I have two numpy ndarrays of the same shape (15081, 56724, 3, 3). What I want to do is as follows:
Say we have a cross section of the first array, array1[1, 1, :, :], looks like this:
[[120, 110, 220], [ 85, 99, 72], [197, 80, 75]]
I want to convert it to a boolean in a way that the max of each row is True and the rest is False. In the whole array, this corresponds to axis=3. So the array looks like this after conversion:
[[False, False, True], [False, True, False], [ True, False, False]]
Now I want to filter the other array, array2, using this boolean array to have something that looks like below. I only want to keeping those values of array2 that correspond to True in array1 and set the rest to zero.
[[ 0, 0, 65], [ 0, 179, 0], [125, 0, 0]]
I can do this using a loop but it takes an age (even more).
I expect something like numpy.where(array1.is_max(axis=3), True, False), but there is no function like is_max in python, besides this way the axis 3 is collapsed and I cannot filter array2 using array1.
Advertisement
Answer
In numpy, is_max is approximately argmax:
indices = array1.argmax(axis=-1)
Instead of a mask, this will give you a linear index, so you can do
array2b = np.zeros_like(array2) index = np.indices(array2.shape[:-1], sparse=True) + (indices,) array2b[index] = array1[index]
You can do something similar with np.take_along_axis and np.put_along_axis:
indices = np.expand_dims(array1.argmax(-1), array1.ndim - 1) array2b = np.zeros_like(array2) np.put_along_axis(array2b, indices, np.take_along_axis(array2, indices, -1), -1)
If you want a mask-based approach, you can create the mask like this:
mask = array1 != array1.max(-1, keepdims=True)
Now you can set all elements to zero directly:
array2[mask] = 0
Alternatively you can do something like
mask = array1 == array1.max(-1, keepdims=True) array2 *= mask
Update
From your description in the comments, you are looking for a different operation entirely. You can start by thresholding array1 (which I assume represents the difference between the blurred and original image):
mask = array1 >= 100 # find a threshold that works array2 *= mask
OR
mask = array1 < 100 array2[mask] = 0
You may also be looking for local minima in an image. You can get those by finding pixels that are bigger than their surroundings. To do that, run a non-linear filter on the image, like scipy.ndimage.maximum_filter:
mask = array1 == scipy.ndimage.maximum_filter(array1, 5) array2 *= mask