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?
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]]