def noise_filter_image(image_array,
windows_size,
window_shape="square",
im_show=True):
"""Noise feature.
Parameters
----------
image_array:numpy.array:
input image
windows_size:int
the size of window
window_shape: str
str is element from dict:{square, disk}
im_show : Bool
if True show result
"""
check_windows_size(windows_size)
kernel = make_kernel(window_shape, windows_size)
#noise
img = noise_filter(image_array, kernel)
#show image
if im_show:
show_image(img)
def check_all():
selem = morphology.disk(1)
refs = np.load(
os.path.join(skimage.data_dir, "rank_filter_tests.npz"))
assert_equal(refs["autolevel"], rank.autolevel(self.image, selem))
assert_equal(refs["autolevel_percentile"],
rank.autolevel_percentile(self.image, selem))
assert_equal(refs["bottomhat"], rank.bottomhat(self.image, selem))
assert_equal(refs["equalize"], rank.equalize(self.image, selem))
assert_equal(refs["gradient"], rank.gradient(self.image, selem))
assert_equal(refs["gradient_percentile"],
rank.gradient_percentile(self.image, selem))
assert_equal(refs["maximum"], rank.maximum(self.image, selem))
assert_equal(refs["mean"], rank.mean(self.image, selem))
assert_equal(refs["geometric_mean"],
rank.geometric_mean(self.image, selem)),
assert_equal(refs["mean_percentile"],
rank.mean_percentile(self.image, selem))
assert_equal(refs["mean_bilateral"],
rank.mean_bilateral(self.image, selem))
assert_equal(refs["subtract_mean"],
rank.subtract_mean(self.image, selem))
assert_equal(refs["subtract_mean_percentile"],
rank.subtract_mean_percentile(self.image, selem))
assert_equal(refs["median"], rank.median(self.image, selem))
assert_equal(refs["minimum"], rank.minimum(self.image, selem))
assert_equal(refs["modal"], rank.modal(self.image, selem))
assert_equal(refs["enhance_contrast"],
rank.enhance_contrast(self.image, selem))
assert_equal(refs["enhance_contrast_percentile"],
rank.enhance_contrast_percentile(self.image, selem))
assert_equal(refs["pop"], rank.pop(self.image, selem))
assert_equal(refs["pop_percentile"],
rank.pop_percentile(self.image, selem))
assert_equal(refs["pop_bilateral"],
rank.pop_bilateral(self.image, selem))
assert_equal(refs["sum"], rank.sum(self.image, selem))
assert_equal(refs["sum_bilateral"],
rank.sum_bilateral(self.image, selem))
assert_equal(refs["sum_percentile"],
rank.sum_percentile(self.image, selem))
assert_equal(refs["threshold"], rank.threshold(self.image, selem))
assert_equal(refs["threshold_percentile"],
rank.threshold_percentile(self.image, selem))
assert_equal(refs["tophat"], rank.tophat(self.image, selem))
assert_equal(refs["noise_filter"],
rank.noise_filter(self.image, selem))
assert_equal(refs["entropy"], rank.entropy(self.image, selem))
assert_equal(refs["otsu"], rank.otsu(self.image, selem))
assert_equal(refs["percentile"],
rank.percentile(self.image, selem))
assert_equal(refs["windowed_histogram"],
rank.windowed_histogram(self.image, selem))
def check_all():
np.random.seed(0)
image = np.random.rand(25, 25)
selem = morphology.disk(1)
refs = np.load(os.path.join(skimage.data_dir, "rank_filter_tests.npz"))
assert_equal(refs["autolevel"], rank.autolevel(image, selem))
assert_equal(refs["autolevel_percentile"], rank.autolevel_percentile(image, selem))
assert_equal(refs["bottomhat"], rank.bottomhat(image, selem))
assert_equal(refs["equalize"], rank.equalize(image, selem))
assert_equal(refs["gradient"], rank.gradient(image, selem))
assert_equal(refs["gradient_percentile"], rank.gradient_percentile(image, selem))
assert_equal(refs["maximum"], rank.maximum(image, selem))
assert_equal(refs["mean"], rank.mean(image, selem))
assert_equal(refs["mean_percentile"], rank.mean_percentile(image, selem))
assert_equal(refs["mean_bilateral"], rank.mean_bilateral(image, selem))
assert_equal(refs["subtract_mean"], rank.subtract_mean(image, selem))
assert_equal(refs["subtract_mean_percentile"], rank.subtract_mean_percentile(image, selem))
assert_equal(refs["median"], rank.median(image, selem))
assert_equal(refs["minimum"], rank.minimum(image, selem))
assert_equal(refs["modal"], rank.modal(image, selem))
assert_equal(refs["enhance_contrast"], rank.enhance_contrast(image, selem))
assert_equal(refs["enhance_contrast_percentile"], rank.enhance_contrast_percentile(image, selem))
assert_equal(refs["pop"], rank.pop(image, selem))
assert_equal(refs["pop_percentile"], rank.pop_percentile(image, selem))
assert_equal(refs["pop_bilateral"], rank.pop_bilateral(image, selem))
assert_equal(refs["sum"], rank.sum(image, selem))
assert_equal(refs["sum_bilateral"], rank.sum_bilateral(image, selem))
assert_equal(refs["sum_percentile"], rank.sum_percentile(image, selem))
assert_equal(refs["threshold"], rank.threshold(image, selem))
assert_equal(refs["threshold_percentile"], rank.threshold_percentile(image, selem))
assert_equal(refs["tophat"], rank.tophat(image, selem))
assert_equal(refs["noise_filter"], rank.noise_filter(image, selem))
assert_equal(refs["entropy"], rank.entropy(image, selem))
assert_equal(refs["otsu"], rank.otsu(image, selem))
assert_equal(refs["percentile"], rank.percentile(image, selem))
assert_equal(refs["windowed_histogram"], rank.windowed_histogram(image, selem))
# 提供图像恢复功能
import numpy as np
from skimage import io, data, util, restoration
from scipy.signal import convolve2d # 用于psf
from skimage.filters.rank import noise_filter
from matplotlib import pyplot as plt
from skimage.morphology import disk
img = data.camera()
noiseGaus = util.random_noise(image=img, mode='gaussian') # 加高斯噪声, 有各种可选参数
noiseSP = util.random_noise(image=img, mode='s&p') # 椒盐噪声
filter = noise_filter(noiseGaus, selem=disk(5)) # 效果不好 可能是出了问题
def wiener_restoration(noisePicture):
# wiener滤波复原
psf = np.ones((5, 5)) / 25
img = convolve2d(noisePicture, psf, 'same')
img += 0.1 * img.std() * np.random.standard_normal(img.shape)
deconvolved_img = restoration.wiener(image=img, psf=psf, balance=1100)
return deconvolved_img
def show_pictures():
plt.figure()
plt.gray()
