how do i correctly handle a multi dimensional numpy array

Question

I'm a Python newbie and struggling a bit with multi dimensional arrays in a for loop. What I have is:

CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
    "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
    "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
    "sofa", "train", "tvmonitor"]
...
...
idxs = np.argsort(preds[0])[::-1][:5]
    print(idxs)
    #loop over top 5 predictions & display them
    for (i, idx) in enumerate(idxs):
        # draw the top prediction on the input image
        print (idx)
        if i == 0:
            print (preds)
            text = "Label: {}, {:.2f}%".format(CLASSES[idx], preds[0][idx] * 100)

            cv2.putText(frame, text, (5, 25),  cv2.FONT_HERSHEY_SIMPLEX,
                0.7, (0, 0, 255), 2)

        # display the predicted label + associated probability to the
        # console   
        print("[INFO] {}. label: {}, probability: {:.5}".format(i + 1,CLASSES[idx], preds[0][idx]))

and I get something like:

[[[ 0.          7.          0.3361728   0.2269333   0.6589312
    0.70067763  0.8960621 ]
  [ 0.         15.          0.44955394  0.5509065   0.4315516
    0.6530549   0.7223625 ]]]
[[[0 3 2 4 5 6 1]
  [0 4 2 3 5 6 1]]]
[[0 3 2 4 5 6 1]
 [0 4 2 3 5 6 1]]
[[[[ 0.          7.          0.3361728   0.2269333   0.6589312
     0.70067763  0.8960621 ]
   [ 0.         15.          0.44955394  0.5509065   0.4315516
     0.6530549   0.7223625 ]]]]
Traceback (most recent call last):
  File "real_time_object_detection.py", line 80, in <module>
    text = "Label: {}, {:.2f}%".format(CLASSES[idx], preds[0][idx] * 100)
TypeError: only integer scalar arrays can be converted to a scalar index

I've copied this code from https://www.pyimagesearch.com/2017/08/21/deep-learning-with-opencv/ but it looks like I'm doing something wrong as idx should be an int but instead is an array

UPDATE:

I tried to figure out what's going on here but I got stuck with the following: why do all argsort calls give the same result? :o

>>> preds[0] = [[[ 0.,          7.,          0.3361728,   0.2269333,   0.6589312,0.70067763,  0.8960621 ],[ 0.,         15.,          0.44955394,  0.5509065,   0.4315516,0.6530549,   0.7223625 ]]]
>>> print(preds[0])
[[[0.0, 7.0, 0.3361728, 0.2269333, 0.6589312, 0.70067763, 0.8960621], [0.0, 15.0, 0.44955394, 0.5509065, 0.4315516, 0.6530549, 0.7223625]]]
>>> import numpy as np
>>> np.argsort(preds[0])
array([[[0, 3, 2, 4, 5, 6, 1],
        [0, 4, 2, 3, 5, 6, 1]]])
>>> np.argsort(preds[0])[::-1]
array([[[0, 3, 2, 4, 5, 6, 1],
        [0, 4, 2, 3, 5, 6, 1]]])
>>> np.argsort(preds[0])[::-1][:5]
array([[[0, 3, 2, 4, 5, 6, 1],
        [0, 4, 2, 3, 5, 6, 1]]])

Plus why does it seem to alter the data, should it not just sort it?

What's the shape of preds? Or more relevantly preds[0]? (1,2,7)? Make sure you shape matches the expectations of the code you are copying. I suspect it has too many dimension. argsort on a 3d array without axis specified looks suspicious. — hpaulj
– hpaulj, Commented Jan 14, 2019 at 4:18
@hpaulj argsort should work fine without axis defined: "Axis along which to sort. The default is -1 (the last axis). If None, the flattened array is used." I've added a print of preds[0] above, I'm not sure how to address this — stdcerr
– stdcerr, Commented Jan 14, 2019 at 5:00
Sorry, I missed the default of -1. Thought it was flattening. — hpaulj
– hpaulj, Commented Jan 14, 2019 at 5:04

hpaulj · Accepted Answer · 2019-01-14 08:11:43Z

Your preds[0], assigned to a variable name is a 3d array:

In [449]: preds0 = np.array([[[ 0.,          7.,          0.3361728,   0.2269333
     ...: ,   0.6589312,0.70067763,  0.8960621 ],[ 0.,         15.,          0.4
     ...: 4955394,  0.5509065,   0.4315516,0.6530549,   0.7223625 ]]])
In [450]: preds0.shape
Out[450]: (1, 2, 7)

argsort applied to that is an array of the same shape:

In [451]: np.argsort(preds0)
Out[451]: 
array([[[0, 3, 2, 4, 5, 6, 1],
        [0, 4, 2, 3, 5, 6, 1]]])
In [452]: _.shape
Out[452]: (1, 2, 7)

With that size 1 initial dimension, not amount of reversing or slicing on that dimension makes a difference. I suspect you wanted to reverse and slice the last dimension, the size 7 one. BUT, be careful about that. The argsort of a multidimensional array, even when applied to one dimension (the default last), is a hard thing to understand, and to use.

The shape matches the array, but the values are the range of 0-6, the last dimension. numpy 1.15 added a couple of functions to make it easier to use the result of argsort (and some other functions):

In [455]: np.take_along_axis(preds0, Out[451], axis=-1)
Out[455]: 
array([[[ 0.        ,  0.2269333 ,  0.3361728 ,  0.6589312 ,
          0.70067763,  0.8960621 ,  7.        ],
        [ 0.        ,  0.4315516 ,  0.44955394,  0.5509065 ,
          0.6530549 ,  0.7223625 , 15.        ]]])

Notice that rows are now sorted, same as produced by np.sort(preds0, axis=-1).

I could pick one 'row' of the index array:

In [459]: idxs = Out[451]
In [461]: idx = idxs[0,0]
In [462]: idx
Out[462]: array([0, 3, 2, 4, 5, 6, 1])
In [463]: idx[::-1]               # reverse
Out[463]: array([1, 6, 5, 4, 2, 3, 0])
In [464]: idx[::-1][:5]           # select
Out[464]: array([1, 6, 5, 4, 2])
In [465]: preds0[0,0,Out[464]]
Out[465]: array([7.        , 0.8960621 , 0.70067763, 0.6589312 , 0.3361728 ])

Now I have the five largest values of preds0[0,0,:] in reverse order.

And to do it to the whole preds0 array:

np.take_along_axis(preds0, idxs[:,:,::-1][:,:,:5], axis=-1)

or for earlier versions:

preds0[[0], [[0],[1]], idxs[:,:,::-1][:,:,:5]]

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