Python element-wise condition on numpy ndarray-CodePudding

I have two numpy ndarrays of the same shape.

A = [[12, 25, 6],
    [28, 52, 74]]
B = [[100, 2, 4],
    [2, 12, 14]]

My goal is to replace every element where there the value in B is <= 5 by 0 in A. So my result should be :

# So C[0][0] = 12 because A[0][0] = 12 and B[0][0] >= 5
C = [[12, 0, 0],
    [0, 52, 74]]

Is there an efficient way to do this? For context, this is to try to do some background substraction on images, and replace all background by black color.

CodePudding user response：

Here you go:

A = np.array([[12, 25, 6],[28, 52, 74]])
B = np.array([[100, 2, 4],[2, 12, 14]])

A = np.where(B <= 5, 0, A)

Output:

array([[12,  0,  0],
       [ 0, 52, 74]])

CodePudding user response：

If you want a new array, I would do this:

C = A.copy()
C[B <= 5] = 0

It's a bit faster than np.where() on my machine anyway.

If you don't mind overwriting A, just do A[B <= 5] = 0.