擅长:python、mysql、java
<p>更简单、更快的解决方案:使用统一的
这里解释了一个“方差技巧”:<a href="http://imagej.net/Integral_Image_Filters" rel="nofollow">http://imagej.net/Integral_Image_Filters</a>(方差是“平方和”和“和的平方之差”)</p>
<pre><code>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
</code></pre>
<p>通用的过滤器比步伐慢40倍。。。在</p>