NumPy array loses dimension upon assignment/copy, why?

Question

I have the following code:

print(type(a1), a1.shape)
a2 = a1                  #.reshape(-1,1,2) this solves my problem
print(type(a2), a2.shape)

The output is:

<class 'numpy.ndarray'> (8, 1, 2)
<class 'numpy.ndarray'> (8, 2)

I know the (commented out) reshape solves my problem, however, I'd like to understand why a simple assignment results in losing the central dimension of the array.

Does anybody know what is going on? Why referring to the array with another name changes its dimensions?

Answer 1

Looking at the openCV script mentioned in the comments, the reshape to three dimensions is necessary because a dimension is being lost via Boolean indexing, and not by the assignment alone.

The names of the arrays in that script which motivated the question are p0 and good_new.

Here is a breakdown of the operations in that script:

1. p0 is a 3D array with shape (17, 1, 2).

2. The line:

   p1, st, err = cv.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
   

   creates new arrays, with array p1 having shape (17, 1, 2) and array st having shape (17, 1).

3. The assignment good_new = p1[st==1] creates a new array object by a Boolean indexing operation on p1. This is a 2D array has shape (17, 2). A dimension has been lost through the indexing operation.

4. The name p0 needs to be assigned back to the array data contained in good_new, but p0 also needs to be 3D. To achieve this, the script uses p0 = good_new.reshape(-1, 1, 2).

---

For completeness, it is worth summarising why the Boolean indexing operation in step (3) results in a dimension disappearing.

The Boolean array st == 1 has shape (17, 1) which matches the initial dimensions of p1, (17, 1, 2).

This means that the selection occurs in the second dimension of p1: the indexer array st == 1 is determining which arrays of shape (2,) should be in the resulting array. The final array will be of shape (n, 2), where n is the number of True values in the Boolean array.

This behaviour is detailed in the NumPy documentation here.

Answer 2

I am not sure why your are getting this.but it should not return like this.Can you please share how your a1 has been created.

I tried like below but not able to re create it

a1=np.ones((8,1,2),dtype=np.uint8)
print(type(a1), a1.shape)

<class 'numpy.ndarray'> (8, 1, 2)

a2=a1

print(type(a2), a2.shape)

<class 'numpy.ndarray'> (8, 1, 2)`