How can I add new dimensions to a Numpy array?

Question

You're asking how to add a dimension to a NumPy array, so that that dimension can then be grown to accommodate new data. A dimension can be added as follows:

image = image[..., np.newaxis]

Alternatively to

image = image[..., np.newaxis]

in @dbliss' answer, you can also use numpy.expand_dims like

image = np.expand_dims(image, <your desired dimension>)

For example (taken from the link above):

x = np.array([1, 2])

print(x.shape)  # prints (2,)

Then

y = np.expand_dims(x, axis=0)

yields

array([[1, 2]])

and

y.shape

gives

(1, 2)

You could just create an array of the correct size up-front and fill it:

frames = np.empty((480, 640, 3, 100))

for k in xrange(nframes):
    frames[:,:,:,k] = cv2.imread('frame_{}.jpg'.format(k))

if the frames were individual jpg file that were named in some particular way (in the example, frame_0.jpg, frame_1.jpg, etc).

Just a note, you might consider using a (nframes, 480,640,3) shaped array, instead.

Pythonic

X = X[:, :, None]

which is equivalent to

X = X[:, :, numpy.newaxis] and X = numpy.expand_dims(X, axis=-1)

But as you are explicitly asking about stacking images, I would recommend going for stacking the list of images np.stack([X1, X2, X3]) that you may have collected in a loop.

If you do not like the order of the dimensions you can rearrange with np.transpose()

You can use np.concatenate() specifying which axis to append, using np.newaxis:

import numpy as np
movie = np.concatenate((img1[:,np.newaxis], img2[:,np.newaxis]), axis=3)

If you are reading from many files:

import glob
movie = np.concatenate([cv2.imread(p)[:,np.newaxis] for p in glob.glob('*.jpg')], axis=3)

Consider Approach 1 with reshape method and Approach 2 with np.newaxis method that produce the same outcome:

#Lets suppose, we have:
x = [1,2,3,4,5,6,7,8,9]
print('I. x',x)

xNpArr = np.array(x)
print('II. xNpArr',xNpArr)
print('III. xNpArr', xNpArr.shape)

xNpArr_3x3 = xNpArr.reshape((3,3))
print('IV. xNpArr_3x3.shape', xNpArr_3x3.shape)
print('V. xNpArr_3x3', xNpArr_3x3)

#Approach 1 with reshape method
xNpArrRs_1x3x3x1 = xNpArr_3x3.reshape((1,3,3,1))
print('VI. xNpArrRs_1x3x3x1.shape', xNpArrRs_1x3x3x1.shape)
print('VII. xNpArrRs_1x3x3x1', xNpArrRs_1x3x3x1)

#Approach 2 with np.newaxis method
xNpArrNa_1x3x3x1 = xNpArr_3x3[np.newaxis, ..., np.newaxis]
print('VIII. xNpArrNa_1x3x3x1.shape', xNpArrNa_1x3x3x1.shape)
print('IX. xNpArrNa_1x3x3x1', xNpArrNa_1x3x3x1)

We have as outcome:

I. x [1, 2, 3, 4, 5, 6, 7, 8, 9]

II. xNpArr [1 2 3 4 5 6 7 8 9]

III. xNpArr (9,)

IV. xNpArr_3x3.shape (3, 3)

V. xNpArr_3x3 [[1 2 3]
 [4 5 6]
 [7 8 9]]

VI. xNpArrRs_1x3x3x1.shape (1, 3, 3, 1)

VII. xNpArrRs_1x3x3x1 [[[[1]
   [2]
   [3]]

  [[4]
   [5]
   [6]]

  [[7]
   [8]
   [9]]]]

VIII. xNpArrNa_1x3x3x1.shape (1, 3, 3, 1)

IX. xNpArrNa_1x3x3x1 [[[[1]
   [2]
   [3]]

  [[4]
   [5]
   [6]]

  [[7]
   [8]
   [9]]]]

There is no structure in numpy that allows you to append more data later.

Instead, numpy puts all of your data into a contiguous chunk of numbers (basically; a C array), and any resize requires allocating a new chunk of memory to hold it. Numpy's speed comes from being able to keep all the data in a numpy array in the same chunk of memory; e.g. mathematical operations can be parallelized for speed and you get less cache misses.

So you will have two kinds of solutions:

1. Pre-allocate the memory for the numpy array and fill in the values, like in JoshAdel's answer, or
2. Keep your data in a normal python list until it's actually needed to put them all together (see below)

---

images = []
for i in range(100):
    new_image = # pull image from somewhere
    images.append(new_image)
images = np.stack(images, axis=3)

---

Note that there is no need to expand the dimensions of the individual image arrays first, nor do you need to know how many images you expect ahead of time.