How can I add new dimensions to a Numpy array

Question

I m starting off with a numpy array of an image   In 1  img   cv2 imread  test jpg     The shape is what you might expect for a 640x480 RGB image   In 2  img shape Out 2    480  640  3    However  this image that I have is a frame of a video  which is 100 frames long  Ideally  I would like to have a single array that contains all the data from this video such that img shape returns  480  640  3  100    What is the best way to add the next frame -- that is  the next set of image data  another 480 x 640 x 3 array -- to my initial array

User · Answer

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

User · Answer

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

User · Answer

I followed this approach   import numpy as np import cv2  ls       for image in image paths      ls append cv2 imread  test jpg     img np   np array ls    shape  100  480  640  3  img np   np rollaxis img np  0  4    shape  480  640  3  100

User · Answer

Alternatively to   image   image      np newaxis    in  dbliss  answer  you can also use numpy expand dims like  image   np expand dims image   lt your desired dimension gt     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

User · Answer

This worked for me  image   image      None

User · Answer

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

User · Answer

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

User · Answer

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

User · Answer

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    Pre-allocate the memory for the numpy array and fill in the values  like in JoshAdel s answer  or 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

[python] How can I add new dimensions to a Numpy array?

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