在numpy中使用as_strided函数的滑动窗口?
问题描述
当我开始使用 python 实现一个滑动窗口来检测静止图像中的对象时，我开始了解这个不错的功能:
As I get to implement a sliding window using python to detect objects in still images, I get to know the nice function:
numpy.lib.stride_tricks.as_strided
所以我尝试制定一个通用规则，以避免在更改我需要的滑动窗口大小时可能会失败的错误.最后我得到了这个表示:
So I tried to achieve a general rule to avoid mistakes I may fail in while changing the size of the sliding windows I need. Finally I got this representation:
all_windows = as_strided(x,((x.shape[0] - xsize)/xstep ,(x.shape[1] - ysize)/ystep ,xsize,ysize), (x.strides[0]*xstep,x.strides[1]*ystep,x.strides[0],x.strides[1])
这会产生一个 4 暗矩阵.前两个代表图像的 x 和 y 轴上的窗口数.其他的代表窗口的大小(xsize,ysize)
which results in a 4 dim matrix. The first two represents the number of windows on the x and y axis of the image. and the others represent the size of the window (xsize,ysize)
step代表两个连续窗口之间的位移.
and the step represents the displacement from between two consecutive windows.
如果我选择方形滑动窗口，这种表示效果很好.但我仍然有一个问题要让它适用于 e.x. 的 Windows.(128,64)，我通常会在其中获得与图像无关的数据.
This representation works fine if I choose a squared sliding windows. but still I have a problem in getting this to work for windows of e.x. (128,64), where I get usually unrelated data to the image.
我的代码有什么问题.有任何想法吗?是否有更好的方法在 python 中让滑动窗口美观整洁地进行图像处理?
What is wrong my code. Any ideas? and if there is a better way to get a sliding windows nice and neat in python for image processing?
谢谢
解决方案
查看这个问题的答案:使用步幅实现高效的移动平均滤波器.基本上跨步不是一个很好的选择，尽管它们有效.
Check out the answers to this question: Using strides for an efficient moving average filter. Basically strides are not a great option, although they work.
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