convert cimg to keras format

[Norcalbiostat]

Hi there.
I'm pretty new at deep learning (~month, into ch5 of Deep learning with R) , and am trying to prepare an image file for use in a convnet, however that image has to first be sliced and diced ahead and split into multiple smaller images. That's fine, i got that, but now I have a cimg object, that has dimensions (height, width, samples, channels). I cannot figure out how to get this into a keras/tensor format where they want (samples, height, width, channels).

Here's a reprex.

library(imager)
set.seed(8675309)
test.img<- array(runif(2*3*10*1),c(2,3,10,1)) 
test.img<- as.cimg(test.img)

dim(test.img)
[1] 2 3 10 1

So I have 10 grayscale images, each of 2x3 height.

Calling keras::image_to_array(test.img) results in the following error:

Error in py_call_impl(callable, dots$args, dots$keywords) : ValueError: Unsupported image shape: (2, 3, 10, 1)

Most examples read the image in from an external image file. I am looking at tens to hundreds of thousands of images. so I'd really prefer to skip the part where i write the cimg to an image file disk, then read it back in.

Any ideas/thoughts?

[Norcalbiostat]

Solved... i was avoiding it the whole time. I completely assumed that manually reshaping the array using a for loop would be so excruciatingly slow it wouldn't be feasible.

Good reminder to always test your assumptions about what will and will not work before you waste hours looking for a solution to a problem that doesn't exist.

 z <- dim(test.img)[3]
 y <- dim(test.img)[1]
 x <- dim(test.img)[2]
 tnsr <- array(NA, dim=c(z, y,x))
  
 for(idx in 1:z){
   tnsr[idx,,] <- drop(frame(test.img,idx))
 }
  
 k.tnsr <- keras::array_reshape(tnsr, c(z, x*y))