在 numpy 数组中切片和在 Python 中切片有什么区别？

Question

If curr_frames is a numpy array, what does the last line mean?

curr_frames = np.array(curr_frames)

idx = map(int,np.linspace(0,len(curr_frames)-1,80))

curr_frames = curr_frames[idx,:,:,:,]

Answer 1

An important distinction from Python's built-in lists to numpy arrays:

• when slicing in the built-in list it creates a copy .

   X=[1,2,3,4,5,6] Y=X[:3] #[1,2,3]

  by slicing X from 0-3 we have created a copy and stored it in the variable Y.

we can verify that by changing the Y and even if we change Y it does not effect X.

    Y[0]=20
    print(Y) # [20,2,3]
    print(X) # [1,2,3,4,5,6]

• when slicing in numpy doesn't create a new copy but it still referring to original array

  A=np.array([1,2,3,4,5,6]) B=A[:3]

By slicing A here and assigning it to B, still B referring to original array A.

We can verify that by changing an element in B and it will change the value in A as well.

    B[0]=20
    print(B) # [20,2,3]
    print(A) # [20,2,3,4,5,6]

Answer 2

The main difference is that numpy slicing can handle multiple dimensions. In your example, curr_frames[idx,:,:,:,] , the array has 4 dimensions and you are slicing by supplying the indices for one dimension ( idx ) and the : notation means to retrieve all for that dimension.

References:

NumPy slicing

Python slicing