Question

我试图过滤图像，使每个像素的值等于围绕它的50x50平方内像素的中值，不包括任何蒙板像素。这是我最近的尝试：

从FITS文件中读取图像（看起来像这样......）
从其他FITS文件应用遮罩
通过屏蔽图像（下面的蒙版图像）传递一个50x50像素的窗口（我认为这是最好的方法...对建议开放）
创建蒙版图像的过滤副本，每个像素的值等于围绕它的50x50平方内像素的中位数值，不包括任何蒙版像素

在此处的代码中，我使用了skimage.util.view_as_windows文档中的一些方法生成过滤后的图像：

在我看来，它忽略了蒙面像素。我的问题是双重的：这是最好的方法吗？如果是这样，为什么它看起来像忽略了面具？

import numpy as np
from astropy.io import fits
from skimage.util.shape import view_as_windows

#   Use the fits files as input image and mask
hdulist = fits.open('xbulge-w1.fits')
image = hdulist[0].data
hdulist3 = fits.open('xbulge-mask.fits')
mask = 1 - hdulist3[0].data
imagemasked = np.ma.masked_array(image, mask = mask)

side = 50
window_shape = (side, side)

Afiltered = view_as_windows(imagemasked, window_shape)

# collapse the last two dimensions in one
flatten_view = Afiltered.reshape(Afiltered.shape[0], Afiltered.shape[1], -1)

# resampling the image by taking median
median_view = np.ma.median(flatten_view, axis=2)

注意：使用＆＃39; side = 50＆＃39;导致相当长的运行时间，因此出于测试目的，我倾向于将其减少到10到25个。

Answer 1

在python中有许多过滤器具有不同的行为，例如对于平均过滤器：

x=np.array([[0.1,0.8,.2],
            [0.5,0.2,np.nan],
            [0.7,0.2,0.9],
            [0.4,0.7,1],
            [np.nan,0.14,1]])

print(uniform_filter(x, size=3, mode='constant'))

[[ 0.17777778         nan         nan]
 [ 0.27777778         nan         nan]
 [ 0.3                nan         nan]
 [        nan         nan         nan]
 [        nan         nan         nan]]

或

from skimage.filters.rank import mean
from skimage.morphology import square
from skimage import img_as_float
x=np.array([[0.1,0.8,.2],
        [0.5,0.2,np.nan],
        [0.7,0.2,0.9],
        [0.4,0.7,1],
        [np.nan,0.14,1]])

print(mean(x, square(3)))

[[102  76  76]
 [106 102  97]
 [114 130 127]
 [ 90 142 167]
 [ 79 137 181]]

print(img_as_float(mean(x, square(3))))

[[ 0.4         0.29803922  0.29803922]
 [ 0.41568627  0.4         0.38039216]
 [ 0.44705882  0.50980392  0.49803922]
 [ 0.35294118  0.55686275  0.65490196]
 [ 0.30980392  0.5372549   0.70980392]]

skimage剂量不支持nan和masking：refrence 或

import numpy as np
# from scipy.signal import convolve
from scipy.signal import convolve2d

x=np.array([[0.1,0.8,.2],
            [0.5,0.2,np.nan],
            [0.7,0.2,0.9],
            [0.4,0.7,1],
            [np.nan,0.14,1]])

core = np.full((3,3),1/3**2)

# convolve(x, core, mode='same')
convolve2d(x, core, mode='same')

[[ 0.17777778         nan         nan]
 [ 0.27777778         nan         nan]
 [ 0.3                nan         nan]
 [        nan         nan  0.43777778]
 [        nan         nan  0.31555556]]

在蒙版图像

1 个答案: