At least one item in your list is either not three dimensional, or its second or third dimension does not match the other elements. If only the first dimension does not match, the arrays are still matched, but as individual objects, no attempt is made to reconcile them into a new (four dimensional) array. Some examples are below:
That is, the offending element’s shape != (?, 224, 3),
or ndim != 3 (with the ? being non-negative integer).
That is what is giving you the error.
You’ll need to fix that, to be able to turn your list into a four (or three) dimensional array. Without context, it is impossible to say if you want to lose a dimension from the 3D items or add one to the 2D items (in the first case), or change the second or third dimension (in the second case).
Here’s an example of the error:
>>> a = [np.zeros((224,224,3)), np.zeros((224,224,3)), np.zeros((224,224))] >>> np.array(a) ValueError: could not broadcast input array from shape (224,224,3) into shape (224,224)
or, different type of input, but the same error:
>>> a = [np.zeros((224,224,3)), np.zeros((224,224,3)), np.zeros((224,224,13))] >>> np.array(a) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: could not broadcast input array from shape (224,224,3) into shape (224,224)
Alternatively, similar but with a different error message:
>>> a = [np.zeros((224,224,3)), np.zeros((224,224,3)), np.zeros((224,100,3))] >>> np.array(a) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: could not broadcast input array from shape (224,224,3) into shape (224)
But the following will work, albeit with different results than (presumably) intended:
>>> a = [np.zeros((224,224,3)), np.zeros((224,224,3)), np.zeros((10,224,3))]
>>> np.array(a)
# long output omitted
>>> newa = np.array(a)
>>> newa.shape
3 # oops
>>> newa.dtype
dtype('O')
>>> newa[0].shape
(224, 224, 3)
>>> newa[1].shape
(224, 224, 3)
>>> newa[2].shape
(10, 224, 3)
>>>