计算方差图像python

问题描述

有没有一种简单的方法来计算使用Python / NumPy / Scipy的图像上的运行方差过滤器？通过运行方差图像，我的意思是计算图像中每个子窗口I的总和（（I - mean（I））^ 2）/ nPixels。

由于图像相当大（12000x12000像素），我想要避免格式之间转换数组的开销，只是为了能够使用不同的库，然后转换回来。

  kernel = np.ones（（winSize，winSize ））/ winSize ** 2 
 image_mean = scipy.ndimage.convolve（image，kernel）
 diff =（image-image_mean）** 2 
＃计算winSize *图片
＃子样本结果
  

但是有更好的东西像stdfilt-功能从Matlab。

任何人都可以指向我有一个函数库的方向，并支持numpy数组，或提示在NumPy / SciPy？

解决更简单的解决方案，也更快：使用SciPy的

  import numpy as np 
 from scipy import ndimage 
 rows，cols = 500，500 
 win_rows，win_cols = 5，5 
 
 img = np。 random.rand（rows，cols）
 win_mean = ndimage.uniform_filter（img，（win_rows，win_cols））
 win_sqr_mean = ndimage.uniform_filter（img ** 2，（win_rows，win_cols））
 win_var = win_sqr_mean  -  win_mean ** 2 
  

而不是可读性。
generic_filter 比步幅慢20倍...

Is there an easy way to calculate a running variance filter on an image using Python/NumPy/Scipy? By running variance image I mean the result of calculating sum((I - mean(I))^2)/nPixels for each sub-window I in the image.

Since the images are quite large (12000x12000 pixels), I want to avoid the overhead of converting the arrays between formats just to be able to use a different library and then convert back.

I guess I could do this manually by finding the mean using something like

kernel = np.ones((winSize, winSize))/winSize**2
image_mean = scipy.ndimage.convolve(image, kernel)
diff = (image - image_mean)**2
# Calculate sum over winSize*winSize sub-images
# Subsample result

but it would be much nicer to have something like the stdfilt-function from Matlab.

Can anyone point me in the direction of a library that has this functionality AND supports numpy arrays, or hint at/provide a way to do this in NumPy/SciPy?

解决方案

Simpler solution and also faster: use SciPy's ndimage.uniform_filter

import numpy as np
from scipy import ndimage
rows, cols = 500, 500
win_rows, win_cols = 5, 5

img = np.random.rand(rows, cols)
win_mean = ndimage.uniform_filter(img, (win_rows, win_cols))
win_sqr_mean = ndimage.uniform_filter(img**2, (win_rows, win_cols))
win_var = win_sqr_mean - win_mean**2

The "stride trick" is beautiful trick, but 4 slower and not that readable.the generic_filter is 20 times slower than the strides...

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