def test_pepper():
seed = 42
cam = img_as_float(camerad)
data_signed = cam * 2.0 - 1.0 # Same image, on range [-1, 1]
cam_noisy = random_noise(cam, seed=seed, mode='pepper', amount=0.15)
peppermask = cam != cam_noisy
# Ensure all changes are to 1.0
assert_allclose(cam_noisy[peppermask], cp.zeros(int(peppermask.sum())))
# Ensure approximately correct amount of noise was added
proportion = float(peppermask.sum()) / (cam.shape[0] * cam.shape[1])
assert 0.11 < proportion <= 0.15
# Check to make sure pepper gets added properly to signed images
orig_zeros = (data_signed == -1).sum()
cam_noisy_signed = random_noise(data_signed,
seed=seed,
mode='pepper',
amount=.15)
proportion = (float((cam_noisy_signed == -1).sum() - orig_zeros) /
(cam.shape[0] * cam.shape[1]))
assert 0.11 < proportion <= 0.15
def test_localvar():
seed = 42
data = cp.zeros((128, 128)) + 0.5
local_vars = cp.zeros((128, 128)) + 0.001
local_vars[:64, 64:] = 0.1
local_vars[64:, :64] = 0.25
local_vars[64:, 64:] = 0.45
data_gaussian = random_noise(data,
mode='localvar',
seed=seed,
local_vars=local_vars,
clip=False)
assert 0.0 < data_gaussian[:64, :64].var() < 0.002
assert 0.095 < data_gaussian[:64, 64:].var() < 0.105
assert 0.245 < data_gaussian[64:, :64].var() < 0.255
assert 0.445 < data_gaussian[64:, 64:].var() < 0.455
# Ensure local variance bounds checking works properly
bad_local_vars = cp.zeros_like(data)
with pytest.raises(ValueError):
random_noise(data,
mode='localvar',
seed=seed,
local_vars=bad_local_vars)
bad_local_vars += 0.1
bad_local_vars[0, 0] = -1
with pytest.raises(ValueError):
random_noise(data,
mode='localvar',
seed=seed,
local_vars=bad_local_vars)
def test_gaussian():
seed = 42
data = cp.zeros((128, 128)) + 0.5
data_gaussian = random_noise(data, seed=seed, var=0.01)
assert 0.008 < data_gaussian.var() < 0.012
data_gaussian = random_noise(data, seed=seed, mean=0.3, var=0.015)
assert 0.28 < data_gaussian.mean() - 0.5 < 0.32
assert 0.012 < data_gaussian.var() < 0.018
def test_poisson():
seed = 42
data = camerad # 512x512 grayscale uint8
cam_noisy = random_noise(data, mode='poisson', seed=seed)
cam_noisy2 = random_noise(data, mode='poisson', seed=seed, clip=False)
cp.random.seed(seed=seed)
expected = cp.random.poisson(img_as_float(data) * 256) / 256.0
assert_allclose(cam_noisy, cp.clip(expected, 0.0, 1.0))
assert_allclose(cam_noisy2, expected)
def test_salt():
seed = 42
cam = img_as_float(camerad)
cam_noisy = random_noise(cam, seed=seed, mode='salt', amount=0.15)
saltmask = cam != cam_noisy
# Ensure all changes are to 1.0
assert_allclose(cam_noisy[saltmask], cp.ones(int(saltmask.sum())))
# Ensure approximately correct amount of noise was added
proportion = float(saltmask.sum()) / (cam.shape[0] * cam.shape[1])
assert 0.11 < proportion <= 0.15
def test_speckle():
seed = 42
data = cp.zeros((128, 128)) + 0.1
cp.random.seed(seed=seed)
noise = cp.random.normal(0.1, 0.02**0.5, (128, 128))
expected = cp.clip(data + data * noise, 0, 1)
data_speckle = random_noise(data,
mode='speckle',
seed=seed,
mean=0.1,
var=0.02)
assert_allclose(expected, data_speckle)
def test_clip_speckle():
seed = 42
data = camerad # 512x512 grayscale uint8
data_signed = img_as_float(
data) * 2.0 - 1.0 # Same image, on range [-1, 1]
# Signed and unsigned, clipped
cam_speckle = random_noise(data, mode='speckle', seed=seed, clip=True)
cam_speckle_sig = random_noise(data_signed,
mode='speckle',
seed=seed,
clip=True)
assert (cam_speckle.max() == 1.0) and (cam_speckle.min() == 0.0)
assert (cam_speckle_sig.max() == 1.0) and (cam_speckle_sig.min() == -1.0)
# Signed and unsigned, unclipped
cam_speckle = random_noise(data, mode='speckle', seed=seed, clip=False)
cam_speckle_sig = random_noise(data_signed,
mode='speckle',
seed=seed,
clip=False)
assert (cam_speckle.max() > 1.219) and (cam_speckle.min() == 0.0)
assert (cam_speckle_sig.max() > 1.219) and (cam_speckle_sig.min() < -1.219)
def test_clip_gaussian():
seed = 42
data = camerad # 512x512 grayscale uint8
data_signed = img_as_float(
data) * 2.0 - 1.0 # Same image, on range [-1, 1]
# Signed and unsigned, clipped
cam_gauss = random_noise(data, mode='gaussian', seed=seed, clip=True)
cam_gauss2 = random_noise(data_signed,
mode='gaussian',
seed=seed,
clip=True)
assert (cam_gauss.max() == 1.0) and (cam_gauss.min() == 0.0)
assert (cam_gauss2.max() == 1.0) and (cam_gauss2.min() == -1.0)
# Signed and unsigned, unclipped
cam_gauss = random_noise(data, mode='gaussian', seed=seed, clip=False)
cam_gauss2 = random_noise(data_signed,
mode='gaussian',
seed=seed,
clip=False)
assert (cam_gauss.max() > 1.22) and (cam_gauss.min() < -0.33)
assert (cam_gauss2.max() > 1.219) and (cam_gauss2.min() < -1.219)
def test_clip_poisson():
seed = 42
data = camerad # 512x512 grayscale uint8
data_signed = img_as_float(
data) * 2.0 - 1.0 # Same image, on range [-1, 1]
# Signed and unsigned, clipped
cam_poisson = random_noise(data, mode='poisson', seed=seed, clip=True)
cam_poisson2 = random_noise(data_signed,
mode='poisson',
seed=seed,
clip=True)
assert (cam_poisson.max() == 1.0) and (cam_poisson.min() == 0.0)
assert (cam_poisson2.max() == 1.0) and (cam_poisson2.min() == -1.0)
# Signed and unsigned, unclipped
cam_poisson = random_noise(data, mode='poisson', seed=seed, clip=False)
cam_poisson2 = random_noise(data_signed,
mode='poisson',
seed=seed,
clip=False)
assert (cam_poisson.max() > 1.15) and (cam_poisson.min() == 0.0)
assert (cam_poisson2.max() > 1.3) and (cam_poisson2.min() == -1.0)
def test_salt_and_pepper():
seed = 42
cam = img_as_float(camerad)
cam_noisy = random_noise(cam,
seed=seed,
mode='s&p',
amount=0.15,
salt_vs_pepper=0.25)
saltmask = cp.logical_and(cam != cam_noisy, cam_noisy == 1.)
peppermask = cp.logical_and(cam != cam_noisy, cam_noisy == 0.)
# Ensure all changes are to 0. or 1.
assert_allclose(cam_noisy[saltmask], cp.ones(int(saltmask.sum())))
assert_allclose(cam_noisy[peppermask], cp.zeros(int(peppermask.sum())))
# Ensure approximately correct amount of noise was added
proportion = float(saltmask.sum() + peppermask.sum()) / (cam.shape[0] *
cam.shape[1])
assert 0.11 < proportion <= 0.18
# Verify the relative amount of salt vs. pepper is close to expected
assert 0.18 < saltmask.sum() / peppermask.sum() < 0.35
import numpy as np
import pytest
from skimage.data import camera, chelsea
# from cucim.skimage.restoration import denoise_wavelet
from skimage.restoration import denoise_wavelet
from cucim.skimage.data import binary_blobs
from cucim.skimage.metrics import mean_squared_error as mse
from cucim.skimage.restoration import calibrate_denoiser, denoise_tv_chambolle
from cucim.skimage.restoration.j_invariant import _invariant_denoise
from cucim.skimage.util import img_as_float, random_noise
test_img = img_as_float(cp.asarray(camera()))
test_img_color = img_as_float(cp.asarray(chelsea()))
test_img_3d = img_as_float(binary_blobs(64, n_dim=3)) / 2
noisy_img = random_noise(test_img, mode="gaussian", var=0.01)
noisy_img_color = random_noise(test_img_color, mode="gaussian", var=0.01)
noisy_img_3d = random_noise(test_img_3d, mode="gaussian", var=0.1)
# TODO: replace with CuPy version once completed
def _denoise_wavelet(image, rescale_sigma=True, **kwargs):
return cp.asarray(
denoise_wavelet(cp.asnumpy(image),
rescale_sigma=rescale_sigma,
**kwargs))
def test_invariant_denoise():
# denoised_img = _invariant_denoise(noisy_img, _denoise_wavelet)
denoised_img = _invariant_denoise(noisy_img, denoise_tv_chambolle)
def test_singleton_dim():
"""Ensure images where size of a given dimension is 1 work correctly."""
image = cp.random.rand(1, 20)
noisy = random_noise(image, mode='salt', amount=0.1, seed=42)
assert cp.sum(noisy == 1) == 3 # GRL: modified to match value for CuPy
def test_salt_p1():
image = cp.random.rand(2, 3)
noisy = random_noise(image, mode='salt', amount=1)
assert_array_equal(noisy, [[1, 1, 1], [1, 1, 1]])
def test_bad_mode():
data = cp.zeros((64, 64))
with pytest.raises(KeyError):
random_noise(data, 'perlin')
def test_set_seed():
seed = 42
cam = cp.asarray(camerad)
test = random_noise(cam, seed=seed)
assert_array_equal(test, random_noise(cam, seed=seed))
