Sliding window using as_strided function in numpy?

Question

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

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])

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)

and the step represents the displacement from between two consecutive windows.

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.

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?

Thanks

Answer 1

There is an issue in your code. Actually this code work good for 2D and no reason to use multi dimensional version (Using strides for an efficient moving average filter). Below is a fixed version:

A = np.arange(100).reshape((10, 10))
print A
all_windows = as_strided(A, ((A.shape[0] - xsize + 1) / xstep, (A.shape[1] - ysize + 1) / ystep, xsize, ysize),
      (A.strides[0] * xstep, A.strides[1] * ystep, A.strides[0], A.strides[1]))
print all_windows