
mask += 20 * np.random.random(mask.shape)
|
||||
mask[mask < 0] = 0
|
||||
mask[mask > 255] = 255
|
||||
res = filter.denoise_tv_chambolle(mask.astype(np.uint8), weight=100)
|
||||
assert res.dtype == np.float
|
||||
assert res.std() * 255 < mask.std()
|
||||
|
||||
# test wrong number of dimensions
|
||||
assert_raises(ValueError, filter.denoise_tv_chambolle,
|
||||
np.random.random((8, 8, 8, 8)))
|
||||
|
||||
|
||||
def test_denoise_tv_bregman_2d():
|
||||
img = lena_gray
|
||||
# add some random noise
|
||||
img += 0.5 * img.std() * np.random.random(img.shape)
|
||||
img = np.clip(img, 0, 1)
|
||||
|
||||
out1 = filter.denoise_tv_bregman(img, weight=10)
|
||||
out2 = filter.denoise_tv_bregman(img, weight=5)
|
||||
|
||||
# make sure noise is reduced
|
||||
assert img.std() > out1.std()
|
||||
assert out1.std() > out2.std()
|
||||
|
||||
|
||||
def test_denoise_tv_bregman_float_result_range():
|
||||
# lena image
|
||||
img = lena_gray
|
||||
int_lena = np.multiply(img, 255).astype(np.uint8)
|
||||
assert np.max(int_lena) > 1
|
||||
denoised_int_lena = filter.denoise_tv_bregman(int_lena, weight=60.0)
|
||||
# test if the value range of output float data is within [0.0:1.0]
|
||||
assert denoised_int_lena.dtype == np.float
|
||||
assert np.max(denoised_int_lena) <= 1.0
|
||||
assert np.min(denoised_int_lena) >= 0.0
|
||||
|
||||
|
||||
def test_denoise_tv_bregman_3d():
|
||||
img = lena
|
||||
# add some random noise
|
||||
img += 0.5 * img.std() * np.random.random(img.shape)
|
||||
img = np.clip(img, 0, 1)
|
||||
|
||||
out1 = filter.denoise_tv_bregman(img, weight=10)
|
||||
out2 = filter.denoise_tv_bregman(img, weight=5)
|
||||
|
||||
# make sure noise is reduced
|
||||
assert img.std() > out1.std()
|
||||
assert out1.std() > out2.std()
|
||||
|
||||
|
||||
def test_denoise_bilateral_2d():
|
||||
img = lena_gray
|
||||
# add some random noise
|
||||
img += 0.5 * img.std() * np.random.random(img.shape)
|
||||
img = np.clip(img, 0, 1)
|
||||
|
||||
out1 = filter.denoise_bilateral(img, sigma_range=0.1, sigma_spatial=20)
|
||||
out2 = filter.denoise_bilateral(img, sigma_range=0.2, sigma_spatial=30)
|
||||
|
||||
# make sure noise is reduced
|
||||
assert img.std() > out1.std()
|
||||
assert out1.std() > out2.std()
|
||||
|
||||
|
||||
def test_denoise_bilateral_3d():
|
||||
img = lena
|
||||
# add some random noise
|
||||
img += 0.5 * img.std() * np.random.random(img.shape)
|
||||
img = np.clip(img, 0, 1)
|
||||
|
||||
out1 = filter.denoise_bilateral(img, sigma_range=0.1, sigma_spatial=20)
|
||||
out2 = filter.denoise_bilateral(img, sigma_range=0.2, sigma_spatial=30)
|
||||
|
||||
# make sure noise is reduced
|
||||
assert img.std() > out1.std()
|
||||
assert out1.std() > out2.std()
|
||||
|
||||
|
||||
def test_nl_means_denoising_2d():
|
||||
img = np.zeros((40, 40))
|
||||
img[10:-10, 10:-10] = 1.
|
||||
img += 0.3*np.random.randn(*img.shape)
|
||||
denoised = filter.nl_means_denoising(img, 7, 5, 0.1)
|
||||
# make sure noise is reduced
|
||||
assert img.std() > denoised.std()
|
||||
|
||||
|
||||
def test_nl_means_denoising_2drgb():
|
||||
# reduce image size because nl means is very slow
|
||||
img = lena[-100:, -100:]
|
||||
# add some random noise
|
||||
img += 0.5 * img.std() * np.random.random(img.shape)
|
||||
img = np.clip(img, 0, 1)
|
||||
denoised = filter.nl_means_denoising(img, 7, 9, 0.08)
|
||||
# make sure noise is reduced
|
||||
assert img.std() > denoised.std()
|
||||
|
||||
|
||||
def test_nl_means_denoising_3d():
|
||||
img = np.zeros((20, 20, 10))
|
||||
img[5:-5, 5:-5, 3:-3] = 1.
|
||||
img += 0.3*np.random.randn(*img.shape)
|
||||
denoised = filter.nl_means_denoising(img, 5, 4, 0.1)
|
||||
# make sure noise is reduced
|
||||
assert img.std() > denoised.std()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
run_module_suite()
|
||||
Reference in New Issue
Block a user